{"id":"https://openalex.org/W2918272865","doi":"https://doi.org/10.1117/12.2512813","title":"Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging","display_name":"Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging","publication_year":2019,"publication_date":"2019-03-04","ids":{"openalex":"https://openalex.org/W2918272865","doi":"https://doi.org/10.1117/12.2512813","mag":"2918272865"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512813","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066916036","display_name":"Angel R. Pineda","orcid":"https://orcid.org/0000-0002-8820-0511"},"institutions":[{"id":"https://openalex.org/I55707380","display_name":"Manhattan College","ror":"https://ror.org/02xhnzg94","country_code":"US","type":"education","lineage":["https://openalex.org/I55707380"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Angel R. Pineda","raw_affiliation_strings":["Manhattan College (United States)"],"affiliations":[{"raw_affiliation_string":"Manhattan College (United States)","institution_ids":["https://openalex.org/I55707380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5066916036"],"corresponding_institution_ids":["https://openalex.org/I55707380"],"apc_list":null,"apc_paid":null,"fwci":0.1693,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52123333,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"9","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9995999932289124,"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/T10844","display_name":"Radiation Dose and Imaging","score":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5325663685798645},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5102217197418213},{"id":"https://openalex.org/keywords/observer","display_name":"Observer (physics)","score":0.45934706926345825},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45171478390693665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4297380745410919},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4094592332839966},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37588924169540405},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3753213882446289},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20565569400787354}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5325663685798645},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5102217197418213},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.45934706926345825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45171478390693665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4297380745410919},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4094592332839966},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37588924169540405},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3753213882446289},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20565569400787354},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512813","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W212537071","https://openalex.org/W1973132343","https://openalex.org/W2002277703","https://openalex.org/W2006943300","https://openalex.org/W2014024561","https://openalex.org/W2091889205","https://openalex.org/W2099631097","https://openalex.org/W2133665775","https://openalex.org/W2789725180","https://openalex.org/W4298304654","https://openalex.org/W6748754204"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2051487156","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2073681303","https://openalex.org/W2358292267","https://openalex.org/W2378166785","https://openalex.org/W2896778670"],"abstract_inverted_index":{"Magnetic":[0],"resonance":[1],"imaging":[2],"(MRI)":[3],"data":[4],"acquisition":[5,68],"is":[6,17,39],"sometimes":[7],"accelerated":[8],"by":[9,19],"pseudo-random":[10],"under-sampling":[11],"of":[12,30,32,51,104,125,134,182,221],"the":[13,33,43,67,122,132,146,167,176,180,183,219,226,231],"frequency":[14],"domain":[15],"which":[16],"followed":[18],"constrained":[20],"reconstruction.":[21,83],"This":[22],"approach":[23,136],"to":[24,120,209],"acceleration":[25,65],"assumes":[26],"a":[27,52,61,80,87,94,141,157,188,210],"certain":[28],"level":[29],"sparsity":[31,38],"object":[34],"being":[35],"imaged.":[36],"The":[37,164],"typically":[40],"considered":[41],"for":[42,197,225,230],"background":[44],"anatomy":[45],"but":[46],"not":[47,207],"explored":[48],"in":[49,66,93,98,214],"terms":[50],"signal":[53,138],"detection":[54,123,139,215],"task.":[55],"In":[56],"this":[57,105,135],"study":[58],"we":[59],"implement":[60],"2.56x":[62],"one":[63,155],"dimensional":[64],"using":[69,140,175,187],"fully":[70],"sampled":[71,76],"low":[72],"frequencies":[73,78],"and":[74,86,107,115,154,179,201,217],"randomly":[75],"high":[77],"with":[79,114,127,191],"total":[81,204],"variation":[82,205],"A":[84],"small":[85],"large":[88],"lesion":[89],"were":[90,118],"synthetically":[91],"placed":[92],"3D":[95],"MRI":[96],"volume":[97,106],"non-overlapping":[99],"regions.":[100],"From":[101],"40":[102],"slices":[103],"16":[108],"regions":[109],"per":[110],"slice,":[111],"640":[112],"sub-images":[113],"without":[116],"signals":[117],"generated":[119],"estimate":[121,184],"performance":[124,216],"lesions":[126],"anatomical":[128],"variation.":[129],"We":[130,194],"compared":[131],"effect":[133,220],"on":[137],"channelized":[142],"Hotelling":[143],"observer":[144,149,159],"approximating":[145,156],"ideal":[147],"linear":[148],"(with":[150,160],"10":[151],"Laguerre-Gauss":[152,227],"channels)":[153],"human":[158],"sparse":[161,232],"difference-of-Gaussians":[162,233],"channels).":[163],"area":[165],"under":[166],"receiver":[168],"operating":[169],"characteristic":[170],"curve":[171],"(AUC)":[172],"was":[173,185,223],"estimated":[174],"Mann-Whitney":[177],"statistic":[178],"uncertainty":[181],"assessed":[186],"bootstrap":[189],"distribution":[190],"10,000":[192],"samples.":[193],"found":[195],"that":[196,218],"these":[198],"two":[199],"tasks":[200],"model":[202,228],"observers,":[203],"did":[206],"lead":[208],"statistically":[211],"significant":[212],"improvement":[213],"regularization":[222],"larger":[224],"than":[229],"model.":[234]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
