{"id":"https://openalex.org/W4410295758","doi":"https://doi.org/10.1109/isbi60581.2025.10980772","title":"Towards Encoding 3D Abdominal MRI Acquisitions as Neural Fields","display_name":"Towards Encoding 3D Abdominal MRI Acquisitions as Neural Fields","publication_year":2025,"publication_date":"2025-04-14","ids":{"openalex":"https://openalex.org/W4410295758","doi":"https://doi.org/10.1109/isbi60581.2025.10980772"},"language":"en","primary_location":{"id":"doi:10.1109/isbi60581.2025.10980772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd 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/A5005061365","display_name":"Nicolas Basty","orcid":"https://orcid.org/0000-0002-1330-0913"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nicolas Basty","raw_affiliation_strings":["Orcino Health Ltd,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Orcino Health Ltd,London,United Kingdom","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gilles Rainer","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gilles Rainer","raw_affiliation_strings":["Imperial College London,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Imperial College London,London,United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067497811","display_name":"E. Louise Thomas","orcid":"https://orcid.org/0000-0003-4235-4694"},"institutions":[{"id":"https://openalex.org/I94951947","display_name":"University of Westminster","ror":"https://ror.org/04ycpbx82","country_code":"GB","type":"education","lineage":["https://openalex.org/I94951947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"E. Louise Thomas","raw_affiliation_strings":["University of Westminster,Research Centre for Optimal Health,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Westminster,Research Centre for Optimal Health,London,United Kingdom","institution_ids":["https://openalex.org/I94951947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057380504","display_name":"Jimmy D. Bell","orcid":"https://orcid.org/0000-0003-3804-1281"},"institutions":[{"id":"https://openalex.org/I94951947","display_name":"University of Westminster","ror":"https://ror.org/04ycpbx82","country_code":"GB","type":"education","lineage":["https://openalex.org/I94951947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jimmy D. Bell","raw_affiliation_strings":["University of Westminster,Research Centre for Optimal Health,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Westminster,Research Centre for Optimal Health,London,United Kingdom","institution_ids":["https://openalex.org/I94951947"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014958681","display_name":"Brandon Whitcher","orcid":"https://orcid.org/0000-0002-6452-2399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brandon Whitcher","raw_affiliation_strings":["Orcino Health Ltd,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Orcino Health Ltd,London,United Kingdom","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5005061365"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14950358,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9754999876022339,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9754999876022339,"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":0.9717000126838684,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9634000062942505,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6692924499511719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5767030715942383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36895108222961426},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3432410955429077}],"concepts":[{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6692924499511719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5767030715942383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36895108222961426},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3432410955429077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi60581.2025.10980772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W3032464132","https://openalex.org/W3109585842","https://openalex.org/W3140413839","https://openalex.org/W4287756134","https://openalex.org/W4312401377","https://openalex.org/W4312749295","https://openalex.org/W4389221690","https://openalex.org/W4390190370","https://openalex.org/W6780338168"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Medical":[0],"imaging":[1,154],"data":[2,35],"is":[3,28,97],"typically":[4],"3D,":[5],"causing":[6],"scan":[7,26],"sizes":[8],"and":[9,51,79,128,142,148],"databases":[10,155],"to":[11,57,116,140],"grow":[12],"cubically":[13],"with":[14,72,145],"resolution,":[15],"unlike":[16],"the":[17,58,61,83,93,109,150],"quadratic":[18],"growth":[19],"in":[20,92,119,137,159],"standard":[21],"computer":[22],"vision":[23],"tasks.":[24],"Compressing":[25],"dimensionality":[27],"essential":[29],"for":[30,45,156],"deep":[31,157],"learning,":[32],"as":[33,76,125],"raw":[34],"often":[36],"exceeds":[37],"GPU":[38],"memory":[39],"limits.":[40],"Autoencoders":[41],"are":[42,55],"commonly":[43],"used":[44],"data-specific":[46],"non-linear":[47],"compression,":[48,144],"balancing":[49],"compactness":[50],"fidelity.":[52],"However,":[53],"they":[54],"limited":[56],"resolution":[59],"of":[60,101,122,135,152],"training":[62],"data.":[63],"Inspired":[64],"by":[65],"Neural":[66],"Fields,":[67],"we":[68],"propose":[69],"an":[70],"autoencoder":[71],"a":[73,98,120],"fully-connected":[74],"network":[75],"its":[77],"decoder,":[78],"train":[80],"it":[81],"on":[82],"UK":[84],"Biobank":[85],"abdominal":[86],"MRI":[87],"dataset.":[88],"Beyond":[89],"more":[90],"fidelity":[91],"reconstruction,":[94],"our":[95,114],"encoding":[96],"continuous":[99],"function":[100],"3D":[102,106],"coordinates":[103],"rather":[104],"than":[105],"rasters":[107],"like":[108],"original":[110],"data,":[111],"which":[112],"enables":[113,149],"architecture":[115],"be":[117],"utilized":[118],"variety":[121],"applications":[123],"such":[124,153],"super-resolution,":[126],"in-painting":[127],"extrapolation.":[129],"We":[130],"show":[131],"that":[132],"this":[133],"change":[134],"paradigm":[136],"representation":[138],"leads":[139],"higher":[141],"better":[143,146],"properties,":[147],"use":[151],"learning":[158],"their":[160],"compressed":[161],"state.":[162]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
