{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T22:30:19Z","timestamp":1789252219915,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","award":["811245"],"award-info":[{"award-number":["811245"]}],"id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Quantification of magnetic resonance spectroscopy (MRS) data using linear combination modeling (LCM) is challenging, partly due to the large number of spectral parameters to be estimated. Popular conventional LCM approaches often place soft constraints on signal amplitude ratios to improve fitting stability, at the cost of introducing bias. Meanwhile, existing deep learning (DL) methods tend to oversimplify the problem by omitting important spectral parameters, limiting their real\u2010world utility. With these considerations in mind, we developed Q\u2010MRS, a DL framework based on a Convolutional vision Transformer (CvT) that combines the strengths of a convolutional neural network (CNN) and a Transformer. The model was trained on a large dataset of simulated spectra and evaluated on high\u2010quality 3T GABA\u2010edited MEGA\u2010PRESS data acquired from health adults in the medial parietal lobe. On simulated data with known ground\u2010truth metabolite levels, the CvT outperformed two baseline models, a simple CNN and an Inception network. When applied to the in\u00a0vivo data, Q\u2010MRS produced fit quality and concentration estimates comparable with those of two established LCM methods, LCModel and Osprey, without imposing constraints on metabolite amplitude ratios. These results suggest that the proposed method is a promising approach for MRS analysis.<\/jats:p>","DOI":"10.1002\/nbm.70279","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:28:05Z","timestamp":1775640485000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Q\u2010MRS: Quantitative Magnetic Resonance Spectral Analysis Using Deep Learning"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6829-1453","authenticated-orcid":false,"given":"Christopher\u00a0J.","family":"Wu","sequence":"first","affiliation":[{"name":"Taub Institute for Research on Alzheimer's Disease and the Aging Brain Columbia University Irving Medical Center  New York New York USA"},{"name":"Department of Biomedical Engineering Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence\u00a0S.","family":"Kegeles","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas\u00a0L.","family":"Rothman","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging Yale University School of Medicine  New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University  New York New York USA"},{"name":"Department of Psychiatry Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4257"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910300604"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.1112"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22579"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108827"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28630"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201081193\u20109"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28941"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29686"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.5008"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910370504"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4482"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28942"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_53"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27727"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106837"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29711"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3354123"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29561"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102802"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"e_1_2_11_23_1","first-page":"9229","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML)","author":"Sun Y.","year":"2020"},{"key":"e_1_2_11_24_1","first-page":"4754","volume-title":"Proceedings of the 39th International Conference on Machine Learning (ICML)","author":"Darestani M. 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