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Med. Biol."],"published-print":{"date-parts":[[2026,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    3D quantitative magnetic resonance imaging (qMRI) enables noninvasive tissue characterization but often requires prolonged acquisition times for multi-contrast imaging, limiting its broader clinical adoption. Although deep learning has shown promise for accelerated MRI reconstruction, supervised methods rely on large-scale fully sampled 3D datasets that are difficult to obtain. Existing implicit neural representation (INR)-based self-supervised methods avoid this requirement but can exhibit spectral bias, potentially leading to over-smoothed image details.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose NL-TINR, a zero-shot self-supervised reconstruction framework that integrates patch-based nonlocal self-similarity (NSS) with INR-based functional tensor decomposition. The NSS prior groups structurally similar image patches into high-dimensional tensors to explicitly exploit nonlocal redundancy and suppress aliasing artifacts while preserving fine anatomical textures. Meanwhile, INRs parameterize the tensor factor functions, providing a compact and flexible representation that captures correlations across spatial and contrast dimensions while reducing the burden of directly modeling the full 3D multi-contrast volume.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Extensive experiments on 3D multi-echo MRI datasets demonstrate that NL-TINR consistently outperforms the compared reconstruction methods, providing improved artifact suppression and anatomical detail preservation, particularly at high acceleration factors.\n                    <jats:italic>Significance.<\/jats:italic>\n                    NL-TINR eliminates the dependence on fully sampled training data while effectively exploiting nonlocal structural redundancy and high-dimensional spatial-contrast correlations, providing a practical zero-shot framework for high-fidelity 3D multi-contrast qMRI reconstruction.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeabed","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T22:54:02Z","timestamp":1790204042000},"page":"195022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["NL-TINR: implicit neural representation based nonlocal functional tensor decomposition for zero-shot 3D multi-contrast MRI reconstruction"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9914-233X","authenticated-orcid":true,"given":"Jingran","family":"Xu","sequence":"first","affiliation":[{"name":"Paul C. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-05-22","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-23","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-07","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}