Accuracy of Iodine Quantification and CT Numbers Using Split-Filter Dual-Energy CT: Influence of Phantom Diameter

Accuracy of Iodine Quantification and CT Numbers Using Split-Filter Dual-Energy CT: Influence of Phantom Diameter | 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 Research Article Accuracy of Iodine Quantification and CT Numbers Using Split-Filter Dual-Energy CT: Influence of Phantom Diameter Masato Kiriki, Maiko Kishigami, Toshiyuki Sakai, Takahiro Minamoto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7055648/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract Background Dual-energy computed tomography (DECT) generates virtual monochromatic images (VMI) and material decomposition images (MDI), facilitating enhanced tissue contrast and quantitative material assessment. However, the accuracy of these measurements may be influenced by object size due to beam hardening and associated spectral changes. Purpose To evaluate the impact of object size on the accuracy of iodine quantification and CT numbers in virtual monochromatic images (VMI) using split-filter dual-energy CT (SFDE), and to compare its performance with sequential acquisition dual-energy CT (SADE). Methods CT scans were performed on phantoms with diameters ranging from 16 to 36 cm using both SFDE and SADE techniques. Virtual monochromatic images and material decomposition images were generated. CT numbers and iodine concentrations were measured from embedded iodine rods, and relative errors were calculated using the 16 cm phantom as a reference. Results CT numbers in VMI obtained from SFDE exhibited increasing variability with larger phantom sizes, particularly at both low and high energy levels. Iodine quantification errors with SFDE exceeded 10% in all phantom sizes and reached approximately 60% in the 36 cm phantom. In contrast, SADE consistently maintained measurement errors within 10%. Conclusion Object size significantly influences the accuracy of CT numbers and iodine quantification using SFDE, with larger phantoms showing marked overestimation. These results suggest that careful interpretation is necessary when applying SFDE-based quantitative imaging in patients with larger body sizes. Dual energy CT Split-filter Virtual monochromatic image Material decomposition image Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Dual-energy computed tomography (DECT) is an advanced imaging modality that utilizes two distinct X-ray energy spectra to acquire diverse diagnostic information. Among the most representative image types generated by DECT are virtual monochromatic images (VMI) and material decomposition images (MDI). VMI has been shown to reduce beam-hardening artifacts and enhance iodine contrast, thereby improving image quality.[1-3] MDI, on the other hand, enables the calculation of material density and effective atomic number, and allows for the quantitative assessment of specific substances such as iodine and calcium.[3, 4] However, one of the factors that can affect the accuracy of DECT is the size of the scanned object. As X-rays traverse longer distances through a material, the X-ray energy spectrum undergoes beam-hardening, resulting in an increase in effective energy. Consequently, the degree of spectral separation may vary depending on object size, potentially influencing the precision of dual-energy analysis. Currently, several acquisition techniques for dual-energy CT (DECT) exist, including dual-source (DS), dual-layer detector (DL), rapid kilovoltage switching, sequential acquisition (SA), and the split-filter (SF) technique.[1, 5] Each technique employs different X-ray energy spectra. Among these, DECT using the SF technique (SFDE) employs a filter composed of 0.05 mm gold (Au) and 0.6 mm tin (Sn), aligned along the Z-axis, to split the 120 kVp X-ray spectrum into low- and high-energy components. A key advantage of SFDE is that it allows dual-energy analysis with a single scan. However, compared to DSCT or DLCT, it has a known limitation of inferior spectral separation capability.[1, 5] Previous studies on VMI obtained from SFDE have primarily focused on aspects such as image noise, spatial resolution, or detectability.[6-9] However, there is a lack of investigations concerning the accuracy of CT attenuation values and iodine quantification. While some studies have assessed the impact of phantom size on accuracy across different DECT techniques,[5, 6, 10] most have been limited to only two or three phantom sizes, which is insufficient for a comprehensive understanding of the effects of object size. In this study, we focused on the behavior of iodine under varying object size. With the SFDE, we acquired VMI and MDI—specifically iodine maps (IM) and virtual non-contrast images (VNC)—from phantoms of various sizes to assess the influence of object diameter on measurement accuracy. Furthermore, we compared the performance of SFDE with that of SADE—DECT using the SA technique at 80 kVp and 140 kVp on the same scanner—to evaluate the accuracy of SFDE analysis. Materials and Methods This study was conducted using phantoms only; thus, institutional review board (IRB) approval was not required. 2.1 CT scan conditions CT scans were performed using a 128-slice CT scanner and associated workstation (SOMATOM Edge Plus and Syngo.via VB60, Siemens Healthineers, Forchheim, Germany). The Mercury 4.0 phantom (Sun Nuclear, Melbourne, FL, USA), which includes five different diameters (16, 21, 26, 31, and 36 cm), was used to simulate various object diameters (Figure 1). The scan parameters and image reconstruction settings are summarized in Table 1. Image reconstruction was conducted using filtered back projection. To determine the radiation dose, automatic exposure control (AEC) was not used. Instead, the tube current was manually adjusted for each diameter section to achieve a standard deviation (SD) of 10 on 5 mm thickness mixed images corresponding to a 120 kVp equivalent. Under these settings, the CTDI vol values for phantom diameters of 16, 21, 26, 31, and 36 cm were 1.4, 2.7, 5.6, 13.6, and 29.6 mGy, respectively, for SADE, and 1.1, 2.4, 5.8, 12.6, and 29.6 mGy, respectively, for SFDE. For both SADE and SFDE, scans were repeated seven times, and both low- and high-kVp images were acquired for each technique. 2.2 Virtual monochromatic image VMI reconstruction was performed using the Monoenergetic Plus application (Siemens Healthineers, Forchheim, Germany).[11] Circular regions of interest (ROIs) with an area of 2.0 mm 2 were placed on iodine rods (10 mg/mL, with a nominal CT number of 245 HU at 120 kVp) located in the material sections of phantom with diameters of 16, 26, and 36 cm. The energy dependence of CT attenuation values (HU curves) was then calculated. Additionally, to assess the accuracy of the CT numbers in VMI at 40, 70, and 140 keV, the relative error was calculated for the 26 cm and 36 cm sections using the 16 cm section as the reference. 