A Pipeline Integrating Cultural Heritage X-ray CR/CT Data Using Digital Imaging and Communications in Medicine

A Pipeline Integrating Cultural Heritage X-ray CR/CT Data Using Digital Imaging and Communications in Medicine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Pipeline Integrating Cultural Heritage X-ray CR/CT Data Using Digital Imaging and Communications in Medicine JUNG-IL SONG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8413368/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2026 Read the published version in npj Heritage Science → Version 1 posted 9 You are reading this latest preprint version Abstract Cultural heritage X-ray and computed tomography (CT) data require conservation-grade reproducibility and interoperability. This study presents the digital imaging and communication in cultural heritage (DICOCH) pipeline, a generation–validation–publication workflow implementing digital imaging and communications in medicine (DICOM) standards for heritage objects. The pipeline integrates automated DICOM generation from computed radiography and CT sources, mandatory machine validation, and automatic creation of publication packages, including International image interoperability framework (IIIF) manifests. A private group (0013,xxxx) preserves heritage-specific context and rights while encoding calibrated Hounsfield unit statistics within a single object. Validation with the Hahoe Mask dataset achieved zero-error compliance and verified interoperability between medical picture archiving and communication system and web-based IIIF viewers. By reconciling standard compliance with open access through a publicly released reference implementation, DICOCH serves as a trusted bridge between scientific preservation and digital reuse, providing a practical framework for global heritage data interoperability. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Non-destructive evaluation (NDE) of cultural heritage sites is a key method for investigating the internal structure, materials, degradation patterns, and manufacturing processes without compromising the physical and chemical stability of the object. X-ray transmission imaging and computed tomography (CT) quantitatively reveal stratigraphic structures, internal defects, foreign materials, and repair traces, directly informing practical decisions such as conservation treatment, exhibition, and transportation, as well as scholarly interpretations, such as production techniques, use-wear analysis, and dating. However, cultural heritage NDE data possess unique demands that distinguish them from medical imaging and industrial NDT, in that (i) the cost and risk of re-imaging are high and the data are highly irreversible; (ii) historical “context”—such as provenance, conservation history, rights, and ethics—must be preserved along with the image 1,2 ; and (iii) they must simultaneously satisfy requirements for long-term preservation, interoperability, and reproducibility across institutions. Despite these requirements, the prevailing practice in the field is the parallel storage of high-resolution raster files (e.g., TIFF/PNG), Excel metadata, PDF reports, and viewer screenshots. This leads to recurring issues such as synchronization failures due to the separation of metadata and images, non-standardized notation of acquisition and reconstruction parameters, differing fields and units across institutions, lack of long-term preservation formats, disconnection from web publications (IIIF), and absence of a machine-readable validation system. For cultural heritage data, which are strongly irreversible, this fragmentation limits reproducibility and subsequent utilization while also hindering the creation of social value through publication, education, and exhibition. International digital heritage policies emphasize these issues. The UNESCO “Charter on the Preservation of Digital Heritage” and the PERSIST guidelines call for lifecycle-oriented strategies (creation–preservation–access) and robust metadata and format choices that maintain the integrity of digital objects over time (UNESCO Charter, 2003, https://unesdoc.unesco.org/ark:/48223/pf0000179529; UNESCO PERSIST Guidelines, 2016, https://unescopersist.org/). In parallel, the FAIR and CARE principles stress that research data should be findable, accessible, interoperable, reusable, and governed in a manner that respects the community and rights holders. Therefore, a unified pipeline that embeds quantitative parameters and contextual information within a standardized container, and that connects machine validation and web publication immediately after generation, is an urgent requirement for cultural heritage NDE. This requirement is particularly critical considering that Cultural heritage X-ray and CT data are resources that combine authenticity and non-repeatability, and there is often only one opportunity to acquire them under safe conditions. Lossless image quality and accurate metadata records at the point of acquisition determine the reliability of subsequent analysis and isomorphic validation. Furthermore, many cultural objects are multi-material composites (e.g., wood, stone, metal, ceramic, and textile); therefore, the transparent disclosure of attenuation properties, scattering and artifact effects, and calibration and reconstruction parameters is essential for interpreting gray values, density contrasts, and segmentation outcomes. At this stage, numerical parameters from the acquisition–reconstruction phases (e.g., kVp/tube current, SOD/SID, pixel/voxel spacing, slice thickness, and reconstruction kernel) and analysis outputs (e.g., Hounsfield unit (HU) statistics and region-of-interest (ROI) measurements) must be accommodated together in a standard container that preserves their units, semantics, and relationships. This container must also be aligned with rights and ethics contexts such as copyright, access policies, and the handling of sensitive information 3 , as well as with semantic knowledge models in the GLAM sector (e.g., CIDOC-CRM and Europeana data model (EDM)) (ISO 21127:2023 CIDOC CRM, https://www.iso.org/obp/ui/en/#iso:std:iso:21127; Europeana Data Model, https://pro.europeana.eu/page/edm-documentation). Merely linking converted proprietary formats is insufficient to satisfy these requirements. A robust, standards-based approach is required that treats the NDE dataset as a single, validated digital object with both scientific and heritage context, in line with the broader policy directions articulated by UNESCO and related digital-heritage frameworks 4 . To address similar interoperability challenges, the medical and industrial domains have successfully adoptedDigital Imaging and Communications in Medicine (DICOM) and Digital Imaging and Communication in Non-Destructive Evaluation (DICONDE) have achieved interoperability as combined container-and-protocol standards, encompassing both file formats and transmission protocols (DICOM PS3.3, https://dicom.nema.org/medical/dicom/current/output/chtml/part03/; ASTM E2339-21, https://www.astm.org/e2339-21.html). The successful standardization of DICOM has been extensively discussed in digital pathology and image archiving, emphasizing the importance of information object definitions (IODs), transfer syntaxes, and conformance statements (Clunie, D.A. Toxicol. Pathol. 2020, https://doi.org/10.1177/0192623320965893). In the cultural heritage field, DICOM-inspired metadata architectures and models for accessibility and adaptive narratives have likewise been proposed 1,2 . Ciortan et al. 1 defined a crucial metadata architecture for multimodal acquisition and articulated how imaging data, annotations, and contextual information can be modeled to support advanced analysis and reuse. Nappi et al. 2 presented important models for accessible knowledge organization and adaptive narratives in IIIF-based environments, focusing on how users can explore and interpret heritage images using enriched manifests and annotations. However, these approaches primarily target annotation toolchains, visualization workflows, and interpretive models. They did not address the generation and machine validation of standard-compliant DICOM Part 10 instances, nor did they provide a technical pipeline for embedding quantitative metrics (e.g., HU/ROI) or linking machine validation (dciodvfy) with web publications (IIIF). Recently, quantitative-focused practices have been reported in heritage-related imaging, such as the reconstruction of 3D CT volumes from 2D X-ray equipment 5 , object-tailored beam filtering to optimize contrast and dose 6 , and ultrahigh-resolution micro-inspection of small-scale features 7 . These studies demonstrated the potential of CT and X-ray imaging as quantitative tools for material and structural analyses. However, research that completes an end-to-end practical pipeline consistently embedding such quantitative metrics into a standardized container and linking them through validation and publication remains rare. In particular, there are limited reports that institutionalize DICOM’s Private Creator and Private Group as a systematic dictionary to accommodate cultural heritage-specific contexts, rights, and quantitative metrics, while also demonstrating round-trip compatibility with both medical PACS viewers and web viewers (IIIF). The present study addresses this gap by focusing on the technical implementation, validation, and publication of the core data container itself. Technically, bridging this gap requires reconciling the distinct architectures of the archival standard and the web interface. DICOM standardizes the imaging container and protocol using a modular structure (PS3.3/3.5/3.6), defining IODs and SOP classes, value representations and multiplicities (VR/VM), unique identifiers (UIDs), and transfer syntaxes(DICOM PS3.3). It also allows for domain extension via private creators and tags, enabling communities such as cultural heritage to define additional attributes without compromising interoperability. Meanwhile, the IIIF Presentation 3.0 specification standardizes high-resolution image publication, multilingual labels, rights/provider information, and thumbnail and renders links through its manifest–canvas–annotation resource model(IIIF Presentation API 3.0, https://iiif.io/api/presentation/3.0/). However, IIIF does not provide a built-in standard for scientific numerical attributes, such as voxel spacing, window/level, reconstruction parameters, or HU semantics. Implementing a trusted bridge between the original scientific container (DICOM) and the web interface (IIIF) is essential. Scalable interoperability is achieved only when the format (file), protocol (transfer), and validation are combined. A dataset must be correctly formed as a DICOM object, verifiable by a machine, and consistently transformed into a stable IIIF representation. Approaches relying solely on conversion libraries to link proprietary formats tend to increase risks as the number of objects and software environments grows. For cultural heritage NDE, a round-trip (write–read–validate) procedure is required: DICOM generation (e.g., via Python/pydicom) → standard-compliance checking (dicom3tools dciodvfy) → IIIF publication through manifests and derivatives (dicom3tools, https://www.dclunie.com/dicom3tools.html;, pydicom, https://pydicom.github.io/pydicom/stable/). Such a procedure should also support the lifecycle-oriented recommendations of UNESCO and align with FAIR/CARE-inspired expectations that heritage-related research data remain trustworthy, reusable, and governed with appropriate community oversight (UNESCO Charter, 2003),(UNESCO PERSIST Guidelines, 2016). Within this context, the technical link between DICOM and IIIF is a key mechanism for implementing policy and ethics requirements in a concrete and verifiable manner(dicom3tools), (pydicom). Unlike previous approaches focused solely on visualization, this study seeks to establish this missing technical link by integrating generation, validation, and publication into a single auditable workflow. To bridge these gaps and establish this missing technical link, we propose and demonstrate the DICOCH ("DICOM for Cultural Heritage") pipeline with four primary contributions. (1) Container extension: A structured subsequence within the DICOM Private Group (0013,xxxx) accommodating cultural heritage context, rights, terminology, IIIF linkage, and HU/ROI calibration/statistics under a fixed Private Creator. (2) Verification-first end-to-end procedure: A generation-validation-publish (GVP) workflow that generates DICOM Part 10 files via Python/Pydicom, validates them with dicom3tools dciodvfy, and automatically produces IIIF Presentation 3.0 manifests. (3) Operational profile and quality metrics: a practical CT/CR minimum metadata profile, VR/VM and UID consistency checks, and a rule-based normalization of warning types (e.g. "Not in IOD,” "Retired,” "Private") to guide quality assurance. (4) Case-based demonstration: A representative CR/CT dataset (Andong Hahoe Mask) that attains dciodvfy errors = 0 and warnings = 0 demonstrates interoperable viewing in both PACS-type DICOM software and an IIIF-based web viewer. Aligned with the international norms of The UNESCO “Charter on the Preservation of Digital Heritage” and PERSIST (2016) guidelines recommend that digital surrogates of cultural heritage be managed as coherent, sustainable objects throughout their lifecycle, from creation through preservation to access (UNESCO Charter, 2003; UNESCO PERSIST Guidelines, 2016). They emphasize format strategies and minimum metadata that preserve interpretability and reuse over long timescales. Within this policy landscape, DICOCH can be understood as an operational profile using DICOM as the canonical archival container and IIIF as a public-facing interface, translating high-level norms into concrete, machine-verifiable data structures and workflows. The DICOCH pipeline targets both 2D X-ray (CR) and 3D CT data of cultural heritage objects. In this study, a representative CR/CT dataset from a Korean cultural heritage site illustrates how the proposed schema and workflow operate on real institutional data. All procedures were applied in a version-controlled environment, ensuring that identical inputs yielded identical outputs, supporting reproducibility and auditability at both the data and software levels. The implementation stack, including Python/pydicom, dicom3tools/dciodvfy, and IIIF Presentation 3.0, along with specific tag sets and materials, is described in the following sections. Section 2 introduces the overall pipeline and DICOCH schema (including the Private 0013 dictionary) and explains the GVP procedure. Section 3 summarizes the materials, datasets, and tag templates used in this study. Section 4 applies the pipeline to case data and reports the quantitative and interoperability results. Section 5 discusses the findings and their implications for cultural heritage NDE and digital heritage policy. Section 6 concludes the paper and outlines directions for future research. 