{"id":"https://openalex.org/W2738743584","doi":"https://doi.org/10.1109/tmi.2018.2799231","title":"Learned Primal-Dual Reconstruction","display_name":"Learned Primal-Dual Reconstruction","publication_year":2018,"publication_date":"2018-01-29","ids":{"openalex":"https://openalex.org/W2738743584","doi":"https://doi.org/10.1109/tmi.2018.2799231","mag":"2738743584","pmid":"https://pubmed.ncbi.nlm.nih.gov/29870362"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2018.2799231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2018.2799231","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/1707.06474","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jonas Adler","orcid":"https://orcid.org/0000-0001-9928-3407"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jonas Adler","raw_affiliation_strings":["Department of Mathematics, KTH - Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-9928-3407","affiliations":[{"raw_affiliation_string":"Department of Mathematics, KTH - Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ozan Oktem","orcid":"https://orcid.org/0000-0002-1118-6483"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ozan Oktem","raw_affiliation_strings":["Department of Mathematics, KTH - Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-1118-6483","affiliations":[{"raw_affiliation_string":"Department of Mathematics, KTH - Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":43.1561,"has_fulltext":false,"cited_by_count":733,"citation_normalized_percentile":{"value":0.99898456,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"37","issue":"6","first_page":"1322","last_page":"1332"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.8561000227928162,"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":0.8561000227928162,"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/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.04089999943971634,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.03440000116825104,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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":0.6786999702453613},{"id":"https://openalex.org/keywords/imaging-phantom","display_name":"Imaging phantom","score":0.6669999957084656},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6126000285148621},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5209000110626221},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.511900007724762},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44339999556541443},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4374000132083893},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.40459999442100525},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.39629998803138733}],"concepts":[{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6786999702453613},{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.6669999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6377000212669373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6340000033378601},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.511900007724762},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5002999901771545},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47760000824928284},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4374000132083893},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40459999442100525},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.36959999799728394},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.35600000619888306},{"id":"https://openalex.org/C76970557","wikidata":"https://www.wikidata.org/wiki/Q1869750","display_name":"Loop unrolling","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C97742081","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Tomographic reconstruction","level":3,"score":0.3337000012397766},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.31279999017715454},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.2800999879837036},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25099998712539673}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"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":"D011856","descriptor_name":"Radiographic Image Enhancement","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D011856","descriptor_name":"Radiographic Image Enhancement","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D011856","descriptor_name":"Radiographic Image Enhancement","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","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}],"locations_count":3,"locations":[{"id":"doi:10.1109/tmi.2018.2799231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2018.2799231","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:29870362","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29870362","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":"Journal