{"id":"https://openalex.org/W7166728860","doi":"https://doi.org/10.48550/arxiv.2606.28787","title":"BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT","display_name":"BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT","publication_year":2026,"publication_date":"2026-06-27","ids":{"openalex":"https://openalex.org/W7166728860","doi":"https://doi.org/10.48550/arxiv.2606.28787"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28787","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28787","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139443937","display_name":"Djahid Abdelmoumene","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdelmoumene, Djahid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058561202","display_name":"Ishak Ayad","orcid":"https://orcid.org/0000-0001-6658-422X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ayad, Ishak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139669271","display_name":"Ma\u00ef K. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Ma\u00ef K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069116151","display_name":"Christian Daveau","orcid":"https://orcid.org/0000-0001-8425-7329"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daveau, Christian","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":false,"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.009600000455975533,"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/T11205","display_name":"Numerical methods in inverse problems","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electrical-impedance-tomography","display_name":"Electrical impedance tomography","score":0.8291000127792358},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5493999719619751},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.49470001459121704},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4668000042438507},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.447299987077713},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.444599986076355},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.423799991607666},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3873000144958496},{"id":"https://openalex.org/keywords/stroke","display_name":"Stroke (engine)","score":0.3862000107765198}],"concepts":[{"id":"https://openalex.org/C155175808","wikidata":"https://www.wikidata.org/wiki/Q1326472","display_name":"Electrical impedance tomography","level":3,"score":0.8291000127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5957000255584717},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5376999974250793},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C2780645631","wikidata":"https://www.wikidata.org/wiki/Q671554","display_name":"Stroke (engine)","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.33640000224113464},{"id":"https://openalex.org/C17829176","wikidata":"https://www.wikidata.org/wiki/Q179043","display_name":"Electrical impedance","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C60591178","wikidata":"https://www.wikidata.org/wiki/Q488986","display_name":"Electrical resistivity tomography","level":3,"score":0.3140999972820282},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C86072612","wikidata":"https://www.wikidata.org/wiki/Q5160239","display_name":"Conformable matrix","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28787","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":"doi:10.48550/arxiv.2606.28787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28787","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-Frequency":[0],"Electrical":[1],"Impedance":[2],"Tomography":[3],"(MF-EIT)":[4],"is":[5,26],"a":[6,52,62,74,90,113],"non-invasive,":[7],"low-cost":[8],"modality":[9],"that":[10,65],"reconstructs":[11],"electrical":[12],"property":[13],"distributions":[14],"from":[15,115],"boundary":[16],"voltages.":[17],"For":[18],"stroke":[19,58],"imaging,":[20],"progress":[21],"in":[22],"3D":[23,56,77,91],"deep-learning":[24],"reconstruction":[25,59],"limited":[27],"by":[28,40,111],"the":[29],"lack":[30],"of":[31],"large-scale":[32],"datasets":[33],"with":[34,141],"paired":[35],"ground-truth":[36],"(GT)":[37],"volumes":[38],"and":[39,47,88,129,136],"non-standardized":[41],"pipelines":[42],"for":[43,55,84],"data":[44,117],"generation,":[45],"simulation,":[46],"evaluation.":[48],"We":[49,122],"introduce":[50],"BREIT,":[51,100],"modular":[53],"framework":[54],"MF-EIT":[57,86],"providing:":[60],"(i)":[61],"neuroimaging-to-EIT":[63],"pipeline":[64],"converts":[66],"CT/MRI":[67],"into":[68,109],"frequency-dependent":[69],"GT":[70],"admittivity":[71],"volumes;":[72],"(ii)":[73],"self-contained":[75],"Python":[76],"Complete":[78],"Electrode":[79],"Model":[80],"(CEM)":[81],"forward":[82],"solver":[83],"simulating":[85],"voltages;":[87],"(iii)":[89],"D-bar":[92,110],"implementation":[93],"supporting":[94],"non-uniform":[95],"electrode":[96],"layouts.":[97],"Building":[98],"on":[99,132],"we":[101],"propose":[102],"dFNO-bar,":[103],"which":[104],"integrates":[105],"Fourier":[106],"Neural":[107],"Operators":[108],"learning":[112],"mapping":[114],"scattering":[116],"$t(\u03be)$":[118],"to":[119],"conductivity":[120],"$\u03c3(x){=}\\Re\\{\u03b3\\}$.":[121],"evaluate":[123],"dFNO-bar":[124],"against":[125],"D-bar,":[126,128],"Deep":[127],"Gauss--Newton":[130],"reconstructions":[131],"UCLH-matched":[133],"synthetic":[134],"data,":[135],"observe":[137],"higher":[138],"brain":[139],"SSIM":[140],"comparable":[142],"CC":[143],"across":[144],"noise":[145],"settings.":[146]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-01T00:00:00"}