2.3 Material decomposition image MDI reconstruction was performed using the Liver VNC application (Siemens Healthineers, Forchheim, Germany), from which iodine maps (IM) and virtual non-contrast images (VNC) were generated. The rel.CM setting, an index representing the iodine slope, was set to 2.0 for SADE and 1.46 for SFDE, both based on the default values of the workstation. Circular ROIs of 2.0 mm 2 were placed on the iodine rods located in the material sections of all phantom diameters. From the IM, iodine concentration and CT numbers were measured, while CT numbers were also obtained from the corresponding VNC images. Results 3.1 CT number of iodine rod at low-and high-kVp images Figure 2 presents the CT numbers of the iodine rods in each phantom section obtained using SADE and SFDE. In SADE, the average CT numbers of the iodine rod in 80 kVp images were 396.5, 397.3, 401.8, 392.6, and 390.2 HU for the 16, 21, 26, 31, and 36 cm phantom sections, respectively. In the 140 kVp images, the corresponding values were 207.3, 212.4, 209.2, 196.3, and 188.1 HU. In contrast, for SFDE, the average CT numbers in 120 kVp (Au-filtered) images were 242.3, 232.1, 220.2, 210.7, and 204.4 HU, and in 120 kVp (Sn-filtered) images were 156.6, 151.8, 146.1, 139.3, and 138.9 HU, respectively. The slopes of CT number changes with respect to phantom diameter were 0.3 for SADE and 2.0 for SFDE. 3.2 HU curves in virtual monochromatic images Figure 3 shows the HU curves of the iodine rods in the 16, 26, and 36 cm phantom sections for both SADE and SFDE. In SADE, the relative error in CT values for the 26 cm section compared to the 16 cm section remained within ±5% across all energy range. For the 36 cm section, the relative errors at 40, 70, and 140 keV were 3%, -6%, and -43%, respectively, indicating a trend of decreasing CT numbers with increasing energy compared to the 16 cm reference. For SFDE, the relative errors for the 26 cm section were 10%, 5%, and -40% at 40, 70, and 140 keV, respectively. In the 36 cm section, the relative errors increased further to 22%, 13%, and -74%, respectively. When using the 16 cm section as a reference, the CT numbers in SFDE exhibited a tendency to be higher at lower energies and lower at higher energies, with a crossover point in the mid-energy range (85 keV for 26 cm, 91 keV for 36 cm). Notably, the CT number of the iodine rod in the 36 cm section fell below that of water (0 HU) at high energy range. 3.3 Quantitative iodine analysis in material decomposition images Table 2 presents the iodine concentrations and CT numbers obtained from the IM and VNC images using SADE and SFDE. Iodine concentrations measured with SADE remained within a 10% error margin from the nominal value of 10 mg/mL across all phantom sections. In contrast, iodine concentrations measured with SFDE exceeded a 10% error in all sections, with errors increasing alongside phantom diameter. A particularly large overestimation of approximately 60% was observed in the 36 cm section. Regarding the CT numbers of the iodine rods, both SADE and SFDE demonstrated an increasing trend in CT values with increasing phantom size. Additionally, CT values obtained with SFDE were consistently higher than those from SADE across all sections. In VNC images, the CT numbers of the iodine rods tended to decrease with increasing phantom diameter in both SADE and SFDE. SFDE consistently yielded lower CT values than SADE. Figure 4 presents representative IM and VNC images for each section. In the SFDE-derived VNC image for the 36 cm section, an inversion of contrast in the iodine rod was observed. Discussion SFDE offers the advantage of simultaneous data acquisition in a single scan and features a relatively simple hardware configuration. However, it also presents a known limitation in terms of spectral separation performance.[1, 5] In this study, we focused on object size—one of the factors effecting the X-ray spectrum—and evaluated its impact on the CT values and quantitative measurements of iodine in SFDE. The two DECT techniques used in this study both employ image-based analysis, allowing for the independent extraction and assessment of low- and high-kVp images, which serve as the foundation for dual-energy analysis. Based on the analysis of the original images, the variation in CT values due to differences in phantom diameter was comparable between the two techniques in high-kVp images (Figure 2). However, in low-kVp images, CT value variation with phantom size was minimal in SADE 80 kVp images (a maximum decrease of 2.9%), whereas in SFDE 120 kVp (Au-filtered) images, a decrease of 15.6% was observed in the 36 cm section. This indicates an X-ray spectral shift due to beam hardening and suggests that the effect of object size on CT values differs depending on the acquisition technique. Consequently, the influence of object size on VMI and MDI results may also vary according to the DECT acquisition technique. Considering the spectral changes observed in the original images, the resulting HU curves in the VMI also exhibited differing trends between the two techniques. In SADE, the HU curves demonstrated convergence of CT numbers in the low keV range, independent of object diameters. In contrast, SFDE showed a trend in which CT values increased in the low keV range and decreased in the high keV range as phantom diameter increased. According to the VMI calculation model reported by Yu et al.[12], low keV VMI is more heavily influenced by low-kVp images. Therefore, if beam-hardening causes a reduction in CT values in the low-kVp images, it would be expected that CT values in low-keV VMIs would also decrease. However, our findings demonstrated the opposite tendency. One possible explanation is that beam hardening may have caused a deviation from the ideal spectral separation by altering the effective energy difference between low- and high-kVp images, particularly in large phantom such as 36 cm diameter. Additionally, although the scan dose was adjusted to maintain an image SD of 10 across different phantom sizes, it was not possible to align the noise frequency characteristics across all sizes. Moreover, the complexity of software-based reconstruction algorithms[11] may have also contributed, making it difficult to reproduce ideal HU curves under varying acquisition conditions. Regarding the quantification of iodine concentration, SADE demonstrated high accuracy across all sections, with measurement errors remaining within 10% of the nominal value. In contrast, SFDE exhibited increasing error with larger phantom diameters, with an overestimation of approximately 60% observed in the 36 cm section. One potential cause of this discrepancy is the influence of the relative contrast medium slope (rel.CM) used in material decomposition. In SFDE, the slope of CT number changes in iodine rods with increasing object diameter was calculated to be 2.0, which exceeds the default rel.CM setting of 1.46 on the workstation. This indicates that the actual CT number changes induced by object diameter surpassed the simulated values assumed by the system for iodine density, likely contributing to reduced quantification accuracy. Furthermore, as shown in Figure 4, contrast inversion of the iodine rod was observed in the VNC image of the 36 cm section obtained with SFDE. This phenomenon can be attributed to excessive subtraction of CT values due to the overestimated iodine concentration. In the context of abdominal imaging, Patel et al.