{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T11:23:14Z","timestamp":1767871394901,"version":"3.49.0"},"reference-count":41,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 CA240808"],"award-info":[{"award-number":["R01 CA240808"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,1,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Purpose.<\/jats:italic>\n                    Diffusion-weighted imaging (DWI) has significant value in disease diagnosis and treatment response monitoring, but its inherent low signal-to-noise ratio (SNR) severely affects image quality and quantification accuracy. Existing denoising techniques often blur important tissue boundary information when suppressing noise.\n                    <jats:italic>Methods.<\/jats:italic>\n                    This study proposes a band-limited implicit neural representation (BL-INR) framework for DWI denoising. The method introduces BL positional encoding based on the frequency response characteristics of the sinc function to restrict INR models from learning high-frequency noise while maintaining strong signal representation capabilities. Furthermore, multi-\n                    <jats:italic>b<\/jats:italic>\n                    -value DWI and structural MRI from the same patient are integrated as anatomical priors, exploiting the correlation between true signals and the statistical independence of noise to achieve effective denoising.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    In clinical DWI data evaluation across four anatomical regions (brain, head and neck, abdomen, and pelvis), BL-INR\u2019s visualization results were superior to existing methods. Under extremely low SNR conditions (SNR = 1) in simulated noise experiments, BL-INR achieved a peak SNR of 35.44 and structural similarity index of 0.933, significantly outperforming other methods. Phantom denoising results showed that BL-INR achieved an average apparent diffusion coefficient value error of only\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mn>4.57<\/mml:mn>\n                          <mml:mo>\u00d7<\/mml:mo>\n                          <mml:msup>\n                            <mml:mn>10<\/mml:mn>\n                            <mml:mrow>\n                              <mml:mo>\u2212<\/mml:mo>\n                              <mml:mn>5<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:msup>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    \u2009mm\n                    <jats:sup>2<\/jats:sup>\n                    \u2009s\n                    <jats:sup>\u22121<\/jats:sup>\n                    , the smallest among all methods.\n                    <jats:italic>Significance.<\/jats:italic>\n                    BL-INR provides a novel approach for DWI denoising by limiting the frequency of INR input positional encoding. Its self-supervised learning characteristics require no paired training data and allow convenient clinical application. The method enables the derivation of accurate diffusion parameters, providing a reliable foundation for DWI-based quantitative analysis with significant clinical application value.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae2a9e","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T22:56:16Z","timestamp":1765320976000},"page":"015033","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Band-limited implicit neural representations for diffusion-weighted imaging denoising"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0622-4710","authenticated-orcid":true,"given":"Yunxiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6285-3194","authenticated-orcid":true,"given":"Yan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5721-0357","authenticated-orcid":true,"given":"Yen-Peng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2518-7989","authenticated-orcid":true,"given":"Jie","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-2755","authenticated-orcid":true,"given":"You","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"pmbae2a9ebib1","article-title":"A review on statistical noise models for magnetic resonance imaging, LPI","author":"Aja-Fern\u00e1ndez","year":"2013","type":"other"},{"key":"pmbae2a9ebib2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.mri.2009.05.025","type":"journal-article","article-title":"Noise estimation in single-and multiple-coil magnetic resonance data based on statistical models","volume":"27","author":"Aja-Fern\u00e1ndez","year":"2009","journal-title":"Magn. 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Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-09","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-01-08","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}