{"id":"https://openalex.org/W7131387759","doi":"https://doi.org/10.48550/arxiv.2602.20289","title":"The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA","display_name":"The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131387759","doi":"https://doi.org/10.48550/arxiv.2602.20289"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.20289","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125323260","display_name":"Zien Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ma, Zien","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126848839","display_name":"S. M. Shermer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shermer, S. M.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126812476","display_name":"Oktay Karaku\u015f","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karaku\u015f, Oktay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5071981008","display_name":"Frank C. Langbein","orcid":"https://orcid.org/0000-0002-3379-0323"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Langbein, Frank C.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5125323260"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9639000296592712,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9639000296592712,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.008100000210106373,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.007600000128149986,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imaging-phantom","display_name":"Imaging phantom","score":0.7289000153541565},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7215999960899353},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6693000197410583},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6632000207901001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5350000262260437},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5253000259399414},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4521999955177307},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39320001006126404}],"concepts":[{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.7289000153541565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7283999919891357},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7215999960899353},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6693000197410583},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6632000207901001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5687999725341797},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5350000262260437},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5253000259399414},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4318999946117401},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C60478076","wikidata":"https://www.wikidata.org/wiki/Q3036835","display_name":"Reference data","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C202799725","wikidata":"https://www.wikidata.org/wiki/Q272035","display_name":"Accuracy and precision","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.20289","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.20289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20289","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.20289","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Magnetic":[0],"resonance":[1],"spectroscopy":[2],"(MRS)":[3],"is":[4,30,226],"used":[5,140],"to":[6,20,33,228],"quantify":[7],"metabolites":[8,24,105],"in":[9,172,202],"vivo":[10],"and":[11,38,43,62,67,120,130,199],"estimate":[12],"biomarkers":[13],"for":[14,47,190],"conditions":[15],"ranging":[16],"from":[17,53,79,99],"neurological":[18],"disorders":[19],"cancers.":[21],"Quantifying":[22],"low-concentration":[23],"such":[25],"as":[26],"GABA":[27,191],"($\u03b3$-aminobutyric":[28],"acid)":[29],"challenging":[31],"due":[32],"low":[34],"signal-to-noise":[35],"ratio":[36],"(SNR)":[37],"spectral":[39],"overlap.":[40],"We":[41,92],"investigate":[42],"validate":[44,93],"deep":[45,183],"learning":[46,184],"quantifying":[48],"complex,":[49],"low-SNR,":[50],"overlapping":[51],"signals":[52],"MEGA-PRESS":[54,81],"spectra,":[55],"devise":[56],"a":[57,63,187,231],"convolutional":[58],"neural":[59],"network":[60],"(CNN)":[61],"Y-shaped":[64],"autoencoder":[65],"(YAE),":[66],"select":[68],"the":[69,138,173,207],"best":[70,181],"models":[71,85,133,147,185],"via":[72],"Bayesian":[73],"optimisation":[74],"on":[75,88,96,236],"10,000":[76],"simulated":[77,90],"spectra":[78,98,195],"slice-profile-aware":[80],"simulations.":[82],"The":[83,180],"selected":[84],"are":[86,134],"trained":[87],"100,000":[89],"spectra.":[91],"their":[94],"performance":[95],"144":[97],"112":[100],"experimental":[101,161],"phantoms":[102],"containing":[103],"five":[104],"of":[106,196],"interest":[107],"(GABA,":[108],"Glu,":[109],"Gln,":[110],"NAA,":[111],"Cr)":[112],"with":[113],"known":[114],"ground":[115,224],"truth":[116,225],"concentrations":[117],"across":[118],"solution":[119],"gel":[121],"series":[122],"acquired":[123],"at":[124],"3":[125],"T":[126],"under":[127],"varied":[128],"bandwidths":[129],"implementations.":[131],"These":[132],"further":[135],"assessed":[136],"against":[137],"widely":[139],"LCModel":[141,210],"quantification":[142],"tool.":[143],"On":[144,160],"simulations,":[145],"both":[146],"achieve":[148],"near-perfect":[149],"agreement":[150],"(small":[151],"MAEs;":[152],"regression":[153],"slopes":[154],"$\\approx":[155],"1.00$,":[156],"$R^2":[157],"\\approx":[158],"1.00$).":[159],"phantom":[162,194],"data,":[163],"errors":[164],"initially":[165],"increased":[166],"substantially.":[167],"However,":[168],"modelling":[169],"variable":[170],"linewidths":[171],"training":[174],"data":[175,218],"significantly":[176],"reduced":[177,221],"this":[178],"gap.":[179],"augmented":[182],"achieved":[186],"mean":[188],"MAE":[189],"over":[192],"all":[193],"0.151":[197],"(YAE)":[198],"0.160":[200],"(FCNN)":[201],"max-normalised":[203],"relative":[204],"concentrations,":[205],"outperforming":[206],"conventional":[208],"baseline":[209],"(0.220).":[211],"A":[212],"sim-to-real":[213],"gap":[214],"remains,":[215],"but":[216],"physics-informed":[217],"augmentation":[219],"substantially":[220],"it.":[222],"Phantom":[223],"needed":[227],"judge":[229],"whether":[230],"method":[232],"will":[233],"perform":[234],"reliably":[235],"real":[237],"data.":[238]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-26T00:00:00"}