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This study assessed the repeatability of neural network (NN)-based IVIM fitting versus classical nonlinear least-squares in pancreatic DWI. The repeatability cohort included ten healthy volunteers and two type 1 diabetes (T1D) individuals, each scanned twice; the glucose-response cohort included three T1D participants and three healthy controls scanned pre- and post-oral glucose. Diffusion data were acquired at 13 b-values (0\u20131200 s\/mm\n                      <jats:inline-formula>\n                        <jats:tex-math>$$^2$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ). Four NN-based methods (IVIM-NET, SUPER-IVIM-DC, U-Net, IVIM-MORPH) were compared with two classical approaches (SLS, SLS-TRF) using full (13-point) and reduced (7-point) protocols. Repeatability was quantified using within-subject coefficient of variation (wCV) and Bland-Altman analysis. All NN-based methods significantly improved test-retest repeatability of the perfusion fraction\n                      <jats:inline-formula>\n                        <jats:tex-math>$$f$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      compared with classical approaches (\n                      <jats:inline-formula>\n                        <jats:tex-math>$$p&lt;0.05$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ), with SUPER-IVIM-DC showing the lowest wCV and IVIM-MORPH offering balanced performance across parameters. A reduced protocol shortened scan time through fewer acquisitions while maintaining or improving repeatability compared to the full protocol. Preliminary glucose-response results show both NN and classical IVIM analyses detect physiologically relevant changes. Parameter estimates varied across NN architectures, requiring further validation to establish accuracy.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11517-026-03637-6","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:30:53Z","timestamp":1785306653000},"page":"3305-3324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural networks improve repeatability of intravoxel incoherent motion (IVIM) parameter estimation in pancreatic diffusion-weighted MRI"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8626-0211","authenticated-orcid":false,"given":"Nitzan","family":"Avidan-Pearl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daphna","family":"Link-Sourani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Weiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moti","family":"Freiman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"issue":"1","key":"3637_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1148\/radiol.2015150244","volume":"278","author":"M Iima","year":"2016","unstructured":"Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion mr imaging: past, present, and future. 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