{"id":"https://openalex.org/W4413787414","doi":"https://doi.org/10.1007/s10278-025-01638-9","title":"An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment","display_name":"An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment","publication_year":2025,"publication_date":"2025-08-28","ids":{"openalex":"https://openalex.org/W4413787414","doi":"https://doi.org/10.1007/s10278-025-01638-9","pmid":"https://pubmed.ncbi.nlm.nih.gov/40877736"},"language":"en","primary_location":{"id":"doi:10.1007/s10278-025-01638-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10278-025-01638-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10278-025-01638-9.pdf","source":{"id":"https://openalex.org/S4393920007","display_name":"Journal of Imaging Informatics in Medicine","issn_l":"2948-2925","issn":["2948-2925","2948-2933"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging Informatics in Medicine","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10278-025-01638-9.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001063257","display_name":"Lu\u00edsa Carvalho Silva","orcid":"https://orcid.org/0000-0003-1990-8759"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Lu\u00edsa C. Silva","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal. luisa.castelbranco.silva@fundacaochampalimaud.pt","Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0003-1990-8759","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal. luisa.castelbranco.silva@fundacaochampalimaud.pt","institution_ids":["https://openalex.org/I113358080"]},{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050835682","display_name":"Cl\u00e1udia S. Constantino","orcid":"https://orcid.org/0000-0003-4320-8437"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Cl\u00e1udia S. Constantino","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0003-4320-8437","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016649968","display_name":"Ricardo Teixeira","orcid":"https://orcid.org/0000-0002-8187-1419"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Ricardo Teixeira","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0002-8187-1419","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041692746","display_name":"Joana C. Castanheira","orcid":"https://orcid.org/0000-0001-7041-1516"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Joana C. Castanheira","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0001-7041-1516","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026211332","display_name":"Francisco P. M. Oliveira","orcid":"https://orcid.org/0000-0001-9468-8894"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Francisco P. M. Oliveira","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0001-9468-8894","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003076929","display_name":"Durval C. Costa","orcid":"https://orcid.org/0000-0001-8039-4924"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Durval C. Costa","raw_affiliation_strings":["Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal"],"raw_orcid":"https://orcid.org/0000-0001-8039-4924","affiliations":[{"raw_affiliation_string":"Champalimaud Clinical Centre, Champalimaud Foundation, Av. Bras\u00edlia, 1400-038, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5001063257"],"corresponding_institution_ids":["https://openalex.org/I113358080"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28575042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":"3","first_page":"2582","last_page":"2592"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":1.0,"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":1.0,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9997000098228455,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"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/nuclear-medicine","display_name":"Nuclear medicine","score":0.5642600059509277},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5165361762046814},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5109971761703491},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.49942851066589355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48656976222991943},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.4730719029903412},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.4378354847431183},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4309535622596741},{"id":"https://openalex.org/keywords/gold-standard","display_name":"Gold standard (test)","score":0.418129026889801},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.33716243505477905},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.33230873942375183},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3103066682815552},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08684030175209045}],"concepts":[{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.5642600059509277},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5165361762046814},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5109971761703491},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.49942851066589355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48656976222991943},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.4730719029903412},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.4378354847431183},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4309535622596741},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.418129026889801},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33716243505477905},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.33230873942375183},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3103066682815552},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08684030175209045}],"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":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","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":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009369","descriptor_name":"Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D009369","descriptor_name":"Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D019275","descriptor_name":"Radiopharmaceuticals","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019275","descriptor_name":"Radiopharmaceuticals","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019788","descriptor_name":"Fluorodeoxyglucose F18","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D019788","descriptor_name":"Fluorodeoxyglucose F18","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D049268","descriptor_name":"Positron-Emission