{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:35:04Z","timestamp":1787027704855,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute for Health and Care Research (NIHR)","award":["II-LA-0216-20007"],"award-info":[{"award-number":["II-LA-0216-20007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. Methods: We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). Results: The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 \u00d7 10\u22123 mm2\/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Conclusion: Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.<\/jats:p>","DOI":"10.3390\/bioengineering11020130","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T08:11:27Z","timestamp":1706515887000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3059-9328","authenticated-orcid":false,"given":"Antonio","family":"Candito","sequence":"first","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Holbrey","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0510-4688","authenticated-orcid":false,"given":"Ana","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0557-9379","authenticated-orcid":false,"given":"Christina","family":"Messiou","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"},{"name":"Department of Radiology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Tunariu","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"},{"name":"Department of Radiology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dow-Mu","family":"Koh","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"},{"name":"Department of Radiology, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-9003","authenticated-orcid":false,"given":"Matthew D.","family":"Blackledge","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.eururo.2016.05.033","article-title":"METastasis Reporting and Data System for Prostate Cancer: Practical Guidelines for Acquisition, Interpretation, and Reporting of Whole-body Magnetic Resonance Imaging-based Evaluations of Multiorgan Involvement in Advanced Prostate Cancer","volume":"71","author":"Padhani","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1148\/radiol.2019181931","article-title":"Imaging Diagnosis and Follow-up of Advanced Prostate Cancer: Clinical Perspectives and State of the Art","volume":"292","author":"Tunariu","year":"2019","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zugni, F., Ruju, F., Pricolo, P., Alessi, S., Iorfida, M., Colleoni, M.A., Bellomi, M., and Petralia, G. (2018). The added value of whole-body magnetic resonance imaging in the management of patients with advanced breast cancer. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205251"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"336","DOI":"10.2214\/AJR.17.17984","article-title":"Whole-Body MRI: Current Applications in Oncology","volume":"209","author":"Morone","year":"2017","journal-title":"Am. J. Roentgenol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e210048","DOI":"10.1148\/rycan.2021210048","article-title":"Prospective Evaluation of Whole-Body MRI versus FDG PET\/CT for Lesion Detection in Participants with Myeloma","volume":"3","author":"Messiou","year":"2021","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1111\/1754-9485.13038","article-title":"Predictors of patient preference for either whole body magnetic resonance imaging (WB-MRI) or CT\/PET-CT for staging colorectal or lung cancer","volume":"64","author":"Miles","year":"2020","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1055\/s-0040-1719018","article-title":"Detection and Characterization of Musculoskeletal Cancer Using Whole-Body Magnetic Resonance Imaging","volume":"24","author":"Isaac","year":"2020","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1148\/radiol.2016160646","article-title":"Diffusion-weighted imaging as a treatment response biomarker for evaluating bone metastases in prostate cancer: A pilot study","volume":"283","author":"Mateo","year":"2017","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s00256-021-03841-5","article-title":"Imaging of treatment response and minimal residual disease in multiple myeloma: State of the art WB-MRI and PET\/CT","volume":"51","author":"Lecouvet","year":"2021","journal-title":"Skelet. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1259\/bjr.20170577","article-title":"Guidelines & recommendations: UK quantitative WB-DWI technical workgroup: Consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer","volume":"91","author":"Barnes","year":"2018","journal-title":"Br. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.mric.2018.06.003","article-title":"Whole-Body Magnetic Resonance Imaging in Oncology: Uses and Indications","volume":"26","author":"Petralia","year":"2018","journal-title":"Magn. Reson. Imaging Clin. N. Am."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"252","DOI":"10.2214\/AJR.11.7866","article-title":"Whole-body diffusion-weighted mri: Tips, tricks, and pitfalls","volume":"199","author":"Koh","year":"2012","journal-title":"Am. J. Roentgenol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Colombo, A., Saia, G., Azzena, A.A., Rossi, A., Zugni, F., Pricolo, P., Summers, P.E., Marvaso, G., Grimm, R., and Bellomi, M. (2021). Semi-automated segmentation of bone metastases from whole-body mri: Reproducibility of apparent diffusion coefficient measurements. Diagnostics, 11.","DOI":"10.3390\/diagnostics11030499"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1097\/RLI.0000000000000838","article-title":"Repeatability and Reproducibility of ADC Measurements and MRI Signal Intensity Measurements of Bone Marrow in Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Multiscanner, Multiprotocol Study","volume":"57","author":"Wennmann","year":"2022","journal-title":"Invest. