{"id":"https://openalex.org/W7165891160","doi":"https://doi.org/10.48550/arxiv.2606.25545","title":"TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs","display_name":"TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7165891160","doi":"https://doi.org/10.48550/arxiv.2606.25545"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25545","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25545","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":null,"license_id":null,"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.2606.25545","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139340682","display_name":"Junhyeok Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Junhyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006749140","display_name":"Kyu Sung Choi","orcid":"https://orcid.org/0000-0002-5175-3307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Kyu Sung","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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.996399998664856,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.996399998664856,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"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/diffusion-mri","display_name":"Diffusion MRI","score":0.7437999844551086},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6491000056266785},{"id":"https://openalex.org/keywords/tractography","display_name":"Tractography","score":0.6391000151634216},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5795000195503235},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5745000243186951},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4611999988555908},{"id":"https://openalex.org/keywords/geodesic","display_name":"Geodesic","score":0.4577000141143799},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.4244000017642975}],"concepts":[{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.7437999844551086},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6491000056266785},{"id":"https://openalex.org/C84787856","wikidata":"https://www.wikidata.org/wiki/Q3076659","display_name":"Tractography","level":4,"score":0.6391000151634216},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5795000195503235},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5745000243186951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5052000284194946},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C165818556","wikidata":"https://www.wikidata.org/wiki/Q213488","display_name":"Geodesic","level":2,"score":0.4577000141143799},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4569999873638153},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41920000314712524},{"id":"https://openalex.org/C8466233","wikidata":"https://www.wikidata.org/wiki/Q757269","display_name":"Metric tensor","level":3,"score":0.4122999906539917},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3817000091075897},{"id":"https://openalex.org/C45715564","wikidata":"https://www.wikidata.org/wiki/Q1292103","display_name":"Connectome","level":3,"score":0.36880001425743103},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.314300000667572},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C89916169","wikidata":"https://www.wikidata.org/wiki/Q17014600","display_name":"Fractional anisotropy","level":4,"score":0.29789999127388},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C99821215","wikidata":"https://www.wikidata.org/wiki/Q1136583","display_name":"Swap (finance)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.251800000667572},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25545","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25545","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.25545","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25545","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":null,"license_id":null,"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":{"Reconstructing":[0],"diffusion":[1,36,92],"tensors":[2,127],"from":[3],"sparse":[4,117],"DWIs":[5],"is":[6],"critical":[7],"for":[8],"accelerating":[9],"Diffusion":[10],"Tensor":[11],"Imaging":[12],"(DTI)":[13],"in":[14,87],"clinical":[15],"settings,":[16],"yet":[17],"current":[18],"deep":[19],"learning":[20],"approaches":[21],"frequently":[22],"yield":[23],"anatomically":[24],"inconsistent":[25],"or":[26,140],"physically":[27],"implausible":[28],"tensors.":[29],"We":[30],"introduce":[31],"TensorLDM,":[32],"a":[33,98,114],"component-wise":[34],"latent":[35,69],"model":[37],"that":[38,66],"processes":[39],"the":[40,68,107,121,138,150],"six":[41],"tensor":[42,73,146],"components":[43],"through":[44],"two":[45],"group-specific":[46],"encoders":[47],"(for":[48],"diagonal":[49],"and":[50,91,126],"off-diagonal":[51],"elements)":[52],"while":[53,97],"maintaining":[54],"anatomical":[55],"consistency":[56],"via":[57],"shared":[58,81],"DWI":[59,101],"conditioning.":[60,105],"TensorLDM":[61,119],"uses":[62],"an":[63],"Anatomy-Conditioned":[64],"Autoencoder":[65],"encourages":[67],"to":[70],"focus":[71],"on":[72],"properties":[74],"rather":[75],"than":[76],"re-encoding":[77],"structural":[78],"information.":[79],"A":[80],"Cross-Component":[82],"Attention":[83],"(CCA)":[84],"mechanism,":[85],"applied":[86],"both":[88],"autoencoder":[89],"refinement":[90],"fine-tuning,":[93],"models":[94],"inter-component":[95],"dependencies,":[96],"Mixture-of-Experts":[99],"(MoE)":[100],"conditioner":[102],"provides":[103],"component-adaptive":[104],"On":[106],"Human":[108],"Connectome":[109],"Project":[110],"(HCP)":[111],"dataset":[112],"under":[113],"single-shell,":[115],"four-volume":[116],"acquisition,":[118],"produces":[120],"most":[122],"accurate":[123],"downstream":[124],"tractography":[125],"with":[128,137],"near-ground-truth":[129],"physical":[130],"validity":[131],"(SPD-violation":[132],"rate":[133],"1.54%":[134],"vs.":[135],"1.40%),":[136],"best":[139],"comparable":[141],"voxel-wise":[142],"reconstruction":[143],"accuracy.":[144],"Geodesic":[145],"error":[147],"measured":[148],"by":[149],"Log-Euclidean":[151],"Metric":[152],"(LEM)":[153],"corroborates":[154],"these":[155],"gains.":[156]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-26T00:00:00"}
