{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T18:12:41Z","timestamp":1789150361249,"version":"build-2803163510"},"reference-count":25,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK125561"],"award-info":[{"award-number":["R01DK125561"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R21DK123569"],"award-info":[{"award-number":["R21DK123569"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS121657"],"award-info":[{"award-number":["R01NS121657"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS133228"],"award-info":[{"award-number":["R01NS133228"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001742","name":"United States-Israel Binational Science Foundation","doi-asserted-by":"publisher","award":["2019056"],"award-info":[{"award-number":["2019056"]}],"id":[{"id":"10.13039\/501100001742","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R21EB029627"],"award-info":[{"award-number":["R21EB029627"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To improve the quality of abdominal diffusion\u2010weighted MR images (DW\u2010MRI) when acquired using single\u2010repetition (NEX = 1) protocols, and thereby increase apparent diffusion coefficient (ADC) map accuracy and lesion conspicuity at high b\u2010values. We aim to reduce the effect of blurring due to motion that obscures small lesions when averaging multiple repetition images at each b\u2010value, which is the current clinical standard.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a self\u2010supervised denoising diffusion probabilistic model (ssDDPM) to improve DW\u2010MRI quality given noisy single\u2010repetition acquisitions in pediatric abdominal scans. The ssDDPM is designed for multi\u2010b\u2010value DW\u2010MRI and incorporates diffusion signal decay model (i.e., ADC model) constraints into its loss term. The model is trained to denoise single\u2010repetition images from multiple b\u2010values while ensuring that the output adheres to the signal decay model. Training was performed on a dataset of 120 pediatric subjects with liver tumors. The performance of ssDDPM was compared with non\u2010local means (NLM) filtering and deep image prior (DIP) denoising techniques. These techniques have the capability to denoise single repetition images unlike the other techniques in literature that requires multiple direction or repetition images. Evaluation included qualitative radiologist's image quality assessment, receiver operating characteristic (ROC) analysis for lesion detection, and ADC fitting accuracy compared with motion\u2010free, breath\u2010hold reference data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ssDDPM demonstrated superior performance over comparison methods in terms of image quality, lesion conspicuity, and ADC map accuracy in NEX = 1 images. It received higher scores in radiologist assessments and showed better lesion discrimination in ROC analysis. Additionally, ssDDPM provided more precise and accurate ADC estimates when compared with the motion\u2010free, breath\u2010hold reference data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The ssDDPM effectively reduces motion related deblurring and enhances the quality of DW\u2010MRI images by directly denoising single\u2010repetition (NEX = 1) images while respecting signal decay model constraints. This method improves the assessment of pediatric liver lesions, offering a more accurate and efficient diagnostic tool with reduced scan times, when compared with current clinical practice and other denoising techniques.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.30536","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T08:25:23Z","timestamp":1746174323000},"page":"1284-1300","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Self\u2010supervised denoising diffusion probabilistic models for abdominal DW\u2010MRI"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5691-0607","authenticated-orcid":false,"given":"Serge Didenko","family":"Vasylechko","sequence":"first","affiliation":[{"name":"Department of Radiology Boston Children's Hospital  Boston Massachusetts USA"},{"name":"Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andy","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Radiology Boston Children's Hospital  Boston Massachusetts USA"},{"name":"Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2112-3205","authenticated-orcid":false,"given":"Onur","family":"Afacan","sequence":"additional","affiliation":[{"name":"Department of Radiology Boston Children's Hospital  Boston Massachusetts USA"},{"name":"Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sila","family":"Kurugol","sequence":"additional","affiliation":[{"name":"Department of Radiology Boston Children's Hospital  Boston Massachusetts USA"},{"name":"Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"107","article-title":"Diffusion\u2010weighted MRI of the liver: Challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion","volume":"11","author":"Wang YXJ","year":"2021","journal-title":"Am J Nucl Med Mol Imaging"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470-7330.2010.9032"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.08.031"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.901238"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2293974"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26059"},{"key":"e_1_2_7_9_1","first-page":"16293","volume-title":"Advances in neural information processing systems","author":"Fadnavis S","year":"2020"},{"key":"e_1_2_7_10_1","unstructured":"XiangT YurtM SyedAB SetsompopK ChaudhariA.DDM2: Self\u2010supervised diffusion MRI denoising with generative diffusion models.arXiv preprint arXiv:2302.03018; 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