Towards standardised sample preparation in X- ray phase contrast imaging: comparison of eight protocols on equine superficial digital flexor tendon (SDFT)

Towards standardised sample preparation in X- ray phase contrast imaging: comparison of eight protocols on equine superficial digital flexor tendon (SDFT) | 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 Towards standardised sample preparation in X- ray phase contrast imaging: comparison of eight protocols on equine superficial digital flexor tendon (SDFT) Charlotte J Maughan Jones, Jayesh Dudhia, Alberto Astolfo, Alessandro Olivo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8842874/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract X-ray phase contrast imaging (XPCi) is becoming increasingly important with regards to three-dimensional imaging of biological tissues, however research has largely been focused on technology development and exploring pre-clinical and research applications, with little consideration to optimal sample preparation techniques and their effect on image quality. This paper aims to fill this gap by comparing 8 different sample preparation techniques on ex vivo biological tissues. Equine superficial digital flexor tendons were prepared using various combinations of PBS, 10% neutral buffered formalin, 70% and 100% ethanol and imaged using an edge illumination XPCi system in a 3D printed container. Results demonstrated that chemical dehydration via ethanol is a recommended precursor to XPCi contrast generation with 100% ethanol the optimal dehydration agent for maximising contrast. Biological sciences/Biological techniques Biological sciences/Biotechnology Physical sciences/Engineering Health sciences/Medical research x-ray phase contrast tendon edge illumination Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction X-ray phase contrast imaging (XPCi) is an emerging tool in preclinical imaging due to its ability to provide increased contrast in biological tissues compared to traditional x-ray imaging, without the need for exogenous contrast media or destructive sample processing methods as in histology. Standard x-ray based imaging methods rely on the difference in absorption properties of materials to produce contrast within an image. XPCi however also utilises the refractive properties of tissues, which can be up to three order of magnitude greater than the corresponding absorption properties, resulting in greater intrinsic contrast. However, despite this, histology remains the current gold standard for investigating the internal three dimensional structure of healthy and diseased biological tissues, both in laboratory and clinical settings. While providing rich information, histology requires lengthy tissue processing and results in the complete destruction of the tissue due to the sectioning required for the application of light microscopy. Histology also remains essentially a 2D technique; although efforts to produce three dimensional data sets from histological slices has been made and commercial systems are now available, reconstruction remains challenging due to preparation induced distortions to the tissue, hampering accurate registration of features between slices [ 1 ]. Furthermore reslicing reconstructed three dimensional histological datasets in different planes can lead to a loss in resolution [ 2 ]. By contrast, XPCi, owing to its natural compatibility with computed tomographic (CT) imaging [ 3 ], allows viewing tissue in its native three dimensional state, helping to uncover the interplay of different structures within the same tissue at high resultion. A significant barrier to the widespread adoption of XPCi as a viable alternative or complement to established histological processes is the lack of consensus on the most effective biological sample preparation techniques, with varied approaches reported in the literature, ranging from formalin fixation [ 4 ], paraffin embedding [ 5 ], aldehyde fixation and osmium impregnantion [ 6 ], chemical dehydration [ 7 ] and critical point drying [ 8 ]. Despite the reporting of many sample preparation techniques, there remains no ‘gold standard’ approach. Furthermore, many of the reported approaches suffer from issues such as bubble formation [ 9 ] or long sample preparation times [ 7 ]. Although it would be ideal to image all tissue fresh (that is, without any processing applied), which some have sucessfully done [ 10 , 11 ], long acquisition times at room temperature render fresh tissue liable to a combination of autolysis and uncontrolled dehydration, which can lead to cellular changes, decomposition and alteration in sample dimension and morphology. Fresh tissues can also pose safety concerns for personel due to potential infectious or zoonotic disease transmission, which may be a health and safety consideration within imaging laboratories. Overarchingly, fresh tissue tends to be difficult to image, especially with techniques that tend to incur lenghty scan times, such as XPCi. Sample processing is therefore required to maintain the biological anatomy, whilst making ‘risky’ tissue safer. As is shown below, sample processing can also lead to enhanced image contrast. Tendon tissue is a suitable model for developing and formalising sample preparation protocols in XPCi due to its well-defined hierarchical organisation and mechanical robustness. Composed primarily of highly aligned collagen fibers arranged in a multi-scale architecture - from fibrils to fascicles - tendons offer a consistent and predictable structure that facilitates image interpretation. The hierarchical structure provides distinct tissue boundaries at multiple scales, making it ideal for evaluating XPCi techniques. Additionally, the tissue’s low cellularity and resistance to deformation during processing make it a practical choice for optimisation of iterative sample preparation techniques which can then be applied to other tissue types. The purpose of this paper is two-fold. By applying a variety of simple and common sample preparation techniques which present stark differences in contrast and feature identification, we demonstrate the need for agreeing upon a reproducible sample preparation pipeline for XPCi. Furthermore, our results demonstrate that the application of a chemical dehydration agent (ethanol) improves contrast relative to the fresh tissue state, and propose its use as a recommended step prior to XPCi. Methods Tissues Normal (healthy) equine superficial digital flexor tendon (SDFT) tissue was excised within 24 h of post-mortem from Thoroughbred-type horses that had been euthanised at abbatoirs for reasons unrelated to this study. Dissected SDFT tissue from the mid-metacarpal was flash frozen in n-hexane at -80 o C and stored at -80 o C at the Royal Veterinary College. The tissue was subsequently defrosted at room temperature and cut into 3 equal lengths (approximately 2cm each axially) ready for processing. Equine tendon tissues were obtained with approval from the Royal Veterinary College Clinical Research Ethical Review Board (URN 2020 2017–2). Horses are a spontaneous and established model of human tendon disease. The SDFT is the functional equivalent of the Achilles tendon as elastic energy storing tendons and and both share remarkable ageing phenotypes[ 12 ] and prevelance of injury (tendinopathy) as a result of athletic activity [ 13 ]. They were chosen for this study in line with the “One Health” principle, as promoted by the World Health Oragisation, which aims to advance the understanding of, and provide mutually beneficial health benefits for, both humans and animals. Tissue Processing Each of the 3 tissue lengths was processed according to one of three pipelines shown in Fig. 1 , resulting in 8 distinct preparation techniques (A1-C3). Storage All tissues were stored at -80 o C immediately after ex vivo dissection