Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7510 LNCS, Issue , 2012, Pages 272-280

  Fang, Ruogu ^a   Chen, Tsuhan ^a   Sanelli, Pina C ^b  

^a School of Operations Research and Industrial Engineering   (United States)

^b WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD; COMPUTERIZED TOMOGRAPHY; HEMODYNAMICS; MEDICAL IMAGING;

CEREBRAL BLOOD FLOW; CEREBROVASCULAR DISEASE; CONTRAST ENHANCEMENT; DECONVOLUTION METHOD; FUNCTIONAL IMAGING; HEMODYNAMIC PARAMETERS; LEARNED DICTIONARIES; OVER-COMPLETE DICTIONARIES;

MEDICAL COMPUTING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BLOOD FLOW; BRAIN; BRAIN CIRCULATION; CEREBROVASCULAR ACCIDENT; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER PROGRAM; DIAGNOSTIC IMAGING; HUMAN; ISCHEMIA; METHODOLOGY; OSCILLOMETRY; PATHOLOGY; PERFUSION; PHYSIOLOGY; RADIOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; CEREBROVASCULAR CIRCULATION; DIAGNOSTIC IMAGING; HUMANS; ISCHEMIA; OSCILLOMETRY; PERFUSION; RADIOLOGY; REGIONAL BLOOD FLOW; SOFTWARE; STROKE; TOMOGRAPHY, X-RAY COMPUTED;

MLCS; MLOWN;

EID: 84872553999     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33415-3_34     Document Type: Conference Paper

References (11)

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