Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging

IEEE Transactions on Medical Imaging

Volumn 23, Issue 2, 2004, Pages 137-152

  Beckmann, Christian F ^a   Smith, Stephen M ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Factor analysis; Functional magnetic resonance imaging; Independent component analysis; Linear structural model; Mixture modeling

Indexed keywords

BLIND SOURCE SEPARATION; GAUSSIAN NOISE (ELECTRONIC); INDEPENDENT COMPONENT ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATION; MEDICAL IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING;

FACTOR ANALYSIS; FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI); LINEAR STRUCTURAL MODELS; MIXTURE MODELING;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BRAIN; BRAIN CORTEX; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; IMAGE ENHANCEMENT; IMAGE QUALITY; METHODOLOGY; NERVE CELL; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; VALIDATION STUDY; VISION;

ALGORITHMS; BRAIN; CEREBRAL CORTEX; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NEURONS; PHANTOMS, IMAGING; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; VISION;

EID: 1342324773     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2003.822821     Document Type: Article

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