as previously described. Once defrosted, they were stored in phosphate buffered saline (PBS) for a maximum of 24 hours prior to further processing. Fixation Tissues were fixed in one of two ways: aldehyde fixation (pipeline 2) via commercially available pre-filled containers of 10% neutral buffered formalin (NBF), or alcohol fixation (pipeline 3) via 70% ethanol (70% EtOH) which was made by diluting 100% ethanol (100% EtOH) with deionised water. In each case, the sample was submerged in approximately 60ml of liquid for a minimum of 48 hours to ensure complete fixation. Tissues were stored within their fixation liquid until imaging or further processing.. Dehydration Initial partial dehydration of tissue was achieved via submersion in 70% ethanol. Complete dehydration was achieved via manual immersion in 200ml of a gradually escalating ethanol series (80%, 80%, 90%, 100%, 100%, 100%) up to 100% for 45 minutes per step as previously reported elsewhere [ 14 , 15 ]. Tissues were stored in either 70% or 100% ethanol until imaging or further processing. Air dried samples were left at room temperature for 24 hours in an open container to allow evapouration of residual ethanol in a completely uncontrolled process. Full dehydration was assumed when the sample was completely hardened and no further visible shrinkage noted. Embedding A 1% low melting point agarose (LMPA) was prepared by boiling 100ml PBS with 1g of LMPA powder for one minute on a hot plate until the powder had fully disolved. LMPA was used due to the lower temperature of gelation, meaning the hot solution could be cooled to approximately 40 o C to prevent heat damage to the tissue prior to solidification. Once cooled, the solution was carefully pipetted around the sample in the container. Care was taken not to introduce any bubbles. The embedded sample was then refridgerated at 4 o C until the agarose had set and the sample was stored in this way until imaging. X-ray Imaging Sample mounting A simple reproducible and watertight sample container and lid were designed using open access online CAD software ‘TinkerCAD’ and 3D printed using thermoplastic polylactic acid (PLA) via the Ultimaker S5 3D printer and associated Cura software. The tube was printed using ‘spiralise’ setting creating container walls of approximately 0.4mm thickness - the resolution limit of the 3D printing system. PLA was chosen as it is chemically compatible with ethanol, formalin and PBS, and minimally absorbs the incident x-ray beam. The container was designed with a 2cm base height as this positioned the sample precisely within the x-ray beam without adjustment to any system hardware to ensure reproducibility in positioning between samples. Once the sample (longitudinally) and any dehydrating or fixation liquid were placed within the container, the lid was sealed shut with commercially available bathroom silicone sealant for added protection against accidental spills, and was then mounted on the sample stage ready for imaging (Fig. 2 ). X-ray Imaging System Samples were imaged using a custom Edge illumination x-ray phase contrast imaging (EI-XPCi) system at UCL consisting of a rotating molybdenum anode source (MicroMax-007HF, Rigaku, Japan) run at 40kVp and 30mA, emitting largely incoherent radiation with a polychromatic spectrum with a mean energy of approximately 18keV. A schematic of the system can be seen in Fig. 2 . The source to detector and source to sample distances were set at 86cm and 70cm, respectively, with the detector mask held approximately 1.5cm in front of the detector. The detector was a CMOS flat panel sensor (C9732DK Hamamatsu, Japan) with a pixel size of 50 µm. Detection of refraction caused by the sample is enabled by two ‘masks’ placed between the source and sample (sample mask) and the sample and detector (detector mask). The masks were manufactured by electroporating 100 microns of gold onto a graphite substrate leaving 10 micron transmitting apertures and a 79µm period in the sample mask, with the detector mask a magnified version based on the system geometry and divergence of the beam. The detector mask is aligned such that its apertures coincide with pixel centres, while the sample mask is aligned with a slight offset, such that the beamlets it creates fall partially onto the exposed pixel areas and partially onto the absorbing septa of the detector mask. Refraction then causes a deflection of the beamlets either onto the absorbing septa of the mask creating a negative signal, or onto the exposed pixel areas causing a positive signal (Fig. 2 .C). Note that, with the mask parameters given above, the detector mask fully covers every other column of detector pixels which would thus not be exposed to photons; this configuration, known as “line-skipping”, reduces the adverse effects of pixel cross talk [ 16 ]. CT Image Acquisition Parameters Samples were rotated within the system allowing for images to be taken at every 0.2 o over the full 360 o angular range. In EI XPCi, the spatial resolution is decoupled from the source blurring and detector pixel size and is instead defined by the width of the apertures in the sample mask [ 17 ]. To leverage this property, samples were moved in 8 sub pixel steps with a 1.2s exposure image taken at each step - a process known as ‘dithering’ [ 18 ]. Dithering was applied at each rotation angle. Flat and dark field images were taken before and after every 200 projections (flat field only) during the scan to facilitate gain and offset correction. Image processing Images were dark and flat field corrected, and the individual frames acquired during dithering combined into up sampled projections. These were then processed according to the so-called single image retrieval method developed for EI-XPCI [ 19 ]. Subsequently, the retrieved projections were reconstructed into axial CT slices via filtered back projection. Results Figure 3 shows CT slices for samples prepared according to the protocols A1 (left) and A2 (right). The images demonstrate the absence of contrast between the sample container, LMPA or PBS and the SDFT within it. It is practically impossible to discern the location of the tendon within the sample container, and no internal structural details can be visualised. Sample B Figure 4 shows CT slices for samples prepared according to the protocols B1 (left), B2 (middle) and B3 (right). Fixing, and submerging fresh SDFT in formalin produces no contrast between the sample, the container and the formalin, again, leaving only traces of the tendon being visible within the container. Dehydrating the tissue in either 70% or 100% ethanol increases the contrast and allows some tissue detail to be observed such as the loose connective tissue, as seen in the close-up images shown in Fig. 6 . Sample C Figure 5 shows CT slices for samples prepared according to the protocols C1 (left), C2 (middle) and C3 (right). These show that fixing and submerging SDFT in 70% ethanol with or without dehydration in 100% ethanol produces contrast that facilitates identification of structures such as loose connective tissue and dehydration to 100% facilitates the faint visualisation of internal structural details (likely fascicle boundaries) as seen in the close-up images in Fig. 6 . Fascicles are bundles of collagen fibres and are the largest subunit of tendons. Further uncontrolled air-drying causes significant shrinkage of the tendon (notable from the reduced size of the sample inside the container), which continued during the image acquisition resulting in blurring due to motion. Finally features likely consistent with tissue cracking possibly due to excessive or sudden dehydration are also apparent in C1, C2 and C3. Contrast to noise ratio (CNR) CNR values were extracted from the images to facilitate a quantitative comparison. However, due to the lack of contrast in the images of