{"id":"https://openalex.org/W2905525600","doi":"https://doi.org/10.1109/acssc.2018.8645364","title":"2.5D Deep Learning For CT Image Reconstruction Using A Multi-GPU Implementation","display_name":"2.5D Deep Learning For CT Image Reconstruction Using A Multi-GPU Implementation","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2905525600","doi":"https://doi.org/10.1109/acssc.2018.8645364","mag":"2905525600"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645364","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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/A5037719051","display_name":"Amirkoushyar Ziabari","orcid":"https://orcid.org/0000-0003-4776-457X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amirkoushyar Ziabari","raw_affiliation_strings":["Electrical and Computer Engineering at Purdue University"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering at Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068927047","display_name":"Dong Hye Ye","orcid":"https://orcid.org/0000-0002-9186-4095"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Hye Ye","raw_affiliation_strings":["Electrical and Computer Engineering at Purdue University"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering at Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109514151","display_name":"Somesh Srivastava","orcid":null},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Somesh Srivastava","raw_affiliation_strings":["GE Healthcare"],"affiliations":[{"raw_affiliation_string":"GE Healthcare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110231549","display_name":"K. Sauer","orcid":null},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ken D. Sauer","raw_affiliation_strings":["Electrical Engineering at University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering at University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109296991","display_name":"Jean\u2010Baptiste Thibault","orcid":null},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jean-Baptiste Thibault","raw_affiliation_strings":["GE Healthcare"],"affiliations":[{"raw_affiliation_string":"GE Healthcare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056657991","display_name":"Charles A. Bouman","orcid":"https://orcid.org/0000-0001-8504-0383"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles A. Bouman","raw_affiliation_strings":["Electrical and Computer Engineering at Purdue University"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering at Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5037719051"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":8.5931,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.97826087,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2044","last_page":"2049"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9962000250816345,"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/computer-science","display_name":"Computer science","score":0.7982624769210815},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7544387578964233},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7452206015586853},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7117325067520142},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6818407773971558},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6147838830947876},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5171091556549072},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5086594223976135},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.49236834049224854},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4502128064632416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41101759672164917},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39715543389320374},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39159226417541504},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3407173752784729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7982624769210815},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7544387578964233},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7452206015586853},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7117325067520142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6818407773971558},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6147838830947876},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5171091556549072},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5086594223976135},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.49236834049224854},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4502128064632416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41101759672164917},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39715543389320374},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39159226417541504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3407173752784729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2018.8645364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645364","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1986262252","https://openalex.org/W2003624223","https://openalex.org/W2087416986","https://openalex.org/W2156387975","https://openalex.org/W2194775991","https://openalex.org/W2296055064","https://openalex.org/W2508457857","https://openalex.org/W2542870090","https://openalex.org/W2556016755","https://openalex.org/W2592991170","https://openalex.org/W2802021665","https://openalex.org/W2904620825","https://openalex.org/W2949117887","https://openalex.org/W2963108767","https://openalex.org/W3104324122","https://openalex.org/W3105425607","https://openalex.org/W3105751747","https://openalex.org/W6638667902","https://openalex.org/W6682889407","https://openalex.org/W6729710357","https://openalex.org/W6757121099","https://openalex.org/W6786000376"],"related_works":["https://openalex.org/W2757389719","https://openalex.org/W2951714568","https://openalex.org/W1963814553","https://openalex.org/W2037595954","https://openalex.org/W1988158806","https://openalex.org/W2507293823","https://openalex.org/W2386146599","https://openalex.org/W2018015402","https://openalex.org/W2144778520","https://openalex.org/W2801815023"],"abstract_inverted_index":{"While":[0],"Model":[1],"Based":[2],"Iterative":[3],"Reconstruction":[4],"(MBIR)":[5],"of":[6],"CT":[7],"scans":[8],"has":[9,24],"been":[10,25],"shown":[11,40],"to":[12,79,124],"have":[13,39],"better":[14],"image":[15],"quality":[16,123],"than":[17],"Filtered":[18],"Back":[19],"Projection":[20],"(FBP),":[21],"its":[22,28],"use":[23],"limited":[26],"by":[27],"high":[29],"computational":[30,132],"cost.":[31,133],"More":[32],"recently,":[33],"deep":[34,70],"convolutional":[35,92],"neural":[36,72,93],"networks":[37],"(CNN)":[38],"great":[41],"promise":[42],"in":[43],"both":[44],"denoising":[45],"and":[46,108,115],"reconstruction":[47,56],"applications.":[48],"In":[49,102],"this":[50],"research,":[51],"we":[52,59,104],"propose":[53,105],"a":[54,69,88],"fast":[55],"algorithm,":[57],"which":[58],"call":[60],"Deep":[61],"Learning":[62],"MBIR":[63,67,85],"(DL-MBIR),":[64],"for":[65],"approximating":[66],"using":[68,87,99],"residual":[71,91],"network.":[73],"The":[74],"DL-MBIR":[75,113],"method":[76,114,120],"is":[77],"trained":[78],"produce":[80],"reconstructions":[81],"that":[82,117],"approximate":[83],"true":[84],"images":[86],"16":[89],"layer":[90],"network":[94],"implemented":[95],"on":[96,111],"multiple":[97],"GPUs":[98],"Google":[100],"Tensorflow.":[101],"addition,":[103],"2D,":[106],"2.5D":[107,119],"3D":[109,127],"variations":[110],"the":[112,118,125],"show":[116],"achieves":[121],"similar":[122],"fully":[126],"method,":[128],"but":[129],"with":[130],"reduced":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}