{"id":"https://openalex.org/W4386528138","doi":"https://doi.org/10.1145/3587716.3587770","title":"Low-Dose Sinogram Restoration in SPECT Imaging Based on Conditional Generative Adversarial Network and LSTM.","display_name":"Low-Dose Sinogram Restoration in SPECT Imaging Based on Conditional Generative Adversarial Network and LSTM.","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4386528138","doi":"https://doi.org/10.1145/3587716.3587770"},"language":"en","primary_location":{"id":"doi:10.1145/3587716.3587770","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587716.3587770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","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":null,"display_name":"Limei Peng","orcid":"https://orcid.org/0009-0006-6332-6595"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limei Peng","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-6332-6595","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100391306","display_name":"Si Li","orcid":"https://orcid.org/0000-0001-5590-7759"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Li","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0001-5590-7759","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"328","last_page":"334"},"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/T11183","display_name":"Advanced X-ray Imaging Techniques","score":0.9947999715805054,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9908000230789185,"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":0.7173911929130554},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7031142711639404},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6694918870925903},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.6516703367233276},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.6232196688652039},{"id":"https://openalex.org/keywords/spect-imaging","display_name":"Spect imaging","score":0.5463233590126038},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5263577699661255},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5221636295318604},{"id":"https://openalex.org/keywords/single-photon-emission-computed-tomography","display_name":"Single-photon emission computed tomography","score":0.5215216279029846},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4465812146663666},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4161219894886017},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3673875331878662},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36026835441589355},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31444060802459717},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23103901743888855},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.20919454097747803},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.19287705421447754}],"concepts":[{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.7173911929130554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7031142711639404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6694918870925903},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.6516703367233276},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.6232196688652039},{"id":"https://openalex.org/C2984398910","wikidata":"https://www.wikidata.org/wiki/Q849737","display_name":"Spect imaging","level":2,"score":0.5463233590126038},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5263577699661255},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5221636295318604},{"id":"https://openalex.org/C2780441642","wikidata":"https://www.wikidata.org/wiki/Q849737","display_name":"Single-photon emission computed tomography","level":2,"score":0.5215216279029846},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4465812146663666},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4161219894886017},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3673875331878662},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36026835441589355},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31444060802459717},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23103901743888855},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.20919454097747803},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.19287705421447754},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587716.3587770","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587716.3587770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[{"id":"https://openalex.org/G3238581596","display_name":null,"funder_award_id":"2022A1515012379","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2584483805","https://openalex.org/W2611467245","https://openalex.org/W2743780012","https://openalex.org/W2894899608","https://openalex.org/W2902508714","https://openalex.org/W2963073614","https://openalex.org/W2963470893","https://openalex.org/W3098281398","https://openalex.org/W3099378996","https://openalex.org/W3105751747","https://openalex.org/W4244955044","https://openalex.org/W4248479044","https://openalex.org/W4282572565","https://openalex.org/W6704369950"],"related_works":["https://openalex.org/W2067323155","https://openalex.org/W2550548326","https://openalex.org/W2023288059","https://openalex.org/W171655601","https://openalex.org/W2113768228","https://openalex.org/W1987865331","https://openalex.org/W2225942053","https://openalex.org/W2553353535","https://openalex.org/W1970980137","https://openalex.org/W1762867498"],"abstract_inverted_index":{"To":[0,83],"address":[1],"the":[2,21,34,46,52,68,77,85,88,91,97,104,109,113,118,122],"problem":[3],"that":[4,112],"low-dose":[5,40,123],"SPECT":[6,41],"imaging":[7],"will":[8],"lead":[9],"to":[10,32,66],"poor-quality":[11],"projection":[12],"images,":[13],"we":[14],"propose":[15],"a":[16,28],"network":[17,25],"structure":[18],"based":[19],"on":[20],"conditional":[22],"generative":[23],"adversarial":[24],"and":[26,48,101,103,108],"add":[27],"convolutional":[29],"LSTM":[30],"module":[31],"combine":[33],"sequence":[35],"features":[36],"of":[37,51,87,121],"sinograms":[38,73],"for":[39],"sinogram":[42,53],"restoration,":[43],"called":[44],"LCGAN,":[45],"spatial":[47],"angular":[49],"information":[50],"can":[54],"be":[55],"better":[56],"utilized.":[57],"Projection":[58],"data":[59],"from":[60],"SIMIND":[61],"software":[62],"simulations":[63],"are":[64],"used":[65],"train":[67],"proposed":[69,114],"model.":[70],"The":[71],"recovered":[72],"were":[74,94],"reconstructed":[75,92],"using":[76,96],"model-based":[78],"iterative":[79],"reconstruction":[80,119],"(MBIR)":[81],"method.":[82],"evaluate":[84],"effectiveness":[86],"LCGAN":[89],"model,":[90],"images":[93],"evaluated":[95],"global":[98],"metrics":[99,106],"PSNR":[100],"NMSE":[102],"local":[105],"COV,":[107],"results":[110],"showed":[111],"method":[115],"significantly":[116],"improved":[117],"quality":[120],"sinograms.":[124]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