samples prepared according to the protocols A1, A2 and B1 and the significant shrinkage in the C3 protocol, CNR was only considered for the images of the remaining 4 preparation techniques (B2, B3, C1, C2), as these remain the only viable techniques moving forward. CNR was calculated as per Eq. 1 : $$\:CNR=\:\frac{{\mu\:}_{T}-{\mu\:}_{B}}{{\sigma\:}_{B}}$$ 1 Here, µ T and µ B denote the mean grey values in a tissue and background region and σ B is the standard deviation in the background region, as depicted in Fig. 7 . Measurements were extracted across 100 CT slices for each sample. CNR results are also displayed in Fig. 7 ; each bar shows the average CNR over the 100 slices, with error bars corresponding to one standard deviation to illustrate interslice variations in contrast and noise. Figure 7 shows that both samples dehydrated to 100% (B3, C2) ethanol display greater CNR than those only dehydrated to 70% (B2 and C1), with sample B2 performing much worse among the four considered sample preparation methods. Discussion Ideally, imaging of biological tissues using XPCi should be performed on fresh ex vivo tissues to preserve native structure and clinical relevance. However, fresh tissues pose challenges, including reduced image contrast and susceptibility to autolysis, dehydration, and shrinkage during long scans in non-refrigerated systems. As shown above, these issues can be mitigated through carefully selected tissue processing techniques. Results show that image contrast in XPCi is highly affected by the sample processing method, especially the presence, lack of and degree of sample dehydration. Samples that were not subject to any ethanol dehydration consistently demonstrated absent contrast regardless of the liquid/gel they were submerged/embedded in. This was found for both fresh and formalin fixed tissues. CNR can provide an estimation as to both the generation of contrast and how much the system is affected by noise. CNR appeared to be highest when tissues were dehydrated to 100% ethanol regardless of the fixation method. Processing tissues in 100% ethanol results in complete dehydration, whereas 70% ethanol leaves residual water within the tissue. To explain these results, the principles behind contrast governance in XPCi can be examined. XPCi generates contrast through both attenuation and refraction, which are described by the complex index of refraction: 𝑛=1−𝛿+𝑖𝛽, with the real (δ) and imaginary (β) parts relating to the attenuation and refraction (phase shifting) properties of a material, respectively. Biological tissues such as tendon, consists of approximately 70% water by mass with the remaining 30% largely collagen [ 20 ] thus, tissue composition is notably altered by dehydration, which removes water from the cellular environment. To evaluate the impact of tissue dehydration on XPCi, δ and β were calculated for tissue and the relevant substances used in the considered preparation techniques at 18 keV. The δ and β values of water, formalin and ethanol were obtained via an open-access X-ray refractive index calculator [ 21 ], informed by density values and chemical compositions obtained from commercial datasheets[ 22 , 23 ] and biomedical databases [ 24 ]. 10% Neutral Buffered Formalin (NBF) was modelled as 96% water and 4% formaldehyde and δ and β values were accordingly calculated as weighted averages, while Low Melting Point (LMP) agarose and Phosphate Buffered Saline (PBS) were treated as water-equivalent due to their high-water content. Collagen, the main constitute of tissue alongside water, is a structurally complex protein that lacks a definitive chemical formula; its δ and β values were thus approximated from calculated δ and β values for the amino acid residues present in equine collagen type I [ 25 ]. Density values for the amino acids were sourced from PubChem [ 24 ], with approximations made using commercial datasheets where reference values were not available. To model progressive dehydration, three distinct hydration states of tissue were prepared. The hydrated state consisted of a 7:3 ratio of water to collagen. Partial dehydration was simulated using a 7:3 ratio of 70% ethanol to collagen, while full dehydration was represented by a 7:3 ratio of 100% ethanol to collagen. Again, δ and β values were accordingly calculated as weighted averages. A full list of chemical formulas and density values used in the calculations is provided in the Supplementary Information, while δ and β values are plotted in Fig. 8 . Figure 8 shows that the difference between δ and β between 100% ethanol and fully dehydrated tissues is 1.7, 2.3, 5.5 and 7.7 times greater respectively than the differences between hydrated tendon and its two submersion liquids (10% NBF and water), with partial dehydration showing an intermediate difference. This explains why CNR was higher for samples dehydrated in 100% ethanol compared to those dehydrated in 70% ethanol, and also why fully hydrated tissues consistently produced no contrast when submerged in any aqueous liquid or material. Although sample dehydration increases contrast, there are potential considerations if downstream histology is required. Depending on their composition, samples may become hard and brittle when dehydrated, making it challenging to slice them via a microtome. In fact, the lack of comparative histology slices in this study is due to this issue, where even after one week of rehydration in saline solution the tendon remained too hard to section. Reducing the final concentration of ethanol from 100% to 70% may mitigate this issue and thus offer a potential compromise between achieving high image quality in XPCi while also allowing downstream tissue processing by histology. Results show that the method of fixation (NBF or 70% ethanol) has negligible impact on the resulting contrast provided subsequent dehydration occurs, however it should be noted that initial fixation with NBF may be protective against excessive dehydration and crack formation in the tissue - effects that were more clearly noted on tissues fixed in 70% ethanol. Other fixation methods such as EMA (ethanol, methanol and acetic acid), which shows greater preservation of tissue morphology than formalin, simultaneously fix and dehydrate tissues [ 26 ]; thus, they may prevent cracking with fewer safety concerns than formalin; however, further investigation is needed. Tendon tissue was selected for this study due to its hierarchical structure across multiple length scales and its representative water content which closely resembles that of most biological tissues; thus, the findings from this study represent an initial step toward establishing a standardized preparation technique applicable to a broad range of ex vivo tissues. However, our results also underscore the potential of XPCi in pre-clinical musculoskeletal biology specifically. The hierarchical organization of tendon presents challenges for traditional histological evaluation, especially when sectioning planes are perpendicular to fibre orientation, limiting insights into the tissue’s three-dimensional architecture. XPCi offers a promising alternative or complement, enabling the visualization of gross anatomy and internal features such as fascicles in three dimensions. Conclusion A simple method has emerged as an effective and reproducible sample preparation protocol for XPCi imaging of biological tissues. The results demonstrate the importance of controllable sample related factors with regards to the final image quality and visualisation of biological structures, an under explored topic in the development of innovative x-ray based imaging techniques such as XPCi. While our work demonstrates the need for dehydration, it also recognises the associated limitations with regards to mechanical properties of the dehydrated tissues and potential difficulties with downstream