{"id":"https://openalex.org/W4409356427","doi":"https://doi.org/10.1109/tmi.2025.3558019","title":"PFCM: Poisson Flow Consistency Models for Low-Dose CT Image Denoising","display_name":"PFCM: Poisson Flow Consistency Models for Low-Dose CT Image Denoising","publication_year":2025,"publication_date":"2025-04-11","ids":{"openalex":"https://openalex.org/W4409356427","doi":"https://doi.org/10.1109/tmi.2025.3558019","pmid":"https://pubmed.ncbi.nlm.nih.gov/40215159"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2025.3558019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2025.3558019","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5076023815","display_name":"Dennis Hein","orcid":"https://orcid.org/0000-0001-7051-6625"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dennis Hein","raw_affiliation_strings":["Department of Radiology, Stanford University, Stanford, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7051-6625","affiliations":[{"raw_affiliation_string":"Department of Radiology, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016049919","display_name":"Grant M. Stevens","orcid":"https://orcid.org/0000-0002-8885-4443"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grant Stevens","raw_affiliation_strings":["GE HealthCare, Waukesha, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GE HealthCare, Waukesha, WI, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063020829","display_name":"Adam Wang","orcid":"https://orcid.org/0000-0001-9234-1264"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Wang","raw_affiliation_strings":["Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, USA","Department of Radiology and the Department of Electrical Engineering, Stanford University, Stanford, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9234-1264","affiliations":[{"raw_affiliation_string":"Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Department of Radiology and the Department of Electrical Engineering, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100400458","display_name":"Ge Wang","orcid":"https://orcid.org/0000-0002-2656-7705"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]},{"id":"https://openalex.org/I4210166946","display_name":"Imaging Center","ror":"https://ror.org/05hsqsk33","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210166946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ge Wang","raw_affiliation_strings":["Department of Biomedical Engineering, School of Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-2656-7705","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, School of Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507","https://openalex.org/I4210166946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0911,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.95231066,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"44","issue":"7","first_page":"2989","last_page":"3001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9939000010490417,"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":0.9939000010490417,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9700000286102295,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.957099974155426,"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/image-denoising","display_name":"Image denoising","score":0.6298863887786865},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5886979103088379},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5582319498062134},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5073949694633484},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.4976253807544708},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43798360228538513},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.41199612617492676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.404492050409317},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40366214513778687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35527756810188293},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18971365690231323}],"concepts":[{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.6298863887786865},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5886979103088379},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5582319498062134},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5073949694633484},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.4976253807544708},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43798360228538513},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.41199612617492676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.404492050409317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40366214513778687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35527756810188293},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18971365690231323}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","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":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D011829","descriptor_name":"Radiation Dosage","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011829","descriptor_name":"Radiation Dosage","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011829","descriptor_name":"Radiation Dosage","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019047","descriptor_name":"Phantoms, 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},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tmi.2025.3558019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2025.3558019","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:40215159","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40215159","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":"IEEE transactions on medical imaging","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320316588","display_name":"G\u00f6ran Gustafssons