{"id":"https://openalex.org/W2603901191","doi":"https://doi.org/10.1109/tmi.2018.2832007","title":"PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction","display_name":"PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction","publication_year":2018,"publication_date":"2018-04-30","ids":{"openalex":"https://openalex.org/W2603901191","doi":"https://doi.org/10.1109/tmi.2018.2832007","mag":"2603901191","pmid":"https://pubmed.ncbi.nlm.nih.gov/29870377"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2018.2832007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2018.2832007","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1703.09165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xuehang Zheng","orcid":"https://orcid.org/0000-0002-4063-0344"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuehang Zheng","raw_affiliation_strings":["University of Michigan\u2013Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4063-0344","affiliations":[{"raw_affiliation_string":"University of Michigan\u2013Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Saiprasad Ravishankar","orcid":"https://orcid.org/0000-0002-5792-5827"},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saiprasad Ravishankar","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-5792-5827","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yong Long","orcid":"https://orcid.org/0000-0002-2055-0662"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Long","raw_affiliation_strings":["University of Michigan\u2013Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2055-0662","affiliations":[{"raw_affiliation_string":"University of Michigan\u2013Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jeffrey A. Fessler","orcid":"https://orcid.org/0000-0001-9998-3315"},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeffrey A. Fessler","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-9998-3315","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.6248,"has_fulltext":false,"cited_by_count":88,"citation_normalized_percentile":{"value":0.98559856,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"37","issue":"6","first_page":"1498","last_page":"1510"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9053000211715698,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9053000211715698,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.027699999511241913,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10844","display_name":"Radiation Dose and Imaging","score":0.017500000074505806,"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/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.7685999870300293},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.598800003528595},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5742999911308289},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5228000283241272},{"id":"https://openalex.org/keywords/imaging-phantom","display_name":"Imaging phantom","score":0.48179998993873596},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4740999937057495},{"id":"https://openalex.org/keywords/augmented-lagrangian-method","display_name":"Augmented Lagrangian method","score":0.4666999876499176},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.39989998936653137}],"concepts":[{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.7685999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7053999900817871},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.598800003528595},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5733000040054321},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5228000283241272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49140000343322754},{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.48179998993873596},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C150452318","wikidata":"https://www.wikidata.org/wiki/Q4820432","display_name":"Augmented Lagrangian method","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.39989998936653137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37279999256134033},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35040000081062317},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.3246000111103058},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C69179731","wikidata":"https://www.wikidata.org/wiki/Q510427","display_name":"Point spread function","level":2,"score":0.25519999861717224}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1109/tmi.2018.2832007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2018.2832007","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:29870377","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29870377","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null},{"id":"pmh:oai:arXiv.org:1703.09165","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1703.09165","pdf_url":"https://arxiv.org/pdf/1703.09165","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pubmedcentral.nih.gov:6034686","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6034686","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Trans