histological processing. Further comparative work is required to investigate the addition of steps to protect against sample cracking and brittleness during dehydration whilst increasing visability of finer tissue details. Declarations Ethical Statement Equine tendon tissues were obtained with approval from the Royal Veterinary College Clinical Research Ethical Review Board (URN 2020 2017–2). Competing intrests The author(s) declare no competing interests Funding CMJ was supported by the Engineering and Physical Sciences Research Council (EPSRC) under the UKRI-EPSRC Doctoral Prize Fellowship scheme (EP/R513143/1). CH acknowledges support from the Royal Academy of Engineering under their Research Fellowship scheme (201617/16A11). AO is supported by the Royal Academy of Engineering under their “Chairs in Emerging Technologies” scheme (Grant CiET1819/2/7). Author Contribution CMJ designed and conducted the experiments, analysed the data, wrote and edited the main manuscript text, JD provided the equine tendon tissue, AA provided laboratory support for x-ray acquisition and data analysis, AO contributed to initial project design, CH provided major manuscript edits, and all authors reviewed the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request References Kartasalo, K. et al. Comparative analysis of tissue reconstruction algorithms for 3D histology. Bioinformatics 34 , 3013–3021 (2018). Jansen, I. et al. Histopathology: ditch the slides, because digital and 3D are on show. World J. Urol. 36 , 549–555 (2018). Withers, P. J. et al. X-ray computed tomography. Nature Reviews Methods Primers vol. 1 Preprint at (2021). https://doi.org/10.1038/s43586-021-00015-4 Massimi, L. et al. Detection of involved margins in breast specimens with X-ray phase-contrast computed tomography. Sci Rep 11 , (2021). Li, K. Y. C. et al. Feasibility and safety of synchrotron-based X-ray phase contrast imaging as a technique complementary to histopathology analysis. Histochem. Cell. Biol. 160 , 377–389 (2023). Barbone, G. E. et al. 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A. et al. Alcoholic fixation over formalin fixation: A new, safer option for morphologic and molecular analysis of tissues. Saudi J. Biol. Sci. 29 , 175–182 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 17 Feb, 2026 Editor invited by journal 17 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 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. 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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-8842874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593178506,"identity":"9c022558-855d-4830-9d75-bce519b2f46d","order_by":0,"name":"Charlotte J Maughan Jones","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYPCCA1DMYMPAwA4RMiBWSxoDAzPxWsDgMGEt/A3ciZ95ft2RN2c8fOzBhz/nE/ubGRg//GA4bIxLi8QB3s3SvH3PDHc2HEs3nNl2O3HGYQZmyR6Gw2a4tBgw8G6Q5u05zLjhwBkzad6G24kbgA6TBrrQBo+Wzb+BWuzBWv78OQfSwvybgJZt0jw/DieCtTCwHQBpYQPZgtNhEod5t1nObTicvOHAsTTJ3rZk4xmHGdssewzScXqfv7138403fw7bbrhx+JjEjz92sv3tzYdv/KiwNmzApQcUC4xtIPsOwIQYGwhHJMMfkH04TR0Fo2AUjIKRDgCiBVwBr0NV0gAAAABJRU5ErkJggg==","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Charlotte","middleName":"J Maughan","lastName":"Jones","suffix":""},{"id":593178507,"identity":"50c31803-2d8e-4f7f-91d3-68964d630720","order_by":1,"name":"Jayesh Dudhia","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Jayesh","middleName":"","lastName":"Dudhia","suffix":""},{"id":593178512,"identity":"5a69f125-c3a4-456c-a756-dd026fa03152","order_by":2,"name":"Alberto Astolfo","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Astolfo","suffix":""},{"id":593178516,"identity":"3c757374-e088-4da7-88ac-b807be15eaa3","order_by":3,"name":"Alessandro Olivo","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Olivo","suffix":""},{"id":593178520,"identity":"1279a2f6-1e74-4981-a87f-19bf2e5589fd","order_by":4,"name":"Charlotte Hagen","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Hagen","suffix":""}],"badges":[],"createdAt":"2026-02-10 15:54:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8842874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8842874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504731,"identity":"24155275-ff89-438e-a8f1-e01389848254","added_by":"auto","created_at":"2026-02-26 13:21:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57324,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram demonstrating the tissue processing pipelines for the 3 equine tendon samples. A total of 8 preparation techniques, labelled A1 – C3, were implemented. The three pipelines are composed of one of more of the following staged: storage (red), fixation (green), dehydration (blue) and embedding (yellow).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/7a4dc4c0baff8578cce102d5.jpg"},{"id":103177242,"identity":"f3876e79-593d-4e17-8c06-143415a89205","added_by":"auto","created_at":"2026-02-22 16:47:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79424,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic cross section of 3D printed sample container demonstrating the key dimensions. B. Photograph of fully sealed and mounted sample container within the EI-XPCI system and C. of EI-XPCi line skipped system, demonstrating the process of signal formation.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/2e4f4935d1970767c28923a2.jpg"},{"id":103505401,"identity":"83f29bb6-6b28-472e-ba3b-4de85351bd7e","added_by":"auto","created_at":"2026-02-26 13:30:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74396,"visible":true,"origin":"","legend":"\u003cp\u003eAxial CT slices of a sample container containing (left) A1. Fresh SDFT tendon submerged in phosphate buffered saline (PBS), or (right) A2. Fresh SDFT embedded in Low melting point agarose (LMPA).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/017a7911f56aa799f8ee491e.jpg"},{"id":103177247,"identity":"f13cebbb-e408-43b9-88b0-56e82cf5d409","added_by":"auto","created_at":"2026-02-22 16:47:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203244,"visible":true,"origin":"","legend":"\u003cp\u003eAxial CT slices of a sample container containing B1: (left) Formalin fixed SDFT submerged in formalin, B2: (middle) Formalin fixed, 70% ethanol dehydrated SDFT submerged in 70% ethanol and B3: (right) formalin fixed, dehydrated to 100% ethanol SDFT, submerged in 100% ethanol. Scale bars = 5mm. Green arrow denotes loose connective tissue.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/237126273521aaf85cf1d2fd.jpg"},{"id":103505214,"identity":"86f4c59d-4fb6-44bc-a997-d823e1e94326","added_by":"auto","created_at":"2026-02-26 13:27:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173613,"visible":true,"origin":"","legend":"\u003cp\u003eAxial CT slices of a sample container containing C1: (left) 70% ethanol fixed SDFT, submerged in 70% ethanol, C2: (middle) 70% Ethanol fixed, 100% ethanol dehydrated SDFT submerged in 100% ethanol and C3: (right) air dried SDFT after previous processing. Scale bars = 5mm. Green and red arrows depict loose connective tissue and cracks respectively.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/1c1aa57282dfadf077f15c14.jpg"},{"id":103177249,"identity":"c07a9680-d7e0-4236-a63d-f56a2ac87aea","added_by":"auto","created_at":"2026-02-22 16:47:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":169752,"visible":true,"origin":"","legend":"\u003cp\u003eClose up detail of loose connective tissue in the samples prepared according to protocols (left to right) B2, B3, C1 and C2. The red arrow indicated the same connective tissue feature in B2 and B3 and the blue arrow indicates a similar feature in C1 and C2. The green arrows denote internal structural details (fascicle boundaries), most visible in C2. Scale bar = 1mm.