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CNNs were trained to output model parameter maps, from which DW images and ADC maps were synthesized, and to account for Rician bias. CNN architectures included were: conventional U\u2010Net, Attention U\u2010Net, and Residual Attention U\u2010Net with biexponential, kurtosis, and gamma distribution as signal models. Moreover, approaches without Rician bias correction and with noise maps as additional input were explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For all signal models, synthetic DW images generated from CNN output are of similar quality compared to OBSIDIAN, a model\u2010based method employing iterative pixel\u2010wise fitting. Computed ADC and parameter maps obtained with CNN models were less noisy than those obtained with OBSIDIAN. Good quantitative agreement with OBSIDIAN in ROI\u2010averaged ADC values and, to a slightly lesser extent, kurtosis model parameters was observed. Results with omitted Rician bias correction clearly deviate, while the impact of the noise map input for the CNNs is less pronounced. Computation time was reduced to seconds compared to several hours for OBSIDIAN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The CNN\u2010based method has potential in clinical use for achieving higher quality DW images and biomarker maps with drastically reduced computation time. Further refinement and larger training datasets are needed to achieve better generalizability and increased robustness.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70373","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T06:24:11Z","timestamp":1776666251000},"page":"928-942","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Self\u2010Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi\u2010b Prostate Diffusion MRI"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3292-8048","authenticated-orcid":false,"given":"Mustafa","family":"Abbas","sequence":"first","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg  Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg  Gothenburg Sweden"},{"name":"Department of Intelligent Systems School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan E.","family":"Maier","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham Women's Hospital Harvard Medical School  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrik","family":"Langkilde","sequence":"additional","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg  Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-8982","authenticated-orcid":false,"given":"Stefan","family":"Kuczera","sequence":"additional","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg  Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,19]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.02.033"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13130420"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.233055"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2015.08.052"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20200869"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.09.3004"},{"issue":"2","key":"e_1_2_10_8_1","first-page":"208","article-title":"High\u2010b\u2010Value Diffusion\u2010Weighted MR Imaging of Hyperacute Ischemic Stroke at 1.5T","volume":"26","author":"Kim H. 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