{"id":"https://openalex.org/W2792083339","doi":"https://doi.org/10.1117/12.2293749","title":"Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI","display_name":"Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2792083339","doi":"https://doi.org/10.1117/12.2293749","mag":"2792083339"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293749","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","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/A5020253934","display_name":"Marijn van Stralen","orcid":"https://orcid.org/0000-0002-3051-5000"},"institutions":[{"id":"https://openalex.org/I3018483916","display_name":"University Medical Center Utrecht","ror":"https://ror.org/0575yy874","country_code":"NL","type":"funder","lineage":["https://openalex.org/I3018483916"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Marijn van Stralen","raw_affiliation_strings":["MRIguidance B.V. (Netherlands)","Univ. Medical Ctr. Utrecht (Netherlands)"],"affiliations":[{"raw_affiliation_string":"MRIguidance B.V. (Netherlands)","institution_ids":[]},{"raw_affiliation_string":"Univ. Medical Ctr. Utrecht (Netherlands)","institution_ids":["https://openalex.org/I3018483916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091823743","display_name":"Phillip Wozny","orcid":null},"institutions":[{"id":"https://openalex.org/I193662353","display_name":"Utrecht University","ror":"https://ror.org/04pp8hn57","country_code":"NL","type":"education","lineage":["https://openalex.org/I193662353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Phillip J. Wozny","raw_affiliation_strings":["Utrecht Univ. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Utrecht Univ. (Netherlands)","institution_ids":["https://openalex.org/I193662353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103245857","display_name":"Yuan Zhou","orcid":"https://orcid.org/0000-0002-6489-0917"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yuan Zhou","raw_affiliation_strings":["Delft Univ. of Technology (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Delft Univ. of Technology (Netherlands)","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113409801","display_name":"Peter R. Seevinck","orcid":"https://orcid.org/0000-0002-6150-8575"},"institutions":[{"id":"https://openalex.org/I3018483916","display_name":"University Medical Center Utrecht","ror":"https://ror.org/0575yy874","country_code":"NL","type":"funder","lineage":["https://openalex.org/I3018483916"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter R. Seevinck","raw_affiliation_strings":["MRIguidance B.V. (Netherlands)","Univ. Medical Ctr. Utrecht (Netherlands)"],"affiliations":[{"raw_affiliation_string":"MRIguidance B.V. (Netherlands)","institution_ids":[]},{"raw_affiliation_string":"Univ. Medical Ctr. Utrecht (Netherlands)","institution_ids":["https://openalex.org/I3018483916"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086060106","display_name":"Marco Loog","orcid":"https://orcid.org/0000-0002-1298-8461"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marco Loog","raw_affiliation_strings":["Delft Univ. of Technology (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Delft Univ. of Technology (Netherlands)","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020253934"],"corresponding_institution_ids":["https://openalex.org/I3018483916"],"apc_list":null,"apc_paid":null,"fwci":1.0609,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77103177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"1612","issue":null,"first_page":"58","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9994999766349792,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9950000047683716,"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":0.6661816835403442},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6626683473587036},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6101969480514526},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6011781692504883},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5585668683052063},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5537298917770386},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5152640342712402},{"id":"https://openalex.org/keywords/peak-signal-to-noise-ratio","display_name":"Peak signal-to-noise