{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T20:51:58Z","timestamp":1788814318748,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","award":["950\u2010233203"],"award-info":[{"award-number":["950\u2010233203"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The purpose of this study is to adapt a Bayesian dual\u2010virtual non\u2010contrast (VNC) method by integrating prior anatomical knowledge from AI\u2010based multi\u2010organ segmentation and to generalize it for spectral photon\u2010counting CT (PCCT) with an arbitrary number of energy\u00a0channels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open\u2010access whole\u2010body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast\u2010enhanced dual\u2010energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy\u2010relevant parameters such as electron density () and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non\u2010contrast scans by setting contrast agent uptake to\u00a0zero.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI\u2010based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and . Mean absolute water\u2010equivalent path length (WEPL) errors confirmed the superiority of segmentation\u2010assisted PCCT over other\u00a0methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This proof of concept demonstrates a flexible, AI\u2010assisted Bayesian framework for extracting quantitative information from contrast\u2010enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is\u00a0needed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Quantitative VNC methods offer the potential to extract radiotherapy\u2010related parameters from contrast\u2010enhanced spectral CT without the need for additional non\u2010contrast imaging. However, the inherently ill\u2010posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced\u00a0techniques.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70095","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T13:15:37Z","timestamp":1761830137000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spectral virtual non\u2010contrast imaging assisted by artificial intelligence segmentation"],"prefix":"10.1002","volume":"52","author":[{"given":"Mohsen Beikali","family":"Soltani","sequence":"first","affiliation":[{"name":"D\u00e9partement de physique Universit\u00e9 de Montr\u00e9al Montr\u00e9al QC Canada"},{"name":"Centre de recherche du Centre hospitalier de l'Universit\u00e9 de Montr\u00e9al Montr\u00e9al QC Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo","family":"Bouchard","sequence":"additional","affiliation":[{"name":"D\u00e9partement de physique Universit\u00e9 de Montr\u00e9al Montr\u00e9al QC Canada"},{"name":"Centre de recherche du Centre hospitalier de l'Universit\u00e9 de Montr\u00e9al Montr\u00e9al QC Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/43\/6\/016"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/57\/11\/R99"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.11.002"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa952f"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aadf2a"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaae91"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/8\/2059"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/55\/5\/006"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/1\/83"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4939082"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad965f"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab1af1"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12934"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-9-168"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14702"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.212579"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bjr\/tqad018"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2023.100515"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2024.101470"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13470"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2018.05.006"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13147"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.03.030"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.01.114"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.10.038"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15793"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2022.100968"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00524"},{"key":"e_1_2_8_30_1","doi-asserted-by":"crossref","unstructured":"OkadaT LinguraruMG HoriM et\u00a0al.Multi\u2010organ segmentation in abdominal CT images. inProceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE;2012:3986\u20103989.","DOI":"10.1109\/EMBC.2012.6346840"},{"key":"e_1_2_8_31_1","first-page":"1","article-title":"Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem","volume":"4","author":"Hofmanninger J","year":"2020","journal-title":"Europe Radiol Experiment"},{"key":"e_1_2_8_32_1","article-title":"Automatic segmentation of colon in 3D CT images and removal of opacified fluid using cascade feed forward neural network","volume":"2015","author":"Devi KG","year":"2015","journal-title":"Comput Math Methods Med"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024"},{"key":"e_1_2_8_34_1","unstructured":"WasserthalJ.TotalSegmentator version v2.4.0. 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