[13] reported that variation in iodine quantification can be reduced by normalizing iodine quantification to the aorta, allowing for better separation of vascular and nonvascular lesions when using IM. This approach enables stable lesion assessment using a threshold of 0.3 mgI/mL. The iodine rods used in our study simulate aortic iodine concentration during the arterial phase; however, SFDE resulted in overestimations of up to 60%. These findings suggest that iodine quantification with SFDE is susceptible to object size, which may affect the reliability of vascular assessments. In vivo study, body habits and anatomical variation across different regions introduce further complexity, raising concerns about the reproducibility of iodine quantification under such conditions. This issue is particularly critical in diagnostic applications involving subtle changes in iodine concentration, such as tumor vascularization assessment or evaluation of therapeutic response, where overestimation may increase the risk of false-positive findings. SFDE employs a split-filter composed of Au and Sn to separate the X-ray spectrum; however, spectral overlap at the interface between the two materials is considered unavoidable.[6-8] When combined with spectral changes induced by increased object size, this overlap further complicates optimal image reconstruction, leading to reduced accuracy in material decomposition and decreased precision in CT values at VMI. To address these challenges, recent studies have reported improvements in spectral separation performance through modifications to filter thickness, as seen in second-generation SFDE systems.[9, 14] Additionally, correction methods incorporating calibration factors have been proposed with the aim of improving the accuracy of SFDE analysis.[15] These developments highlight the potential for future advancements in both hardware and software to enhance the overall performance of SFDE. This study has several limitations. First, the iodine concentration used in the phantom experiments was limited to a nominal value of 10 mg/mL. This does not fully reflect the complexity of clinical scenarios, where diverse tissue compositions and contrast enhancement are often encountered. Further investigations that consider the potential interactions in mixed material compositions are needed. In addition, the present study was restricted to accuracy validation using physical phantoms and did not evaluate the relationship between image-based measurements and diagnostic performance in actual clinical settings. Future studies should include clinical validation using patient data to assess the practical applicability of the findings. Moreover, this study focused exclusively on the evaluation of VMI, IM, and VNC in relation to iodine. Comprehensive assessment of image quality remains necessary. Future work should incorporate clinically relevant task-based performance—such as noise power spectrum, task transfer function, and detectability index—to provide an overall evaluation of image quality. Conclusion This study clarified the impact of object size on VMI and MDI in SFDE. The results demonstrated that both CT values and iodine quantification accuracy are highly dependent on object thickness, with a notable tendency toward overestimation in larger phantoms. These findings suggest that careful consideration is required when interpreting iodine concentration in clinical settings, particularly in patients with larger body habits. Continued technical refinement and validation efforts are essential to improve the accuracy and reliability of SFDE-based quantitative imaging. Declarations Funding The authors have no funding sources to declare. Conflicts of interest The authors declare that they have no competing interests. Author contributions M.K. conceived the study and designed the experimental protocol. M.K. performed the phantom scans, data collection, and data analysis. T.S. and T.M. supervised the overall project and provided critical revisions. All authors contributed to the writing and approved the final manuscript. Acknowledgements The authors would like to thank M.K. and R.U. for their technical assistance during the CT examinations, as well as the radiological technologists at our institution. Ethics approval and consent to participate This research did not require Institutional Review Board approval because this study did not involve human participants or animals. Availability of data Data required for this study may be made available by the authors upon reasonable request. References Johnson TRC. Dual-energy CT: general principles. AJR Am J Roentgenol. 2012; 199(5 Suppl): S3–S8. doi:10.2214/AJR.12.9116. Albrecht MH, Vogl TJ, Martin SS, et al. Review of clinical applications for virtual monoenergetic dual-energy CT. Radiology. 2019; 293(2): 260–271. doi:10.1148/radiol.2019182297. Parakh A, Lennartz S, An C, et al. Dual-energy CT images: pearls and pitfalls. Radiographics. 2021; 41(1): 98–119. doi:10.1148/rg.2021200102. Tatsugami F, Higaki T, Nakamura Y, et al. Dual-energy CT: minimal essentials for radiologists. Jpn J Radiol. 2022; 40(6): 547–559. doi:10.1007/s11604-021-01233-2. Almeida IP, Schyns LEJR, Öllers MC, et al. Dual-energy CT quantitative imaging: a comparison study between twin-beam and dual-source CT scanners. Med Phys . 2017; 44(1): 171–179. doi:10.1002/mp.12000. Greffier J, Si-Mohamed S, Dabli D, et al. Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data. Eur Radiol. 2021; 31(7): 5324–5334. doi:10.1007/s00330-020-07671-2. Goo HW, Goo JM. Dual-energy CT: new horizon in medical imaging. Korean J Radiol . 2017; 18(4): 555–569. doi:10.3348/kjr.2017.18.4.555. Greffier J, Si-Mohamed S, Guiu B, et al. Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction. Quant Imaging Med Surg. 2022; 12(2): 1149–1162. doi:10.21037/qims-21-708. Greffier J, Van Ngoc Ty C, Fitton I, et al. Spectral performance of two split-filter dual-energy CT systems: a phantom study. Med Phys. 2023; 50(11): 6828–6835. doi:10.1002/mp.16701. Ohira S, Mochizuki J, Niwa T, et al. Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique. Radiol Phys Technol. 2024; 17(2): 458–466. doi:10.1007/s12194-024-00802-0. Grant KL, Flohr TG, Krauss B, et al. Assessment of an advanced image-based technique to calculate virtual monoenergetic computed tomographic images from a dual-energy examination to improve contrast-to-noise ratio in examinations using iodinated contrast media. Invest Radiol. 2014; 49(9): 586–592. doi:10.1097/RLI.0000000000000060. Yu L, Christner JA, Leng S, et al. Virtual monochromatic imaging in dual-source dual-energy CT: radiation dose and image quality. Med Phys. 2011; 38(12): 6371–6379. doi:10.1118/1.3658568. Patel BN, Vernuccio F, Meyer M, et al. Dual-energy CT material density iodine quantification for distinguishing vascular from nonvascular renal lesions: normalization reduces intermanufacturer threshold variability. AJR Am J Roentgenol. 