2. Methods This study proposes a consistent procedure (the DICOCH pipeline) for generating, validating, and publishing cultural heritage NDE images in compliance with the DICOM standard. The overall GVP workflow is illustrated in Fig. 1. 2.1 Design Principles The DICOCH pipeline is based on the following four key principles: Standards Compliance: Adheres primarily to the IOD·VR/VM rules of DICOM PS3.3/PS3.5/PS3.6, aiming for 'errors=0' at each stage as verified by the official validator (dciodvfy). Independence: Strictly maintain the integrity of standard public tags. Cultural-heritage-specific context, quantification, rights, and web linkage information were independently added using Private Group (0013,xxxx) sequences. Web Interoperability: Embeds stable URIs for the IIIF Presentation 3.0 Manifest and Image Service, enabling the generation–validation–publication–viewing chain to operate seamlessly. Reproducibility: Ensures an audit trail by packaging transformation parameters, tool versions, checksums, and private dictionary text with identical timestamps. 2.2 DICOCH Schema and Tag Mapping An Excel- or CSV-based tag table was used as the tag input. This table defines the core information for constructing the DICOM dataset. Tag Address (hex, e.g., 0x0013,0x1100) Value Representation (VR) Standard Keyword Actual Value Human-readable Label (used for IIIF metadata, etc.) The parser reads this table, cross-validates the tag address and keywords, and prioritizes the VR rule for data serialization if conflicts occur (e.g., if VR = DS). Missing values (blanks, "NONE,” etc.) are processed according to DICOM Type 1/2/3 rules. 2.3 DICOCH Tag Schema Mapping Principles Tag injection rules were divided into modality-specific (CT, CR) and common rules, all complying with the DICOM IOD definitions (PS3.3) and the Data Dictionary (PS3.6). 2.3.1 CT (Computed Tomography) CT data uses CT Image Storage (1.2.840.10008.5.1.4.1.1.2) as the default SOP Class. For quantitative reproducibility, RescaleIntercept (0028,1052), RescaleSlope (0028,1053), and RescaleType (0028,1054=HU) were mandatory. For 3D registration, ImagePositionPatient (0020,0032), ImageOrientationPatient (0020,0037), and FrameOfReferenceUID (0020,0052) are completed, along with Z-axis parameters such as SliceThickness (0018,0050). PixelSpacing (0028,0030) was used for the in-plane scale, and WindowCenter (0028,1050) / WindowWidth (0028,1051) were recorded as the initial display hints. 2.3.2 CR (Computed Radiography) CR data used CR Image Storage (1.2.840.10008.5.1.4.1.1.1) as the default SOP Class. The standard for length/distortion interpretation is unified under Imager Pixel Spacing (0018,1164) and is not conflated with CT pixel Spacing (0028,0030). The projection context (ViewPosition 0018,5101) and target context (BodyPartExamined 0018,0015) are met as Type 2, and the related parameters (e.g., Cassette/Plate/Filter/Exposure) are included. 2.3.3 Common Rules Nonstandard input (e.g., missing source metadata) falls back to Secondary Capture (1.2.840.10008.5.1.4.1.1.7), for the reason specified in the DerivationDescription (0008,2111). The transfer syntax was fixed to Explicit VR Little Endian (1.2.840.10008.1.2.1). The internal consistency of the pixel core (e.g., photometric interpretation =MONOCHROME2) was ensured. All UIDs are guaranteed to be globally unique and SpecificCharacterSet = ISO_IR 192 (UTF-8) is declared for multilingual labels. 2.4 Core Tag Set Structure In accordance with DICOM PS3.3/PS3.6, tags were organized into modality-shared core and modality-specific items. This separation is required to satisfy both PACS/general viewer compatibility and densification validation stability requirements. The shared core (Table 2) defines the fundamental pixel data and display parameters, whereas the modality-specific sets define the geometry and quantitative meaning of CT (Table 3) and CR (Table 4). 2.4.1 Acquisition Protocol and Tag CT Set The acquisition protocol specified SliceThickness, SpacingBetweenSlices (if applicable), PixelSpacing, ImagePosition/OrientationPatient, and FrameOfReferenceUID. For quantitative reproducibility, RescaleIntercept = -1024, RescaleSlope = 1, and RescaleType = HU were set as defaults. If inter-equipment/inter-scan calibration exists, the calibration standards and coefficients are recorded in the HU Calibration Sequence (0013,1400). 2.4.2 Acquisition Protocol and Tag CR Set The protocol recorded KVP, tube current, exposure, and source-to-object/detector distances. Resolution was reported using ImagerPixelSpacing (0018,1164). For cases where human anatomical terms were unsuitable, BodyPartExamined and ViewPosition maintained standard codes, while context was supplemented in the private group to ensure interpretation within the projection/detector context. 2.5 DICOCH Private Group (0013,xxxx) Structure A Private Group (0013,xxxx) was designed to encapsulate heritage-specific metadata (context, quantification, Web, and rights). All private blocks are named Private Creator (0013,0010) = NRICH_DICOCH. 2.5.1 Design Principles: Independence: Augments public tags without overwriting them. Readability: Strictly adheres to the VR/VM rules and the UTF-8 chart. Validation Usability: Embeds a self-describing private dictionary to allow external tools to identify tags. Web Interoperability: Isolates IIIF links in a separate sequence to maintain viewer independence. 2.5.2 Root Sequences (Table 1): (A) HeritageMetadataSequence (0013,11xx): Humanities/conservation context (name, ID, period, material, condition). (B) GrayValue-HU_PointSequence / GrayValue-HU_ROISequence (0013,12xx / 13xx): Coordinates (point/polygon) and Gray/HU statistics (Mean/SD/Min/Max). (C) HUCalibrationSequence (0013,14xx): Basis and coefficients for HU calibration. (Current version uses medical defaults: Intercept = -1024, Slope = 1, Type = HU). (D) IIIFInternationalNormLinkSequence (0013,15xx): Embedded stable URIs for Manifest, Image Service, and Rights. (E) SecurityAccessSequence (0013,16xx): Access/integrity (AccessLevel, EmbargoUntil, Checksum). (F) ModalityExtension (0013,17xx/18xx): Descriptive mirror (summary layer) for CR/CT. (G) PrivateDictionary/Readme (0013,19xx): Dictionary text and version. 2.6 Python Pipeline Implementation (GVP) The pipeline is implemented in Python, chosen for its extensive ecosystem of open-source libraries for image processing and DICOM handling (e.g., pydicom, numpy), as well as its cross-platform compatibility. It uses the core libraries of pydicom (DICOM I/O), numpy (numerical processing), pandas (tag table loading), and pillow/tiff-file (image loading). The pipeline was operated using a series of Python-based graphical user interfaces (GUIs) (Figs. 2–4). 2.6.1 Generation File-meta & SOP Synchronization: The SOP Class is selected based on the IOD, and the file metadata and dataset UIDs are generated simultaneously to prevent mismatches (see Fig. 2). Pixel/Display Core Automation: Check the dynamic range of the source images (TIFF/PNG, etc.) and normalize the bit depth. WindowCenter/Width was auto-estimated based on percentiles, preserving any user-defined values. Modality Branching: The CT path prioritizes 3D coordinate/quantification tags (e.g., FrameOfReferenceUID, Rescale). The CR path maintains 2D projection attributes (e.g., ImagerPixelSpacing) and removes unnecessary 3D tags (e.g., IPP/IOP) to ensure IOD purity. Tag Parser: Parses the Excel/CSV tag sheet, auto-configures sequence (SQ) hierarchies based on parent tags, and generates Private Dictionary text. 2.6.2 Validation dciodvfy Execution: Calls the standard validator (dciodvfy.exe) to verify IOD, module, and VR/VM compliance of the generated DICOM files. Dictionary Injection: When calling the validator, the private DICOCH dictionary is injected using the DCMDICTPATH environment variable. This allows the validator to recognize (0013,xxxx) tags by their defined labels instead of "Unrecognized tag," producing a human-readable validation log. Result Reporting: Validation results were output as per-file JSONL/text dumps and an aggregate HTML report (see Fig. 3). 2.6.3 Publication DICOM→JPEG Derivative (8-bit) Generation Algorithm: Generates 8-bit JPEG derivatives from the original DICOM for IIIF web viewer compatibility (e.g., Mirador) (see Fig. 4). Algorithm (i) applies the Modality LUT (rescale), (ii) applies the VOI LUT (window/level), (iii) uses percentile scaling if no VOI is present, and (iv) performs 8-bit mapping and JPEG encoding. All transformation parameters were logged. IIIF Presentation 3.0 Manifest Generation: An IIIF manifest (JSON) file is autogenerated for each DICOM instance. The Canvas metadata field of the manifest includes a summary of the key tags (public and private) from the original DICOM. Right information (e.g., rights and providers) is also standardized at this stage. Note: The DICOCH pipeline does not implement the IIIF Image Server (e.g., Cantaloupe, IIPImage). The “Publication” step refers to generating a server-ready package consisting of 8-bit JPEG derivatives and structured metadata (IIIF Presentation 3.0 Manifest). This package is automatically generated from the validated DICOM Part 10 file and is ready for immediate deployment on any standard IIIF-compliant server. 2.7 Case Study Object and Equipment This section describes the case study object, imaging equipment and environment, acquisition protocols for CR and CT, and the structure of the tag sets used (shared, modality-specific, and Private Group 0013). 2.7.1 Case Study Object The case study object was the Andong Hahoe Mask (National Treasure No. 121) (Figs. 5 and 6). Hahoe Masks are the oldest surviving wooden masks in Korea, dating back to the Goryeo Dynasty (mid-12th century). Used in the Hahoe Pyolshin-gut Tal-nori (Mask Dance Drama), these masks are carved from alder wood and are renowned for their separate chin structure (in the case of the Yangban mask), which allows for a variety of expressions. As a designated national treasure, these masks represent a critical class of organic cultural heritage that requires strict conservation monitoring. This object was selected as a representative case study for low-attenuation organic materials, which presents a baseline challenge for data standardization. The unique cultural heritage context, including the object's proper name, management number, material, and conservation status, was recorded in the structured HeritageMetadataSequence (0013,1100) within the DICOCH Private Group. This serves to permanently embed object-centric historical, rights, and institutional contexts within the DICOM file, which are not covered by standard medical or industrial protocols. 2.7.2 Equipment and Environment CR Equipment: A GE CRxVision system (for imaging) and Rhythm Review (for reading and verification) were used. Length/distortion analysis was based on ImagerPixelSpacing (0018,1164), supplemented with ViewPosition, Cassette/Plate/Filter, and Exposure/ExposureTime (detailed in Table 6). CT Equipment: An SEC (Republic of Korea) X-eye 7000b system was used with CT-eye software for reconstruction. Key acquisition/reconstruction parameters (kVp, tube current/exposure, projections if available, SOD/SID, PixelSpacing, SliceThickness/SpacingBetweenSlices) were recorded according to the standard core (detailed in Table 7). For quantitative reproducibility, RescaleIntercept/Slope/Type were set to Hounsfield units (HU). Equipment and software identifiers (Manufacturer, ManufacturerModelName, Software Versions, StationName, DeviceSerialNumber) were recorded as public tags, with summary metadata mirrored in the Private 0013 ModalityExtension (CT/CR). 3. Results This section reports the results of applying the DICOCH-based generation-validation-publication pipeline to the Hahoe Mask case study, based on the equipment, environment, and tag sets described in Section 3. 3.1 Case Study Overview and Dataset The subject of this study was the Hahoe Mask (National Treasure No. 121), a wooden cultural heritage object from Korea. This object was selected as a representative case study for low-attenuation organic materials, which present a baseline challenge for data standardization. As noted in the limitations (Section 6), high-density or composite materials (e.g., metals and ceramics), which are known to produce significant imaging artifacts, were not included in this study and represent a key area for future validation of the pipeline's robustness. Original images were processed in the DICOCH format alongside the vendor DICOM formats for generation, validation, and publication. Input metadata were transcribed from the provided tag spreadsheets (see Table 5), and outputs included checksums and validation logs. 3.2 CR Application Results: Generation and Tag Conformance The SOP Class was set to CR Image Storage (1.2.840.10008.5.1.4.1.1.1). Internal consistency of the pixel module was confirmed (PhotometricInterpretation=MONOCHROME2, Rows/Columns, BitsAllocated/Stored/HighBit; PixelRepresentation). Resolution was specified using ImagerPixelSpacing (0018,1164). BodyPartExamined (0018,0015) and ViewPosition (0018,5101) were recorded as Type 2. The cultural heritage context (object name, identifier, material, condition, investigation purpose, etc.) was structured into a Private Group (0013,xxxx) via the HeritageMetadataSequence. 3.3 CT Application Results: A 'Context-First' Approach to HU Standardization and ROI Statistics A primary challenge in standardizing CT data for cultural heritage is the reliable interpretation of quantitative values (Hounsfield units, HU). As discussed in detail in Section 5.5, HU values derived from medical standards are not directly comparable across different scanners or non-human tissue materials (e.g., wood and metal). Therefore, the goal of the DICOCH pipeline is not to assert absolute material-calibrated HU values but rather to (1) preserve the internal monotonic data for reproducibility and (2) structurally record the full context of measurement (calibration parameters, ROIs, and statistics) for verifiable, context-aware analysis. 3.3.1 Generation and Geometric Consistency CT output was generated using CT Image Storage (1.2.840.10008.5.1.4.1.1.2). Three-dimensional geometric consistency was ensured through ImagePosition/Orientation (Patient), FrameOfReferenceUID, SliceThickness, and PixelSpacing. The representative slice (instance #1177) shows a high-density feature on the right, likely a metal fastener, with associated streaking artifacts (Fig. 7). 