{"id":"https://openalex.org/W2998256487","doi":"https://doi.org/10.1145/3364836.3364867","title":"Hybrid 3D/2D-Based Deep Convolutional Neural Network for Spatio-Temporal Denoising of Angiography","display_name":"Hybrid 3D/2D-Based Deep Convolutional Neural Network for Spatio-Temporal Denoising of Angiography","publication_year":2019,"publication_date":"2019-08-24","ids":{"openalex":"https://openalex.org/W2998256487","doi":"https://doi.org/10.1145/3364836.3364867","mag":"2998256487"},"language":"en","primary_location":{"id":"doi:10.1145/3364836.3364867","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","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/A5100425983","display_name":"Pu Zhang","orcid":"https://orcid.org/0000-0002-3438-3340"},"institutions":[{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pu Zhang","raw_affiliation_strings":["Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China","institution_ids":["https://openalex.org/I4210134419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075863007","display_name":"Chengyang Wu","orcid":"https://orcid.org/0000-0002-1863-6045"},"institutions":[{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengyang Wu","raw_affiliation_strings":["Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China","institution_ids":["https://openalex.org/I4210134419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445660","display_name":"Yan Xu","orcid":"https://orcid.org/0000-0002-2636-7594"},"institutions":[{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Xu","raw_affiliation_strings":["Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Neusoft Medical Systems Co. Ltd, Courtyard East, Beijing, China","institution_ids":["https://openalex.org/I4210134419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103711068","display_name":"Jingwu Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingwu Yao","raw_affiliation_strings":["Neusoft Medical Systems USA, Inc., Houston, TX"],"affiliations":[{"raw_affiliation_string":"Neusoft Medical Systems USA, Inc., Houston, TX","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100425983"],"corresponding_institution_ids":["https://openalex.org/I4210134419"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15952655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"157","last_page":"160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9961000084877014,"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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7733180522918701},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7525782585144043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7322415709495544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7103589773178101},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6357137560844421},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6106972098350525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5473026633262634},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5320091247558594},{"id":"https://openalex.org/keywords/angiography","display_name":"Angiography","score":0.428737610578537},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.414060115814209},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37710219621658325},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.0770919919013977},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.05104437470436096}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7733180522918701},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7525782585144043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322415709495544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7103589773178101},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6357137560844421},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6106972098350525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5473026633262634},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5320091247558594},{"id":"https://openalex.org/C2780643987","wikidata":"https://www.wikidata.org/wiki/Q468414","display_name":"Angiography","level":2,"score":0.428737610578537},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.414060115814209},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37710219621658325},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0770919919013977},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.05104437470436096},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3364836.3364867","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W94230774","https://openalex.org/W1978749115","https://openalex.org/W2037642501","https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2097073572","https://openalex.org/W2098477387","https://openalex.org/W2103504761","https://openalex.org/W2103559027","https://openalex.org/W2121845348","https://openalex.org/W2132192990","https://openalex.org/W2153663612","https://openalex.org/W2157233332","https://openalex.org/W2330127310","https://openalex.org/W2508457857","https://openalex.org/W2527006979","https://openalex.org/W2536599074","https://openalex.org/W2964204553","https://openalex.org/W2979814141","https://openalex.org/W3143835446","https://openalex.org/W4244531973","https://openalex.org/W4247924304","https://openalex.org/W4249142012","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2012531322","https://openalex.org/W2402761219","https://openalex.org/W4321487865","https://openalex.org/W2785900585","https://openalex.org/W4313906399","https://openalex.org/W2353730437","https://openalex.org/W2490303674","https://openalex.org/W2609066826","https://openalex.org/W4391266461"],"abstract_inverted_index":{"Image":[0],"denoising":[1,25],"is":[2,82],"one":[3],"of":[4,11,33,58,87,103,107,112],"the":[5,30,79,92],"important":[6],"issues":[7],"for":[8,65],"X-ray":[9,104],"angiography":[10,50,96],"C-arm":[12],"systems.":[13],"Most":[14],"existing":[15],"methods":[16],"in":[17],"this":[18],"field":[19],"focus":[20],"only":[21],"on":[22],"2D":[23],"image":[24,34],"from":[26],"frame-by-frame":[27],"independently,":[28],"losing":[29],"temporal":[31,46],"information":[32,86],"sequence":[35],"to":[36,41,49,75,95],"some":[37],"extents.":[38],"In":[39],"order":[40],"handle":[42],"both":[43,108],"spatial":[44],"and":[45,78,100],"noises":[47],"simultaneously":[48],"imaging,":[51],"we":[52],"propose":[53],"a":[54],"novel":[55],"deep":[56],"architecture":[57],"hybrid":[59],"3D/2D":[60],"CNN":[61],"(convolutional":[62],"neural":[63],"network)":[64],"spatio-temporal":[66],"noise":[67],"reduction.":[68],"The":[69],"developed":[70],"model":[71,93],"takes":[72],"multiple":[73],"frames":[74],"input":[76],"channels,":[77],"final":[80],"result":[81],"obtained":[83],"through":[84],"combining":[85],"all":[88],"channels.":[89],"We":[90],"evaluate":[91],"applied":[94],"images":[97],"with":[98],"normal":[99],"low":[101],"doses":[102],"exposures.":[105],"Results":[106],"cases":[109],"outperform":[110],"those":[111],"state-of-the-art":[113],"methods.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