2019; 212(2): 366–376. doi:10.2214/AJR.18.20115. Greffier J, Van Ngoc Ty C, Fitton I, et al. Impact of phantom size on low-energy virtual monoenergetic images of three dual-energy CT platforms. Diagnostics (Basel) . 2023; 13(19): 3039. doi:10.3390/diagnostics13193039. Jacobsen MC, Schellingerhout D, Wood CA, et al. Intermanufacturer comparison of dual-energy CT iodine quantification and monochromatic attenuation: a phantom study. Radiology. 2018; 287(1): 224–234. doi:10.1148/radiol.2017170896. Tables Table 1 Scan and reconstruction parameters Sequential acquisition Split-filter Tube voltage (kVp) 80 / 140 120 (Au/Sn filter) Rotation time (s/rot) 0.5 0.5 Pitch factor 0.5 / 1.0 0.25 Beam width (mm) 38.4 19.2 Slice thickness (mm) 5.0 5.0 Field of view (mm 2 ) 384 × 384 384 × 384 Kernel Qr40 Qr40 Table 2 Iodine concentration and CT numbers for Sequential and Split filter DECT at various phantom diameters Phantom diameter Sequential acquisition Split-filter IM (mg/mL) IM (HU) VNC (HU) IM (mg/mL) IM (HU) VNC (HU) 16 cm 9.5 ± 0.1 244.5 ± 0.9 19.7 ± 1.1 11.9 ± 0.1 256.7 ± 1.2 -31.3 ± 1.3 21 cm 9.3 ± 0.0 236.9 ± 0.8 30.9 ± 1.1 12.5 ± 0.1 258.7 ± 1.4 -42.3 ± 1.5 26 cm 9.7 ± 0.0 249.1 ± 0.9 17.8 ± 0.8 13.4 ± 0.2 265.8 ± 2.2 -59.3 ± 2.0 31 cm 10.1 ± 0.1 256.6 ± 1.3 -1.6 ± 1.4 14.8 ± 0.3 276.5 ± 2.2 -79.8 ± 2.7 36 cm 10.4 ± 0.1 268.1 ± 1.1 -19.4 ± 1.2 16.0 ± 0.2 284.2 ± 2.6 -92.8 ± 2.6 VNC, virtual non-contrast image IM, iodine map Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Editorial decision: Major revisions 24 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Editor invited by journal 21 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 08 Jul, 2025 First submitted to journal 05 Jul, 2025 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. 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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-7055648","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482930207,"identity":"1ee09abd-c6e2-4225-96ee-b9fad7ed43d9","order_by":0,"name":"Masato Kiriki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHACZoYEBgkDNvYeKP8AiOAhRgvPGVK0AIEBg0QOshY8QLf9ALPBwz0WxnySb49JfGDYJsd3gPnhBwaZOzi1mJ1JYE5IeCZhxiadlyY5g+G2seQBNmMJBp5nuLUcyP98IOGAhA2bdI6ZNO+/24kbDjCYAf1yGLeW8w+YIVokz5hJ8zDcrt9wgP0bfi03QA47AHSYBA9YS4LBAR4Cttx4wGwA1GLMxpNjbAn0i+HMwzzFEgn4/HI+gVnyx4E6w/ntZwxvfGC4Lc93vH3jh489uEMMGbBIgClQPCX2HCBKC/MHBPsHcVpGwSgYBaNgRAAAafNQIG55jjAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-0818-0177","institution":"Hyogo Medical University Hospital: Hyogo Ika Daigaku Byoin","correspondingAuthor":true,"prefix":"","firstName":"Masato","middleName":"","lastName":"Kiriki","suffix":""},{"id":482930208,"identity":"2c98e15f-f3c2-4019-b195-25fe543f3ec6","order_by":1,"name":"Maiko Kishigami","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine Faculty of Medicine: Kyoto Daigaku Daigakuin Igaku Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"Maiko","middleName":"","lastName":"Kishigami","suffix":""},{"id":482930209,"identity":"0c877a52-37a0-46a2-ad23-5bba3cbdc800","order_by":2,"name":"Toshiyuki Sakai","email":"","orcid":"","institution":"Hyogo Medical University Hospital: Hyogo Ika Daigaku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Toshiyuki","middleName":"","lastName":"Sakai","suffix":""},{"id":482930210,"identity":"60e9b50a-35c4-4add-885c-0bc50d992670","order_by":3,"name":"Takahiro Minamoto","email":"","orcid":"","institution":"Hyogo Medical University Hospital: Hyogo Ika Daigaku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Minamoto","suffix":""}],"badges":[],"createdAt":"2025-07-06 03:52:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7055648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7055648/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13246-025-01658-3","type":"published","date":"2025-10-06T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86662646,"identity":"8035681a-bfbb-4c65-9285-31285cde713e","added_by":"auto","created_at":"2025-07-14 10:45:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1932543,"visible":true,"origin":"","legend":"\u003cp\u003eStructure and materials of the Mercury Phantom.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7055648/v1/e1c76db3ec700d3125fbb211.png"},{"id":86663946,"identity":"4798d480-8fd4-46f5-90a2-5c19f2b860e6","added_by":"auto","created_at":"2025-07-14 10:53:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":301942,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of CT numbers from low and high kVp images at each phantom section.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7055648/v1/aae68918c462cae58b239af0.png"},{"id":86663947,"identity":"034f3551-f062-43d3-bb17-2dd5bb98f3c3","added_by":"auto","created_at":"2025-07-14 10:53:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":391918,"visible":true,"origin":"","legend":"\u003cp\u003eHU curves of (a) Sequential and (b) Split-filter dual-energy CT at three phantom diameters.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7055648/v1/d9863b8e48f568ac11cfea65.png"},{"id":86662652,"identity":"c60e1524-7370-450f-8f09-393273c90d9b","added_by":"auto","created_at":"2025-07-14 10:45:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1205651,"visible":true,"origin":"","legend":"\u003cp\u003eIodine map and virtual non-contrast images (VNC) from Sequential and Split filter dual-energy (SADE and SFDE) CT at various phantom diameters. The VNC image by SFDE shows contrast inversion in the iodine rod in the 36 cm phantom (white arrowhead).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7055648/v1/9588f451cb8b1d6adb957a54.png"},{"id":93419725,"identity":"1f00be52-58d0-40c4-b53e-fb879db5fc62","added_by":"auto","created_at":"2025-10-13 16:06:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4903670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7055648/v1/9bd3350f-7701-4dbb-981a-09a90c6db475.pdf"}],"financialInterests":"","formattedTitle":"Accuracy of Iodine Quantification and CT Numbers Using Split-Filter Dual-Energy CT: Influence of Phantom Diameter","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDual-energy computed tomography (DECT) is an advanced imaging modality that utilizes two distinct X-ray energy spectra to acquire diverse diagnostic information. Among the most representative image types generated by DECT are virtual monochromatic images (VMI) and material decomposition images (MDI). VMI has been shown to reduce beam-hardening artifacts and enhance iodine contrast, thereby improving image quality.[1-3] MDI, on the other hand, enables the calculation of material density and effective atomic number, and allows for the quantitative assessment of specific substances such as iodine and calcium.[3, 4]\u003c/p\u003e\n\u003cp\u003eHowever, one of the factors that can affect the accuracy of DECT is the size of the scanned object. As X-rays traverse longer distances through a material, the X-ray energy spectrum undergoes beam-hardening, resulting in an increase in effective energy. Consequently, the degree of spectral separation may vary depending on object size, potentially influencing the precision of dual-energy analysis.