3.3.2 HU scaling: limitations, rationale, and remedy To provide a manufacturer-independent, reproducible physical scale compatible with standard medical viewers, the baseline medical transforms RescaleIntercept = -1024, RescaleSlope = 1, and RescaleType = HU were applied, preserving a monotonic mapping from stored pixel values to HU-formatted CT numbers. DICOCH introduces structured storage for HU records to ensure scientific interpretability and machine reuse: GrayValue-HU_PointSequence (0013,12xx) and DICOCH_GrayValue-HU_ROISequence (0013,13xx) store coordinate-based points and 64×64 px square ROI statistics as paired grayscale + HU-formatted values. The HUCalibrationSequence (0013,14xx) captures the calibration provenance: phantom type/levels, tube potential and filtration, reconstruction kernel, rescaled parameters, and uncertainty metrics. The Python DICOM ROI Cropper (Fig. 4) allows runtime setting of RescaleSlope and RescaleIntercept, persisting in both the standard pixel transform (0028,1052/1053/1054) and the HU Calibration Sequence (0013,14xx). The default remains (−1024, 1) for medical compatibility. Note on HU values: The HU values presented in Table 8 are 'HU-formatted CT numbers' derived from the standard medical rescale (RescaleIntercept = −1024, RescaleSlope = 1). These values provide reproducible internal contrast for this scan but should not be interpreted as absolute material properties. Structured DICOCH storage (0013,14xx) captures the necessary context. 3.3.3 ROI/Point Measurement Results Coordinate-based points were recorded in GrayValue-HU_PointSequence (0013,12xx), and 64×64 px ROI statistics in DICOCH_GrayValue-HU_ROISequence (0013,13xx). For example, ROI-1 (air) measured −1024.0 HU (SD 0.0), while ROI-3 (wood core) and ROI-4 (pigment) fell in the 984–987 HU range. ROI-6 (artifact affected by streaking) showed higher variability (≈1042 HU, SD 34.5) (Table 8). 3.3.4 Implications for interoperability and AI reuse By encoding both the applied transform (slope/intercept) and the calibration/uncertainty context in machine-readable sequences, DICOCH separates “HU-formatted” values from “calibrated HU,” preserves traceability, and supports reproducible analysis and model training. This approach also aligns with curatorial standards for long-term stewardship. 3.4 Standards Conformance, Vendor Benchmarking, and Interoperability We validated both the DICOCH outputs and vendor-native DICOM files (Vendor DCM) from the same equipment under identical conditions and then performed real-world interoperability tests across a PACS-style viewer and a web-publication workflow (IIIF). The consolidated findings are summarized in Table 9. 3.4.1 Key Outcome For both CR and CT, DICOCH achieved 0 errors / 0 warnings using dciodvfy. Informational notices arose only from heritage-specific private tags (Group 0013), whose dictionaries are not yet publicly registered: 146 notices for CR and 157 for CT. These notices are not standards violations; the validator reports them as “unrecognized tag—assuming explicit VR OK.” Under the same validator and options, the Vendor DCM exhibited non-trivial issues: CR: 23 errors / 10 warnings; CT: 5 errors / 10 warnings—dominated by: (i) missing required Type 1/2/2C attributes, (ii) file-meta vs. dataset UID mismatches, (iii) invalid VR/VM (e.g., non-numeric strings in IS/DS), and (iv) attribute conflicts (e.g., PixelAspectRatio vs. PixelSpacing). By contrast, DICOCH prevents these error classes through IOD-specific template enforcement, value range checks, explicit type normalization, and unified UID propagation across the file meta and dataset. For downstream use, DICOCH files behaved normally in a PACS viewer (tag browser, grayscale display, and spatial scale) and in the IIIF pipeline (manifest generation and rendering), whereas some vendor files with the above errors showed degraded behavior in specific viewers (e.g., frame-of-reference linkages and display-scale inconsistencies). 3.4.2 Interpretation Note A dciodvfy pass (0 errors / 0 warnings) is necessary but not sufficient for end-to-end interoperability. Accordingly, we document viewer/server checks (PACS-style viewer and IIIF web delivery) alongside full validator environments, commands, and raw logs in the Supplementary Material to allow exact reproduction of results. The vendor errors (Table 10) typically include: (i) missing required Type 1/2/2C attributes, (ii) file-meta/dataset UID mismatches, (iii) invalid VR/VM, and (iv) conflicting attributes. The DICOCH pipeline preemptively resolves these issues through template enforcement, value-range validation, type conversion, and unified UID propagation, achieving 0 Errors/0 Warnings. 3.4.3 Interoperability and Publication The DICOCH-generated CR and CT files were verified to run without issues in a commercial DICOM viewer (MicroDicom 2025.3) including the proper operation of the metadata panel, pixel inspection, window/level control, and tag browser, as documented in Figs. 8 and 9. In parallel, the automatically produced IIIF Presentation 3.0 manifests were successfully rendered in the Mirador web viewer, enabling a standard-based metadata display, high-resolution zoom and panning, and multi-image comparison (Figs. 10–11). Taken together, these results confirm that a single dciodvfy-validated DICOCH source object can simultaneously supply the medical/industrial viewing toolchain (PACS-type DICOM viewers) and the cultural-heritage web ecosystem (IIIF-based viewers such as Mirador) from one authoritative dataset (MicroDicom, https:/ / www.microdicom.com/; Mirador, https : //projectmirador.org/). 3.5 Reproducibility and Public Availability To ensure full reproducibility, a complete package—including generation scripts (versioned with dependencies), dciodvfy logs, IIIF manifests, and a Private Tag dictionary—is provided. As detailed in the "Data Availability" and "Code Availability" declarations, the full, version-controlled codebase and generated datasets will be deposited in a public GitHub repository maintained by the National Research Institute of Cultural Heritage (NRICH) and archived with a DOI. This allows external users to replicate the GVP workflow and validate outputs against the NRICH reference results 3.6 Summary of Results Both the CR and CT outputs satisfied all mandatory and conditional tags, maintaining the internal conformance of the IOD, pixel, and geometry modules. The CT data preserved monotonic HU transformation by applying Rescale(−1024, 1, HU) and produced standardized 64×64 px ROI/Point statistics. The DICOCH Private Group (0013,xxxx) successfully structured and provided extended metadata, including the heritage context, IIIF links, security/rights information, and quantitative ROI/point statistics. The discrepancy results (zero errors, zero warnings), especially when contrasted with vendor-native files, confirmed the necessity and efficacy of the verification-first approach. The model was validated through successful interoperability tests using MicroDicom and Mirador software. 4. Discussion This study proposes and demonstrates the DICOCH pipeline, which integrates the entire generation–validation–publication (GVP) process(Table 11.). for cultural heritage NDE data into a single, standardized procedure. The key contributions are: (i) designing an orthogonal Private Group (0013,xxxx) to co-locate cultural context, rights, IIIF links, and HU/ROI quantitative metadata with standard DICOM tags within the same object; (ii) establishing an automated "verification-first" workflow; and (iii) confirming interoperability across both commercial PACS viewers and web-based IIIF viewers. A key finding is the DICOCH Private Group’s (0013,14xx) ability to record calibration context, shifting the field beyond simple CT numbers toward verifiable quantitative analyses. In this model, DICOCH acts as the audited “trusted bridge” connecting the preservation-grade original (DICOM) with the access-ready derivatives (IIIF Manifest, JPEGs), ensuring metadata consistency between archival sources and web dissemination. The design of the (0013,xxxx) Private Group is intended not as a rigid institutional silo but as an “open reference implementation” for adoption and co-governance by the wider cultural heritage community. By publicly releasing the code and Private Dictionary on GitHub, other institutions can be invited to validate and extend this schema. This open-access approach is a necessary first step toward building the consensus required for a formal DICOM PS3.6 Change Proposal, ensuring long-term sustainability. Cultural heritage NDE data must satisfy specific requirements—provenance, material condition, rights, and IIIF publication—that differ from clinical or industrial contexts. Table 11 outlines how DICOCH systematically fills the remaining gaps through existing frameworks(Table 12.). The pipeline adheres to a "verification-first" principle. Python/Pydicom scripts enforce Type 1/2/1C/2C conditions and VR formats (e.g., IS/DS) during generation. CT data preserve HU monotonicity and geometric completeness. For validation, dciodvfy performs full IOD conformance checks with a fail-fast (errors = 0) policy. Private items are treated as notices, and a public-private dictionary ensures interpretability. This automated chain (Generation → dciodvfy → IIIF) enhances reproducibility by ensuring identical output for identical input in a version-locked environment and increases data reliability through proactive enforcement of DICOM rules. Despite these advancements, several limitations must be acknowledged. Currently, HU interpretation relies on medical reference data, which may not be fully valid for heterogeneous heritage materials (wood, ceramics, metals) with variable moisture and degradation states. HU values are highly sensitive to equipment, kVp, reconstruction kernel, and object size; inter-scanner and size-dependent variations have been widely reported 8,9,15 . Clinical CT is further affected by beam hardening and manufacturer-specific algorithms, causing HU shifts of several units or percent even for the same material 13,14 . Furthermore, this study was validated using a single organic object (the Hahoe Mask). High-density materials like lead-glazed ceramics or gilt-bronzes present additional challenges, such as photon starvation and severe streaking artifacts, which may affect both quantitative ROI analysis and visual coherence in web derivatives. Future pathways involve establishing material-aware HU calibration profiles using custom heritage phantoms and theoretical models based on NIST XCOM mass attenuation coefficients (https://physics.nist.gov/PhysRefData/Xcom/html/xcom1.html). Technical alternatives like DECT/Spectral CT and PCD-CT may reduce energy dependency and improve quantification accuracy 11,12 . We recommend performing regular number of CT accuracy audits referencing QA frameworks such as AAPM TG-233 (https://www.aapm.org/pubs/reports/RPT_233.pdf). Recent studies demonstrate the viability of clinical CT for wood density via cross-validation with FTIR/Raman spectroscopy 16 and the potential for expanded CT use in museums through 3D reconstruction from 2D equipment 5,17 . In conclusion, DICOCH reconciles standard compliance with openness, aligning with international digital preservation principles for long-term access. To operationalize these findings, we will release the DICOCH scripts, generated DICOM instances, and IIIF manifests in a public GitHub repository maintained by the Cultural Heritage Conservation Science Center, NRICH. This open framework provides a practical foundation to move beyond ambiguous imaging toward robust, verifiable, and FAIR-compliant data for the global cultural heritage community. Abbreviations Acronyms Expansion AAPM American Association of Physicists in Medicine CARE Collective Benefit, Authority to Control, Responsibility, and Ethics (Data Principles) CIDOC-CRM CIDOC Conceptual Reference Model (ISO 21127) CR Computed Radiography CT Computed Tomography DECT Dual-Energy Computed Tomography DICOM Digital Imaging and Communications in Medicine DICOCH Digital Imaging and Communication in Cultural Heritage (Proposed Pipeline) DICONDE Digital Imaging and Communication in Non-Destructive Evaluation DOI Digital Object Identifier DS Decimal String (DICOM Value Representation) EDM Europeana Data Model FAIR Findable, Accessible, Interoperable, and Reusable (Data Principles) FTIR Fourier Transform Infrared Spectroscopy GLAM Galleries, Libraries, Archives, and Museums GUI Graphical User Interface GVP Generation-Validation-Publication (Workflow) HU Hounsfield Unit IIIF International Image Interoperability Framework IOD Information Object Definition ISO International Organization for Standardization JPEG Joint Photographic Experts Group JSON JavaScript Object Notation JSONL JavaScript Object Notation Lines kVp Kilovoltage Peak LUT Look-Up Table MPR Multi-Planar Reconstruction NDE Non-Destructive Evaluation NDT Non-Destructive Testing NIST National Institute of Standards and Technology NRICH National Research Institute of Cultural Heritage PACS Picture Archiving and Communication System PCD-CT Photon-Counting Detector Computed Tomography PERSIST Platform to Enhance the Sustainability of the Information Society (UNESCO Guidelines) PNG Portable Network Graphics QA Quality Assurance ROI Region of Interest SD Standard Deviation SID Source-to-Image Distance SOD Source-to-Object Distance SOP Service-Object Pair TIFF Tagged Image File Format UID Unique Identifier URI Uniform Resource Identifier VM Value Multiplicity VOI Value of Interest VR Value Representation XCOM Photon Cross Sections Database (NIST) dciodvfy DICOM Validator Software (part of dicom3tools) Declarations Data Availability The Supplementary Information package accompanies this study to ensure full reproducibility. It is organized into distinct resources for Computed Tomography (CT) and Computed Radiography (CR) and contains modality-specific DICOCH private dictionaries, image input templates, and sample IIIF manifests. Importantly, to substantiate the comparative analysis presented in Table 9, raw Dciodvfy audit logs are provided for both the DICOCH-generated files (confirming zero errors) and the original vendor-native files. Upon acceptance of this article, the complete and versioned datasets will be deposited in a permanent, publicly accessible repository (e.g., Zenodo or GitHub) maintained by the Cultural Heritage Conservation Science Center of the National Research Institute of Cultural Heritage (NRICH). Full-resolution CR/CT data of cultural heritage objects will be released only in downsampled or anonymized forms in accordance with NRICH data-sharing policies. Code Availability The custom Python software implementing the DICOCH pipeline is provided in the Supplementary Information (Source_Code.zip). The package includes three core modules: (a) the DICOM Converter for standard-compliant generation, (b) the Validation & Publication module for automated dciodvfy integration and IIIF manifest creation, and (c) the ROI Cropper tool for quantitative HU analysis and statistical embedding. Detailed execution instructions and dependency requirements are included in the accompanying README file. After acceptance, the complete version-controlled codebase will be made publicly available in a GitHub repository and archived with a DOI through Zenodo to ensure long-term accessibility and citability. Acknowledgments Funding This study was conducted as part of the Scientific Diagnosis and Research on the Application of Conservation Treatment Technology for Organic Cultural Heritage research project, supported by the National Research Institute of Cultural Heritage through the Cultural Heritage R&D Program. No specific grant number. Ethical Approval & Consent The present study involved non-human subjects and adhered to institutional ethics guidelines. The publication of images and metadata followed the policies of the holding institutions. Author Contributions (CRediT) IJS: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing — Original Draft, Writing — Review and Editing, Visualization, Supervision, Project Administration, and Funding acquisition. Competing Interests The authors declare no competing interests. References Ciortan, I. M. et al. A DICOM-inspired metadata architecture for managing multimodal acquisitions in cultural heritage. in Proceedings of the Digital Cultural Heritage (ed. Ioannides, M.) 37-49. 