{"id":"https://openalex.org/W2521446193","doi":"https://doi.org/10.1007/978-3-319-46630-9_16","title":"MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention","display_name":"MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2521446193","doi":"https://doi.org/10.1007/978-3-319-46630-9_16","mag":"2521446193"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-46630-9_16","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-46630-9_16","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://kclpure.kcl.ac.uk/ws/files/103098752/MRI_TRUS_Image_Synthesis_with_ONOFREY_Firstonline23September2016_GREEN_AAM.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083553196","display_name":"John A. Onofrey","orcid":"https://orcid.org/0000-0002-9432-0448"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"John A. Onofrey","raw_affiliation_strings":["Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA"],"affiliations":[{"raw_affiliation_string":"Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080738214","display_name":"\u0130lkay \u00d6ks\u00fcz","orcid":"https://orcid.org/0000-0001-6478-0534"},"institutions":[{"id":"https://openalex.org/I127077003","display_name":"IMT School for Advanced Studies Lucca","ror":"https://ror.org/035gh3a49","country_code":"IT","type":"education","lineage":["https://openalex.org/I127077003"]},{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["IT","US"],"is_corresponding":false,"raw_author_name":"Ilkay Oksuz","raw_affiliation_strings":["Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","IMT Institute for Advanced Studies Lucca, Lucca, Italy"],"affiliations":[{"raw_affiliation_string":"Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"IMT Institute for Advanced Studies Lucca, Lucca, Italy","institution_ids":["https://openalex.org/I127077003"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103745980","display_name":"Saradwata Sarkar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145712","display_name":"Applied Design Labs (United States)","ror":"https://ror.org/0439dh211","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145712"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saradwata Sarkar","raw_affiliation_strings":["Eigen, Grass Valley, CA, 95945, USA"],"affiliations":[{"raw_affiliation_string":"Eigen, Grass Valley, CA, 95945, USA","institution_ids":["https://openalex.org/I4210145712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102921911","display_name":"Rajesh Venkataraman","orcid":"https://orcid.org/0009-0001-2554-4130"},"institutions":[{"id":"https://openalex.org/I4210145712","display_name":"Applied Design Labs (United States)","ror":"https://ror.org/0439dh211","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145712"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajesh Venkataraman","raw_affiliation_strings":["Eigen, Grass Valley, CA, 95945, USA"],"affiliations":[{"raw_affiliation_string":"Eigen, Grass Valley, CA, 95945, USA","institution_ids":["https://openalex.org/I4210145712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078494006","display_name":"Lawrence H. Staib","orcid":"https://orcid.org/0000-0002-9516-5136"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence H. Staib","raw_affiliation_strings":["Biomedical Engineering, Yale University, New Haven, CT, 06520, USA","Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","Electrical Engineering, Yale University, New Haven, CT, 06520, USA"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Electrical Engineering, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026211955","display_name":"Xenophon Papademetris","orcid":"https://orcid.org/0000-0001-7711-3335"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xenophon Papademetris","raw_affiliation_strings":["Biomedical Engineering, Yale University, New Haven, CT, 06520, USA","Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5083553196"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":0.2215,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65525593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"157","last_page":"166"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9983999729156494,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9983999729156494,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980999827384949,"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":0.9972000122070312,"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/computer-science","display_name":"Computer science","score":0.7555445432662964},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.6545349359512329},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.6512359380722046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5656589269638062},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.556006908416748},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5007758140563965},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.4540179967880249},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4197661876678467},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.31324106454849243},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3015022277832031},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2821483016014099}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7555445432662964},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.6545349359512329},