Tomography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D049268","descriptor_name":"Positron-Emission Tomography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D051598","descriptor_name":"Whole Body Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D051598","descriptor_name":"Whole Body Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1007/s10278-025-01638-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10278-025-01638-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10278-025-01638-9.pdf","source":{"id":"https://openalex.org/S4393920007","display_name":"Journal of Imaging Informatics in Medicine","issn_l":"2948-2925","issn":["2948-2925","2948-2933"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging Informatics in Medicine","raw_type":"journal-article"},{"id":"pmid:40877736","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40877736","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":"Journal of imaging informatics in medicine","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:13230395","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13230395/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Imaging Inform Med","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s10278-025-01638-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10278-025-01638-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10278-025-01638-9.pdf","source":{"id":"https://openalex.org/S4393920007","display_name":"Journal of Imaging Informatics in Medicine","issn_l":"2948-2925","issn":["2948-2925","2948-2933"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging Informatics in Medicine","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413787414.pdf","grobid_xml":"https://content.openalex.org/works/W4413787414.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1901129140","https://openalex.org/W2048865332","https://openalex.org/W2056452610","https://openalex.org/W2133665775","https://openalex.org/W2145938281","https://openalex.org/W2581082771","https://openalex.org/W2784499877","https://openalex.org/W2898241136","https://openalex.org/W2908201961","https://openalex.org/W2921613140","https://openalex.org/W3014974815","https://openalex.org/W3037582100","https://openalex.org/W3097841242","https://openalex.org/W3098949126","https://openalex.org/W3112701542","https://openalex.org/W3133816304","https://openalex.org/W3136586316","https://openalex.org/W3160440345","https://openalex.org/W3194623075","https://openalex.org/W3215324224","https://openalex.org/W4200146459","https://openalex.org/W4210547008","https://openalex.org/W4225390283","https://openalex.org/W4234180827","https://openalex.org/W4280492177","https://openalex.org/W4281264079","https://openalex.org/W4288704578","https://openalex.org/W4365504496","https://openalex.org/W4391560826","https://openalex.org/W4399263341","https://openalex.org/W4403004058"],"related_works":["https://openalex.org/W3027020613","https://openalex.org/W2016533837","https://openalex.org/W3167885074","https://openalex.org/W2892386716","https://openalex.org/W1998563493","https://openalex.org/W4306164210","https://openalex.org/W4313316311","https://openalex.org/W4362608745","https://openalex.org/W2383143032","https://openalex.org/W2082728368"],"abstract_inverted_index":{"Abstract":[0],"Benefits":[1],"in":[2,58,134,213,235,275],"patient":[3],"comfort,":[4],"efficiency,":[5],"and":[6,44,61,88,97,107,111,137,143,154,173,179,187,189,211,248],"sustainability":[7],"can":[8],"come":[9],"from":[10],"reducing":[11],"positron":[12],"emission":[13],"tomography":[14],"(PET)":[15],"scan\u2019s":[16],"acquisition":[17],"duration.":[18],"This":[19],"study":[20],"assesses":[21],"the":[22,59,79,92,113,138,166,199,204,214,223,236,264,279],"clinical":[23,76],"adequacy":[24],"of":[25,52,63,91,95,103,112,116,118,168,225,266],"restoring":[26],"fast-acquisition":[27,267],"18":[28,31,48,270],"F-fluorodeoxyglucose":[29],"([":[30],"F]FDG)":[32],"PET":[33,50],"to":[34,160,165,260,278],"its":[35],"standard-of-care":[36,205,280],"image":[37,163],"quality":[38,164],"through":[39],"deep-learning-based":[40],"(DL)":[41],"methods.":[42,287],"Fast":[43],"standard":[45,115,167,224,285],"whole-body":[46,268],"[":[47,269],"F]FDG":[49,271],"acquisitions":[51],"117":[53],"oncological":[54],"patients":[55],"were":[56,121,177],"included":[57],"training":[60,72],"testing":[62],"three":[64,124],"convolutional":[65],"neural":[66],"networks.":[67],"The":[68,227,255],"best-performing":[69],"network":[70],"during":[71],"was":[73,130,238],"chosen":[74],"for":[75,182,191,243,250,263],"evaluation":[77],"on":[78],"test":[80],"set":[81],"(":[82,206],"N":[83],"=":[84],"25).":[85],"Visual":[86,150],"assessment":[87,151],"lesion":[89],"detectability":[90],"fast":[93],"acquisitions,":[94],"20":[96,153,183,244],"30":[98,155,192,251],"seconds":[99],"per":[100],"axial":[101],"field":[102],"view":[104],"(s/AFOV),":[105],"with":[106,157,203],"without":[108],"DL-based":[109,158,196,257,282],"denoising,":[110],"local":[114],"care,":[117],"70":[119],"s/AFOV,":[120],"performed":[122],"by":[123],"experienced":[125],"nuclear":[126],"medicine":[127],"physicians.":[128],"Quantification":[129],"conducted":[131],"globally":[132],"(voxel-wise),":[133],"healthy":[135],"organs":[136],"reported":[139],"lesions.":[140],"Optimised":[141],"Gaussian":[142],"non-local":[144],"means":[145],"filters":[146],"served":[147],"as":[148,239,241],"benchmarks.":[149],"revealed":[152],"s/AFOV":[156,184,193,245,252],"denoising":[159,197,283],"have":[161],"similar":[162,277],"care.":[169,226],"Average":[170],"lesion-based":[171],"sensitivity":[172],"positive":[174],"predictive":[175],"value":[176,233],"74%":[178],"72%,":[180],"respectively,":[181],"+":[185,194,246,253],"DL":[186,247],"72%":[188],"80%":[190],"DL.":[195,254],"displayed":[198],"highest":[200],"voxel-wise":[201],"agreement":[202],"p":[207],"&lt;":[208],"0.001).":[209],"Liver":[210],"lungs":[212],"DL-denoised":[215],"images":[216,276],"exhibited":[217],"a":[218],"higher":[219],"signal-to-noise":[220],"ratio":[221],"than":[222],"median":[228],"absolute":[229],"maximum":[230],"standardised":[231],"uptake":[232],"deviation":[234],"lesions":[237],"low":[240],"0.39":[242],"0.30":[249],"proposed":[256],"method":[258],"proved":[259],"be":[261],"suitable":[262],"restoration":[265],"PET,":[272],"having":[273],"resulted":[274],"acquisitions.":[281],"outperformed":[284],"benchmark":[286]},"counts_by_year":[],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-08-29T00:00:00"}