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/0d9b24278275040571a577b0.jpg"},{"id":103177250,"identity":"f7de5053-e62e-4603-9f47-978f0ccb0605","added_by":"auto","created_at":"2026-02-22 16:47:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89961,"visible":true,"origin":"","legend":"\u003cp\u003eCNR in the images obtained from 4 of the sample preparation techniques. Error bars show +/- one standard deviation of CNR values calculated across the 100 slices.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/62016743a4638ec192bddb5d.jpg"},{"id":104397537,"identity":"65b04ab9-535b-4718-b4a0-6afa121ffdbc","added_by":"auto","created_at":"2026-03-11 11:50:44","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":61325,"visible":true,"origin":"","legend":"\u003cp\u003eCalculated delta and beta values for tendon tissue and associated liquids.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/5cba44913b68cff7d5a78e17.jpg"},{"id":104407295,"identity":"952b5bf6-b948-405b-8ff8-7b3ff1d19f85","added_by":"auto","created_at":"2026-03-11 12:36:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1439258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/aa1d585c-798d-405e-8246-76718705f2f7.pdf"},{"id":103505400,"identity":"8e921f17-94bb-4866-a778-5e03f8b6785b","added_by":"auto","created_at":"2026-02-26 13:30:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":139853,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8842874/v1/d6c8a58bcfd627a6fb2db6ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards standardised sample preparation in X- ray phase contrast imaging: comparison of eight protocols on equine superficial digital flexor tendon (SDFT)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eX-ray phase contrast imaging (XPCi) is an emerging tool in preclinical imaging due to its ability to provide increased contrast in biological tissues compared to traditional x-ray imaging, without the need for exogenous contrast media or destructive sample processing methods as in histology. Standard x-ray based imaging methods rely on the difference in absorption properties of materials to produce contrast within an image. XPCi however also utilises the refractive properties of tissues, which can be up to three order of magnitude greater than the corresponding absorption properties, resulting in greater intrinsic contrast.\u003c/p\u003e \u003cp\u003eHowever, despite this, histology remains the current gold standard for investigating the internal three dimensional structure of healthy and diseased biological tissues, both in laboratory and clinical settings. While providing rich information, histology requires lengthy tissue processing and results in the complete destruction of the tissue due to the sectioning required for the application of light microscopy. Histology also remains essentially a 2D technique; although efforts to produce three dimensional data sets from histological slices has been made and commercial systems are now available, reconstruction remains challenging due to preparation induced distortions to the tissue, hampering accurate registration of features between slices [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore reslicing reconstructed three dimensional histological datasets in different planes can lead to a loss in resolution [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By contrast, XPCi, owing to its natural compatibility with computed tomographic (CT) imaging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], allows viewing tissue in its native three dimensional state, helping to uncover the interplay of different structures within the same tissue at high resultion.\u003c/p\u003e \u003cp\u003eA significant barrier to the widespread adoption of XPCi as a viable alternative or complement to established histological processes is the lack of consensus on the most effective biological sample preparation techniques, with varied approaches reported in the literature, ranging from formalin fixation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], paraffin embedding [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], aldehyde fixation and osmium impregnantion [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], chemical dehydration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and critical point drying [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite the reporting of many sample preparation techniques, there remains no \u0026lsquo;gold standard\u0026rsquo; approach. Furthermore, many of the reported approaches suffer from issues such as bubble formation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] or long sample preparation times [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough it would be ideal to image all tissue fresh (that is, without any processing applied), which some have sucessfully done [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], long acquisition times at room temperature render fresh tissue liable to a combination of autolysis and uncontrolled dehydration, which can lead to cellular changes, decomposition and alteration in sample dimension and morphology. Fresh tissues can also pose safety concerns for personel due to potential infectious or zoonotic disease transmission, which may be a health and safety consideration within imaging laboratories. Overarchingly, fresh tissue tends to be difficult to image, especially with techniques that tend to incur lenghty scan times, such as XPCi. Sample processing is therefore required to maintain the biological anatomy, whilst making \u0026lsquo;risky\u0026rsquo; tissue safer. As is shown below, sample processing can also lead to enhanced image contrast.\u003c/p\u003e \u003cp\u003eTendon tissue is a suitable model for developing and formalising sample preparation protocols in XPCi due to its well-defined hierarchical organisation and mechanical robustness. Composed primarily of highly aligned collagen fibers arranged in a multi-scale architecture - from fibrils to fascicles - tendons offer a consistent and predictable structure that facilitates image interpretation. The hierarchical structure provides distinct tissue boundaries at multiple scales, making it ideal for evaluating XPCi techniques. Additionally, the tissue\u0026rsquo;s low cellularity and resistance to deformation during processing make it a practical choice for optimisation of iterative sample preparation techniques which can then be applied to other tissue types.\u003c/p\u003e \u003cp\u003eThe purpose of this paper is two-fold. By applying a variety of simple and common sample preparation techniques which present stark differences in contrast and feature identification, we demonstrate the need for agreeing upon a reproducible sample preparation pipeline for XPCi. Furthermore, our results demonstrate that the application of a chemical dehydration agent (ethanol) improves contrast relative to the fresh tissue state, and propose its use as a recommended step prior to XPCi.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTissues\u003c/h2\u003e \u003cp\u003eNormal (healthy) equine superficial digital flexor tendon (SDFT) tissue was excised within 24 h of post-mortem from Thoroughbred-type horses that had been euthanised at abbatoirs for reasons unrelated to this study. Dissected SDFT tissue from the mid-metacarpal was flash frozen in n-hexane at -80 \u003csup\u003eo\u003c/sup\u003eC and stored at -80\u003csup\u003eo\u003c/sup\u003eC at the Royal Veterinary College. The tissue was subsequently defrosted at room temperature and cut into 3 equal lengths (approximately 2cm each axially) ready for processing. Equine tendon tissues were obtained with approval from the Royal Veterinary College Clinical Research Ethical Review Board (URN 2020 2017\u0026ndash;2).