{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:40:00Z","timestamp":1780368000268,"version":"3.54.1"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,3,16]],"date-time":"2027-03-16T00:00:00Z","timestamp":1805155200000},"content-version":"am","delay-in-days":288,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-CA248506"],"award-info":[{"award-number":["R01-CA248506"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-CA272702"],"award-info":[{"award-number":["R01-CA272702"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007185","name":"University of California, Los Angeles","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011075","name":"David Geffen School of Medicine, University of California, Los Angeles","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011075","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.media.2026.104033","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:53:09Z","timestamp":1773273189000},"page":"104033","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI"],"prefix":"10.1016","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2496-0829","authenticated-orcid":false,"given":"Kai","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8814-4417","authenticated-orcid":false,"given":"Alex","family":"Ling Yu Hung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaifeng","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parsa","family":"Hajipour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2585-5916","authenticated-orcid":false,"given":"Holden","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6013-0689","authenticated-orcid":false,"given":"Steve","family":"Raman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4175-5322","authenticated-orcid":false,"given":"Kyunghyun","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.media.2026.104033_bib0001","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/TMI.2019.2901928","article-title":"Joint prostate cancer detection and gleason score prediction in mp-MRI via focalnet","volume":"38","author":"Cao","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0002","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","article-title":"Voxresnet: deep voxelwise residual networks for brain segmentation from 3d MR images","volume":"170","author":"Chen","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2026.104033_bib0003","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1724","article-title":"Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation","author":"Cho","year":"2014"},{"issue":"2","key":"10.1016\/j.media.2026.104033_bib0004","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TBME.2020.2993528","article-title":"Deep learning regression for prostate cancer detection and grading in bi-parametric MRI","volume":"68","author":"De Vente","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.media.2026.104033_bib0005","series-title":"International Conference on Learning Representations","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.media.2026.104033_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102347","article-title":"Prostattention-net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI scans","volume":"77","author":"Duran","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104033_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102347","article-title":"Prostattention-net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI scans","volume":"77","author":"Duran","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104033_bib0008","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Hungry hungry hippos: towards language modeling with state space models","author":"Fu","year":"2023"},{"key":"10.1016\/j.media.2026.104033_bib0009","unstructured":"Gu, A., Dao, T., 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv: 2312.00752."},{"key":"10.1016\/j.media.2026.104033_bib0010","series-title":"International Conference on Learning Representations","article-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2022"},{"key":"10.1016\/j.media.2026.104033_bib0011","series-title":"Proceedings of the IEEE\/CVF winter conference on applications of computer vision","first-page":"574","article-title":"Unetr: transformers for 3d medical image segmentation","author":"Hatamizadeh","year":"2022"},{"issue":"4","key":"10.1016\/j.media.2026.104033_bib0012","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1007\/s00330-021-08320-y","article-title":"Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge","volume":"32","author":"Hosseinzadeh","year":"2022","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.media.2026.104033_bib0013","doi-asserted-by":"crossref","first-page":"3754","DOI":"10.1007\/s00330-020-07494-1","article-title":"Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)\u2013do we still need contrast media?","volume":"31","author":"Huebner","year":"2021","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0014","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TMI.2022.3211764","article-title":"Cat-net: a cross-slice attention transformer model for prostate zonal segmentation in mri","volume":"42","author":"Hung","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0015","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"113","article-title":"Cross-slice attention and evidential critical loss for uncertainty-aware prostate cancer detection","author":"Hung","year":"2024"},{"issue":"2","key":"10.1016\/j.media.2026.104033_bib0016","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Method."},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0017","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/s13244-023-01439-0","article-title":"Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study","volume":"14","author":"Karagoz","year":"2023","journal-title":"Insight. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0018","series-title":"International conference on MICCAI","first-page":"489","article-title":"Deep convolutional encoder-decoders for prostate cancer detection and classification","author":"Kiraly","year":"2017"},{"key":"10.1016\/j.media.2026.104033_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107374","article-title":"Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI","volume":"165","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.media.2026.104033_bib0020","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/TMI.2014.2303821","article-title":"Computer-aided detection of prostate cancer in MRI","volume":"33","author":"Litjens","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0021","unstructured":"Ma, J., Li, F., Wang, B., 2024. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv: 2401.04722."},{"issue":"12","key":"10.1016\/j.media.2026.104033_bib0022","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1111\/iju.15280","article-title":"Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: added value of dynamic contrast-enhanced imaging","volume":"30","author":"Matsuoka","year":"2023","journal-title":"Int. J. Urol."