{"id":"https://openalex.org/W4320170124","doi":"https://doi.org/10.48550/arxiv.2206.05278","title":"Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT","display_name":"Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4320170124","doi":"https://doi.org/10.48550/arxiv.2206.05278"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2206.05278","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05278","pdf_url":"https://arxiv.org/pdf/2206.05278","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.05278","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017790811","display_name":"Xiongchao Chen","orcid":"https://orcid.org/0000-0003-4112-8492"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiongchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335355","display_name":"Bo Zhou","orcid":"https://orcid.org/0000-0003-1063-2493"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037354068","display_name":"Huidong Xie","orcid":"https://orcid.org/0000-0002-1124-3548"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Huidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010533392","display_name":"Xueqi Guo","orcid":"https://orcid.org/0000-0002-0416-2811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Xueqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101491863","display_name":"Jiazhen Zhang","orcid":"https://orcid.org/0000-0002-3078-1907"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiazhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041557267","display_name":"Albert J. Sinusas","orcid":"https://orcid.org/0000-0003-0972-9589"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sinusas, Albert J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083553196","display_name":"John A. Onofrey","orcid":"https://orcid.org/0000-0002-9432-0448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Onofrey, John A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041148305","display_name":"Chi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"liu, Chi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","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"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9988999962806702,"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":0.9968000054359436,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6964123845100403},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6636344194412231},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.651190459728241},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6110531091690063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5956900715827942},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5726931095123291},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.5701501965522766},{"id":"https://openalex.org/keywords/single-photon-emission-computed-tomography","display_name":"Single-photon emission computed tomography","score":0.5381826162338257},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4940374493598938},{"id":"https://openalex.org/keywords/correction-for-attenuation","display_name":"Correction for attenuation","score":0.48318830132484436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45858877897262573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45115554332733154},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.4012833535671234},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.3009132742881775},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23672828078269958},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20797666907310486}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6964123845100403},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6636344194412231},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.651190459728241},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6110531091690063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5956900715827942},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5726931095123291},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.5701501965522766},{"id":"https://openalex.org/C2780441642","wikidata":"https://www.wikidata.org/wiki/Q849737","display_name":"Single-photon emission computed tomography","level":2,"score":0.5381826162338257},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4940374493598938},{"id":"https://openalex.org/C123688308","wikidata":"https://www.wikidata.org/wiki/Q7309537","display_name":"Correction for attenuation","level":3,"score":0.48318830132484436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45858877897262573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45115554332733154},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.4012833535671234},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.3009132742881775},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23672828078269958},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20797666907310486},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2206.05278","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05278","pdf_url":"https://arxiv.org/pdf/2206.05278","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2206.05278","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2206.05278","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.05278","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05278","pdf_url":"https://arxiv.org/pdf/2206.05278","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W1966307499","https://openalex.org/W2068748575","https://openalex.org/W2523010270","https://openalex.org/W2143424335","https://openalex.org/W1523361089","https://openalex.org/W2058895041","https://openalex.org/W4229903143","https://openalex.org/W2545726818"],"abstract_inverted_index":{"Single-photon":[0],"emission":[1],"computed":[2,22],"tomography":[3,23],"(SPECT)":[4],"is":[5],"a":[6,94,128,182],"widely":[7],"applied":[8],"imaging":[9],"approach":[10],"for":[11,27,63,70,93,133,156],"diagnosis":[12],"of":[13,35,112,136],"coronary":[14],"artery":[15],"diseases.":[16],"Attenuation":[17],"maps":[18],"(u-maps)":[19],"derived":[20],"from":[21,146],"(CT)":[24],"are":[25,42,60],"utilized":[26],"attenuation":[28],"correction":[29],"(AC)":[30],"to":[31,149,167],"improve":[32],"diagnostic":[33],"accuracy":[34],"cardiac":[36,113,137],"SPECT.":[37],"However,":[38],"SPECT":[39,114,138,197],"and":[40,115,139,153,192],"CT":[41],"obtained":[43],"sequentially":[44],"in":[45],"clinical":[46,178],"practice,":[47],"which":[48],"potentially":[49],"induces":[50],"misregistration":[51],"between":[52],"the":[53,105,134,144],"two":[54,76,89],"scans.":[55],"Convolutional":[56],"neural":[57],"networks":[58],"(CNN)":[59],"powerful":[61],"tools":[62],"medical":[64],"image":[65,86],"registration.":[66],"Previous":[67],"CNN-based":[68],"methods":[69,98],"cross-modality":[71,106],"registration":[72,111,135,190],"either":[73],"directly":[74],"concatenated":[75],"input":[77],"modalities":[78,148],"as":[79],"an":[80],"early":[81],"feature":[82,169],"fusion":[83,170],"or":[84,103],"extracted":[85],"features":[87,155],"using":[88,177],"separate":[90],"CNN":[91],"modules":[92],"late":[95],"fusion.":[96],"These":[97],"do":[99],"not":[100,119],"fully":[101],"extract":[102],"fuse":[104],"information.":[107],"Besides,":[108],"deep-learning-based":[109],"rigid":[110],"CT-derived":[116,140],"u-maps":[117],"has":[118],"been":[120],"investigated":[121],"before.":[122],"In":[123],"this":[124],"paper,":[125],"we":[126],"propose":[127],"Dual-Branch":[129],"Squeeze-Fusion-Excitation":[130],"(DuSFE)":[131],"module":[132],"u-maps.":[141],"DuSFE":[142,159,186],"fuses":[143],"knowledge":[145],"multiple":[147,164],"recalibrate":[150],"both":[151],"channel-wise":[152],"spatial":[154,173],"each":[157],"modality.":[158],"can":[160],"be":[161],"embedded":[162,184],"at":[163,171],"convolutional":[165],"layers":[166],"enable":[168],"different":[172],"dimensions.":[174],"Our":[175],"studies":[176],"data":[179],"demonstrated":[180],"that":[181],"network":[183],"with":[185],"generated":[187],"substantial":[188],"lower":[189],"errors":[191],"therefore":[193],"more":[194],"accurate":[195],"AC":[196],"images":[198],"than":[199],"previous":[200],"methods.":[201]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