10.1007/978-3-319-75826-8_4 (Lecture Notes in Computer Science (Springer, Cham, 2018)). Nappi, M. L., Buono, M., Chivăran, C. & Giusto, R. M. Models and tools for the digital organisation of knowledge: accessible and adaptive narratives for cultural heritage. Herit. Sci. 12 , 112 (2024). 10.1186/s40494-024-01219-z. Stylianidis, E. Recording and documenting cultural heritage. in Proceedings of theexploring the Ethical Dimension in Recording and Documenting Cultural Heritage 25-46 (Springer Nature, Cham, 2025). 10.1007/978-3-031-80034-4_2. Amico, N. & Felicetti, A. 3D data long-term preservation in cultural heritage. (2024). 10.48550/arXiv.2409.04507. Bossema, F. G. et al. Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. Nat. Commun. 15 , 3939 (2024). 10.1038/s41467-024-48102-w, PubMed: 38744870. Kiss, M. B. et al. Beam filtration for object-tailored X-ray CT of multi-material cultural heritage objects. Herit. Sci. 11 , 130 (2023). 10.1186/s40494-023-00970-z. Jaques, V. A. J. et al. X-ray high resolution computed tomography for cultural heritage material micro-inspection. Proc. SPIE 11784 (2021). 10.1117/12.2592310. Sande, E. P. S., Martinsen, A. C. T., Hole, E. O. & Olerud, H. M. Interphantom and interscanner variations for Hounsfield units—establishment of reference values for HU in a commercial QA phantom. Phys. Med. Biol. 55 , 5123-5135 (2010). 10.1088/0031-9155/55/17/015, PubMed: 20714048. Zheng, X. et al. Body size and tube voltage dependent corrections for Hounsfield Unit in medical X-ray computed tomography: theory and experiments. Sci. Rep. 10 , 15696 (2020). 10.1038/s41598-020-72707-y, PubMed: 32973237. Szyszko, J. A., Aldieri, A., La Mattina, A. A. & Viceconti, M. Phantomless calibration of CT scans for hip fracture risk prediction in silico: comparison with phantom-based calibration. PLOS One 19 , e0305474 (2024). 10.1371/journal.pone.0305474, PubMed: 38875268. Greffier, J., Villani, N., Defez, D., Dabli, D. & Si-Mohamed, S. Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn. Interv. Imaging 104 , 167–177 (2023). 10.1016/j.diii.2022.11.003, PubMed: 36414506. Salyapongse, A. M. et al. CT number accuracy and association with object size: a phantom study comparing energy-integrating detector CT and deep silicon photon-counting detector CT. AJR Am. J. Roentgenol. 221 , 539–547 (2023). 10.2214/AJR.23.29463, PubMed: 37255042. Marques, J. B., Renha, S. K., Mendonça Pereira, H. M., Lima, T. V. M. & Simões, R. F. P. Effects of convolution filter with beam hardening correction on computed tomography image quality. Phys. Med. 110 , 102599 (2023). 10.1016/j.ejmp.2023.102599, PubMed: 37167777. Chacko, M. S., Wu, D. H., Grewal, H. S. & Sonnad, J. R. Impact of beam-hardening corrections on proton relative stopping power estimates from single- and dual-energy CT. J. Appl. Clin. Med. Phys. 23 , e13711 (2022). 10.1002/acm2.13711, PubMed: 35816460. Nakao, M. et al. CT number calibration audit in photon radiation therapy. Med. Phys. 51 , 1571-1582 (2024). 10.1002/mp.16887, PubMed: 38112216. Longo, S., Corsaro, C., Granata, F. & Fazio, E. Clinical CT densitometry for wooden cultural heritage analysis validated by FTIR and Raman spectroscopies. Radiat. Phys. Chem. 199 , 110376 (2022). 10.1016/j.radphyschem.2022.110376. Vavřík, D. et al. Non-destructive exploration of late Gothic panel painting using X-ray tomography and flattening of the reconstructed data. Eur. Phys. J. Plus 138 , 618 (2023). 10.1140/epjp/s13360-023-04212-w. Tables Tables 1 to 12 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table.docx SupplementaryInformationfinal.zip Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2026 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 11 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 11 Jan, 2026 First submitted to journal 11 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8413368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573878334,"identity":"2a2029d9-a8ce-4570-b2f1-c0a34412f4ee","order_by":0,"name":"JUNG-IL SONG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYNCCin9yEhBWArFazhwwJlELY9uBxBlEa5HvP3zs4Zczd9Jntvc+fPCDIS2foBaDG2npxjIVz3Jn8xw3NuxhyLFsIKhFgsdMWuIMc+48iTQ2CR6GCgMiHHbGTFqyjTldTiKN/ecfYrQwHMgxk/zYdjhBGmgLMw9DDmEtQL+kSTOcSTOc2XOMWVrGII0Yhx0+JvmjwkZe4ngb48c3FclEOAwIgO6BW0qUBmBM/iBS4SgYBaNgFIxQAACyqzjky6xmpAAAAABJRU5ErkJggg==","orcid":"","institution":"National Research Institute of Cultural Heritage","correspondingAuthor":true,"prefix":"","firstName":"JUNG-IL","middleName":"","lastName":"SONG","suffix":""}],"badges":[],"createdAt":"2025-12-20 16:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8413368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8413368/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s40494-026-02480-0","type":"published","date":"2026-04-04T15:59:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100405577,"identity":"20f4fc0c-6e28-4ce5-98bd-ab347e176150","added_by":"auto","created_at":"2026-01-16 12:07:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13626027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe DICOCH Generation-Validation-Publish (GVP) workflow. Schematic of the pipeline processing input data (X-ray images, Excel metadata) through (a) the DICOCH Converter to generate (b) standardized DICOM Part 10 files, which are then (c) validated for compliance (dciodvfy) and (d) published for web access (IIIF).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/0c113da9f1ccfbb90ab6cbab.png"},{"id":100406429,"identity":"6fa609c1-6aa0-466f-9e86-54bba5f60a25","added_by":"auto","created_at":"2026-01-16 13:01:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1972243,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical User Interfaces (GUIs) for the DICOCH GVP pipeline, showing the main modules. (a) The Generation (G) module for creating DICOM files from source images and tag sheets.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/4c3c7e383d711d8e07b9cd8a.png"},{"id":100405581,"identity":"0ce36f28-bc95-47c3-979e-e3b641432be9","added_by":"auto","created_at":"2026-01-16 12:08:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8204583,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical User Interfaces (GUIs) for the DICOCH GVP pipeline, showing the main modules. (b) The Validation (V) and Publication (P) module for dciodvfy validation and IIIF manifest generation.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/c35d6a7fab94928b8f6a6e9c.png"},{"id":100421456,"identity":"1b849b7e-dc73-46bf-b7fd-b45a93ad448b","added_by":"auto","created_at":"2026-01-16 13:32:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4865004,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical User Interfaces (GUIs) for the DICOCH GVP pipeline, showing the main modules. (c) The supplementary analysis tool(DICOM ROI Cropper) for quantitative HU/ROI measurements and embedding statistics into the DICOM private tags.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/03cd1f39cada809ab8301549.png"},{"id":100406597,"identity":"9f31a0b1-8b12-4555-96a7-c0784e702b6c","added_by":"auto","created_at":"2026-01-16 13:03:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3777068,"visible":true,"origin":"","legend":"\u003cp\u003eX-RAY CR image : CR_Andong Hahoe Mask\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/7ba2b842962f968d371ba74a.png"},{"id":100404946,"identity":"10c283cb-b4be-4e4e-ace4-50768c6eabea","added_by":"auto","created_at":"2026-01-16 12:04:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":686006,"visible":true,"origin":"","legend":"\u003cp\u003eX-RAY CT slice image : CT_slice1177_Andong Hahoe Mask\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/cb4f71193b70c47aa5424573.png"},{"id":100406606,"identity":"94fbe438-4f3c-4f73-9808-835435011303","added_by":"auto","created_at":"2026-01-16 13:03:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1609354,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative CT slice and ROI/point layout (ROI_1–6). \u003cstrong\u003eNote.\u003c/strong\u003eHigh-density features and associated streak artifacts are visible on the right side. ROI/point statistics were stored in 0013,12xx/13xx sequences, with acquisition and calibration context in 0013,14xx sequences.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/9ce0c7a96bb9787ca494b1e9.png"},{"id":100406629,"identity":"97908b14-f46e-4bac-981c-5a9421d3ab9b","added_by":"auto","created_at":"2026-01-16 13:03:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9173833,"visible":true,"origin":"","legend":"\u003cp\u003eMicroDicom view of the CR image—metadata panel, pixel view, and window/level controls.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/c7359fb85f0c34d2996f4f0a.png"},{"id":100406745,"identity":"8dccae95-e10b-4dcc-8511-af9d004c21f0","added_by":"auto","created_at":"2026-01-16 13:03:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5352363,"visible":true,"origin":"","legend":"\u003cp\u003eMicroDicom view of the CT slice—slice viewer, frame of reference, and tag browser\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/f3da4a29c4dad26194f93d92.png"},{"id":100405607,"identity":"f4b93e37-9775-428d-bfab-09493fa8d951","added_by":"auto","created_at":"2026-01-16 12:08:26","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":9821248,"visible":true,"origin":"","legend":"\u003cp\u003eCR rendered in IIIF-Mirador—metadata panel and zoom/pan UI.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/ec45cab63549ef4fee0e2b58.png"},{"id":100406193,"identity":"a73cead8-ca68-4b84-a25a-6347cbdaab59","added_by":"auto","created_at":"2026-01-16 12:53:30","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3050444,"visible":true,"origin":"","legend":"\u003cp\u003eCT rendered in IIIF-Mirador—web-derived image under identical settings..\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/a076483e49c68d9c475b9139.png"},{"id":106343768,"identity":"0f6d0aa6-5e05-4c3c-848a-fb96d306e8ab","added_by":"auto","created_at":"2026-04-07 16:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":54976491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/6accfe4d-847f-4189-9445-7b0fc8ebe254.pdf"},{"id":100406624,"identity":"b17640a1-bfbf-4837-9da6-398bf671af06","added_by":"auto","created_at":"2026-01-16 13:03:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28919,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/e3e3330570a52753b0221e6b.docx"},{"id":100405882,"identity":"4c72a433-6e7a-4eb9-9e33-d73137ef596e","added_by":"auto","created_at":"2026-01-16 12:24:24","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":741799865,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationfinal.zip","url":"https://assets-eu.researchsquare.com/files/rs-8413368/v1/d035a451c8df40cd8708626b.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Pipeline Integrating Cultural Heritage X-ray CR/CT Data Using Digital Imaging and Communications in Medicine","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-destructive evaluation (NDE) of cultural heritage sites is a key method for investigating the internal structure, materials, degradation patterns, and manufacturing processes without compromising the physical and chemical stability of the object. X-ray transmission imaging and computed tomography (CT) quantitatively reveal stratigraphic structures, internal defects, foreign materials, and repair traces, directly informing practical decisions such as conservation treatment, exhibition, and transportation, as well as scholarly interpretations, such as production techniques, use-wear analysis, and dating. However, cultural heritage NDE data possess unique demands that distinguish them from medical imaging and industrial NDT, in that (i) the cost and risk of re-imaging are high and the data are highly irreversible; (ii) historical “context”—such as provenance, conservation history, rights, and ethics—must be preserved along with the image\u003csup\u003e1,2\u003c/sup\u003e; and (iii) they must simultaneously satisfy requirements for long-term preservation, interoperability, and reproducibility across institutions.\u003c/p\u003e\n\u003cp\u003eDespite these requirements, the prevailing practice in the field is the parallel storage of high-resolution raster files (e.g., TIFF/PNG), Excel metadata, PDF reports, and viewer screenshots. This leads to recurring issues such as synchronization failures due to the separation of metadata and images, non-standardized notation of acquisition and reconstruction parameters, differing fields and units across institutions, lack of long-term preservation formats, disconnection from web publications (IIIF), and absence of a machine-readable validation system. For cultural heritage data, which are strongly irreversible, this fragmentation limits reproducibility and subsequent utilization while also hindering the creation of social value through publication, education, and exhibition.