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S1470-2045(19)30684-9","article-title":"Olaparib in patients with metastatic castration-resistant prostate cancer with DNA repair gene aberrations (TOPARP-B): A multicentre, open-label, randomised, phase 2 trial","volume":"21","author":"Mateo","year":"2020","journal-title":"Lancet Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TMI.2008.2004429","article-title":"Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging","volume":"28","author":"Jager","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41598-021-04467-2","article-title":"Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone","volume":"12","author":"Digma","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"704","DOI":"10.3389\/fonc.2020.00704","article-title":"Noise-Corrected, Exponentially Weighted, Diffusion-Weighted MRI (niceDWI) Improves Image Signal Uniformity in Whole-Body Imaging of Metastatic Prostate Cancer","volume":"10","author":"Blackledge","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ceranka, J., Verga, S., Lecouvet, F., Metens, T., De Mey, J., and Vandemeulebroucke, J. (2018, January 16). Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI. Proceedings of the Computational Methods and Clinical Applications for Spine Imaging: 5th International Workshop and Challenge, CSI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain.","DOI":"10.1007\/978-3-030-13736-6_7"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"163","DOI":"10.2214\/AJR.11.8185","article-title":"Assessing the relation between bone marrow signal intensity and apparent diffusion coefficient in diffusion-weighted MRI","volume":"200","author":"Padhani","year":"2013","journal-title":"Am. J. Roentgenol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.crad.2021.04.001","article-title":"Whole-body MRI: A practical guide for imaging patients with malignant bone disease","volume":"76","author":"Winfield","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1148\/radiol.2021201740","article-title":"Oncologically Relevant Findings Reporting and Data System (ONCO-RADS): Guidelines for the Acquisition, Interpretation, and Reporting of Whole-Body MRI for Cancer screening","volume":"299","author":"Petralia","year":"2021","journal-title":"Radiology"},{"key":"ref_23","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1148\/radiol.11101919","article-title":"Computed Diffusion-weighted MR Imaging May Improve Tumor Detection","volume":"261","author":"Blackledge","year":"2011","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jadon, S. (2020, January 27\u201329). A survey of loss functions for semantic segmentation. Proceedings of the Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Via del Mar, Chile.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","article-title":"Combo loss: Handling input and output imbalance in multi-organ segmentation","volume":"75","author":"Taghanaki","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_27","unstructured":"Sadegh, S., Salehi, M., Erdogmus, D., and Gholipour, A. (2017). International Workshop on Machine Learning in Medical Imaging, Springer International Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abraham, N., and Khan, N.M. (2019, January 8\u201311). A novel focal tversky loss function with improved attention u-net for lesion segmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/j.neuroimage.2013.07.060","article-title":"Automatic Magnetic Resonance Spinal Cord Segmentation with Topology Constraints for Variable Fields of View","volume":"23","author":"Chen","year":"2013","journal-title":"Neuroimage"},{"key":"ref_30","unstructured":"Blackledge, M.D., Koh, D.M., Orton, M.R., Collins, D.J., and Leach, M.O. (2012). A Fast and Simple Post-Processing Procedure for the Correction of Mis-Registration between Sequentially Acquired Stations in Whole-Body Diffusion Weighted MRI, International Society for Magnetic Resonance in Medicine."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1002\/mrm.26787","article-title":"Registration strategies for multi-modal whole-body MRI mosaicking","volume":"79","author":"Ceranka","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102766","DOI":"10.1016\/j.nicl.2021.102766","article-title":"Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning","volume":"31","author":"Lemay","year":"2021","journal-title":"NeuroImage Clin."},{"key":"ref_33","first-page":"737","article-title":"Convolutional neural network\u2013based automated segmentation of the spinal cord and contusion injury: Deep learning biomarker correlates of motor impairment in acute spinal cord injury","volume":"40","author":"McCoy","year":"2019","journal-title":"Am. J. Neuroradiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.neuroimage.2018.09.081","article-title":"Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks","volume":"184","author":"Gros","year":"2019","journal-title":"NeuroImage"},{"key":"ref_35","unstructured":"Candito, A., Blackledge, M.D., Holbrey, R., and Koh, D.M. (2022, January 6\u20138). Automated tool to quantitatively assess bone disease on Whole-Body Diffusion Weighted Imaging for patients with Advanced Prostate Cancer. Proceedings of the Medical Imaging with Deep Learning, Zurich, Switzerland."},{"key":"ref_36","unstructured":"Qaiser, T., Winzeck, S., Barfoot, T., Barwick, T., Doran, S.J., Kaiser, M.F., and Glocker, B. (October, January 27). Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI, Strasbourg, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1097\/RLI.0000000000000932","article-title":"Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements from Whole-Body Magnetic Resonance Imaging in Patients with Multiple Myeloma: A Retrospective Multicenter Study","volume":"58","author":"Wennmann","year":"2023","journal-title":"Invest. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.mric.2018.06.002","article-title":"Quantitative Whole-Body Diffusion-Weighted MR Imaging","volume":"26","author":"Donners","year":"2018","journal-title":"Magn. Reson. Imaging Clin. N. Am."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/2\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:51:09Z","timestamp":1760104269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/2\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["bioengineering11020130"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11020130","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]}}}