\u003c/p\u003e \u003cp\u003eHorses are a spontaneous and established model of human tendon disease. The SDFT is the functional equivalent of the Achilles tendon as elastic energy storing tendons and and both share remarkable ageing phenotypes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and prevelance of injury (tendinopathy) as a result of athletic activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They were chosen for this study in line with the \u0026ldquo;One Health\u0026rdquo; principle, as promoted by the World Health Oragisation, which aims to advance the understanding of, and provide mutually beneficial health benefits for, both humans and animals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue Processing\u003c/h3\u003e\n\u003cp\u003eEach of the 3 tissue lengths was processed according to one of three pipelines shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, resulting in 8 distinct preparation techniques (A1-C3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStorage\u003c/p\u003e \u003cp\u003eAll tissues were stored at -80\u003csup\u003eo\u003c/sup\u003eC immediately after \u003cem\u003eex vivo\u003c/em\u003e dissection as previously described. Once defrosted, they were stored in phosphate buffered saline (PBS) for a maximum of 24 hours prior to further processing.\u003c/p\u003e \u003cp\u003eFixation\u003c/p\u003e \u003cp\u003eTissues were fixed in one of two ways: aldehyde fixation (pipeline 2) via commercially available pre-filled containers of 10% neutral buffered formalin (NBF), or alcohol fixation (pipeline 3) via 70% ethanol (70% EtOH) which was made by diluting 100% ethanol (100% EtOH) with deionised water. In each case, the sample was submerged in approximately 60ml of liquid for a minimum of 48 hours to ensure complete fixation. Tissues were stored within their fixation liquid until imaging or further processing..\u003c/p\u003e \u003cp\u003eDehydration\u003c/p\u003e \u003cp\u003eInitial partial dehydration of tissue was achieved via submersion in 70% ethanol. Complete dehydration was achieved via manual immersion in 200ml of a gradually escalating ethanol series (80%, 80%, 90%, 100%, 100%, 100%) up to 100% for 45 minutes per step as previously reported elsewhere [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Tissues were stored in either 70% or 100% ethanol until imaging or further processing.\u003c/p\u003e \u003cp\u003eAir dried samples were left at room temperature for 24 hours in an open container to allow evapouration of residual ethanol in a completely uncontrolled process. Full dehydration was assumed when the sample was completely hardened and no further visible shrinkage noted.\u003c/p\u003e \u003cp\u003eEmbedding\u003c/p\u003e \u003cp\u003eA 1% low melting point agarose (LMPA) was prepared by boiling 100ml PBS with 1g of LMPA powder for one minute on a hot plate until the powder had fully disolved. LMPA was used due to the lower temperature of gelation, meaning the hot solution could be cooled to approximately 40\u003csup\u003eo\u003c/sup\u003eC to prevent heat damage to the tissue prior to solidification. Once cooled, the solution was carefully pipetted around the sample in the container. Care was taken not to introduce any bubbles. The embedded sample was then refridgerated at 4\u003csup\u003eo\u003c/sup\u003eC until the agarose had set and the sample was stored in this way until imaging.\u003c/p\u003e\n\u003ch3\u003eX-ray Imaging\u003c/h3\u003e\n\u003cp\u003eSample mounting\u003c/p\u003e \u003cp\u003eA simple reproducible and watertight sample container and lid were designed using open access online CAD software \u0026lsquo;TinkerCAD\u0026rsquo; and 3D printed using thermoplastic polylactic acid (PLA) via the Ultimaker S5 3D printer and associated Cura software. The tube was printed using \u0026lsquo;spiralise\u0026rsquo; setting creating container walls of approximately 0.4mm thickness - the resolution limit of the 3D printing system. PLA was chosen as it is chemically compatible with ethanol, formalin and PBS, and minimally absorbs the incident x-ray beam. The container was designed with a 2cm base height as this positioned the sample precisely within the x-ray beam without adjustment to any system hardware to ensure reproducibility in positioning between samples.\u003c/p\u003e \u003cp\u003eOnce the sample (longitudinally) and any dehydrating or fixation liquid were placed within the container, the lid was sealed shut with commercially available bathroom silicone sealant for added protection against accidental spills, and was then mounted on the sample stage ready for imaging (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eX-ray Imaging System\u003c/p\u003e \u003cp\u003eSamples were imaged using a custom Edge illumination x-ray phase contrast imaging (EI-XPCi) system at UCL consisting of a rotating molybdenum anode source (MicroMax-007HF, Rigaku, Japan) run at 40kVp and 30mA, emitting largely incoherent radiation with a polychromatic spectrum with a mean energy of approximately 18keV. A schematic of the system can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe source to detector and source to sample distances were set at 86cm and 70cm, respectively, with the detector mask held approximately 1.5cm in front of the detector. The detector was a CMOS flat panel sensor (C9732DK Hamamatsu, Japan) with a pixel size of 50 \u0026micro;m. Detection of refraction caused by the sample is enabled by two \u0026lsquo;masks\u0026rsquo; placed between the source and sample (sample mask) and the sample and detector (detector mask). The masks were manufactured by electroporating 100 microns of gold onto a graphite substrate leaving 10 micron transmitting apertures and a 79\u0026micro;m period in the sample mask, with the detector mask a magnified version based on the system geometry and divergence of the beam. The detector mask is aligned such that its apertures coincide with pixel centres, while the sample mask is aligned with a slight offset, such that the beamlets it creates fall partially onto the exposed pixel areas and partially onto the absorbing septa of the detector mask. Refraction then causes a deflection of the beamlets either onto the absorbing septa of the mask creating a negative signal, or onto the exposed pixel areas causing a positive signal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.C). Note that, with the mask parameters given above, the detector mask fully covers every other column of detector pixels which would thus not be exposed to photons; this configuration, known as \u0026ldquo;line-skipping\u0026rdquo;, reduces the adverse effects of pixel cross talk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCT Image Acquisition Parameters\u003c/p\u003e \u003cp\u003eSamples were rotated within the system allowing for images to be taken at every 0.2\u003csup\u003eo\u003c/sup\u003e over the full 360\u003csup\u003eo\u003c/sup\u003e angular range. In EI XPCi, the spatial resolution is decoupled from the source blurring and detector pixel size and is instead defined by the width of the apertures in the sample mask [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To leverage this property, samples were moved in 8 sub pixel steps with a 1.2s exposure image taken at each step - a process known as \u0026lsquo;dithering\u0026rsquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dithering was applied at each rotation angle. Flat and dark field images were taken before and after every 200 projections (flat field only) during the scan to facilitate gain and offset correction.\u003c/p\u003e \u003cp\u003eImage processing\u003c/p\u003e \u003cp\u003eImages were dark and flat field corrected, and the individual frames acquired during dithering combined into up sampled projections. These were then processed according to the so-called single image retrieval method developed for EI-XPCI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, the retrieved projections were reconstructed into axial CT slices via filtered back projection.