\u003c/p\u003e\n\u003cp\u003eCurrently, several acquisition techniques for dual-energy CT (DECT) exist, including dual-source (DS), dual-layer detector (DL), rapid kilovoltage switching, sequential acquisition (SA), and the split-filter (SF) technique.[1, 5] Each technique employs different X-ray energy spectra. Among these, DECT using the SF technique (SFDE) employs a filter composed of 0.05 mm gold (Au) and 0.6 mm tin (Sn), aligned along the Z-axis, to split the 120 kVp X-ray spectrum into low- and high-energy components. A key advantage of SFDE is that it allows dual-energy analysis with a single scan. However, compared to DSCT or DLCT, it has a known limitation of inferior spectral separation capability.[1, 5]\u003c/p\u003e\n\u003cp\u003ePrevious studies on VMI obtained from SFDE have primarily focused on aspects such as image noise, spatial resolution, or detectability.[6-9] However, there is a lack of investigations concerning the accuracy of CT attenuation values and iodine quantification. While some studies have assessed the impact of phantom size on accuracy across different DECT techniques,[5, 6, 10] most have been limited to only two or three phantom sizes, which is insufficient for a comprehensive understanding of the effects of object size.\u003c/p\u003e\n\u003cp\u003eIn this study, we focused on the behavior of iodine under varying object size. With the SFDE, we acquired VMI and MDI\u0026mdash;specifically iodine maps (IM) and virtual non-contrast images (VNC)\u0026mdash;from phantoms of various sizes to assess the influence of object diameter on measurement accuracy. Furthermore, we compared the performance of SFDE with that of SADE\u0026mdash;DECT using the SA technique at 80 kVp and 140 kVp on the same scanner\u0026mdash;to evaluate the accuracy of SFDE analysis.\u003c/p\u003e\n"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study was conducted using phantoms only; thus, institutional review board (IRB) approval was not required.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e2.1 CT scan conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT scans were performed using a 128-slice CT scanner and associated workstation (SOMATOM Edge Plus and Syngo.via VB60, Siemens Healthineers, Forchheim, Germany). The Mercury 4.0 phantom (Sun Nuclear, Melbourne, FL, USA), which includes five different diameters (16, 21, 26, 31, and 36 cm), was used to simulate various object diameters (Figure 1). The scan parameters and image reconstruction settings are summarized in Table 1. Image reconstruction was conducted using filtered back projection.\u003c/p\u003e\n\u003cp\u003eTo determine the radiation dose, automatic exposure control (AEC) was not used. Instead, the tube current was manually adjusted for each diameter section to achieve a standard deviation (SD) of 10 on 5 mm thickness mixed images corresponding to a 120 kVp equivalent. Under these settings, the CTDI\u003csub\u003evol\u003c/sub\u003e values for phantom diameters of 16, 21, 26, 31, and 36 cm were 1.4, 2.7, 5.6, 13.6, and 29.6 mGy, respectively, for SADE, and 1.1, 2.4, 5.8, 12.6, and 29.6 mGy, respectively, for SFDE. For both SADE and SFDE, scans were repeated seven times, and both low- and high-kVp images were acquired for each technique.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Virtual monochromatic image\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVMI reconstruction was performed using the Monoenergetic Plus application (Siemens Healthineers, Forchheim, Germany).[11] Circular regions of interest (ROIs) with an area of 2.0 mm\u003csup\u003e2\u003c/sup\u003e were placed on iodine rods (10 mg/mL, with a nominal CT number of 245 HU at 120 kVp) located in the material sections of phantom with diameters of 16, 26, and 36 cm. The energy dependence of CT attenuation values (HU curves) was then calculated. Additionally, to assess the accuracy of the CT numbers in VMI at 40, 70, and 140 keV, the relative error was calculated for the 26 cm and 36 cm sections using the 16 cm section as the reference.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e2.3 Material decomposition image\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMDI reconstruction was performed using the Liver VNC application (Siemens Healthineers, Forchheim, Germany), from which iodine maps (IM) and virtual non-contrast images (VNC) were generated. The rel.CM setting, an index representing the iodine slope, was set to 2.0 for SADE and 1.46 for SFDE, both based on the default values of the workstation. Circular ROIs of 2.0 mm\u003csup\u003e2\u003c/sup\u003e were placed on the iodine rods located in the material sections of all phantom diameters. From the IM, iodine concentration and CT numbers were measured, while CT numbers were also obtained from the corresponding VNC images.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 CT number of iodine rod at low-and high-kVp images\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 presents the CT numbers of the iodine rods in each phantom section obtained using SADE and SFDE. In SADE, the average CT numbers of the iodine rod in 80 kVp images were 396.5, 397.3, 401.8, 392.6, and 390.2 HU for the 16, 21, 26, 31, and 36 cm phantom sections, respectively. In the 140 kVp images, the corresponding values were 207.3, 212.4, 209.2, 196.3, and 188.1 HU.\u003c/p\u003e\n\u003cp\u003eIn contrast, for SFDE, the average CT numbers in 120 kVp (Au-filtered) images were 242.3, 232.1, 220.2, 210.7, and 204.4 HU, and in 120 kVp (Sn-filtered) images were 156.6, 151.8, 146.1, 139.3, and 138.9 HU, respectively. The slopes of CT number changes with respect to phantom diameter were 0.3 for SADE and 2.0 for SFDE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 HU curves in virtual monochromatic images\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the HU curves of the iodine rods in the 16, 26, and 36 cm phantom sections for both SADE and SFDE. In SADE, the relative error in CT values for the 26 cm section compared to the 16 cm section remained within \u0026plusmn;5% across all energy range. For the 36 cm section, the relative errors at 40, 70, and 140 keV were 3%, -6%, and -43%, respectively, indicating a trend of decreasing CT numbers with increasing energy compared to the 16 cm reference.\u003c/p\u003e\n\u003cp\u003eFor SFDE, the relative errors for the 26 cm section were 10%, 5%, and -40% at 40, 70, and 140 keV, respectively. In the 36 cm section, the relative errors increased further to 22%, 13%, and -74%, respectively. When using the 16 cm section as a reference, the CT numbers in SFDE exhibited a tendency to be higher at lower energies and lower at higher energies, with a crossover point in the mid-energy range (85 keV for 26 cm, 91 keV for 36 cm). Notably, the CT number of the iodine rod in the 36 cm section fell below that of water (0 HU) at high energy range.