A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion

IEEE Transactions on Medical Imaging

Volumn 30, Issue 5, 2011, Pages 1076-1089

  Lee, Kangjoo ^a   Tak, Sungho ^a   Ye, Jong Chul ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Compressed sensing; data driven functional magnetic resonance imaging (fMRI) analysis; K SVD; minimum description length (MDL) principle; sparse dictionary learning; sparse generalized linear model; statistical parametric mapping

Indexed keywords

COMPRESSED SENSING; DATA-DRIVEN; GENERALIZED LINEAR MODEL; K-SVD; MINIMUM DESCRIPTION LENGTH (MDL) PRINCIPLE; SPARSE DICTIONARY LEARNING; STATISTICAL PARAMETRIC MAPPING;

BRAIN; ELECTROPHYSIOLOGY; EXPERIMENTS; INFORMATION THEORY; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; MEDICAL IMAGING; MULTIVARIANT ANALYSIS; OPTIMIZATION; RESONANCE; SIGNAL RECONSTRUCTION;

INDEPENDENT COMPONENT ANALYSIS;

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BRAIN; EVOKED AUDITORY RESPONSE; EVOKED MUSCLE RESPONSE; HISTOLOGY; HUMAN; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING; STATISTICAL MODEL;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; EVOKED POTENTIALS, AUDITORY; EVOKED POTENTIALS, MOTOR; HUMANS; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 79955571265     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2097275     Document Type: Article

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