\u003c/p\u003e\n\u003cp\u003eInternational digital heritage policies emphasize these issues. The UNESCO “Charter on the Preservation of Digital Heritage” and the PERSIST guidelines call for lifecycle-oriented strategies (creation–preservation–access) and robust metadata and format choices that maintain the integrity of digital objects over time (UNESCO Charter, 2003, https://unesdoc.unesco.org/ark:/48223/pf0000179529; UNESCO PERSIST Guidelines, 2016, https://unescopersist.org/). In parallel, the FAIR and CARE principles stress that research data should be findable, accessible, interoperable, reusable, and governed in a manner that respects the community and rights holders. Therefore, a unified pipeline that embeds quantitative parameters and contextual information within a standardized container, and that connects machine validation and web publication immediately after generation, is an urgent requirement for cultural heritage NDE.\u003c/p\u003e\n\u003cp\u003eThis requirement is particularly critical considering that Cultural heritage X-ray and CT data are resources that combine authenticity and non-repeatability, and there is often only one opportunity to acquire them under safe conditions. Lossless image quality and accurate metadata records at the point of acquisition determine the reliability of subsequent analysis and isomorphic validation. Furthermore, many cultural objects are multi-material composites (e.g., wood, stone, metal, ceramic, and textile); therefore, the transparent disclosure of attenuation properties, scattering and artifact effects, and calibration and reconstruction parameters is essential for interpreting gray values, density contrasts, and segmentation outcomes.\u003c/p\u003e\n\u003cp\u003eAt this stage, numerical parameters from the acquisition–reconstruction phases (e.g., kVp/tube current, SOD/SID, pixel/voxel spacing, slice thickness, and reconstruction kernel) and analysis outputs (e.g., Hounsfield unit (HU) statistics and region-of-interest (ROI) measurements) must be accommodated together in a standard container that preserves their units, semantics, and relationships. This container must also be aligned with rights and ethics contexts such as copyright, access policies, and the handling of sensitive information\u003csup\u003e3\u003c/sup\u003e, as well as with semantic knowledge models in the GLAM sector (e.g., CIDOC-CRM and Europeana data model (EDM)) (ISO 21127:2023 CIDOC CRM, https://www.iso.org/obp/ui/en/#iso:std:iso:21127; Europeana Data Model, https://pro.europeana.eu/page/edm-documentation). Merely linking converted proprietary formats is insufficient to satisfy these requirements. A robust, standards-based approach is required that treats the NDE dataset as a single, validated digital object with both scientific and heritage context, in line with the broader policy directions articulated by UNESCO and related digital-heritage frameworks\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo address similar interoperability challenges, the medical and industrial domains have successfully adoptedDigital Imaging and Communications in Medicine (DICOM) and Digital Imaging and Communication in Non-Destructive Evaluation (DICONDE) have achieved interoperability as combined container-and-protocol standards, encompassing both file formats and transmission protocols (DICOM PS3.3, https://dicom.nema.org/medical/dicom/current/output/chtml/part03/; ASTM E2339-21, https://www.astm.org/e2339-21.html). The successful standardization of DICOM has been extensively discussed in digital pathology and image archiving, emphasizing the importance of information object definitions (IODs), transfer syntaxes, and conformance statements (Clunie, D.A. Toxicol. Pathol. 2020, https://doi.org/10.1177/0192623320965893). In the cultural heritage field, DICOM-inspired metadata architectures and models for accessibility and adaptive narratives have likewise been proposed\u003csup\u003e1,2\u003c/sup\u003e. Ciortan et al.\u003csup\u003e1\u003c/sup\u003e defined a crucial metadata architecture for multimodal acquisition and articulated how imaging data, annotations, and contextual information can be modeled to support advanced analysis and reuse. Nappi et al.\u003csup\u003e2\u003c/sup\u003e presented important models for accessible knowledge organization and adaptive narratives in IIIF-based environments, focusing on how users can explore and interpret heritage images using enriched manifests and annotations. However, these approaches primarily target annotation toolchains, visualization workflows, and interpretive models. They did not address the generation and machine validation of standard-compliant DICOM Part 10 instances, nor did they provide a technical pipeline for embedding quantitative metrics (e.g., HU/ROI) or linking machine validation (dciodvfy) with web publications (IIIF).\u003c/p\u003e\n\u003cp\u003eRecently, quantitative-focused practices have been reported in heritage-related imaging, such as the reconstruction of 3D CT volumes from 2D X-ray equipment\u003csup\u003e5\u003c/sup\u003e, object-tailored beam filtering to optimize contrast and dose\u003csup\u003e6\u003c/sup\u003e, and ultrahigh-resolution micro-inspection of small-scale features\u003csup\u003e7\u003c/sup\u003e. These studies demonstrated the potential of CT and X-ray imaging as quantitative tools for material and structural analyses. However, research that completes an end-to-end practical pipeline consistently embedding such quantitative metrics into a standardized container and linking them through validation and publication remains rare. In particular, there are limited reports that institutionalize DICOM’s Private Creator and Private Group as a systematic dictionary to accommodate cultural heritage-specific contexts, rights, and quantitative metrics, while also demonstrating round-trip compatibility with both medical PACS viewers and web viewers (IIIF). The present study addresses this gap by focusing on the technical implementation, validation, and publication of the core data container itself.\u003c/p\u003e\n\u003cp\u003eTechnically, bridging this gap requires reconciling the distinct architectures of the archival standard and the web interface. DICOM standardizes the imaging container and protocol using a modular structure (PS3.3/3.5/3.6), defining IODs and SOP classes, value representations and multiplicities (VR/VM), unique identifiers (UIDs), and transfer syntaxes(DICOM PS3.3). It also allows for domain extension via private creators and tags, enabling communities such as cultural heritage to define additional attributes without compromising interoperability. Meanwhile, the IIIF Presentation 3.0 specification standardizes high-resolution image publication, multilingual labels, rights/provider information, and thumbnail and renders links through its manifest–canvas–annotation resource model(IIIF Presentation API 3.0, https://iiif.io/api/presentation/3.0/). \u003c/p\u003e\n\u003cp\u003eHowever, IIIF does not provide a built-in standard for scientific numerical attributes, such as voxel spacing, window/level, reconstruction parameters, or HU semantics. Implementing a trusted bridge between the original scientific container (DICOM) and the web interface (IIIF) is essential. Scalable interoperability is achieved only when the format (file), protocol (transfer), and validation are combined. A dataset must be correctly formed as a DICOM object, verifiable by a machine, and consistently transformed into a stable IIIF representation. Approaches relying solely on conversion libraries to link proprietary formats tend to increase risks as the number of objects and software environments grows.\u003c/p\u003e\n\u003cp\u003eFor cultural heritage NDE, a round-trip (write–read–validate) procedure is required: DICOM generation (e.g., via Python/pydicom) → standard-compliance checking (dicom3tools dciodvfy) → IIIF publication through manifests and derivatives (dicom3tools, https://www.dclunie.com/dicom3tools.html;, pydicom, https://pydicom.github.io/pydicom/stable/). Such a procedure should also support the lifecycle-oriented recommendations of UNESCO and align with FAIR/CARE-inspired expectations that heritage-related research data remain trustworthy, reusable, and governed with appropriate community oversight (UNESCO Charter, 2003),(UNESCO PERSIST Guidelines, 2016). Within this context, the technical link between DICOM and IIIF is a key mechanism for implementing policy and ethics requirements in a concrete and verifiable manner(dicom3tools), (pydicom). Unlike previous approaches focused solely on visualization, this study seeks to establish this missing technical link by integrating generation, validation, and publication into a single auditable workflow.\u003c/p\u003e\n\u003cp\u003eTo bridge these gaps and establish this missing technical link, we propose and demonstrate the DICOCH (\"DICOM for Cultural Heritage\") pipeline with four primary contributions. (1) Container extension: A structured subsequence within the DICOM Private Group (0013,xxxx) accommodating cultural heritage context, rights, terminology, IIIF linkage, and HU/ROI calibration/statistics under a fixed Private Creator. (2) Verification-first end-to-end procedure: A generation-validation-publish (GVP) workflow that generates DICOM Part 10 files via Python/Pydicom, validates them with dicom3tools dciodvfy, and automatically produces IIIF Presentation 3.0 manifests. (3) Operational profile and quality metrics: a practical CT/CR minimum metadata profile, VR/VM and UID consistency checks, and a rule-based normalization of warning types (e.g. \"Not in IOD,” \"Retired,” \"Private\") to guide quality assurance. (4) Case-based demonstration: A representative CR/CT dataset (Andong Hahoe Mask) that attains dciodvfy errors = 0 and warnings = 0 demonstrates interoperable viewing in both PACS-type DICOM software and an IIIF-based web viewer.\u003c/p\u003e\n\u003cp\u003eAligned with the international norms of The UNESCO “Charter on the Preservation of Digital Heritage” and PERSIST (2016) guidelines recommend that digital surrogates of cultural heritage be managed as coherent, sustainable objects throughout their lifecycle, from creation through preservation to access (UNESCO Charter, 2003; UNESCO PERSIST Guidelines, 2016). They emphasize format strategies and minimum metadata that preserve interpretability and reuse over long timescales. Within this policy landscape, DICOCH can be understood as an operational profile using DICOM as the canonical archival container and IIIF as a public-facing interface, translating high-level norms into concrete, machine-verifiable data structures and workflows.\u003c/p\u003e\n\u003cp\u003eThe DICOCH pipeline targets both 2D X-ray (CR) and 3D CT data of cultural heritage objects. In this study, a representative CR/CT dataset from a Korean cultural heritage site illustrates how the proposed schema and workflow operate on real institutional data. All procedures were applied in a version-controlled environment, ensuring that identical inputs yielded identical outputs, supporting reproducibility and auditability at both the data and software levels. The implementation stack, including Python/pydicom, dicom3tools/dciodvfy, and IIIF Presentation 3.0, along with specific tag sets and materials, is described in the following sections. Section 2 introduces the overall pipeline and DICOCH schema (including the Private 0013 dictionary) and explains the GVP procedure. Section 3 summarizes the materials, datasets, and tag templates used in this study. Section 4 applies the pipeline to case data and reports the quantitative and interoperability results. Section 5 discusses the findings and their implications for cultural heritage NDE and digital heritage policy. Section 6 concludes the paper and outlines directions for future research.\u003c/p\u003e"},{"header":"2. Methods ","content":"\u003cp\u003eThis study proposes a consistent procedure (the DICOCH pipeline) for generating, validating, and publishing cultural heritage NDE images in compliance with the DICOM standard. The overall GVP workflow is illustrated in Fig. 1.\u003c/p\u003e\n\u003cp\u003e2.1 Design Principles\u003c/p\u003e\n\u003cp\u003eThe DICOCH pipeline is based on the following four key principles:\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eStandards Compliance: Adheres primarily to the IOD\u0026middot;VR/VM rules of DICOM PS3.3/PS3.5/PS3.6, aiming for \u0026apos;errors=0\u0026apos; at each stage as verified by the official validator (dciodvfy).\u003c/li\u003e\n \u003cli\u003eIndependence: Strictly maintain the integrity of standard public tags. Cultural-heritage-specific context, quantification, rights, and web linkage information were independently added using Private Group (0013,xxxx) sequences.\u003c/li\u003e\n \u003cli\u003eWeb Interoperability: Embeds stable URIs for the IIIF Presentation 3.0 Manifest and Image Service, enabling the generation\u0026ndash;validation\u0026ndash;publication\u0026ndash;viewing chain to operate seamlessly.\u003c/li\u003e\n \u003cli\u003eReproducibility: Ensures an audit trail by packaging transformation parameters, tool versions, checksums, and private dictionary text with identical timestamps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 DICOCH Schema and Tag Mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Excel- or CSV-based tag table was used as the tag input. This table defines the core information for constructing the DICOM dataset.\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eTag Address (hex, e.g., 0x0013,0x1100)\u003c/li\u003e\n \u003cli\u003eValue Representation (VR)\u003c/li\u003e\n \u003cli\u003eStandard Keyword\u003c/li\u003e\n \u003cli\u003eActual Value\u003c/li\u003e\n \u003cli\u003eHuman-readable Label (used for IIIF metadata, etc.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe parser reads this table, cross-validates the tag address and keywords, and prioritizes the VR rule for data serialization if conflicts occur (e.g., if VR = DS). Missing values (blanks, \u0026quot;NONE,\u0026rdquo; etc.) are processed according to DICOM Type 1/2/3 rules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 DICOCH Tag Schema Mapping Principles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTag injection rules were divided into modality-specific (CT, CR) and common rules, all complying with the DICOM IOD definitions (PS3.3) and the Data Dictionary (PS3.