Stiftelser","ror":"https://ror.org/05s5h0e62"},{"id":"https://openalex.org/F4320332457","display_name":"GE Healthcare","ror":"https://ror.org/03yt24h27"},{"id":"https://openalex.org/F4320333178","display_name":"Foundation for Neonatal Research and Education","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1972150100","https://openalex.org/W1986806190","https://openalex.org/W2003624223","https://openalex.org/W2037511378","https://openalex.org/W2102104181","https://openalex.org/W2110496314","https://openalex.org/W2114286204","https://openalex.org/W2119259145","https://openalex.org/W2133665775","https://openalex.org/W2171697262","https://openalex.org/W2520302833","https://openalex.org/W2554137722","https://openalex.org/W2584483805","https://openalex.org/W2617128058","https://openalex.org/W2743780012","https://openalex.org/W2768814045","https://openalex.org/W2889741596","https://openalex.org/W2962162024","https://openalex.org/W2962785568","https://openalex.org/W3040980702","https://openalex.org/W3041466907","https://openalex.org/W3045244891","https://openalex.org/W3049688872","https://openalex.org/W3082833877","https://openalex.org/W3096001627","https://openalex.org/W3098281398","https://openalex.org/W3110147920","https://openalex.org/W3112965401","https://openalex.org/W3155072588","https://openalex.org/W3187413956","https://openalex.org/W3206439203","https://openalex.org/W3212516020","https://openalex.org/W4200039099","https://openalex.org/W4296142362","https://openalex.org/W4306247281","https://openalex.org/W4308555638","https://openalex.org/W4312293341","https://openalex.org/W4312773500","https://openalex.org/W4318620303","https://openalex.org/W4362694255","https://openalex.org/W4385650939","https://openalex.org/W4387211214","https://openalex.org/W4399526318","https://openalex.org/W4400905853","https://openalex.org/W4402754926","https://openalex.org/W4403069472","https://openalex.org/W4403464026","https://openalex.org/W4408703205","https://openalex.org/W6635810480","https://openalex.org/W6679045638","https://openalex.org/W6767164110","https://openalex.org/W6779823529","https://openalex.org/W6783713337","https://openalex.org/W6786375611","https://openalex.org/W6788990321","https://openalex.org/W6795288823","https://openalex.org/W6803078664","https://openalex.org/W6804811285","https://openalex.org/W6838452192","https://openalex.org/W6843749293","https://openalex.org/W6849273598","https://openalex.org/W6849666819","https://openalex.org/W6850614898","https://openalex.org/W6852396960","https://openalex.org/W6854332868","https://openalex.org/W6857470303","https://openalex.org/W6862535282"],"related_works":["https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W4407169614","https://openalex.org/W2387428419","https://openalex.org/W4401571043","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W2765479697","https://openalex.org/W4406054402","https://openalex.org/W4312627788"],"abstract_inverted_index":{"X-ray":[0],"computed":[1],"tomography":[2],"(CT)":[3],"is":[4,207],"widely":[5],"used":[6],"for":[7,88,112],"medical":[8],"diagnosis":[9],"and":[10,69,189,250],"treatment":[11],"planning;":[12],"however,":[13],"concerns":[14],"about":[15],"ionizing":[16],"radiation":[17],"exposure":[18],"drive":[19],"efforts":[20],"to":[21,64,84,106,209],"optimize":[22],"image":[23,118],"quality":[24],"at":[25,171,251],"lower":[26],"doses.":[27],"This":[28],"study":[29],"introduces":[30,142],"Poisson":[31,161],"Flow":[32],"Consistency":[33],"Models":[34],"(PFCM),":[35],"a":[36,67,75,121,143,222,241,247,252],"novel":[37,109,228],"family":[38],"of":[39,47,54,78,115,148,156,169,186,221,237,254],"deep":[40],"generative":[41,110,127],"models":[42,206],"that":[43,103,124,155,165],"combines":[44],"the":[45,50,82,91,113,126,135,160,166,192,215,219],"robustness":[46,87,168],"PFGM++":[48,65],"with":[49,134],"efficient":[51],"single-step":[52],"sampling":[53],"consistency":[55,62,205],"models.":[56],"PFCM":[57,80,170],"are":[58,152],"derived":[59],"by":[60,129,226],"generalizing":[61],"distillation":[63],"through":[66],"change-of-variables":[68],"an":[70,131,199],"updated":[71],"noise":[72,146],"distribution.":[73],"As":[74],"distilled":[76],"version":[77],"PFGM++,":[79],"inherit":[81],"ability":[83],"trade":[85],"off":[86],"rigidity":[89],"via":[90,120],"hyperparameter":[92],"$\\text":[93],"{D}":[94],"\\in":[95],"\\text":[96,98],"{(}{0},\\infty":[97],"{)}$":[99],".":[100],"A":[101],"fact":[102],"we":[104,233],"exploit":[105],"adapt":[107],"this":[108,140,176],"model":[111],"task":[114],"low-dose":[116,136,149,194],"CT":[117,137,150,195],"denoising,":[119],"\"task-specific\"":[122],"sampler":[123,180,201],"\"hijacks\"":[125],"process":[128],"replacing":[130],"intermediate":[132,157],"state":[133],"image.":[138],"While":[139],"\"hijacking\"":[141],"severe":[144],"mismatch-the":[145],"characteristics":[147],"images":[151,239],"different":[153],"from":[154,240],"states":[158],"in":[159,184],"flow":[162],"process-we":[163],"show":[164,234],"inherent":[167],"small":[172],"D":[173,224],"effectively":[174],"mitigates":[175],"issue.":[177],"The":[178],"resulting":[179],"achieves":[181],"excellent":[182],"performance":[183],"terms":[185],"LPIPS,":[187],"SSIM,":[188],"PSNR":[190],"on":[191,203],"Mayo":[193],"dataset.":[196],"By":[197],"contrast,":[198],"analogous":[200],"based":[202],"standard":[204],"found":[208],"be":[210],"significantly":[211],"less":[212],"robust":[213],"under":[214],"same":[216],"conditions,":[217],"highlighting":[218],"importance":[220],"tunable":[223],"afforded":[225],"our":[227],"framework.":[229],"To":[230],"highlight":[231],"generalizability,":[232],"effective":[235],"denoising":[236],"clinical":[238],"prototype":[242],"photon-counting":[243],"system":[244],"reconstructed":[245],"using":[246],"sharper":[248],"kernel":[249],"range":[253],"energy":[255],"levels.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}