{"id":"https://openalex.org/W3152685198","doi":"https://doi.org/10.1109/access.2021.3073944","title":"Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks","display_name":"Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3152685198","doi":"https://doi.org/10.1109/access.2021.3073944","mag":"3152685198"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3073944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073944","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09406803.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09406803.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013068815","display_name":"Miao Tian","orcid":"https://orcid.org/0000-0002-1155-3220"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Miao Tian","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102080803","display_name":"Kaikai Song","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaikai Song","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China","Department of Automation, University of Science and Technology of China, Hefei 230027, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Department of Automation, University of Science and Technology of China, Hefei 230027, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5013068815"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.234,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.89701389,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"62266","last_page":"62275"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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":0.9991999864578247,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9922999739646912,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9873999953269958,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7871338129043579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7654819488525391},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7533324956893921},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6548871397972107},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6371266841888428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.634611189365387},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5322686433792114},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5138909816741943},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4420740306377411},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3972853720188141}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7871338129043579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7654819488525391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533324956893921},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6548871397972107},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6371266841888428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.634611189365387},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5322686433792114},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5138909816741943},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4420740306377411},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3972853720188141},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3073944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073944","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09406803.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fb56cab6b3f846e68ce09cd70df19b0d","is_oa":true,"landing_page_url":"https://doaj.org/article/fb56cab6b3f846e68ce09cd70df19b0d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 62266-62275 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3073944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073944","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09406803.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3152685198.pdf","grobid_xml":"https://content.openalex.org/works/W3152685198.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W1593077517","https://openalex.org/W1899329334","https://openalex.org/W1901129140","https://openalex.org/W1971735224","https://openalex.org/W1978084472","https://openalex.org/W2001234856","https://openalex.org/W2001697618","https://openalex.org/W2014940628","https://openalex.org/W2015057244","https://openalex.org/W2037642501","https://openalex.org/W2072128103","https://openalex.org/W2073660032","https://openalex.org/W2081859730","https://openalex.org/W2099471712","https://openalex.org/W2107840100","https://openalex.org/W2120480077","https://openalex.org/W2133665775","https://openalex.org/W2134920263","https://openalex.org/W2135036909","https://openalex.org/W2135184018","https://openalex.org/W2136396015","https://openalex.org/W2137676365","https://openalex.org/W2142592339","https://openalex.org/W2145094598","https://openalex.org/W2146337213","https://openalex.org/W2160637922","https://openalex.org/W2173520492","https://openalex.org/W2181347294","https://openalex.org/W2271840356","https://openalex.org/W2298992465","https://openalex.org/W2339754110","https://openalex.org/W2510850936","https://openalex.org/W2520164769","https://openalex.org/W2546938941","https://openalex.org/W2764207251","https://openalex.org/W2887579808","https://openalex.org/W2890139949","https://openalex.org/W2890847992","https://openalex.org/W2904016341","https://openalex.org/W2956134935","https://openalex.org/W2962793481","https://openalex.org/W2963017889","https://openalex.org/W2963073614","https://openalex.org/W2963444790","https://openalex.org/W2963470893","https://openalex.org/W2963684088","https://openalex.org/W2964046669","https://openalex.org/W2982671374","https://openalex.org/W2997574889","https://openalex.org/W3014516197","https://openalex.org/W3083068801","https://openalex.org/W3084280717","https://openalex.org/W3104725225","https://openalex.org/W3118496544","https://openalex.org/W3165173837","https://openalex.org/W4231109964","https://openalex.org/W4298201114","https://openalex.org/W4300912893","https://openalex.org/W4320013936","https://openalex.org/W6621378261","https://openalex.org/W6680588873","https://openalex.org/W6681096077","https://openalex.org/W6681686951","https://openalex.org/W6685352114","https://openalex.org/W6685777803","https://openalex.org/W6694517276","https://openalex.org/W6726381175","https://openalex.org/W6729383884","https://openalex.org/W6729966448"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W4308217387"],"abstract_inverted_index":{"Denoising":[0],"plays":[1],"an":[2],"important":[3],"role":[4],"in":[5,72,131,174,245],"the":[6,25,55,70,73,78,83,106,120,129,132,162,189,198,214,223,234,242,247,253],"Magnetic":[7],"Resonance":[8],"Imaging":[9],"(MRI)":[10],"applications":[11],"for":[12,33],"medical":[13],"diagnosis.":[14,29],"MRI":[15,34,50,134,155],"images":[16,135,165],"usually":[17],"contain":[18],"undesired":[19],"noises":[20],"which":[21,90],"would":[22],"negatively":[23],"affect":[24],"exactitude":[26],"of":[27,92,176,188,222,252],"pathological":[28],"Recently,":[30],"many":[31],"models":[32],"denoising":[35,52,248],"have":[36],"been":[37],"developed":[38],"from":[39,82,204],"deep":[40],"learning":[41],"networks.":[42],"In":[43,117],"this":[44],"paper,":[45],"we":[46],"propose":[47],"a":[48,62,87,93,97,101,110,114,178],"novel":[49],"image":[51,79,95,99,112,199],"method":[53,171,192,209,226,236],"using":[54,149],"conditional":[56,84],"Generative":[57],"Adversarial":[58],"Networks":[59],"(GANs).":[60],"Specifically,":[61],"Convolutional":[63],"Neural":[64],"Network":[65],"(CNN)":[66],"is":[67,86,125,143,237],"utilized":[68],"as":[69,136,138],"discriminator":[71],"process":[74],"to":[75,127,206],"distinguish":[76],"whether":[77],"pair":[80,89,103],"obtained":[81],"GAN":[85],"real":[88,153],"consists":[91],"noisy":[94,111,133],"and":[96,113,152,240,250,256],"noise-free":[98],"or":[100],"fake":[102],"which,":[104],"on":[105,161],"other":[107],"hand,":[108],"contains":[109],"denoised":[115],"image.":[116],"our":[118,170,208],"design,":[119],"convolutional":[121],"encoder-decoder":[122],"networks-based":[123],"generator":[124],"used":[126],"remove":[128],"noise":[130,168,200],"much":[137],"possible.":[139],"The":[140],"whole":[141],"architecture":[142],"trained":[144],"by":[145],"adversarial":[146],"learning.":[147],"Experiments":[148],"both":[150,246],"synthetic":[151,163],"clinical":[154],"datasets":[156],"are":[157],"conducted.":[158],"When":[159],"tested":[160],"T1w":[164],"with":[166],"10%":[167],"level,":[169],"performed":[172],"better":[173],"terms":[175],"reaching":[177],"high":[179],"structural":[180],"similarity":[181],"index":[182],"(SSIM)":[183],"at":[184],"0.9489":[185],"while":[186,220],"that":[187,213,221,233],"next":[190,224],"best":[191,225],"was":[193,202,210],"only":[194,216],"0.7485.":[195],"Moreover,":[196],"when":[197],"level":[201,249],"increased":[203],"1%":[205],"10%,":[207],"more":[211,238],"stable":[212],"SSIM":[215],"dropped":[217,227],"about":[218,228],"3.2%":[219],"23.7%.":[229],"Simulation":[230],"results":[231],"demonstrate":[232],"proposed":[235],"robust":[239],"outperforms":[241],"conventional":[243],"methods":[244],"preservation":[251],"anatomical":[254],"structures":[255],"defined":[257],"contrast.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}