ratio","score":0.4390893578529358},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43002742528915405},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2421354353427887}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6661816835403442},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6626683473587036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6101969480514526},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6011781692504883},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5585668683052063},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5537298917770386},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5152640342712402},{"id":"https://openalex.org/C154579607","wikidata":"https://www.wikidata.org/wiki/Q3373850","display_name":"Peak signal-to-noise ratio","level":3,"score":0.4390893578529358},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43002742528915405},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2421354353427887},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293749","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","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/W2021177063","https://openalex.org/W2035397698","https://openalex.org/W2044967973","https://openalex.org/W2057743740","https://openalex.org/W2080858163","https://openalex.org/W2133287637","https://openalex.org/W2133665775","https://openalex.org/W2140351287","https://openalex.org/W2208340121","https://openalex.org/W2523468284","https://openalex.org/W2567079332","https://openalex.org/W6727576312","https://openalex.org/W6731670082"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W2113666009","https://openalex.org/W2145649715","https://openalex.org/W2183753013","https://openalex.org/W2405106204","https://openalex.org/W2133102185"],"abstract_inverted_index":{"Predicting":[0],"pseudo":[1,82],"CT":[2,27,83,97,102],"images":[3,84],"from":[4,85],"MRI":[5,90,108],"data":[6,91,98,103],"has":[7],"received":[8],"increasing":[9],"attention":[10],"for":[11,25,42,68,79,109,173],"use":[12,63],"in":[13,146,158,236,273],"MRI-only":[14],"radiation":[15],"therapy":[16],"planning":[17],"and":[18,38,44,92,111,117,125,128,130,139,169,182,209,224],"PET-MRI":[19],"attenuation":[20],"correction,":[21],"eliminating":[22],"the":[23,62,140,147,150,176,197,200,214,243,263],"need":[24],"harmful":[26],"scanning.":[28],"Current":[29],"approaches":[30],"focus":[31],"on":[32,187,210,221],"voxelwise":[33,115,247],"mean":[34],"absolute":[35],"error":[36],"(MAE)":[37],"peak":[39],"signal-to-noise-ratio":[40],"(PSNR)":[41],"optimization":[43,206,231],"evaluation.":[45,112],"Contextual":[46,251],"losses":[47,67,272],"such":[48],"as":[49],"structural":[50],"similarity":[51,152],"(SSIM)":[52],"are":[53,184],"known":[54],"to":[55,94,107],"promote":[56],"perceptual":[57,239],"image":[58,240],"quality.":[59],"We":[60,113],"investigate":[61],"of":[64,81,99,142,175,178,196,199,245,258,262,270],"these":[65],"contextual":[66,122,271],"optimization.":[69],"Patch-based":[70],"3D":[71,86],"fully":[72],"convolutional":[73],"neural":[74],"networks":[75],"(FCN)":[76],"were":[77],"optimized":[78,202],"prediction":[80,203,256],"gradient":[87],"echo":[88],"pelvic":[89],"compared":[93,114],"ground":[95],"truth":[96],"26":[100],"patients.":[101],"was":[104,133,155],"non-rigidly":[105],"registered":[106],"training":[110],"L1":[116,124,201],"L2":[118,126],"loss":[119,252],"functions,":[120],"with":[121,165,189],"multi-scale":[123],"(MSL1":[127],"MSL2),":[129],"SSIM.":[131],"Performance":[132],"evaluated":[134],"using":[135,232],"MAE,":[136,222],"PSNR,":[137],"SSIM":[138,179,225,233],"overlap":[141],"segmented":[143],"cortical":[144],"bone":[145],"reconstructions,":[148],"by":[149],"dice":[151],"metric.":[153],"Evaluation":[154],"carried":[156],"out":[157],"cross-validation.":[159],"All":[160],"optimizations":[161],"successfully":[162],"converged":[163],"well":[164],"PSNR":[166,223],"between":[167],"25":[168],"30":[170],"HU,":[171],"except":[172],"one":[174],"folds":[177],"optimizations.":[180],"MSL1":[181,193],"MSL2":[183,205],"at":[185,242],"least":[186],"par":[188],"their":[190],"single-scale":[191],"counterparts.":[192],"overcomes":[194],"some":[195],"instabilities":[198],"models.":[204],"is":[207],"stable,":[208],"average,":[211],"outperforms":[212],"all":[213],"other":[215],"losses,":[216],"although":[217],"quantitative":[218,248],"evaluations":[219],"based":[220],"only":[226],"show":[227],"minor":[228],"differences.":[229],"Direct":[230],"visually":[234],"excelled":[235],"terms":[237],"subjective":[238,268],"quality":[241],"expense":[244],"a":[246],"performance":[249,257],"drop.":[250],"functions":[253],"can":[254],"improve":[255],"FCNs":[259],"without":[260],"change":[261],"network":[264],"architecture.":[265],"The":[266],"suggested":[267],"superiority":[269],"reconstructing":[274],"local":[275],"structures":[276],"merits":[277],"further":[278],"investigations.":[279]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}