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However, traditional voxel\u2010wise independent fitting methods are highly sensitive to noise and do not utilize spatial correlation, resulting in unstable parameter estimation. Although existing deep learning methods have shown improvements, they are still limited by local receptive\u00a0fields.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address this, we propose a two\u2010stage IVIM parameter estimation framework based on Implicit Neural Representation (IVIM\u2010INR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Our IVIM\u2010INR method achieves global spatial perception through coordinate encoding and enhances spatial context modeling by leveraging local 3D patch information from multi\u2010\n                      <jats:italic>b<\/jats:italic>\n                      \u2010value images. The first stage INR performs signal denoising, and the second stage INR accurately fits IVIM\u00a0parameters.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluation on brain digital phantoms, AAPM breast IVIM\u2010dMRI Challenge data, and clinical Glioblastoma (GBM) patient data demonstrates significant advantages of the proposed method over existing techniques. In brain simulation data, when SNR = 50, the normalized mean absolute errors (NMAEs) in tumor regions were 0.160.10 for , 0.020.01 for , and 0.070.05 for , all lower than comparison methods. In the tumor tissues of the 100 cases from the AAPM breast IVIM\u2010dMRI challenge dataset,  error was reduced by 58% compared to ConvNet. Intraclass correlation coefficient (ICC) analysis of real clinical data indicates that our method achieves the best performance with an ICC of  in normal tissues reaching 0.629.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By combining INR's continuous function modeling capability with spatial\u2010aware feature design, IVIM\u2010INR overcomes the inherent limitations of traditional methods under noisy conditions, providing a more reliable tool for clinical IVIM quantitative\u00a0analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70599","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T16:50:41Z","timestamp":1785257441000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation"],"prefix":"10.1002","volume":"53","author":[{"given":"Yunxiang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen\u2010Peng","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.10.5515"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.2463\/mrms.rev.2019-0124"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep07208"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00062-016-0510-7"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.12.037"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.08.030"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22740"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-014-3554-6"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.20508"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad3db8"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194590"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000262"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/0806023"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac9a1f"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28852"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-018-0639-9"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"MolaeiA AminimehrA TavakoliA et\u00a0al.Implicit neural representation in medical imaging: a comparative survey. 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