{"id":"https://openalex.org/W2805342764","doi":"https://doi.org/10.1109/isbi.2018.8363589","title":"Accelerated high b-value diffusion-weighted MR imaging via phase-constrained low-rank tensor model","display_name":"Accelerated high b-value diffusion-weighted MR imaging via phase-constrained low-rank tensor model","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2805342764","doi":"https://doi.org/10.1109/isbi.2018.8363589","mag":"2805342764"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2018.8363589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2018.8363589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5054386295","display_name":"Lianli Liu","orcid":"https://orcid.org/0000-0002-1095-0953"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lianli Liu","raw_affiliation_strings":["Departrnent of Radiation Oncology, University of Michingan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departrnent of Radiation Oncology, University of Michingan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032786866","display_name":"Adam Johansson","orcid":"https://orcid.org/0000-0001-9849-2143"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adam Johansson","raw_affiliation_strings":["Departrnent of Radiation Oncology, University of Michingan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departrnent of Radiation Oncology, University of Michingan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032245802","display_name":"James M. Balter","orcid":"https://orcid.org/0000-0003-1513-4936"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James M. Balter","raw_affiliation_strings":["Departrnent of Radiation Oncology, University of Michingan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departrnent of Radiation Oncology, University of Michingan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020712237","display_name":"Yue Cao","orcid":"https://orcid.org/0000-0003-3910-3261"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue Cao","raw_affiliation_strings":["Departrnent of Radiation Oncology, University of Michingan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departrnent of Radiation Oncology, University of Michingan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027207271","display_name":"Jeffrey A. Fessler","orcid":"https://orcid.org/0000-0001-9998-3315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeffrey A. Fessler","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10324878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":null,"first_page":"344","last_page":"348"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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":0.9990000128746033,"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/T12303","display_name":"Tensor decomposition and applications","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.8554369807243347},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.5551074147224426},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5448103547096252},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5258410573005676},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.49289608001708984},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4798792898654938},{"id":"https://openalex.org/keywords/aliasing","display_name":"Aliasing","score":0.4772724509239197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4583032429218292},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4513123333454132},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4425395727157593},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42764273285865784},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.3948916792869568},{"id":"https://openalex.org/keywords/nuclear-magnetic-resonance","display_name":"Nuclear magnetic resonance","score":0.35617128014564514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3268177807331085},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30448341369628906},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2512194514274597},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2315838634967804},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.22020554542541504},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.17160698771476746},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09127426147460938}],"concepts":[{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.8554369807243347},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.5551074147224426},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5448103547096252},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5258410573005676},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.49289608001708984},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4798792898654938},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.4772724509239197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4583032429218292},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4513123333454132},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4425395727157593},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42764273285865784},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3948916792869568},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.35617128014564514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3268177807331085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30448341369628906},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2512194514274597},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2315838634967804},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.22020554542541504},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.17160698771476746},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09127426147460938},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2018.8363589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2018.8363589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","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":20,"referenced_works":["https://openalex.org/W1497904071","https://openalex.org/W1508744047","https://openalex.org/W1545171847","https://openalex.org/W1870281475","https://openalex.org/W2013912476","https://openalex.org/W2045510807","https://openalex.org/W2049169055","https://openalex.org/W2055913665","https://openalex.org/W2096615275","https://openalex.org/W2099801199","https://openalex.org/W2100566252","https://openalex.org/W2111388536","https://openalex.org/W2113914986","https://openalex.org/W2117756735","https://openalex.org/W2131752914","https://openalex.org/W2171333384","https://openalex.org/W2293488915","https://openalex.org/W2338760177","https://openalex.org/W2507398220","https://openalex.org/W2536450744"],"related_works":["https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2569661359","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W2897298721","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W3199841771"],"abstract_inverted_index":{"High":[0],"b-value":[1],"Diffusion-weighted":[2],"MRI":[3],"(DWI)":[4],"is":[5,65,81],"promising":[6],"in":[7],"cancer":[8],"imaging":[9,62,88,124],"but":[10],"suffers":[11],"from":[12],"long":[13],"acquisition":[14,42,92],"time":[15],"and":[16,35,55,89,94,105,117],"low":[17],"signal-to-noise":[18],"ratio":[19],"(SNR).":[20],"We":[21],"propose":[22],"a":[23],"low-rank":[24,132],"tensor":[25],"model":[26],"that":[27,64],"exploits":[28],"correlation":[29],"across":[30],"both":[31,103],"diffusion-induced":[32],"signal":[33],"decays":[34],"neighboring":[36],"k-space":[37],"samples,":[38],"to":[39,122],"accelerate":[40],"the":[41],"of":[43,49,112],"DWI":[44],"using":[45,102],"an":[46,61,109],"extended":[47],"range":[48],"b-values":[50,80],"(0":[51],"s/mm2to":[52],"2500":[53],"s/mm2)":[54],"limited":[56],"(orthogonal":[57],"only)":[58],"diffusion":[59],"directions,":[60],"scheme":[63],"increasingly":[66],"used":[67],"for":[68,76],"brain":[69],"gliomas":[70],"evaluation.":[71],"A":[72],"phase":[73,77],"constraint":[74],"accounts":[75],"variations":[78],"between":[79],"also":[82],"applied.":[83],"Our":[84],"method":[85,126],"integrates":[86],"parallel":[87,123],"partial":[90],"Fourier":[91],"naturally,":[93],"undersamples":[95],"along":[96],"phase-encoding":[97],"direction":[98],"only.":[99],"Reconstruction":[100],"results":[101],"patient":[104],"simulated":[106],"data":[107],"with":[108],"acceleration":[110],"factor":[111],"8":[113],"show":[114],"improved":[115],"SNR":[116],"reduced":[118],"aliasing,":[119],"as":[120,127,129],"compared":[121],"only":[125],"well":[128],"two":[130],"other":[131],"model-based":[133],"methods.":[134]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
