{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T07:37:49Z","timestamp":1792741069351,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Magnetic resonance spectroscopic imaging (MRSI) is a non\u2010invasive neuroimaging tool that maps brain metabolites, providing insights into brain physiology and pathology. However, clinical adoption remains limited due to low spatial resolution and partial volume effects, where acquired signals detect metabolism from different tissue types, preventing clear identification of tissue\u2010specific metabolic patterns. We developed a statistical framework based on non\u2010negative geographically weighted regression (NNGWR) designed to overcome these limitations. The method integrates tissue partial volumes with MRSI data using spatially adaptive regression to interpolate metabolite levels across the imaging volume while separating distinct tissue contributions. Importantly, our model accounts for spatial heterogeneity within tissues rather than assuming metabolic uniformity, providing more physiologically realistic estimates. Simulations using synthetic ground truth data show that our method consistently achieves a lower median absolute percentage error than cubic spline and zero\u2010filling interpolations, especially under high noise or low\u2010resolution conditions. A total of 120 MRSI datasets were acquired at 3 T from a cohort of 36 healthy volunteers to assess predictive performance. To handle real\u2010world data, we implemented a robust reweighting scheme that addresses variable spectral quality by progressively downweighting degraded data rather than discarding it, balancing data retention with model stability. Application to a glioblastoma clinical case demonstrated successful separation of metabolite ratios across healthy, tumour and necrotic tissues. The model identified intratumoural metabolic heterogeneity and provided local confidence for transparent clinical interpretation. By decomposing metabolic contributions by tissue type and accounting for natural spatial variations, the approach enables improved brain tumour characterisation. This model is applicable to other neurological conditions where tissue\u2010specific metabolic alterations serve as disease biomarkers, helping both clinical diagnostics and research into brain pathophysiology.<\/jats:p>","DOI":"10.1002\/nbm.70424","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:42:42Z","timestamp":1792737762000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Separation of Anatomical Contributions in MRSI Using a Non\u2010Negative Geographically Weighted Regression Model"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9569-8664","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"Giraud","sequence":"first","affiliation":[{"name":"Department of Imaging University Hospital Center of Poitiers  Poitiers France"},{"name":"DACTIM\u2010MIS team, Laboratory of Mathematics and Applications Labcom I3M  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3553-6695","authenticated-orcid":false,"given":"Arnaud","family":"Poinas","sequence":"additional","affiliation":[{"name":"DACTIM\u2010MIS team, Laboratory of Mathematics and Applications Labcom I3M  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1075-4730","authenticated-orcid":false,"given":"Carole","family":"Guillevin","sequence":"additional","affiliation":[{"name":"Department of Imaging University Hospital Center of Poitiers  Poitiers France"},{"name":"DACTIM\u2010MIS team, Laboratory of Mathematics and Applications Labcom I3M  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u00e9my","family":"Guillevin","sequence":"additional","affiliation":[{"name":"Department of Imaging University Hospital Center of Poitiers  Poitiers France"},{"name":"DACTIM\u2010MIS team, Laboratory of Mathematics and Applications Labcom I3M  Poitiers France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,22]]},"reference":[{"issue":"6","key":"e_1_2_13_2_1","article-title":"Early Metabolic Alteration Predicts Outcome With Middle Cerebral Artery Occlusion Treated by Mechanical Thrombectomy","volume":"3","author":"Herpe G.","year":"2023","journal-title":"Stroke: Vascular and Interventional Neurology"},{"key":"e_1_2_13_3_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.pscychresns.2017.08.010","article-title":"1H Magnetic Resonance Spectroscopy Suggests Neural Membrane Alteration in Specific Regions Involved in Obsessive\u2010Compulsive Disorder","volume":"269","author":"Hatchondo L.","year":"2017","journal-title":"Psychiatry Research: Neuroimaging"},{"issue":"7","key":"e_1_2_13_4_1","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1002\/acn3.50825","article-title":"1H\u201031P Magnetic Resonance Spectroscopy: Effect of Biotin in Multiple Sclerosis","volume":"6","author":"Guillevin C.","year":"2019","journal-title":"Annals of Clinical and Translational Neurology"},{"key":"e_1_2_13_5_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jmr.2015.11.003","article-title":"Magnetic Resonance Spectroscopic Imaging at Superresolution: Overview and Perspectives","volume":"263","author":"Kasten J.","year":"2016","journal-title":"Journal of Magnetic Resonance"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910080308"},{"key":"e_1_2_13_7_1","doi-asserted-by":"crossref","unstructured":"S.Dong G.Hangel W.Bogner et\u00a0al. \u201cMulti\u2010Scale Super\u2010Resolution Magnetic Resonance Spectroscopic Imaging With Adjustable Sharpness \u201d inInternational Conference on Medical Image Computing and Computer\u2010Assisted Intervention(Springer Nature Switzerland 2022) 410\u2013420.","DOI":"10.1007\/978-3-031-16446-0_39"},{"key":"e_1_2_13_8_1","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.3389\/fonc.2019.01010","article-title":"Super\u2010Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning","volume":"9","author":"Iqbal Z.","year":"2019","journal-title":"Frontiers in Oncology"},{"key":"e_1_2_13_9_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1017\/CBO9780511544958.005","article-title":"Artifacts and Pitfalls in MR Spectroscopy","volume":"2004","author":"Hurd R. 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