{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:04:57Z","timestamp":1790395497341,"version":"4.1.0"},"reference-count":17,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,3]]},"abstract":"\n Background<\/jats:title>\n Deep learning\u2010based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning\u2010based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR).<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To investigate a convolutional neural network\u2010based SR (CNN\u2010SR) approach for simultaneous frequency\u2010and\u2010phase correction (FPC) of single\u2010voxel Meshcher\u2013Garwood point\u2010resolved spectroscopy (MEGA\u2010PRESS) MRS data.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Subjects<\/jats:title>\n Forty thousand simulated MEGA\u2010PRESS datasets generated from FID Appliance (FID\u2010A) were used and split into the following: 32,000\/4000\/4000 for training\/validation\/testing. A 101 MEGA\u2010PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets.<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 3T, MEGA\u2010PRESS.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were \u221220 to 20\u2009Hz and \u221290\u00b0 to 90\u00b0 and were uniformly distributed for the simulation dataset at different signal\u2010to\u2010noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0\u20135\u2009Hz; 0\u201320\u00b0), medium offsets (5\u201310\u2009Hz; 20\u201345\u00b0), and large offsets (10\u201320\u2009Hz; 45\u201390\u00b0).<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Two\u2010tailed paired\n t<\/jats:italic>\n \u2010tests for model performances in the simulation and in vivo datasets were used and a\n P<\/jats:italic>\n \u2010value <0.05 was considered statistically significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n CNN\u2010SR model was capable of correcting frequency offsets (0.014\u2009\u00b1\u20090.010\u2009Hz at SNR 20 and 0.058\u2009\u00b1\u20090.050\u2009Hz at SNR 2.5 with line broadening) and phase offsets (0.104\u2009\u00b1\u20090.076\u00b0 at SNR 20 and 0.416\u2009\u00b1\u20090.317\u00b0 at SNR 2.5 with line broadening). Using in vivo datasets, CNN\u2010SR achieved the best performance without (0.000055\u2009\u00b1\u20090.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062\u2009\u00b1\u20090.000068 at small, \u22120.000033\u2009\u00b1\u20090.000023 at medium, 0.000067\u2009\u00b1\u20090.000102 at large) applied.<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n The proposed CNN\u2010SR method is an efficient and accurate approach for simultaneous FPC of single\u2010voxel MEGA\u2010PRESS MRS data.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 4<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.28868","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T07:21:17Z","timestamp":1688455277000},"page":"964-975","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning"],"prefix":"10.1002","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6096-4408","authenticated-orcid":false,"given":"David J.","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanting","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Harguindeguy","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott A.","family":"Small","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Columbia University New York New York USA"},{"name":"Department of Neurology Columbia University New York New York USA"},{"name":"Taub Institute Research on Alzheimer's Disease and the Aging Brain, Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Columbia University New York New York USA"},{"name":"Columbia University Irving Medical Center, Columbia University, New York State Psychiatric Institute New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas L.","family":"Rothman","sequence":"additional","affiliation":[{"name":"Radiology and Biomedical Imaging of Biomedical Engineering, Yale University New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Columbia University New York New York USA"},{"name":"Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2015-0010"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.12.004"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25094"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108827"},{"key":"e_1_2_7_6_1","unstructured":"LiuC MaDJ ZhuN et al.JET \u2013 A Matlab toolkit for automated J\u2010difference\u2010edited MR spectra processing of in vivo mouse MEGA\u2010PRESS study at 9.4T. 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