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1 CT (Computed Tomography)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT data uses CT Image Storage (1.2.840.10008.5.1.4.1.1.2) as the default SOP Class. For quantitative reproducibility, RescaleIntercept (0028,1052), RescaleSlope (0028,1053), and RescaleType (0028,1054=HU) were mandatory. For 3D registration, ImagePositionPatient (0020,0032), ImageOrientationPatient (0020,0037), and FrameOfReferenceUID (0020,0052) are completed, along with Z-axis parameters such as SliceThickness (0018,0050). PixelSpacing (0028,0030) was used for the in-plane scale, and WindowCenter (0028,1050) / WindowWidth (0028,1051) were recorded as the initial display hints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 CR (Computed Radiography)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCR data used CR Image Storage (1.2.840.10008.5.1.4.1.1.1) as the default SOP Class. The standard for length/distortion interpretation is unified under Imager Pixel Spacing (0018,1164) and is not conflated with CT pixel Spacing (0028,0030). The projection context (ViewPosition 0018,5101) and target context (BodyPartExamined 0018,0015) are met as Type 2, and the related parameters (e.g., Cassette/Plate/Filter/Exposure) are included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 Common Rules\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNonstandard input (e.g., missing source metadata) falls back to Secondary Capture (1.2.840.10008.5.1.4.1.1.7), for the reason specified in the DerivationDescription (0008,2111). The transfer syntax was fixed to Explicit VR Little Endian (1.2.840.10008.1.2.1). The internal consistency of the pixel core (e.g., photometric interpretation =MONOCHROME2) was ensured. All UIDs are guaranteed to be globally unique and SpecificCharacterSet = ISO_IR 192 (UTF-8) is declared for multilingual labels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Core Tag Set Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with DICOM PS3.3/PS3.6, tags were organized into modality-shared core and modality-specific items. This separation is required to satisfy both PACS/general viewer compatibility and densification validation stability requirements. The shared core (Table 2) defines the fundamental pixel data and display parameters, whereas the modality-specific sets define the geometry and quantitative meaning of CT (Table 3) and CR (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcquisition Protocol and Tag CT Set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe acquisition protocol specified SliceThickness, SpacingBetweenSlices (if applicable), PixelSpacing, ImagePosition/OrientationPatient, and FrameOfReferenceUID. For quantitative reproducibility, RescaleIntercept = -1024, RescaleSlope = 1, and RescaleType = HU were set as defaults. If inter-equipment/inter-scan calibration exists, the calibration standards and coefficients are recorded in the HU Calibration Sequence (0013,1400).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcquisition Protocol and Tag CR Set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol recorded KVP, tube current, exposure, and source-to-object/detector distances. Resolution was reported using ImagerPixelSpacing (0018,1164). For cases where human anatomical terms were unsuitable, BodyPartExamined and ViewPosition maintained standard codes, while context was supplemented in the private group to ensure interpretation within the projection/detector context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 DICOCH Private Group (0013,xxxx) Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Private Group (0013,xxxx) was designed to encapsulate heritage-specific metadata (context, quantification, Web, and rights). All private blocks are named Private Creator (0013,0010) = NRICH_DICOCH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1 Design Principles:\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eIndependence: Augments public tags without overwriting them.\u003c/li\u003e\n \u003cli\u003eReadability: Strictly adheres to the VR/VM rules and the UTF-8 chart.\u003c/li\u003e\n \u003cli\u003eValidation Usability: Embeds a self-describing private dictionary to allow external tools to identify tags.\u003c/li\u003e\n \u003cli\u003eWeb Interoperability: Isolates IIIF links in a separate sequence to maintain viewer independence.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2 Root Sequences (Table 1):\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003e(A) HeritageMetadataSequence (0013,11xx): Humanities/conservation context (name, ID, period, material, condition).\u003c/li\u003e\n \u003cli\u003e(B) GrayValue-HU_PointSequence / GrayValue-HU_ROISequence (0013,12xx / 13xx): Coordinates (point/polygon) and Gray/HU statistics (Mean/SD/Min/Max).\u003c/li\u003e\n \u003cli\u003e(C) HUCalibrationSequence (0013,14xx): Basis and coefficients for HU calibration. (Current version uses medical defaults: Intercept = -1024, Slope = 1, Type = HU).\u003c/li\u003e\n \u003cli\u003e(D) IIIFInternationalNormLinkSequence (0013,15xx): Embedded stable URIs for Manifest, Image Service, and Rights.\u003c/li\u003e\n \u003cli\u003e(E) SecurityAccessSequence (0013,16xx): Access/integrity (AccessLevel, EmbargoUntil, Checksum).\u003c/li\u003e\n \u003cli\u003e(F) ModalityExtension (0013,17xx/18xx): Descriptive mirror (summary layer) for CR/CT.\u003c/li\u003e\n \u003cli\u003e(G) PrivateDictionary/Readme (0013,19xx): Dictionary text and version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Python Pipeline Implementation (GVP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pipeline is implemented in Python, chosen for its extensive ecosystem of open-source libraries for image processing and DICOM handling (e.g., pydicom, numpy), as well as its cross-platform compatibility. It uses the core libraries of pydicom (DICOM I/O), numpy (numerical processing), pandas (tag table loading), and pillow/tiff-file (image loading). The pipeline was operated using a series of Python-based graphical user interfaces (GUIs) (Figs. 2\u0026ndash;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1 Generation\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eFile-meta \u0026amp; SOP Synchronization: The SOP Class is selected based on the IOD, and the file metadata and dataset UIDs are generated simultaneously to prevent mismatches (see Fig. 2).\u003c/li\u003e\n \u003cli\u003ePixel/Display Core Automation: Check the dynamic range of the source images (TIFF/PNG, etc.) and normalize the bit depth. WindowCenter/Width was auto-estimated based on percentiles, preserving any user-defined values.\u003c/li\u003e\n \u003cli\u003eModality Branching: The CT path prioritizes 3D coordinate/quantification tags (e.g., FrameOfReferenceUID, Rescale). The CR path maintains 2D projection attributes (e.g., ImagerPixelSpacing) and removes unnecessary 3D tags (e.g., IPP/IOP) to ensure IOD purity.\u003c/li\u003e\n \u003cli\u003eTag Parser: Parses the Excel/CSV tag sheet, auto-configures sequence (SQ) hierarchies based on parent tags, and generates Private Dictionary text.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.2 Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cul class=\"decimal_type\" start=\"50\"\u003e\n \u003cli\u003edciodvfy Execution: Calls the standard validator (dciodvfy.exe) to verify IOD, module, and VR/VM compliance of the generated DICOM files.\u003c/li\u003e\n \u003cli\u003eDictionary Injection: When calling the validator, the private DICOCH dictionary is injected using the DCMDICTPATH environment variable. This allows the validator to recognize (0013,xxxx) tags by their defined labels instead of \u0026quot;Unrecognized tag,\u0026quot; producing a human-readable validation log.\u003c/li\u003e\n \u003cli\u003eResult Reporting: Validation results were output as per-file JSONL/text dumps and an aggregate HTML report (see Fig. 3).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.3 Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eDICOM\u0026rarr;JPEG Derivative (8-bit) Generation Algorithm: Generates 8-bit JPEG derivatives from the original DICOM for IIIF web viewer compatibility (e.g., Mirador) (see Fig. 4).\u0026nbsp;Algorithm (i) applies the Modality LUT (rescale), (ii) applies the VOI LUT (window/level), (iii) uses percentile scaling if no VOI is present, and (iv) performs 8-bit mapping and JPEG encoding. All transformation parameters were logged.\u003c/li\u003e\n \u003cli\u003eIIIF Presentation 3.0 Manifest Generation: An IIIF manifest (JSON) file is autogenerated for each DICOM instance. The Canvas metadata field of the manifest includes a summary of the key tags (public and private) from the original DICOM. Right information (e.g., rights and providers) is also standardized at this stage.\u003c/li\u003e\n \u003cli\u003eNote: The DICOCH pipeline does not implement the IIIF Image Server (e.g., Cantaloupe, IIPImage). The \u0026ldquo;Publication\u0026rdquo; step refers to generating a server-ready package consisting of 8-bit JPEG derivatives and structured metadata (IIIF Presentation 3.0 Manifest). This package is automatically generated from the validated DICOM Part 10 file and is ready for immediate deployment on any standard IIIF-compliant server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Case Study Object and Equipment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section describes the case study object, imaging equipment and environment, acquisition protocols for CR and CT, and the structure of the tag sets used (shared, modality-specific, and Private Group 0013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCase Study Object\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe case study object was the Andong Hahoe Mask (National Treasure No. 121) (Figs. 5 and 6). Hahoe Masks are the oldest surviving wooden masks in Korea, dating back to the Goryeo Dynasty (mid-12th century). Used in the Hahoe Pyolshin-gut Tal-nori (Mask Dance Drama), these masks are carved from alder wood and are renowned for their separate chin structure (in the case of the Yangban mask), which allows for a variety of expressions. As a designated national treasure, these masks represent a critical class of organic cultural heritage that requires strict conservation monitoring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis object was selected as a representative case study for low-attenuation organic materials, which presents a baseline challenge for data standardization. The unique cultural heritage context, including the object\u0026apos;s proper name, management number, material, and conservation status, was recorded in the structured HeritageMetadataSequence (0013,1100) within the DICOCH Private Group. This serves to permanently embed object-centric historical, rights, and institutional contexts within the DICOM file, which are not covered by standard medical or industrial protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEquipment and Environment\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCR Equipment: A GE CRxVision system (for imaging) and Rhythm Review (for reading and verification) were used. Length/distortion analysis was based on ImagerPixelSpacing (0018,1164), supplemented with ViewPosition, Cassette/Plate/Filter, and Exposure/ExposureTime (detailed in Table 6).\u003c/li\u003e\n \u003cli\u003eCT Equipment: An SEC (Republic of Korea) X-eye 7000b system was used with CT-eye software for reconstruction. Key acquisition/reconstruction parameters (kVp, tube current/exposure, projections if available, SOD/SID, PixelSpacing, SliceThickness/SpacingBetweenSlices) were recorded according to the standard core (detailed in Table 7). For quantitative reproducibility, RescaleIntercept/Slope/Type were set to Hounsfield units (HU). Equipment and software identifiers (Manufacturer, ManufacturerModelName, Software Versions, StationName, DeviceSerialNumber) were recorded as public tags, with summary metadata mirrored in the Private 0013 ModalityExtension (CT/CR).\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis section reports the results of applying the DICOCH-based generation-validation-publication pipeline to the Hahoe Mask case study, based on the equipment, environment, and tag sets described in Section 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Case Study Overview and Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subject of this study was the Hahoe Mask (National Treasure No. 121), a wooden cultural heritage object from Korea. This object was selected as a representative case study for low-attenuation organic materials, which present a baseline challenge for data standardization. As noted in the limitations (Section 6), high-density or composite materials (e.g., metals and ceramics), which are known to produce significant imaging artifacts, were not included in this study and represent a key area for future validation of the pipeline's robustness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOriginal images were processed in the DICOCH format alongside the vendor DICOM formats for generation, validation, and publication. Input metadata were transcribed from the provided tag spreadsheets (see Table 5), and outputs included checksums and validation logs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 CR Application Results: Generation and Tag Conformance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SOP Class was set to CR Image Storage (1.2.840.10008.5.1.4.1.1.1). Internal consistency of the pixel module was confirmed (PhotometricInterpretation=MONOCHROME2, Rows/Columns, BitsAllocated/Stored/HighBit; PixelRepresentation). Resolution was specified using ImagerPixelSpacing (0018,1164). BodyPartExamined (0018,0015) and ViewPosition (0018,5101) were recorded as Type 2. The cultural heritage context (object name, identifier, material, condition, investigation purpose, etc.) was structured into a Private Group (0013,xxxx) via the HeritageMetadataSequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 CT Application Results: A 'Context-First' Approach to HU Standardization and ROI Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA primary challenge in standardizing CT data for cultural heritage is the reliable interpretation of quantitative values (Hounsfield units, HU). As discussed in detail in Section 5.5, HU values derived from medical standards are not directly comparable across different scanners or non-human tissue materials (e.g., wood and metal). Therefore, the goal of the DICOCH pipeline is not to assert absolute material-calibrated HU values but rather to (1) preserve the internal monotonic data for reproducibility and (2) structurally record the full context of measurement (calibration parameters, ROIs, and statistics) for verifiable, context-aware analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Generation and Geometric Consistency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT output was generated using CT Image Storage (1.2.840.10008.5.1.4.1.1.2). Three-dimensional geometric consistency was ensured through ImagePosition/Orientation (Patient), FrameOfReferenceUID, SliceThickness, and PixelSpacing. The representative slice (instance #1177) shows a high-density feature on the right, likely a metal fastener, with associated streaking artifacts (Fig. 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 HU scaling: limitations, rationale, and remedy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide a manufacturer-independent, reproducible physical scale compatible with standard medical viewers, the baseline medical transforms RescaleIntercept = -1024, RescaleSlope = 1, and RescaleType = HU were applied, preserving a monotonic mapping from stored pixel values to HU-formatted CT numbers.