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Quantitative iodine analysis in material decomposition images\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the iodine concentrations and CT numbers obtained from the IM and VNC images using SADE and SFDE. Iodine concentrations measured with SADE remained within a 10% error margin from the nominal value of 10 mg/mL across all phantom sections.\u003c/p\u003e\n\u003cp\u003eIn contrast, iodine concentrations measured with SFDE exceeded a 10% error in all sections, with errors increasing alongside phantom diameter. A particularly large overestimation of approximately 60% was observed in the 36 cm section. Regarding the CT numbers of the iodine rods, both SADE and SFDE demonstrated an increasing trend in CT values with increasing phantom size. Additionally, CT values obtained with SFDE were consistently higher than those from SADE across all sections.\u003c/p\u003e\n\u003cp\u003eIn VNC images, the CT numbers of the iodine rods tended to decrease with increasing phantom diameter in both SADE and SFDE. SFDE consistently yielded lower CT values than SADE. Figure 4 presents representative IM and VNC images for each section. In the SFDE-derived VNC image for the 36 cm section, an inversion of contrast in the iodine rod was observed.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSFDE offers the advantage of simultaneous data acquisition in a single scan and features a relatively simple hardware configuration. However, it also presents a known limitation in terms of spectral separation performance.[1, 5] In this study, we focused on object size\u0026mdash;one of the factors effecting the X-ray spectrum\u0026mdash;and evaluated its impact on the CT values and quantitative measurements of iodine in SFDE.\u003c/p\u003e\n\u003cp\u003eThe two DECT techniques used in this study both employ image-based analysis, allowing for the independent extraction and assessment of low- and high-kVp images, which serve as the foundation for dual-energy analysis. Based on the analysis of the original images, the variation in CT values due to differences in phantom diameter was comparable between the two techniques in high-kVp images (Figure 2). However, in low-kVp images, CT value variation with phantom size was minimal in SADE 80 kVp images (a maximum decrease of 2.9%), whereas in SFDE 120 kVp (Au-filtered) images, a decrease of 15.6% was observed in the 36 cm section. This indicates an X-ray spectral shift due to beam hardening and suggests that the effect of object size on CT values differs depending on the acquisition technique. Consequently, the influence of object size on VMI and MDI results may also vary according to the DECT acquisition technique.\u003c/p\u003e\n\u003cp\u003eConsidering the spectral changes observed in the original images, the resulting HU curves in the VMI also exhibited differing trends between the two techniques. In SADE, the HU curves demonstrated convergence of CT numbers in the low keV range, independent of object diameters. In contrast, SFDE showed a trend in which CT values increased in the low keV range and decreased in the high keV range as phantom diameter increased. According to the VMI calculation model reported by Yu et al.[12], low keV VMI is more heavily influenced by low-kVp images. Therefore, if beam-hardening causes a reduction in CT values in the low-kVp images, it would be expected that CT values in low-keV VMIs would also decrease. However, our findings demonstrated the opposite tendency. One possible explanation is that beam hardening may have caused a deviation from the ideal spectral separation by altering the effective energy difference between low- and high-kVp images, particularly in large phantom such as 36 cm diameter. Additionally, although the scan dose was adjusted to maintain an image SD of 10 across different phantom sizes, it was not possible to align the noise frequency characteristics across all sizes. Moreover, the complexity of software-based reconstruction algorithms[11] may have also contributed, making it difficult to reproduce ideal HU curves under varying acquisition conditions.\u003c/p\u003e\n\u003cp\u003eRegarding the quantification of iodine concentration, SADE demonstrated high accuracy across all sections, with measurement errors remaining within 10% of the nominal value. In contrast, SFDE exhibited increasing error with larger phantom diameters, with an overestimation of approximately 60% observed in the 36 cm section. One potential cause of this discrepancy is the influence of the relative contrast medium slope (rel.CM) used in material decomposition. In SFDE, the slope of CT number changes in iodine rods with increasing object diameter was calculated to be 2.0, which exceeds the default rel.CM setting of 1.46 on the workstation. This indicates that the actual CT number changes induced by object diameter surpassed the simulated values assumed by the system for iodine density, likely contributing to reduced quantification accuracy.\u0026nbsp;Furthermore, as shown in Figure 4, contrast inversion of the iodine rod was observed in the VNC image of the 36 cm section obtained with SFDE. This phenomenon can be attributed to excessive subtraction of CT values due to the overestimated iodine concentration. In the context of abdominal imaging, Patel et al.[13] reported that variation in iodine quantification can be reduced by normalizing iodine quantification to the aorta, allowing for better separation of vascular and nonvascular lesions when using IM. This approach enables stable lesion assessment using a threshold of 0.3 mgI/mL. The iodine rods used in our study simulate aortic iodine concentration during the arterial phase; however, SFDE resulted in overestimations of up to 60%. These findings suggest that iodine quantification with SFDE is susceptible to object size, which may affect the reliability of vascular assessments. In vivo study, body habits and anatomical variation across different regions introduce further complexity, raising concerns about the reproducibility of iodine quantification under such conditions. This issue is particularly critical in diagnostic applications involving subtle changes in iodine concentration, such as tumor vascularization assessment or evaluation of therapeutic response, where overestimation may increase the risk of false-positive findings.\u003c/p\u003e\n\u003cp\u003eSFDE employs a split-filter composed of Au and Sn to separate the X-ray spectrum; however, spectral overlap at the interface between the two materials is considered unavoidable.[6-8] When combined with spectral changes induced by increased object size, this overlap further complicates optimal image reconstruction, leading to reduced accuracy in material decomposition and decreased precision in CT values at VMI.\u0026nbsp;To address these challenges, recent studies have reported improvements in spectral separation performance through modifications to filter thickness, as seen in second-generation SFDE systems.[9, 14] Additionally, correction methods incorporating calibration factors have been proposed with the aim of improving the accuracy of SFDE analysis.