Article"},{"id":"pmh:oai:arXiv.org:1707.06474","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1707.06474","pdf_url":"https://arxiv.org/pdf/1707.06474","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1707.06474","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1707.06474","pdf_url":"https://arxiv.org/pdf/1707.06474","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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/G1178954299","display_name":"3D rekonstruktion med simulerade fram\u00e5tmodeller","funder_award_id":"ID14-0055","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"},{"id":"https://openalex.org/G2182370445","display_name":"L\u00e5gkomplexitetsrekonstruktionsmetoder f\u00f6r medicin","funder_award_id":"AM13-0049","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"},{"id":"https://openalex.org/G4593765863","display_name":null,"funder_award_id":"EB017095","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"},{"id":"https://openalex.org/G7048746577","display_name":null,"funder_award_id":"EB017185","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"}],"funders":[{"id":"https://openalex.org/F4320320940","display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning","ror":"https://ror.org/044wr7g58"},{"id":"https://openalex.org/F4320321396","display_name":"Elekta","ror":"https://ror.org/02j2cf778"},{"id":"https://openalex.org/F4320337363","display_name":"National Institute of Biomedical Imaging and Bioengineering","ror":"https://ror.org/00372qc85"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W967573802","https://openalex.org/W1677182931","https://openalex.org/W1988115241","https://openalex.org/W2004602201","https://openalex.org/W2021347102","https://openalex.org/W2074567512","https://openalex.org/W2092663520","https://openalex.org/W2094366314","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2469946482","https://openalex.org/W2520016695","https://openalex.org/W2533020866","https://openalex.org/W2542870090","https://openalex.org/W2574952845","https://openalex.org/W2584483805","https://openalex.org/W2604388535","https://openalex.org/W2607406448","https://openalex.org/W2949128855","https://openalex.org/W6631943919","https://openalex.org/W6639824700","https://openalex.org/W6702130928","https://openalex.org/W6712909570","https://openalex.org/W6729455701","https://openalex.org/W7048263784"],"related_works":["https://openalex.org/W3107474891","https://openalex.org/W2386067591","https://openalex.org/W3174762625","https://openalex.org/W2158065114","https://openalex.org/W3141106550","https://openalex.org/W3087617596","https://openalex.org/W4214924096","https://openalex.org/W2393478627","https://openalex.org/W4234881090","https://openalex.org/W4246418678"],"abstract_inverted_index":{"We":[0,67],"propose":[1],"the":[2,32,71,93,113,133,146],"Learned":[3],"Primal-Dual":[4],"algorithm":[5,10,43,139],"for":[6,12,149],"tomographic":[7],"reconstruction.":[8],"The":[9,42],"accounts":[11],"a":[13,19,25,129],"(possibly":[14],"non-linear)":[15],"forward":[16],"operator":[17],"in":[18,132],"deep":[20,86],"neural":[21,40],"network":[22],"by":[23],"unrolling":[24],"proximal":[26,33],"primal-dual":[27],"optimization":[28],"method,":[29],"but":[30],"where":[31],"operators":[34],"have":[35],"been":[36],"replaced":[37],"with":[38,128],"convolutional":[39],"networks.":[41],"is":[44,116],"trained":[45],"end-to-end,":[46],"working":[47],"directly":[48],"from":[49],"raw":[50],"measured":[51],"data":[52],"and":[53,85,121],"it":[54],"does":[55],"not":[56],"depend":[57],"on":[58,74],"any":[59],"initial":[60],"reconstruction":[61,79],"such":[62],"as":[63],"filtered":[64],"back-projection":[65],"(FBP).":[66],"compare":[68],"performance":[69],"of":[70,90],"proposed":[72],"method":[73,147],"low":[75],"dose":[76],"computed":[77],"tomography":[78],"against":[80,106],"FBP,":[81],"total":[82],"variation":[83],"(TV),":[84],"learning":[87],"based":[88],"post-processing":[89,126],"FBP.":[91],"For":[92,110],"Shepp-Logan":[94],"phantom":[95],"we":[96],"obtain":[97],">6":[98],"dB":[99,118,123],"peak":[100],"signal":[101],"to":[102],"noise":[103],"ratio":[104],"improvement":[105,115,131],"all":[107],"compared":[108],"methods.":[109],"human":[111],"phantoms":[112],"corresponding":[114],"6.6":[117],"over":[119,124],"TV":[120],"2.2":[122],"learned":[125],"along":[127],"substantial":[130],"structural":[134],"similarity":[135],"index.":[136],"Finally,":[137],"our":[138],"involves":[140],"only":[141],"ten":[142],"forward-back-projection":[143],"computations,":[144],"making":[145],"feasible":[148],"time":[150],"critical":[151],"clinical":[152],"applications.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":49},{"year":2025,"cited_by_count":90},{"year":2024,"cited_by_count":102},{"year":2023,"cited_by_count":111},{"year":2022,"cited_by_count":116},{"year":2021,"cited_by_count":146},{"year":2020,"cited_by_count":52},{"year":2019,"cited_by_count":50},{"year":2018,"cited_by_count":16},{"year":2012,"cited_by_count":1}],"updated_date":"2026-08-30T07:30:35.949966","created_date":"2017-07-31T00:00:00"}