},{"key":"10.1016\/j.media.2026.104033_bib0023","series-title":"2016 fourth international conference on 3D vision (3DV)","first-page":"565","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"issue":"8","key":"10.1016\/j.media.2026.104033_bib0024","first-page":"561","article-title":"Magnetic resonance dispersion imaging for localization of angiogenesis and cancer growth","volume":"49","author":"Mischi","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102512","article-title":"Deep learning DCE-MRI parameter estimation: application in pancreatic cancer","volume":"80","author":"Ottens","year":"2022","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2026.104033_bib0026","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1002\/mrm.21066","article-title":"Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI","volume":"56","author":"Parker","year":"2006","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0027","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1038\/s41598-022-06730-6","article-title":"Deep learning for fully automatic detection, segmentation, and gleason grade estimation of prostate cancer in multiparametric magnetic resonance images","volume":"12","author":"Pellicer-Valero","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2026.104033_bib0028","series-title":"Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"7","key":"10.1016\/j.media.2026.104033_bib0029","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.media.2026.104033_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102155","article-title":"End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction","volume":"73","author":"Saha","year":"2021","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.media.2026.104033_bib0031","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.ejrad.2010.04.023","article-title":"Mr-perfusion (mrp) and diffusion-weighted imaging (dwi) in prostate cancer: quantitative and model-based gadobenate dimeglumine mrp parameters in detection of prostate cancer","volume":"76","author":"Scherr","year":"2010","journal-title":"Eur. J. Radiol."},{"issue":"6","key":"10.1016\/j.media.2026.104033_bib0032","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1002\/mp.14855","article-title":"Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging","volume":"48","author":"Seetharaman","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2026.104033_bib0033","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0034","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.media.2026.104033_bib0035","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Simplified state space layers for sequence modeling","author":"Smith","year":"2023"},{"issue":"6","key":"10.1016\/j.media.2026.104033_bib0036","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1002\/jmri.26047","article-title":"Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI","volume":"48","author":"Song","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"issue":"4","key":"10.1016\/j.media.2026.104033_bib0037","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1002\/jmri.26685","article-title":"Modified MR dispersion imaging in prostate dynamic contrast-enhanced MRI","volume":"50","author":"Sung","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0038","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1007\/s00261-018-1807-6","article-title":"Investigating the role of DCE-MRI, over t2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy","volume":"44","author":"Taghipour","year":"2019","journal-title":"Abdominal Radiol."},{"issue":"3","key":"10.1016\/j.media.2026.104033_bib0039","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S","article-title":"Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: standardized quantities and symbols","volume":"10","author":"Tofts","year":"1999","journal-title":"J. Magn. Reson. Imaging"},{"issue":"5","key":"10.1016\/j.media.2026.104033_bib0040","doi-asserted-by":"crossref","first-page":"W242","DOI":"10.2214\/AJR.17.19215","article-title":"Evaluation of dispersion MRI for improved prostate cancer diagnosis in a multicenter study","volume":"211","author":"Turco","year":"2018","journal-title":"Am. J. Roentgenol."},{"issue":"3","key":"10.1016\/j.media.2026.104033_bib0041","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2","volume":"76","author":"Turkbey","year":"2019","journal-title":"Eur. Urol."},{"issue":"2","key":"10.1016\/j.media.2026.104033_bib0042","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TBME.2020.2993528","article-title":"Deep learning regression for prostate cancer detection and grading in bi-parametric MRI","volume":"68","author":"Vente","year":"2020","journal-title":"IEEE Transact. Biomed. Eng. (TBME)"},{"issue":"6","key":"10.1016\/j.media.2026.104033_bib0043","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.2214\/AJR.12.8510","article-title":"Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management","volume":"198","author":"Verma","year":"2012","journal-title":"Am. J. Roentgenol."},{"issue":"3","key":"10.1016\/j.media.2026.104033_bib0044","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.eururo.2013.05.045","article-title":"Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 t","volume":"64","author":"Vos","year":"2013","journal-title":"Eur. Urol."},{"key":"10.1016\/j.media.2026.104033_bib0045","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","article-title":"Pi-rads prostate imaging-reporting and data system: 2015, version 2","volume":"69","author":"Weinreb","year":"2016","journal-title":"Eur. Urol."},{"key":"10.1016\/j.media.2026.104033_bib0046","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"578","article-title":"Segmamba: long-range sequential modeling mamba for 3d medical image segmentation","author":"Xing","year":"2024"},{"key":"10.1016\/j.media.2026.104033_bib0047","doi-asserted-by":"crossref","first-page":"117033","DOI":"10.1109\/ACCESS.2022.3214309","article-title":"Seresu-net for multimodal brain tumor segmentation","volume":"10","author":"Yan","year":"2022","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.media.2026.104033_bib0048","doi-asserted-by":"crossref","first-page":"91","DOI":"10.2174\/157340507780619179","article-title":"Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples","volume":"3","author":"Yankeelov","year":"2007","journal-title":"Curr. Med. Imaging"},{"key":"10.1016\/j.media.2026.104033_bib0049","first-page":"1355","article-title":"False positive reduction using multiscale contextual features for prostate cancer detection in multi-Parametric MRI scans","author":"Yu","year":"2020","journal-title":"IEEE ISBI"},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0050","doi-asserted-by":"crossref","first-page":"42","DOI":"10.2478\/raon-2023-0007","article-title":"Effects of dynamic contrast enhancement on transition zone prostate cancer in prostate imaging reporting and data system version 2.1","volume":"57","author":"Zhang","year":"2023","journal-title":"Radiol. Oncol."},{"issue":"17","key":"10.1016\/j.media.2026.104033_bib0051","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.3390\/cancers16172983","article-title":"A deep learning-based framework for highly accelerated prostate MR dispersion imaging","volume":"16","author":"Zhao","year":"2024","journal-title":"Cancer. (Basel)"},{"issue":"1","key":"10.1016\/j.media.2026.104033_bib0052","doi-asserted-by":"crossref","first-page":"5740","DOI":"10.1038\/s41598-024-56405-7","article-title":"AtPCa-net: anatomical-aware prostate cancer detection network on multi-parametric MRI","volume":"14","author":"Zheng","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2026.104033_bib0053","series-title":"Forty-first International Conference on Machine Learning","article-title":"Vision mamba: efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001027?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001027?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:06:15Z","timestamp":1778803575000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526001027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":53,"alternative-id":["S1361841526001027"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104033","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104033","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104033"}}