\u003c/p\u003e\n\u003cp\u003eDICOCH introduces structured storage for HU records to ensure scientific interpretability and machine reuse:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGrayValue-HU_PointSequence (0013,12xx) and DICOCH_GrayValue-HU_ROISequence (0013,13xx) store coordinate-based points and 64×64 px square ROI statistics as paired grayscale + HU-formatted values.\u003c/li\u003e\n \u003cli\u003eThe HUCalibrationSequence (0013,14xx) captures the calibration provenance: phantom type/levels, tube potential and filtration, reconstruction kernel, rescaled parameters, and uncertainty metrics.\u003c/li\u003e\n \u003cli\u003eThe Python DICOM ROI Cropper (Fig. 4) allows runtime setting of RescaleSlope and RescaleIntercept, persisting in both the standard pixel transform (0028,1052/1053/1054) and the HU Calibration Sequence (0013,14xx). The default remains (−1024, 1) for medical compatibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote on HU values: The HU values presented in Table 8 are 'HU-formatted CT numbers' derived from the standard medical rescale (RescaleIntercept =\u0026nbsp;−1024, RescaleSlope = 1). These values provide reproducible internal contrast for this scan but should not be interpreted as absolute material properties. Structured DICOCH storage (0013,14xx) captures the necessary context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3 ROI/Point Measurement Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoordinate-based points were recorded in GrayValue-HU_PointSequence (0013,12xx), and 64×64 px ROI statistics in DICOCH_GrayValue-HU_ROISequence (0013,13xx). For example, ROI-1 (air) measured\u0026nbsp;−1024.0 HU (SD 0.0), while ROI-3 (wood core) and ROI-4 (pigment) fell in the 984–987 HU range. ROI-6 (artifact affected by streaking) showed higher variability (≈1042 HU, SD 34.5) (Table 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eImplications for interoperability and AI reuse\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy encoding both the applied transform (slope/intercept) and the calibration/uncertainty context in machine-readable sequences, DICOCH separates “HU-formatted” values from “calibrated HU,” preserves traceability, and supports reproducible analysis and model training. This approach also aligns with curatorial standards for long-term stewardship. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Standards Conformance, Vendor Benchmarking, and Interoperability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe validated both the DICOCH outputs and vendor-native DICOM files (Vendor DCM) from the same equipment under identical conditions and then performed real-world interoperability tests across a PACS-style viewer and a web-publication workflow (IIIF). The consolidated findings are summarized in Table 9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 Key Outcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor both CR and CT, DICOCH achieved 0 errors / 0 warnings using dciodvfy. Informational notices arose only from heritage-specific private tags (Group 0013), whose dictionaries are not yet publicly registered: 146 notices for CR and 157 for CT. These notices are not standards violations; the validator reports them as “unrecognized tag—assuming explicit VR OK.”\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder the same validator and options, the Vendor DCM exhibited non-trivial issues: CR: 23 errors / 10 warnings; CT: 5 errors / 10 warnings—dominated by: (i) missing required Type 1/2/2C attributes, (ii) file-meta vs. dataset UID mismatches, (iii) invalid VR/VM (e.g., non-numeric strings in IS/DS), and (iv) attribute conflicts (e.g., PixelAspectRatio vs. PixelSpacing). By contrast, DICOCH prevents these error classes through IOD-specific template enforcement, value range checks, explicit type normalization, and unified UID propagation across the file meta and dataset. For downstream use, DICOCH files behaved normally in a PACS viewer (tag browser, grayscale display, and spatial scale) and in the IIIF pipeline (manifest generation and rendering), whereas some vendor files with the above errors showed degraded behavior in specific viewers (e.g., frame-of-reference linkages and display-scale inconsistencies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Interpretation Note\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA dciodvfy pass (0 errors / 0 warnings) is necessary but not sufficient for end-to-end interoperability. Accordingly, we document viewer/server checks (PACS-style viewer and IIIF web delivery) alongside full validator environments, commands, and raw logs in the Supplementary Material to allow exact reproduction of results.\u003c/p\u003e\n\u003cp\u003eThe vendor errors (Table 10) typically include: (i) missing required Type 1/2/2C attributes, (ii) file-meta/dataset UID mismatches, (iii) invalid VR/VM, and (iv) conflicting attributes. The DICOCH pipeline preemptively resolves these issues through template enforcement, value-range validation, type conversion, and unified UID propagation, achieving 0 Errors/0 Warnings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3 Interoperability and Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The DICOCH-generated CR and CT files were verified to run without issues in a commercial DICOM viewer (MicroDicom 2025.3) including the proper operation of the metadata panel, pixel inspection, window/level control, and tag browser, as documented in Figs. 8 and 9.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn parallel, the automatically produced IIIF Presentation 3.0 manifests were successfully rendered in the Mirador web viewer, enabling a standard-based metadata display, high-resolution zoom and panning, and multi-image comparison (Figs. 10–11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these results confirm that a single dciodvfy-validated DICOCH source object can simultaneously supply the medical/industrial viewing toolchain (PACS-type DICOM viewers) and the cultural-heritage web ecosystem (IIIF-based viewers such as Mirador) from one authoritative dataset (MicroDicom, https:/\u003ca href=\"https://www.microdicom.com\"\u003e/\u003c/a\u003ewww.microdicom.com/; Mirador, https\u003ca href=\"https://projectmirador.org\"\u003e:\u003c/a\u003e//projectmirador.org/).\u003c/p\u003e\n\u003ch3\u003e3.5 Reproducibility and Public Availability\u003c/h3\u003e\n\u003cp\u003eTo ensure full reproducibility, a complete package—including generation scripts (versioned with dependencies), dciodvfy logs, IIIF manifests, and a Private Tag dictionary—is provided. As detailed in the \"Data Availability\" and \"Code Availability\" declarations, the full, version-controlled codebase and generated datasets will be deposited in a public GitHub repository maintained by the National Research Institute of Cultural Heritage (NRICH) and archived with a DOI. This allows external users to replicate the GVP workflow and validate outputs against the NRICH reference results\u003c/p\u003e\n\u003ch3\u003e3.6 Summary of Results\u003c/h3\u003e\n\u003cp\u003eBoth the CR and CT outputs satisfied all mandatory and conditional tags, maintaining the internal conformance of the IOD, pixel, and geometry modules. The CT data preserved monotonic HU transformation by applying Rescale(−1024, 1, HU) and produced standardized 64×64 px ROI/Point statistics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DICOCH Private Group (0013,xxxx) successfully structured and provided extended metadata, including the heritage context, IIIF links, security/rights information, and quantitative ROI/point statistics. The discrepancy results (zero errors, zero warnings), especially when contrasted with vendor-native files, confirmed the necessity and efficacy of the verification-first approach. The model was validated through successful interoperability tests using MicroDicom and Mirador software.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study proposes and demonstrates the DICOCH pipeline, which integrates the entire generation–validation–publication (GVP) process(Table 11.). for cultural heritage NDE data into a single, standardized procedure. The key contributions are: (i) designing an orthogonal Private Group (0013,xxxx) to co-locate cultural context, rights, IIIF links, and HU/ROI quantitative metadata with standard DICOM tags within the same object; (ii) establishing an automated \"verification-first\" workflow; and (iii) confirming interoperability across both commercial PACS viewers and web-based IIIF viewers. A key finding is the DICOCH Private Group’s (0013,14xx) ability to record calibration context, shifting the field beyond simple CT numbers toward verifiable quantitative analyses. In this model, DICOCH acts as the audited “trusted bridge” connecting the preservation-grade original (DICOM) with the access-ready derivatives (IIIF Manifest, JPEGs), ensuring metadata consistency between archival sources and web dissemination.\u003c/p\u003e\n\u003cp\u003eThe design of the (0013,xxxx) Private Group is intended not as a rigid institutional silo but as an “open reference implementation” for adoption and co-governance by the wider cultural heritage community. By publicly releasing the code and Private Dictionary on GitHub, other institutions can be invited to validate and extend this schema. This open-access approach is a necessary first step toward building the consensus required for a formal DICOM PS3.6 Change Proposal, ensuring long-term sustainability. Cultural heritage NDE data must satisfy specific requirements—provenance, material condition, rights, and IIIF publication—that differ from clinical or industrial contexts. Table 11 outlines how DICOCH systematically fills the remaining gaps through existing frameworks(Table 12.).\u003c/p\u003e\n\u003cp\u003eThe pipeline adheres to a \"verification-first\" principle. Python/Pydicom scripts enforce Type 1/2/1C/2C conditions and VR formats (e.g., IS/DS) during generation. CT data preserve HU monotonicity and geometric completeness. For validation, dciodvfy performs full IOD conformance checks with a fail-fast (errors = 0) policy. Private items are treated as notices, and a public-private dictionary ensures interpretability. This automated chain (Generation → dciodvfy → IIIF) enhances reproducibility by ensuring identical output for identical input in a version-locked environment and increases data reliability through proactive enforcement of DICOM rules.\u003c/p\u003e\n\u003cp\u003eDespite these advancements, several limitations must be acknowledged. Currently, HU interpretation relies on medical reference data, which may not be fully valid for heterogeneous heritage materials (wood, ceramics, metals) with variable moisture and degradation states. HU values are highly sensitive to equipment, kVp, reconstruction kernel, and object size; inter-scanner and size-dependent variations have been widely reported\u003csup\u003e8,9,15\u003c/sup\u003e. Clinical CT is further affected by beam hardening and manufacturer-specific algorithms, causing HU shifts of several units or percent even for the same material\u003csup\u003e13,14\u003c/sup\u003e. Furthermore, this study was validated using a single organic object (the Hahoe Mask). High-density materials like lead-glazed ceramics or gilt-bronzes present additional challenges, such as photon starvation and severe streaking artifacts, which may affect both quantitative ROI analysis and visual coherence in web derivatives.\u003c/p\u003e\n\u003cp\u003eFuture pathways involve establishing material-aware HU calibration profiles using custom heritage phantoms and theoretical models based on NIST XCOM mass attenuation coefficients (https://physics.nist.gov/PhysRefData/Xcom/html/xcom1.html). Technical alternatives like DECT/Spectral CT and PCD-CT may reduce energy dependency and improve quantification accuracy\u003csup\u003e11,12\u003c/sup\u003e. We recommend performing regular number of CT accuracy audits referencing QA frameworks such as AAPM TG-233 (https://www.aapm.org/pubs/reports/RPT_233.pdf). Recent studies demonstrate the viability of clinical CT for wood density via cross-validation with FTIR/Raman spectroscopy\u003csup\u003e16\u003c/sup\u003e and the potential for expanded CT use in museums through 3D reconstruction from 2D equipment\u003csup\u003e5,17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn conclusion, DICOCH reconciles standard compliance with openness, aligning with international digital preservation principles for long-term access. To operationalize these findings, we will release the DICOCH scripts, generated DICOM instances, and IIIF manifests in a public GitHub repository maintained by the Cultural Heritage Conservation Science Center, NRICH. This open framework provides a practical foundation to move beyond ambiguous imaging toward robust, verifiable, and FAIR-compliant data for the global cultural heritage community.