[15] These developments highlight the potential for future advancements in both hardware and software to enhance the overall performance of SFDE.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study has several limitations. First, the iodine concentration used in the phantom experiments was limited to a nominal value of 10 mg/mL. This does not fully reflect the complexity of clinical scenarios, where diverse tissue compositions and contrast enhancement are often encountered. Further investigations that consider the potential interactions in mixed material compositions are needed. In addition, the present study was restricted to accuracy validation using physical phantoms and did not evaluate the relationship between image-based measurements and diagnostic performance in actual clinical settings. Future studies should include clinical validation using patient data to assess the practical applicability of the findings. Moreover, this study focused exclusively on the evaluation of VMI, IM, and VNC in relation to iodine. Comprehensive assessment of image quality remains necessary. Future work should incorporate clinically relevant task-based performance\u0026mdash;such as noise power spectrum, task transfer function, and detectability index\u0026mdash;to provide an overall evaluation of image quality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study clarified the impact of object size on VMI and MDI in SFDE. The results demonstrated that both CT values and iodine quantification accuracy are highly dependent on object thickness, with a notable tendency toward overestimation in larger phantoms. These findings suggest that careful consideration is required when interpreting iodine concentration in clinical settings, particularly in patients with larger body habits. Continued technical refinement and validation efforts are essential to improve the accuracy and reliability of SFDE-based quantitative imaging.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no funding sources to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K. conceived the study and designed the experimental protocol. M.K. performed the phantom scans, data collection, and data analysis. T.S. and T.M. supervised the overall project and provided critical revisions. All authors contributed to the writing and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank M.K. and R.U. for their technical assistance during the CT examinations, as well as the radiological technologists at our institution.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not require Institutional Review Board approval because\u0026nbsp;this study did not involve human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData required for this study may be made available by the authors upon reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJohnson TRC. Dual-energy CT: general principles. \u003cem\u003eAJR Am J Roentgenol.\u003c/em\u003e 2012; 199(5 Suppl): S3\u0026ndash;S8. doi:10.2214/AJR.12.9116.\u003c/li\u003e\n\u003cli\u003eAlbrecht MH, Vogl TJ, Martin SS, et al. Review of clinical applications for virtual monoenergetic dual-energy CT. \u003cem\u003eRadiology.\u003c/em\u003e 2019; 293(2): 260\u0026ndash;271. doi:10.1148/radiol.2019182297.\u003c/li\u003e\n\u003cli\u003eParakh A, Lennartz S, An C, et al. Dual-energy CT images: pearls and pitfalls. \u003cem\u003eRadiographics.\u003c/em\u003e 2021; 41(1): 98\u0026ndash;119. doi:10.1148/rg.2021200102.\u003c/li\u003e\n\u003cli\u003eTatsugami F, Higaki T, Nakamura Y, et al. Dual-energy CT: minimal essentials for radiologists.\u003cem\u003e Jpn J Radiol.\u003c/em\u003e 2022; 40(6): 547\u0026ndash;559. doi:10.1007/s11604-021-01233-2.\u003c/li\u003e\n\u003cli\u003eAlmeida IP, Schyns LEJR, \u0026Ouml;llers MC, et al. Dual-energy CT quantitative imaging: a comparison study between twin-beam and dual-source CT scanners. \u003cem\u003eMed Phys\u003c/em\u003e. 2017; 44(1): 171\u0026ndash;179. doi:10.1002/mp.12000.\u003c/li\u003e\n\u003cli\u003eGreffier J, Si-Mohamed S, Dabli D, et al. Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data. \u003cem\u003eEur Radiol.\u003c/em\u003e 2021; 31(7): 5324\u0026ndash;5334. doi:10.1007/s00330-020-07671-2. \u003c/li\u003e\n\u003cli\u003eGoo HW, Goo JM. Dual-energy CT: new horizon in medical imaging. \u003cem\u003eKorean J Radiol\u003c/em\u003e. 2017; 18(4): 555\u0026ndash;569. doi:10.3348/kjr.2017.18.4.555.\u003c/li\u003e\n\u003cli\u003eGreffier J, Si-Mohamed S, Guiu B, et al. Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction. \u003cem\u003eQuant Imaging Med Surg.\u003c/em\u003e 2022; 12(2): 1149\u0026ndash;1162. doi:10.21037/qims-21-708.\u003c/li\u003e\n\u003cli\u003eGreffier J, Van Ngoc Ty C, Fitton I, et al. Spectral performance of two split-filter dual-energy CT systems: a phantom study. \u003cem\u003eMed Phys.\u003c/em\u003e 2023; 50(11): 6828\u0026ndash;6835. doi:10.1002/mp.16701.\u003c/li\u003e\n\u003cli\u003eOhira S, Mochizuki J, Niwa T, et al. Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique. \u003cem\u003eRadiol Phys Technol.\u003c/em\u003e 2024; 17(2): 458\u0026ndash;466. doi:10.1007/s12194-024-00802-0. \u003c/li\u003e\n\u003cli\u003eGrant KL, Flohr TG, Krauss B, et al. Assessment of an advanced image-based technique to calculate virtual monoenergetic computed tomographic images from a dual-energy examination to improve contrast-to-noise ratio in examinations using iodinated contrast media. \u003cem\u003eInvest Radiol.\u003c/em\u003e 2014; 49(9): 586\u0026ndash;592. doi:10.1097/RLI.0000000000000060.\u003c/li\u003e\n\u003cli\u003eYu L, Christner JA, Leng S, et al. Virtual monochromatic imaging in dual-source dual-energy CT: radiation dose and image quality. \u003cem\u003eMed Phys.\u003c/em\u003e 2011; 38(12): 6371\u0026ndash;6379. doi:10.1118/1.3658568. \u003c/li\u003e\n\u003cli\u003ePatel BN, Vernuccio F, Meyer M, et al. Dual-energy CT material density iodine quantification for distinguishing vascular from nonvascular renal lesions: normalization reduces intermanufacturer threshold variability. \u003cem\u003eAJR Am J Roentgenol.\u003c/em\u003e 2019; 212(2): 366\u0026ndash;376. doi:10.2214/AJR.18.20115.\u003c/li\u003e\n\u003cli\u003eGreffier J, Van Ngoc Ty C, Fitton I, et al. Impact of phantom size on low-energy virtual monoenergetic images of three dual-energy CT platforms. \u003cem\u003eDiagnostics (Basel)\u003c/em\u003e. 2023; 13(19): 3039. doi:10.3390/diagnostics13193039.\u003c/li\u003e\n\u003cli\u003eJacobsen MC, Schellingerhout D, Wood CA, et al. Intermanufacturer comparison of dual-energy CT iodine quantification and monochromatic attenuation: a phantom study. \u003cem\u003eRadiology.\u003c/em\u003e 2018; 287(1): 224\u0026ndash;234. doi:10.1148/radiol.2017170896.