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We developed and evaluated DeDistortNet, a generative dual-conditioned diffusion model for correcting geometric distortions in prostate DWI without experimentally acquired paired distorted-undistorted data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      We utilized the public PROSTATEx dataset, divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009135, 1,893 slices), validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094, 64 slices), and test (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009189, 2,623 slices) cohorts stratified by distortion severity. Model training used only undistorted DWIs, with simulated distortions to generate paired examples. DeDistortNet combines contextual guidance from distorted DWIs with structural guidance from T2-weighted images to synthesize distortion-free DWIs. Performance was evaluated using quantitative analysis on simulated distortions and indirect validation on clinically distorted DWIs through anatomical concordance with T2-derived prostate masks.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The cohort included prostate MRI exams from 328 male subjects. The DWIs from 139 exams were undistorted for training\/validation, while 189 were distorted for testing. In simulated data, DeDistortNet restored image quality across distortion severities, improving peak signal-to-noise ratio by 36% and structural similarity by 55% in the peripheral zone (PZ) under extreme distortion. In clinically distorted data, concordance with T2-weighted references (PZ Dice similarity) improved by 37% under severe distortion and by 72% under extreme distortion. Radiologist assessment further supported improved geometric fidelity and prostate boundary delineation after correction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DeDistortNet, trained on undistorted DWIs with simulated distortions, effectively corrected distortions in prostate DWI and restored anatomical fidelity, particularly in the PZ, without requiring additional acquisitions or specialized imaging protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>By correcting severe distortions without additional imaging sequences, DeDistortNet restores anatomical fidelity in prostate diffusion-weighted imaging, particularly in the peripheral zone, enabling more reliable image interpretation and reducing the need for repeat scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>DeDistortNet corrects geometric distortion in prostate diffusion MRI.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Our method integrates anatomical guidance from T2-weighted images for distortion-free reconstruction.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Our method was trained on simulated distortions without experimentally acquired paired distorted-undistorted data or additional MRI sequences.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00735-w","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:35:20Z","timestamp":1778664920000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-conditioned diffusion model with anatomical guidance for geometric distortion correction in prostate MRI"],"prefix":"10.1186","volume":"10","author":[{"given":"Inye","family":"Na","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyunghyun","family":"Sung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5681-8918","authenticated-orcid":false,"given":"Hyunjin","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"735_CR1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1002\/mrm.1910340111","volume":"34","author":"P Jezzard","year":"1995","unstructured":"Jezzard P, Balaban RS (1995) Correction for geometric distortion in echo planar images from B0 field variations. 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