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcronyms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpansion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAPM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eAmerican Association of Physicists in Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCARE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eCollective Benefit, Authority to Control, Responsibility, and Ethics (Data Principles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIDOC-CRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eCIDOC Conceptual Reference Model (ISO 21127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eComputed Radiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDECT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDual-Energy Computed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDICOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDigital Imaging and Communications in Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDICOCH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDigital Imaging and Communication in Cultural Heritage (Proposed Pipeline)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDICONDE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDigital Imaging and Communication in Non-Destructive Evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDigital Object Identifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDecimal String (DICOM Value Representation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eEuropeana Data Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eFindable, Accessible, Interoperable, and Reusable (Data Principles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFTIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eFourier Transform Infrared Spectroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eGalleries, Libraries, Archives, and Museums\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGUI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eGraphical User Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGVP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eGeneration-Validation-Publication (Workflow)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eHounsfield Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIIIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eInternational Image Interoperability Framework\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIOD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eInformation Object Definition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eInternational Organization for Standardization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJPEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eJoint Photographic Experts Group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJSON\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eJavaScript Object Notation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJSONL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eJavaScript Object Notation Lines\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ekVp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eKilovoltage Peak\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eLook-Up Table\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eMulti-Planar Reconstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eNon-Destructive Evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eNon-Destructive Testing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eNational Institute of Standards and Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRICH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eNational Research Institute of Cultural Heritage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePACS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003ePicture Archiving and Communication System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003ePhoton-Counting Detector Computed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePERSIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003ePlatform to Enhance the Sustainability of the Information Society (UNESCO Guidelines)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePNG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003ePortable Network Graphics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eQuality Assurance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eRegion of Interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eSource-to-Image Distance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eSource-to-Object Distance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eService-Object Pair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIFF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eTagged Image File Format\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eUnique Identifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eURI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eUniform Resource Identifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eValue Multiplicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eValue of Interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eValue Representation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eXCOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003ePhoton Cross Sections Database (NIST)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edciodvfy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 507px;\"\u003e\n \u003cp\u003eDICOM Validator Software (part of dicom3tools)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Supplementary Information package accompanies this study to ensure full reproducibility. It is organized into distinct resources for Computed Tomography (CT) and Computed Radiography (CR) and contains modality-specific DICOCH private dictionaries, image input templates, and sample IIIF manifests. Importantly, to substantiate the comparative analysis presented in Table 9, raw Dciodvfy audit logs are provided for both the DICOCH-generated files (confirming zero errors) and the original vendor-native files. Upon acceptance of this article, the complete and versioned datasets will be deposited in a permanent, publicly accessible repository (e.g., Zenodo or GitHub) maintained by the Cultural Heritage Conservation Science Center of the National Research Institute of Cultural Heritage (NRICH). Full-resolution CR/CT data of cultural heritage objects will be released only in downsampled or anonymized forms in accordance with NRICH data-sharing policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe custom Python software implementing the DICOCH pipeline is provided in the Supplementary Information (Source_Code.zip). The package includes three core modules: (a) the DICOM Converter for standard-compliant generation, (b) the Validation \u0026amp; Publication module for automated dciodvfy integration and IIIF manifest creation, and (c) the ROI Cropper tool for quantitative HU analysis and statistical embedding. Detailed execution instructions and dependency requirements are included in the accompanying README file. After acceptance, the complete version-controlled codebase will be made publicly available in a GitHub repository and archived with a DOI through Zenodo to ensure long-term accessibility and citability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of the Scientific Diagnosis and Research on the Application of Conservation Treatment Technology for Organic Cultural Heritage research project, supported by the National Research Institute of Cultural Heritage through the Cultural Heritage R\u0026amp;D Program. No specific grant number.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval \u0026amp; Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study involved non-human subjects and adhered to institutional ethics guidelines. The publication of images and metadata followed the policies of the holding institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions (CRediT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIJS: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing \u0026mdash; Original Draft, Writing \u0026mdash; Review and Editing, Visualization, Supervision, Project Administration, and Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCiortan, I. M. et al. A DICOM-inspired metadata architecture for managing multimodal acquisitions in cultural heritage. in \u003cem\u003eProceedings of the Digital Cultural Heritage\u003c/em\u003e (ed. Ioannides, M.) 37-49. 10.1007/978-3-319-75826-8_4 (Lecture Notes in Computer Science (Springer, Cham, 2018)).\u003c/li\u003e\n\u003cli\u003eNappi, M. L., Buono, M., Chivăran, C. \u0026amp; Giusto, R. M. Models and tools for the digital organisation of knowledge: accessible and adaptive narratives for cultural heritage. \u003cem\u003eHerit. Sci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 112 (2024). 10.1186/s40494-024-01219-z.\u003c/li\u003e\n\u003cli\u003eStylianidis, E. Recording and documenting cultural heritage. in \u003cem\u003eProceedings of theexploring the Ethical Dimension in Recording and Documenting Cultural Heritage\u003c/em\u003e 25-46 (Springer Nature, Cham, 2025). 10.1007/978-3-031-80034-4_2.\u003c/li\u003e\n\u003cli\u003eAmico, N. \u0026amp; Felicetti, A. 3D data long-term preservation in cultural heritage. (2024). 10.48550/arXiv.2409.04507. \u003c/li\u003e\n\u003cli\u003eBossema, F. G. et al. Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 3939 (2024). 10.1038/s41467-024-48102-w, PubMed: 38744870.\u003c/li\u003e\n\u003cli\u003eKiss, M. B. et al. Beam filtration for object-tailored X-ray CT of multi-material cultural heritage objects. \u003cem\u003eHerit. Sci.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 130 (2023). 10.1186/s40494-023-00970-z.\u003c/li\u003e\n\u003cli\u003eJaques, V. A. J. et al. X-ray high resolution computed tomography for cultural heritage material micro-inspection. \u003cem\u003eProc. \u003c/em\u003e\u003cem\u003eSPIE\u003c/em\u003e \u003cstrong\u003e11784\u003c/strong\u003e (2021). 10.1117/12.2592310.\u003c/li\u003e\n\u003cli\u003eSande, E. P. S., Martinsen, A. C. T., Hole, E. O. \u0026amp; Olerud, H. M. Interphantom and interscanner variations for Hounsfield units\u0026mdash;establishment of reference values for HU in a commercial QA phantom. \u003cem\u003ePhys. Med. Biol.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 5123-5135 (2010). 10.1088/0031-9155/55/17/015, PubMed: 20714048.\u003c/li\u003e\n\u003cli\u003eZheng, X. et al. Body size and tube voltage dependent corrections for Hounsfield Unit in medical X-ray computed tomography: theory and experiments. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 15696 (2020). 10.1038/s41598-020-72707-y, PubMed: 32973237.\u003c/li\u003e\n\u003cli\u003eSzyszko, J. A., Aldieri, A., La Mattina, A. A. \u0026amp; Viceconti, M. Phantomless calibration of CT scans for hip fracture risk prediction in silico: comparison with phantom-based calibration. \u003cem\u003ePLOS One\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, e0305474 (2024). 10.1371/journal.pone.0305474, PubMed: 38875268.\u003c/li\u003e\n\u003cli\u003eGreffier, J., Villani, N., Defez, D., Dabli, D. \u0026amp; Si-Mohamed, S. Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. \u003cem\u003eDiagn. \u003c/em\u003e\u003cem\u003eInterv. Imaging\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 167\u0026ndash;177 (2023). 10.1016/j.diii.2022.11.003, PubMed: 36414506.\u003c/li\u003e\n\u003cli\u003eSalyapongse, A. M. et al. CT number accuracy and association with object size: a phantom study comparing energy-integrating detector CT and deep silicon photon-counting detector CT. \u003cem\u003eAJR Am. J. Roentgenol.\u003c/em\u003e \u003cstrong\u003e221\u003c/strong\u003e, 539\u0026ndash;547 (2023). 10.2214/AJR.23.29463, PubMed: 37255042.\u003c/li\u003e\n\u003cli\u003eMarques, J. B., Renha, S. K., Mendon\u0026ccedil;a Pereira, H. M., Lima, T. V. M. \u0026amp; Sim\u0026otilde;es, R. F. P. Effects of convolution filter with beam hardening correction on computed tomography image quality. \u003cem\u003ePhys. Med.\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 102599 (2023). 10.1016/j.ejmp.2023.102599, PubMed: 37167777.\u003c/li\u003e\n\u003cli\u003eChacko, M. S., Wu, D. H., Grewal, H. S. \u0026amp; Sonnad, J. R. Impact of beam-hardening corrections on proton relative stopping power estimates from single- and dual-energy CT. \u003cem\u003eJ. Appl. \u003c/em\u003e\u003cem\u003eClin. Med. Phys.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e13711 (2022). 10.1002/acm2.13711, PubMed: 35816460.\u003c/li\u003e\n\u003cli\u003eNakao, M. et al. CT number calibration audit in photon radiation therapy. \u003cem\u003eMed. Phys.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1571-1582 (2024). 10.1002/mp.16887, PubMed: 38112216.\u003c/li\u003e\n\u003cli\u003eLongo, S., Corsaro, C., Granata, F. \u0026amp; Fazio, E. Clinical CT densitometry for wooden cultural heritage analysis validated by FTIR and Raman spectroscopies. \u003cem\u003eRadiat. Phys. Chem.\u003c/em\u003e \u003cstrong\u003e199\u003c/strong\u003e, 110376 (2022). 10.1016/j.radphyschem.2022.110376.\u003c/li\u003e\n\u003cli\u003eVavř\u0026iacute;k, D. et al. Non-destructive exploration of late Gothic panel painting using X-ray tomography and flattening of the reconstructed data. \u003cem\u003eEur. Phys. J. Plus\u003c/em\u003e \u003cstrong\u003e138\u003c/strong\u003e, 618 (2023). 10.1140/epjp/s13360-023-04212-w.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 12 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8413368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8413368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cultural heritage X-ray and computed tomography (CT) data require conservation-grade reproducibility and interoperability. This study presents the digital imaging and communication in cultural heritage (DICOCH) pipeline, a generation–validation–publication workflow implementing digital imaging and communications in medicine (DICOM) standards for heritage objects. The pipeline integrates automated DICOM generation from computed radiography and CT sources, mandatory machine validation, and automatic creation of publication packages, including International image interoperability framework (IIIF) manifests. A private group (0013,xxxx) preserves heritage-specific context and rights while encoding calibrated Hounsfield unit statistics within a single object. Validation with the Hahoe Mask dataset achieved zero-error compliance and verified interoperability between medical picture archiving and communication system and web-based IIIF viewers. By reconciling standard compliance with open access through a publicly released reference implementation, DICOCH serves as a trusted bridge between scientific preservation and digital reuse, providing a practical framework for global heritage data interoperability.","manuscriptTitle":"A Pipeline Integrating Cultural Heritage X-ray CR/CT Data Using Digital Imaging and Communications in Medicine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 10:39:25","doi":"10.21203/rs.3.rs-8413368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-11T15:32:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T14:48:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T12:43:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252452924239068706169575361755899353417","date":"2026-01-20T15:44:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75396516863497321253876545917369039706","date":"2026-01-15T12:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T18:20:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T15:57:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T04:17:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Heritage Science","date":"2026-01-12T04:10:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4426030e-53b4-46cf-8d52-c4e8bd5042a1","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:04:09+00:00","versionOfRecord":{"articleIdentity":"rs-8413368","link":"https://doi.org/10.1038/s40494-026-02480-0","journal":{"identity":"npj-heritage-science","isVorOnly":false,"title":"npj Heritage Science"},"publishedOn":"2026-04-04 15:59:13","publishedOnDateReadable":"April 4th, 2026"},"versionCreatedAt":"2026-01-16 10:39:25","video":"","vorDoi":"10.1038/s40494-026-02480-0","vorDoiUrl":"https://doi.org/10.1038/s40494-026-02480-0","workflowStages":[]},"version":"v1","identity":"rs-8413368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8413368","identity":"rs-8413368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}