{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:50:47Z","timestamp":1786978247157,"version":"build-2736575974"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100014434","name":"Narodowa Agencja Wymiany Akademickiej","doi-asserted-by":"publisher","award":["25\/NAWA\u2010PROM\u2010UR\/2019"],"award-info":[{"award-number":["25\/NAWA\u2010PROM\u2010UR\/2019"]}],"id":[{"id":"10.13039\/501100014434","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014434","name":"Narodowa Agencja Wymiany Akademickiej","doi-asserted-by":"publisher","award":["STER BPI\/STE\/2023\/1\/00001\/U\/00001"],"award-info":[{"award-number":["STER BPI\/STE\/2023\/1\/00001\/U\/00001"]}],"id":[{"id":"10.13039\/501100014434","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Magnetic resonance elastography (MRE) enables non\u2010invasive quantification of liver stiffness and plays a pivotal role in the assessment of hepatic fibrosis. However, clinical implementation remains limited by the need for manual delineation of regions of interest (ROIs), which is time\u2010consuming, requires expert input, and is prone to interobserver variability. This study assesses the feasibility of a single deep learning (DL) pipeline to fully automate this process\u2014from acquired MRE data to liver\u2010stiffness quantification\u2014within a controlled proof\u2010of\u2010concept setting. To investigate this, we developed and validated a convolutional neural network\u2010based framework capable of reconstructing stiffness maps and generating liver segmentation masks directly from magnitude and phase images. The models were trained using MRE data from 83 adult volunteers (56 women, 27 men; median age 21\u2009years), primarily representing healthy individuals or those with early\u2010stage disease. This homogeneous cohort was deliberately selected to facilitate proof\u2010of\u2010concept development and reduce confounding sources of biological variability during model optimization. Across several neural network architectures (U\u2010Net, ResNet, and CycleGAN hybrids), the pipeline produced liver\u2010stiffness estimates with less than 11% deviation from the reference values in all cases, and less than 3% in the best performing configurations. Agreement between automated and manual measurements was comparable to interreader agreement, with intraclass correlation coefficients\u2014ICC(A,1), calculated using a two\u2010way random effects model for absolute agreement\u2014of 0.86 (95% CI: 0.71\u20130.95) and 0.89 (95% CI: 0.74\u20130.97), respectively. Total inference time per examination was 23.7\u2009\u00b1\u20094.4\u2009s. These findings demonstrate the technical feasibility of a fully automated, AI\u2010driven postprocessing pipeline for liver MRE within a controlled proof\u2010of\u2010concept setting using data from healthy volunteers. Although the approach shows promise for reducing analysis time and operator dependence, further validation in diverse clinical populations is required before broader generalization. This work represents an initial step toward more accessible, scalable, and standardized liver\u2010stiffness assessment in research and, potentially, future clinical applications.<\/jats:p>","DOI":"10.1002\/nbm.70258","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T11:05:42Z","timestamp":1773054342000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Unified Deep Learning Framework for Liver MR Elastography Postprocessing: Proof\u2010of\u2010Concept Study"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0715-9578","authenticated-orcid":false,"given":"Vitaliy","family":"Atamaniuk","sequence":"first","affiliation":[{"name":"Department of Physics and Medical Engineering, Faculty of Mathematics and Applied Physics Rzesz\u00f3w University of Technology  Rzesz\u00f3w Poland"},{"name":"Department of Radiology Independent Public Health Care Center of the Ministry of Interior and Administration in Rzesz\u00f3w  Rzesz\u00f3w Poland"},{"name":"Laboratory of Innovative Medical Imaging Methods, Faculty of Health Sciences and Psychology, Collegium Medicum University of Rzesz\u00f3w  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrii","family":"Pozaruk","sequence":"additional","affiliation":[{"name":"Institute of Physics, Faculty of Exact and Technical Sciences University of Rzesz\u00f3w  Rzesz\u00f3w Poland"},{"name":"Department of Photomedicine and Physical Chemistry, Faculty of Medicine, Collegium Medicum University of Rzesz\u00f3w  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micha\u0142","family":"Madera","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science Rzesz\u00f3w University of Technology  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miko\u0142aj","family":"Wcis\u0142o","sequence":"additional","affiliation":[{"name":"NOVA Health Center  Domaradz Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogdan","family":"Obrzut","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, Collegium Medicum University of Rzesz\u00f3w  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marian","family":"Cholewa","sequence":"additional","affiliation":[{"name":"Institute of Physics, Faculty of Exact and Technical Sciences University of Rzesz\u00f3w  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacek","family":"Kluska","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science Rzesz\u00f3w University of Technology  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzanna","family":"Obrzut","sequence":"additional","affiliation":[{"name":"Laboratory of Innovative Medical Imaging Methods, Faculty of Health Sciences and Psychology, Collegium Medicum University of Rzesz\u00f3w  Rzesz\u00f3w Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916\u2010014\u20100145\u2010y"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.4254\/wjh.v7.i3.607"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/hep.20974"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cld.740"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1159\/000171507"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.7569924"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23731"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/ca.21006"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2019190034"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23980"},{"key":"e_1_2_12_12_1","first-page":"10134","volume-title":"Proc 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M.He X. Y.Zhang S. Q.Ren andJ.Sun \u201cDeep Residual Learning for Image Recognition \u201d2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas NV USA(2016):770\u2013778. 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