{"id":"https://openalex.org/W7161854060","doi":"https://doi.org/10.1109/isbi61048.2026.11515953","title":"Low Performing Pixel Correction in Computed Tomography with Unrolled Network and Synthetic Data Training","display_name":"Low Performing Pixel Correction in Computed Tomography with Unrolled Network and Synthetic Data Training","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161854060","doi":"https://doi.org/10.1109/isbi61048.2026.11515953"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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/A5029334093","display_name":"Hongxu Yang","orcid":"https://orcid.org/0000-0003-2447-094X"},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Hongxu Yang","raw_affiliation_strings":["STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040407089","display_name":"Levente Lippenszky","orcid":"https://orcid.org/0009-0000-7835-2071"},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Levente Lippenszky","raw_affiliation_strings":["STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136577175","display_name":"Edina Timko","orcid":null},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Edina Timko","raw_affiliation_strings":["STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046480861","display_name":"Lehel Ferenczi","orcid":null},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Lehel Ferenczi","raw_affiliation_strings":["STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare","institution_ids":["https://openalex.org/I189448455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020175648","display_name":"Gopal Avinash","orcid":null},"institutions":[{"id":"https://openalex.org/I189448455","display_name":"General Electric (Spain)","ror":"https://ror.org/04gbh9b75","country_code":"ES","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I189448455"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Gopal Avinash","raw_affiliation_strings":["STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STO-Artificial Intelligence &#x0026; Machine Learning, GE HealthCare","institution_ids":["https://openalex.org/I189448455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82061502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.5271000266075134,"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":0.5271000266075134,"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":0.1331000030040741,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.10580000281333923,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/computed-tomography","display_name":"Computed tomography","score":0.5309000015258789},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.520799994468689},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5128999948501587},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45249998569488525},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42250001430511475},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.31450000405311584}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6309999823570251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6116999983787537},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5667999982833862},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C116321358","wikidata":"https://www.wikidata.org/wiki/Q1662063","display_name":"Industrial computed tomography","level":3,"score":0.25949999690055847},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2121984376","https://openalex.org/W2464708700","https://openalex.org/W2798559986","https://openalex.org/W2920406184","https://openalex.org/W2995848654","https://openalex.org/W4220854553","https://openalex.org/W4292361847","https://openalex.org/W4312938066","https://openalex.org/W4402727345","https://openalex.org/W4409985501","https://openalex.org/W4411599750","https://openalex.org/W7138254799"],"related_works":[],"abstract_inverted_index":{"Low":[0],"performance":[1],"pixels":[2],"(LPP)":[3],"in":[4,16,37,42],"Computed":[5],"Tomography":[6],"(CT)":[7],"detectors":[8,145],"would":[9],"lead":[10],"to":[11,32,62,100,129,184],"ring":[12],"and":[13,113,180],"streak":[14],"artifacts":[15,131,170],"the":[17,38,43,76,80,84,105,111,126,148,150,154,172],"reconstructed":[18],"images,":[19,124],"making":[20],"them":[21],"clinically":[22],"unusable.":[23],"In":[24,87,138],"recent":[25],"years,":[26],"several":[27],"solutions":[28],"have":[29],"been":[30],"proposed":[31,151],"correct":[33,101,130,168],"LPP":[34,102,109,169],"artifacts,":[35],"either":[36,69],"image":[39,114],"domain":[40,45],"or":[41,72],"sinogram":[44,112],"using":[46],"supervised":[47],"deep":[48],"learning":[49],"methods.":[50],"However,":[51],"these":[52],"methods":[53],"require":[54],"dedicated":[55],"datasets":[56],"for":[57,177,188],"training,":[58,179],"which":[59],"are":[60,116],"expensive":[61],"collect.":[63],"Moreover,":[64],"existing":[65],"approaches":[66,156],"focus":[67],"solely":[68],"on":[70,97],"image-space":[71],"sinogram-space":[73],"correction,":[74],"ignoring":[75],"intrinsic":[77,106],"correlations":[78,107],"from":[79,122],"forward":[81],"operation":[82],"of":[83,108,174],"CT":[85],"geometry.":[86],"this":[88],"work,":[89],"we":[90],"propose":[91],"an":[92],"unrolled":[93],"dual-domain":[94],"method":[95,152],"based":[96],"synthetic":[98,119],"data":[99,120,175],"artifacts.":[103],"Specifically,":[104],"between":[110],"domains":[115],"leveraged":[117],"through":[118],"generated":[121],"natural":[123],"enabling":[125],"trained":[127],"model":[128,178],"without":[132,171],"requiring":[133],"any":[134],"realworld":[135],"clinical":[136],"data.":[137],"experiments":[139],"simulating":[140],"<tex":[141],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[142],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$1-2":[143],"\\%$</tex>":[144],"defect":[146],"near":[147],"isocenter,":[149],"outperformed":[153],"state-of-the-art":[155],"by":[157],"a":[158],"large":[159],"margin.":[160],"The":[161],"results":[162],"indicate":[163],"that":[164],"our":[165],"solution":[166],"can":[167],"cost":[173],"collection":[176],"it":[181],"is":[182],"adaptable":[183],"different":[185],"scanner":[186],"settings":[187],"software-based":[189],"applications.":[190]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-21T00:00:00"}