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.6512359380722046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656589269638062},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.556006908416748},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5007758140563965},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.4540179967880249},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4197661876678467},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.31324106454849243},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3015022277832031},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2821483016014099}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/978-3-319-46630-9_16","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-46630-9_16","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},{"id":"pmh:oai:kclpure.kcl.ac.uk:publications/1a61a56a-4054-4fa3-a958-627a137362c7","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/1a61a56a-4054-4fa3-a958-627a137362c7","pdf_url":"https://kclpure.kcl.ac.uk/ws/files/103098752/MRI_TRUS_Image_Synthesis_with_ONOFREY_Firstonline23September2016_GREEN_AAM.pdf","source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Onofrey , J A , Oksuz , I , Sarkar , S , Venkataraman , R , Staib , L H & Papademetris , X 2016 , ' MRI-TRUS image synthesis with application to image-guided prostate intervention ' , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pp. 157-166 . https://doi.org/10.1007/978-3-319-46630-9_16","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:kclpure.kcl.ac.uk:publications/1a61a56a-4054-4fa3-a958-627a137362c7","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/1a61a56a-4054-4fa3-a958-627a137362c7","pdf_url":"https://kclpure.kcl.ac.uk/ws/files/103098752/MRI_TRUS_Image_Synthesis_with_ONOFREY_Firstonline23September2016_GREEN_AAM.pdf","source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Onofrey , J A , Oksuz , I , Sarkar , S , Venkataraman , R , Staib , L H & Papademetris , X 2016 , ' MRI-TRUS image synthesis with application to image-guided prostate intervention ' , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pp. 157-166 . https://doi.org/10.1007/978-3-319-46630-9_16","raw_type":"article"},"sustainable_development_goals":[{"score":0.6600000262260437,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2521446193.pdf","grobid_xml":"https://content.openalex.org/works/W2521446193.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W95913851","https://openalex.org/W1491111105","https://openalex.org/W1604152368","https://openalex.org/W1794421049","https://openalex.org/W1975467353","https://openalex.org/W1979775925","https://openalex.org/W1988900798","https://openalex.org/W2008437871","https://openalex.org/W2021063134","https://openalex.org/W2060847096","https://openalex.org/W2072513344","https://openalex.org/W2086538808","https://openalex.org/W2113576511","https://openalex.org/W2124539070","https://openalex.org/W2143620145","https://openalex.org/W2160547390","https://openalex.org/W2171490473","https://openalex.org/W4246096830"],"related_works":["https://openalex.org/W2351153092","https://openalex.org/W3021493803","https://openalex.org/W2257755506","https://openalex.org/W2011111248","https://openalex.org/W2462603952","https://openalex.org/W4256129901","https://openalex.org/W2471562626","https://openalex.org/W2461863667","https://openalex.org/W2295804706","https://openalex.org/W3168515805"],"abstract_inverted_index":{"Accurate":[0],"and":[1,45,87],"robust":[2],"fusion":[3,30],"of":[4,55,113,125,160],"pre-procedure":[5],"magnetic":[6],"resonance":[7],"imaging":[8,15],"(MRI)":[9],"to":[10,64,80,99,120,139],"intra-procedure":[11],"trans-rectal":[12],"ultrasound":[13],"(TRUS)":[14],"is":[16,77],"necessary":[17],"for":[18,28,68],"image-guided":[19],"prostate":[20,39,58],"cancer":[21],"biopsy":[22],"procedures.":[23],"The":[24],"current":[25],"clinical":[26,116],"standard":[27],"image":[29,69,97,118,145],"relies":[31],"on":[32],"non-rigid":[33],"surface-based":[34,48],"registration":[35,49,76,106],"between":[36,85],"semi-automatically":[37],"segmented":[38],"surfaces":[40],"in":[41],"both":[42],"the":[43,62,141],"MRI":[44,86],"TRUS.":[46,88],"This":[47],"method":[50],"does":[51],"not":[52],"take":[53],"advantage":[54],"internal":[56],"anatomical":[57],"structures,":[59],"which":[60,148],"have":[61],"potential":[63],"provide":[65],"useful":[66],"information":[67],"registration.":[70,155],"However,":[71],"non-rigid,":[72],"multi-modal":[73],"intensity-based":[74,154],"MRI-TRUS":[75,117],"challenging":[78],"due":[79],"highly":[81],"non-linear":[82],"intensities":[83],"relationships":[84],"In":[89],"this":[90,101,134,161],"paper,":[91],"we":[92,132,149],"present":[93,157],"preliminary":[94,158],"work":[95],"using":[96,109],"synthesis":[98],"cast":[100],"problem":[102],"into":[103],"a":[104,110,122],"mono-modal":[105,153],"task":[107],"by":[108],"large":[111],"database":[112],"over":[114],"100":[115],"pairs":[119],"learn":[121],"joint":[123,136],"model":[124,138],"MR-TRUS":[126],"appearance.":[127],"Thus,":[128],"given":[129],"an":[130],"MRI,":[131],"use":[133],"learned":[135],"appearance":[137,146],"synthesize":[140],"patient\u2019s":[142],"corresponding":[143],"TRUS":[144],"with":[147],"could":[150],"potentially":[151],"perform":[152],"We":[156],"results":[159],"approach.":[162]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}