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows CT slices for samples prepared according to the protocols A1 (left) and A2 (right). The images demonstrate the absence of contrast between the sample container, LMPA or PBS and the SDFT within it. It is practically impossible to discern the location of the tendon within the sample container, and no internal structural details can be visualised.\u003c/p\u003e\n\u003ch3\u003eSample B\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows CT slices for samples prepared according to the protocols B1 (left), B2 (middle) and B3 (right). Fixing, and submerging fresh SDFT in formalin produces no contrast between the sample, the container and the formalin, again, leaving only traces of the tendon being visible within the container. Dehydrating the tissue in either 70% or 100% ethanol increases the contrast and allows some tissue detail to be observed such as the loose connective tissue, as seen in the close-up images shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample C\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows CT slices for samples prepared according to the protocols C1 (left), C2 (middle) and C3 (right). These show that fixing and submerging SDFT in 70% ethanol with or without dehydration in 100% ethanol produces contrast that facilitates identification of structures such as loose connective tissue and dehydration to 100% facilitates the faint visualisation of internal structural details (likely fascicle boundaries) as seen in the close-up images in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Fascicles are bundles of collagen fibres and are the largest subunit of tendons. Further uncontrolled air-drying causes significant shrinkage of the tendon (notable from the reduced size of the sample inside the container), which continued during the image acquisition resulting in blurring due to motion. Finally features likely consistent with tissue cracking possibly due to excessive or sudden dehydration are also apparent in C1, C2 and C3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContrast to noise ratio (CNR)\u003c/h3\u003e\n\u003cp\u003eCNR values were extracted from the images to facilitate a quantitative comparison. However, due to the lack of contrast in the images of samples prepared according to the protocols A1, A2 and B1 and the significant shrinkage in the C3 protocol, CNR was only considered for the images of the remaining 4 preparation techniques (B2, B3, C1, C2), as these remain the only viable techniques moving forward. CNR was calculated as per Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:CNR=\\:\\frac{{\\mu\\:}_{T}-{\\mu\\:}_{B}}{{\\sigma\\:}_{B}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u0026micro;\u003csub\u003eT\u003c/sub\u003e and \u0026micro;\u003csub\u003eB\u003c/sub\u003e denote the mean grey values in a tissue and background region and σ\u003csub\u003eB\u003c/sub\u003e is the standard deviation in the background region, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Measurements were extracted across 100 CT slices for each sample. CNR results are also displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; each bar shows the average CNR over the 100 slices, with error bars corresponding to one standard deviation to illustrate interslice variations in contrast and noise.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that both samples dehydrated to 100% (B3, C2) ethanol display greater CNR than those only dehydrated to 70% (B2 and C1), with sample B2 performing much worse among the four considered sample preparation methods.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIdeally, imaging of biological tissues using XPCi should be performed on fresh \u003cem\u003eex vivo\u003c/em\u003e tissues to preserve native structure and clinical relevance. However, fresh tissues pose challenges, including reduced image contrast and susceptibility to autolysis, dehydration, and shrinkage during long scans in non-refrigerated systems. As shown above, these issues can be mitigated through carefully selected tissue processing techniques.\u003c/p\u003e \u003cp\u003eResults show that image contrast in XPCi is highly affected by the sample processing method, especially the presence, lack of and degree of sample dehydration. Samples that were not subject to any ethanol dehydration consistently demonstrated absent contrast regardless of the liquid/gel they were submerged/embedded in. This was found for both fresh and formalin fixed tissues. CNR can provide an estimation as to both the generation of contrast and how much the system is affected by noise. CNR appeared to be highest when tissues were dehydrated to 100% ethanol regardless of the fixation method. Processing tissues in 100% ethanol results in complete dehydration, whereas 70% ethanol leaves residual water within the tissue.\u003c/p\u003e \u003cp\u003eTo explain these results, the principles behind contrast governance in XPCi can be examined. XPCi generates contrast through both attenuation and refraction, which are described by the complex index of refraction: \u0026#119899;=1\u0026minus;\u0026#120575;+\u0026#119894;\u0026#120573;, with the real (δ) and imaginary (β) parts relating to the attenuation and refraction (phase shifting) properties of a material, respectively. Biological tissues such as tendon, consists of approximately 70% water by mass with the remaining 30% largely collagen [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] thus, tissue composition is notably altered by dehydration, which removes water from the cellular environment.\u003c/p\u003e \u003cp\u003eTo evaluate the impact of tissue dehydration on XPCi, δ and β were calculated for tissue and the relevant substances used in the considered preparation techniques at 18 keV. The δ and β values of water, formalin and ethanol were obtained via an open-access X-ray refractive index calculator [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], informed by density values and chemical compositions obtained from commercial datasheets[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and biomedical databases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 10% Neutral Buffered Formalin (NBF) was modelled as 96% water and 4% formaldehyde and δ and β values were accordingly calculated as weighted averages, while Low Melting Point (LMP) agarose and Phosphate Buffered Saline (PBS) were treated as water-equivalent due to their high-water content. Collagen, the main constitute of tissue alongside water, is a structurally complex protein that lacks a definitive chemical formula; its δ and β values were thus approximated from calculated δ and β values for the amino acid residues present in equine collagen type I [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Density values for the amino acids were sourced from PubChem [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with approximations made using commercial datasheets where reference values were not available. To model progressive dehydration, three distinct hydration states of tissue were prepared. The hydrated state consisted of a 7:3 ratio of water to collagen. Partial dehydration was simulated using a 7:3 ratio of 70% ethanol to collagen, while full dehydration was represented by a 7:3 ratio of 100% ethanol to collagen. Again, δ and β values were accordingly calculated as weighted averages. A full list of chemical formulas and density values used in the calculations is provided in the Supplementary Information, while δ and β values are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the difference between δ and β between 100% ethanol and fully dehydrated tissues is 1.7, 2.3, 5.5 and 7.7 times greater respectively than the differences between hydrated tendon and its two submersion liquids (10% NBF and water), with partial dehydration showing an intermediate difference. This explains why CNR was higher for samples dehydrated in 100% ethanol compared to those dehydrated in 70% ethanol, and also why fully hydrated tissues consistently produced no contrast when submerged in any aqueous liquid or material.