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Deep learning (DL) reconstruction has the potential to enhance the quality of head and neck DWI. This study aims to evaluate the performance of an accelerated, DL-reconstructed DWI (DWI\n                      <jats:sub>DL<\/jats:sub>\n                      ) in terms of image quality and diagnostic confidence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study included patients who underwent clinically indicated head and neck DWI at 1.5\u202fT and 3\u202fT between August 2023 and January 2024 at a\u00a0tertiary care center. Imaging was performed at low b\u2011values (0 or 50\u202fsec\/mm\n                      <jats:sup>2<\/jats:sup>\n                      ) and high b\u2011values (800\u202fsec\/mm\n                      <jats:sup>2<\/jats:sup>\n                      ), and apparent diffusion coefficient (ADC) maps were computed. After acquiring standard single-shot echoplanar imaging DWI sequences, the raw MR datasets underwent simulated acceleration by reducing the number of signal averages. These accelerated exams were then reconstructed using a\u00a0novel DL-based algorithm that combined DL-based k\u2011space to image reconstruction with DL-based super-resolution processing (DWI\n                      <jats:sub>DL<\/jats:sub>\n                      ). Three readers analyzed the images using a\u00a0visual Likert score to evaluate image sharpness, artifacts, noise, overall image quality, and diagnostic confidence. Comparisons were made using the Wilcoxon signed-rank test. A\u00a0quantitative analysis of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient values (ADC) was also performed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study included 30\u00a0patients (mean age, 55\u202f\u00b1\u200919\u00a0years; range, 24\u201384; 18\u00a0men) with various pathologies. Scan times were reduced by 67% at 1.5\u202fT and up to 55% at 3\u202fT. The quantitative analysis revealed a\u00a0minimal but statistically significant decrease in SNR and CNR in the deep learning-reconstructed images (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u20090.002 and\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001, respectively). However, readers reported no significant differences between DWI and DWI\n                      <jats:sub>DL<\/jats:sub>\n                      regarding image quality parameters or diagnostic confidence for both low and high b\u2011value images, as well as the ADC (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&gt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL reconstruction of head and neck DWI is feasible, significantly reducing examination time without compromising image quality or diagnostic confidence. This technique enables accelerated and effective diagnostic DWI of the head and neck.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00062-025-01604-6","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T15:11:09Z","timestamp":1767798669000},"page":"523-533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Reconstruction of Diffusion-weighted MRI Enables Shorter Examination Times While Maintaining Image Quality in Head and Neck Imaging"],"prefix":"10.1007","volume":"36","author":[{"given":"Haidara","family":"Almansour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Michael","family":"Brendel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Neatu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Gassenmaier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Herrmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Werner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitali","family":"Koch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Darwich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Weiland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Benkert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Altmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Kronfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed E.","family":"Othman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Nikolaou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saif","family":"Afat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"issue":"1","key":"1604_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1102\/1470-7330.2010.0030","volume":"10","author":"HC Thoeny","year":"2010","unstructured":"Thoeny\u00a0HC. 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Almansour, J. Brendel, C. Neatu, S. Gassenmaier, J. Herrmann, S. Werner, V. Koch, S. Altmann, A. Kronfeld, A. Othman, K. Nikolaou and S. Afat declare that there are no competing interests. O. Darwich, E. Weiland and T. Benkert are employees of Siemens Healthineers AG, Forchheim, Germany.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}