{"id":"https://openalex.org/W3011268293","doi":"https://doi.org/10.1117/12.2549412","title":"Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion","display_name":"Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W3011268293","doi":"https://doi.org/10.1117/12.2549412","mag":"3011268293"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549412","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","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/A5075352040","display_name":"Dariya Malyarenko","orcid":"https://orcid.org/0000-0003-0403-1501"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dariya Malyarenko","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027717698","display_name":"Yuxi Pang","orcid":"https://orcid.org/0000-0001-5039-0236"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxi Pang","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001095372","display_name":"Ghoncheh Amouzandeh","orcid":"https://orcid.org/0000-0002-9423-7003"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ghoncheh Amouzandeh","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017319649","display_name":"Thomas L. Chenevert","orcid":"https://orcid.org/0000-0003-1476-4274"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas L. Chenevert","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5873,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70389122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"31"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.9997000098228455,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9997000098228455,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9984999895095825,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9754999876022339,"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/imaging-phantom","display_name":"Imaging phantom","score":0.6299006938934326},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5923631191253662},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5523654222488403},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5061690211296082},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4759126305580139},{"id":"https://openalex.org/keywords/kurtosis","display_name":"Kurtosis","score":0.46190744638442993},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45044010877609253},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.42163509130477905},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.4175075888633728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37137681245803833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34189939498901367},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3282381296157837},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10340258479118347},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.09457772970199585}],"concepts":[{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.6299006938934326},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5923631191253662},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5523654222488403},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5061690211296082},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4759126305580139},{"id":"https://openalex.org/C166963901","wikidata":"https://www.wikidata.org/wiki/Q287251","display_name":"Kurtosis","level":2,"score":0.46190744638442993},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45044010877609253},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.42163509130477905},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.4175075888633728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37137681245803833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34189939498901367},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3282381296157837},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10340258479118347},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.09457772970199585},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549412","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4381516319","https://openalex.org/W2037499216","https://openalex.org/W1506384729","https://openalex.org/W4225568567","https://openalex.org/W4286378979","https://openalex.org/W2075698830","https://openalex.org/W3216026256","https://openalex.org/W3127045225","https://openalex.org/W2059891554","https://openalex.org/W29630554"],"abstract_inverted_index":{"Clinical":[0],"applications":[1],"of":[2,17,35,84,102,111,149,197],"quantitative":[3],"diffusion-weighted":[4],"imaging":[5],"(qDWI)":[6],"require":[7],"confidence":[8],"intervals":[9],"for":[10,39,52,72,91,109,123,228],"derived":[11,38],"diffusion":[12,41,81,179],"parameters":[13],"to":[14,28,187,212],"aid":[15],"differentiation":[16],"technical":[18],"errors":[19],"from":[20],"tissue":[21],"characteristics.":[22],"This":[23],"study":[24],"outlines":[25],"practical":[26],"procedures":[27,235],"evaluate":[29],"precision":[30,88,220],"(uncertainty)":[31],"and":[32,67,78,87,127,143,206,227,244],"accuracy":[33,86],"(bias)":[34],"parametric":[36],"maps":[37],"non-Gaussian":[40],"models":[42],"using":[43],"numerical":[44],"DWI":[45,117,135],"phantoms":[46],"(digital":[47],"reference":[48],"objects":[49],"(DROs))":[50],"generated":[51,59],"advanced":[53],"qDWI":[54,69,238],"clinical":[55],"trial":[56],"protocols.":[57],"The":[58,233],"DROs":[60],"include":[61],"simulated":[62],"acquisition":[63,243],"noise,":[64],"DICOM":[65],"scaling":[66],"clinically-relevant":[68],"parameter":[70,155,199],"ranges":[71],"perfusion-fraction":[73],"intra-voxel":[74],"incoherent":[75],"motion,":[76],"kurtosis":[77],"stretched":[79],"exponential":[80],"models.":[82],"Evaluation":[83],"fit":[85,146,154,175,198,213],"is":[89,166,185,209,221],"illustrated":[90],"unsupervised":[92],"linear":[93],"least-squares":[94],"(LLS)":[95],"versus":[96,173,225,230],"nonlinear":[97],"minimization":[98,202],"(NLM)":[99],"algorithms":[100],"ignorant":[101],"noise.":[103],"DRO":[104,140],"application":[105],"examples":[106],"are":[107],"shown":[108],"calibration":[110],"noise-induced":[112,181],"uncertainty":[113,214],"with":[114,158],"a":[115],"physical":[116],"phantom,":[118],"retrospective":[119],"model":[120,142,231],"fidelity":[121],"analysis":[122,137],"brain":[124],"tumor":[125],"data":[126],"prospective":[128],"error":[129],"mapping":[130],"in":[131,153,171,183,189],"renal":[132],"tissue.":[133],"Physical":[134],"phantom":[136],"confirms":[138],"adequate":[139],"noise":[141,164],"realistic":[144],"predicted":[145],"errors.":[147,246],"Detection":[148],"step-like":[150],"bias":[151,182,188,201],"activation":[152],"space,":[156],"consistent":[157],"modeldependent":[159],"signal":[160],"truncation":[161],"at":[162],"the":[163,190],"floor,":[165],"proposed":[167],"based":[168],"on":[169],"differences":[170],"NLM":[172],"LLS":[174,224],"maps.":[176],"For":[177,194,216],"all":[178],"models,":[180],"diffusivity":[184,229],"anti-correlated":[186],"model-specific":[191],"(non-diffusivity)":[192],"parameter.":[193,232],"confident":[195],"assessment":[196],"precision,":[200],"through":[203],"b-range":[204],"constraints":[205],"bdependent":[207],"averaging":[208],"advised":[210],"prior":[211],"mapping.":[215],"low":[217],"bias,":[218],"higher":[219],"achieved":[222],"by":[223],"NLM,":[226],"described":[234],"allow":[236],"efficient":[237],"protocol":[239],"optimization":[240],"toward":[241],"reduced":[242],"model-dependent":[245]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}