\u003c/p\u003e \u003cp\u003eAlthough sample dehydration increases contrast, there are potential considerations if downstream histology is required. Depending on their composition, samples may become hard and brittle when dehydrated, making it challenging to slice them via a microtome. In fact, the lack of comparative histology slices in this study is due to this issue, where even after one week of rehydration in saline solution the tendon remained too hard to section. Reducing the final concentration of ethanol from 100% to 70% may mitigate this issue and thus offer a potential compromise between achieving high image quality in XPCi while also allowing downstream tissue processing by histology.\u003c/p\u003e \u003cp\u003eResults show that the method of fixation (NBF or 70% ethanol) has negligible impact on the resulting contrast provided subsequent dehydration occurs, however it should be noted that initial fixation with NBF may be protective against excessive dehydration and crack formation in the tissue - effects that were more clearly noted on tissues fixed in 70% ethanol. Other fixation methods such as EMA (ethanol, methanol and acetic acid), which shows greater preservation of tissue morphology than formalin, simultaneously fix and dehydrate tissues [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; thus, they may prevent cracking with fewer safety concerns than formalin; however, further investigation is needed.\u003c/p\u003e \u003cp\u003eTendon tissue was selected for this study due to its hierarchical structure across multiple length scales and its representative water content which closely resembles that of most biological tissues; thus, the findings from this study represent an initial step toward establishing a standardized preparation technique applicable to a broad range of \u003cem\u003eex vivo\u003c/em\u003e tissues. However, our results also underscore the potential of XPCi in pre-clinical musculoskeletal biology specifically. The hierarchical organization of tendon presents challenges for traditional histological evaluation, especially when sectioning planes are perpendicular to fibre orientation, limiting insights into the tissue\u0026rsquo;s three-dimensional architecture. XPCi offers a promising alternative or complement, enabling the visualization of gross anatomy and internal features such as fascicles in three dimensions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA simple method has emerged as an effective and reproducible sample preparation protocol for XPCi imaging of biological tissues. The results demonstrate the importance of controllable sample related factors with regards to the final image quality and visualisation of biological structures, an under explored topic in the development of innovative x-ray based imaging techniques such as XPCi. While our work demonstrates the need for dehydration, it also recognises the associated limitations with regards to mechanical properties of the dehydrated tissues and potential difficulties with downstream histological processing. Further comparative work is required to investigate the addition of steps to protect against sample cracking and brittleness during dehydration whilst increasing visability of finer tissue details.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eEquine tendon tissues were obtained with approval from the Royal Veterinary College Clinical Research Ethical Review Board (URN 2020 2017\u0026ndash;2).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting intrests\u003c/h2\u003e \u003cp\u003eThe author(s) declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eCMJ was supported by the Engineering and Physical Sciences Research Council (EPSRC) under the UKRI-EPSRC Doctoral Prize Fellowship scheme (EP/R513143/1).\u003c/p\u003e \u003cp\u003eCH acknowledges support from the Royal Academy of Engineering under their Research Fellowship scheme (201617/16A11).\u003c/p\u003e \u003cp\u003eAO is supported by the Royal Academy of Engineering under their \u0026ldquo;Chairs in Emerging Technologies\u0026rdquo; scheme (Grant CiET1819/2/7).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCMJ designed and conducted the experiments, analysed the data, wrote and edited the main manuscript text, JD provided the equine tendon tissue, AA provided laboratory support for x-ray acquisition and data analysis, AO contributed to initial project design, CH provided major manuscript edits, and all authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKartasalo, K. et al. 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Alcoholic fixation over formalin fixation: A new, safer option for morphologic and molecular analysis of tissues. \u003cem\u003eSaudi J. Biol. Sci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 175\u0026ndash;182 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"x-ray phase contrast, tendon, edge illumination","lastPublishedDoi":"10.21203/rs.3.rs-8842874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8842874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX-ray phase contrast imaging (XPCi) is becoming increasingly important with regards to three-dimensional imaging of biological tissues, however research has largely been focused on technology development and exploring pre-clinical and research applications, with little consideration to optimal sample preparation techniques and their effect on image quality. This paper aims to fill this gap by comparing 8 different sample preparation techniques on \u003cem\u003eex vivo\u003c/em\u003e biological tissues. Equine superficial digital flexor tendons were prepared using various combinations of PBS, 10% neutral buffered formalin, 70% and 100% ethanol and imaged using an edge illumination XPCi system in a 3D printed container. Results demonstrated that chemical dehydration via ethanol is a recommended precursor to XPCi contrast generation with 100% ethanol the optimal dehydration agent for maximising contrast.\u003c/p\u003e","manuscriptTitle":"Towards standardised sample preparation in X- ray phase contrast imaging: comparison of eight protocols on equine superficial digital flexor tendon (SDFT)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:47:34","doi":"10.21203/rs.3.rs-8842874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T10:08:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T08:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23712322560219504160985360385982069805","date":"2026-03-26T08:55:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T18:40:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9360319745513914946650693029458148909","date":"2026-03-23T11:15:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T22:30:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T21:55:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-17T06:26:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T17:38:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-13T17:34:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7f5f510-c036-4719-9cf5-73e175aaef3e","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63118882,"name":"Biological sciences/Biological techniques"},{"id":63118883,"name":"Biological sciences/Biotechnology"},{"id":63118884,"name":"Physical sciences/Engineering"},{"id":63118885,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-08T08:26:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:47:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8842874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8842874","identity":"rs-8842874","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}