Med Imaging","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1703.09165","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1703.09165","pdf_url":"https://arxiv.org/pdf/1703.09165","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2289761694","display_name":null,"funder_award_id":"61501292","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1972266574","https://openalex.org/W1974218973","https://openalex.org/W1976709621","https://openalex.org/W1980003430","https://openalex.org/W1986262252","https://openalex.org/W1989833002","https://openalex.org/W1994281301","https://openalex.org/W1995758426","https://openalex.org/W2002932002","https://openalex.org/W2003624223","https://openalex.org/W2017692724","https://openalex.org/W2032237670","https://openalex.org/W2037511378","https://openalex.org/W2042547280","https://openalex.org/W2048417186","https://openalex.org/W2049641710","https://openalex.org/W2053958583","https://openalex.org/W2058808032","https://openalex.org/W2066771519","https://openalex.org/W2075157914","https://openalex.org/W2076181982","https://openalex.org/W2090457307","https://openalex.org/W2094366314","https://openalex.org/W2098092211","https://openalex.org/W2107214962","https://openalex.org/W2114122776","https://openalex.org/W2115429828","https://openalex.org/W2126534699","https://openalex.org/W2128659236","https://openalex.org/W2133665775","https://openalex.org/W2141039087","https://openalex.org/W2142419873","https://openalex.org/W2142884793","https://openalex.org/W2145889472","https://openalex.org/W2149400409","https://openalex.org/W2152142562","https://openalex.org/W2153663612","https://openalex.org/W2157812230","https://openalex.org/W2160547390","https://openalex.org/W2163398148","https://openalex.org/W2168543121","https://openalex.org/W2168668658","https://openalex.org/W2189938900","https://openalex.org/W2293090580","https://openalex.org/W2349047606","https://openalex.org/W2487371665","https://openalex.org/W2567331605","https://openalex.org/W2578922756","https://openalex.org/W2952026614","https://openalex.org/W2962757575","https://openalex.org/W2963294657","https://openalex.org/W6605295242","https://openalex.org/W6660475368","https://openalex.org/W6676727762","https://openalex.org/W6705181680"],"related_works":[],"abstract_inverted_index":{"The":[0,53,73,95,206],"development":[1],"of":[2,24,49,55,68,115,128,155,174],"computed":[3],"tomography":[4],"(CT)":[5],"image":[6,18,62,85,97,180,198,203,213],"reconstruction":[7,39,86,98,161],"methods":[8],"that":[9,41,111,141,196],"significantly":[10,151],"reduce":[11],"patient":[12],"radiation":[13],"exposure,":[14],"while":[15],"maintaining":[16],"high":[17],"quality":[19,154,199,214],"is":[20,58,78,100,223],"an":[21,46],"important":[22],"area":[23],"research":[25],"in":[26,194],"low-dose":[27,146],"CT":[28,69,84,96,126],"imaging.":[29],"We":[30,189],"propose":[31],"a":[32,66,83,89,103,163,172,184],"new":[33],"penalized":[34],"weighted":[35],"least":[36],"squares":[37],"(PWLS)":[38],"method":[40,108,150],"exploits":[42],"regularization":[43,169],"based":[44,170],"on":[45,171],"efficient":[47],"Union":[48],"Learned":[50],"TRAnsforms":[51],"(PWLS-ULTRA).":[52],"union":[54,173],"square":[56,187],"transforms":[57,176],"pre-learned":[59],"from":[60,65],"numerous":[61],"patches":[63],"extracted":[64],"dataset":[67],"images":[70,157],"or":[71,211],"volumes.":[72],"proposed":[74,149,207],"PWLS-based":[75],"cost":[76],"function":[77],"optimized":[79],"by":[80,102],"alternating":[81],"between":[82],"step,":[87],"and":[88,92,117,123,133,137,145,200],"sparse":[90],"coding":[91],"clustering":[93],"step.":[94],"step":[99],"accelerated":[101],"relaxed":[104],"linearized":[105],"augmented":[106],"Lagrangian":[107],"with":[109,121,162,168],"ordered-subsets":[110],"reduces":[112],"the":[113,129,148,153],"number":[114],"forward":[116],"back":[118],"projections.":[119],"Simulations":[120],"2-D":[122],"3-D":[124,134],"axial":[125],"scans":[127,139],"extended":[130],"cardiac-torso":[131],"phantom":[132],"helical":[135],"chest":[136],"abdomen":[138],"show":[140],"for":[142],"both":[143],"normal-dose":[144],"levels,":[147],"improves":[152],"reconstructed":[156],"compared":[158,215],"to":[159,178,216],"PWLS":[160,167],"nonadaptive":[164],"edge-preserving":[165],"regularizer.":[166],"learned":[175,186,217],"leads":[177],"better":[179,212],"reconstructions":[181],"than":[182],"using":[183],"single":[185],"transform.":[188],"also":[190],"incorporate":[191],"patch-based":[192],"weights":[193],"PWLS-ULTRA":[195],"enhance":[197],"help":[201],"improve":[202],"resolution":[204],"uniformity.":[205],"approach":[208],"achieves":[209],"comparable":[210],"overcomplete":[218],"synthesis":[219],"dictionaries,":[220],"but":[221],"importantly,":[222],"much":[224],"faster":[225],"(computationally":[226],"more":[227],"efficient).":[228]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2017-04-07T00:00:00"}