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Scan and reconstruction parameters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"52%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSequential acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSplit-filter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Tube voltage (kVp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 / 140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (Au/Sn filter)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Rotation time (s/rot)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Pitch factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 / 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Beam width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Slice thickness (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eField of view (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e384 \u0026times; 384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e384 \u0026times; 384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Kernel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQr40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQr40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Iodine concentration and CT numbers for Sequential and Split filter DECT at various phantom diameters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"775\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePhantom\u003c/p\u003e\n \u003cp\u003ediameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 327px;\"\u003e\n \u003cp\u003eSequential acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 327px;\"\u003e\n \u003cp\u003eSplit-filter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eIM (mg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eIM (HU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVNC (HU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eIM (mg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eIM (HU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVNC (HU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;16 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e244.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19.7 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e11.9 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e256.7 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-31.3 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;21 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9.3 \u0026plusmn; 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e236.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e30.9 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e258.7 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-42.3 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;26 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9.7 \u0026plusmn; 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e249.1 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e17.8 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e265.8 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-59.3 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;31 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10.1 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e256.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-1.6 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e14.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e276.5 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-79.8 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;36 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10.4 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e268.1 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-19.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e16.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e284.2 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-92.8 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 775px;\"\u003e\n \u003cp\u003eVNC, virtual non-contrast image\u003c/p\u003e\n \u003cp\u003eIM, iodine map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dual energy CT, Split-filter, Virtual monochromatic image, Material decomposition image","lastPublishedDoi":"10.21203/rs.3.rs-7055648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7055648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\n\nDual-energy computed tomography (DECT) generates virtual monochromatic images (VMI) and material decomposition images (MDI), facilitating enhanced tissue contrast and quantitative material assessment. However, the accuracy of these measurements may be influenced by object size due to beam hardening and associated spectral changes.\n\nPurpose\n\nTo evaluate the impact of object size on the accuracy of iodine quantification and CT numbers in virtual monochromatic images (VMI) using split-filter dual-energy CT (SFDE), and to compare its performance with sequential acquisition dual-energy CT (SADE).\n\nMethods\n\nCT scans were performed on phantoms with diameters ranging from 16 to 36 cm using both SFDE and SADE techniques. Virtual monochromatic images and material decomposition images were generated. CT numbers and iodine concentrations were measured from embedded iodine rods, and relative errors were calculated using the 16 cm phantom as a reference.\n\nResults\n\nCT numbers in VMI obtained from SFDE exhibited increasing variability with larger phantom sizes, particularly at both low and high energy levels. Iodine quantification errors with SFDE exceeded 10% in all phantom sizes and reached approximately 60% in the 36 cm phantom. In contrast, SADE consistently maintained measurement errors within 10%.\n\nConclusion\n\nObject size significantly influences the accuracy of CT numbers and iodine quantification using SFDE, with larger phantoms showing marked overestimation. These results suggest that careful interpretation is necessary when applying SFDE-based quantitative imaging in patients with larger body sizes.","manuscriptTitle":"Accuracy of Iodine Quantification and CT Numbers Using Split-Filter Dual-Energy CT: Influence of Phantom Diameter","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:45:15","doi":"10.21203/rs.3.rs-7055648/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-08-24T21:12:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-01T05:43:09+00:00","index":0,"fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2025-07-21T08:13:55+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T11:31:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T13:35:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2025-07-05T23:51:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ad28899a-1bf9-4ea5-91c7-cca510da2832","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:00:55+00:00","versionOfRecord":{"articleIdentity":"rs-7055648","link":"https://doi.org/10.1007/s13246-025-01658-3","journal":{"identity":"physical-and-engineering-sciences-in-medicine","isVorOnly":false,"title":"Physical and Engineering Sciences in Medicine"},"publishedOn":"2025-10-06 15:57:07","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2025-07-14 10:45:15","video":"","vorDoi":"10.1007/s13246-025-01658-3","vorDoiUrl":"https://doi.org/10.1007/s13246-025-01658-3","workflowStages":[]},"version":"v1","identity":"rs-7055648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7055648","identity":"rs-7055648","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}