A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry

NeuroImage

Volumn 56, Issue 3, 2011, Pages 1181-1201

  Goh, Alvina ^a,d   Lenglet, Christophe ^b   Thompson, Paul M ^c   Vidal, René ^d  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b UNIVERSITY OF MINNESOTA MEDICAL SCHOOL   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d Johns Hopkins University   (United States)

Author keywords

Diffusion weighted MRI; High angular resolution diffusion imaging

Indexed keywords

ARTICLE; AVERAGING; BRAIN ASYMMETRY; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; HIGH ANGULAR RESOLUTION DIFFUSION IMAGING; IMAGE PROCESSING; NEUROIMAGING; NONPARAMETRIC TEST; PHANTOM; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SIGNAL DETECTION; SYSTEM ANALYSIS;

ALGORITHMS; ANIMALS; ANISOTROPY; BRAIN; DATA INTERPRETATION, STATISTICAL; DIFFUSION MAGNETIC RESONANCE IMAGING; FUNCTIONAL LATERALITY; HUMANS; IMAGE ENHANCEMENT; IMAGE PROCESSING, COMPUTER-ASSISTED; LINEAR MODELS; MODELS, STATISTICAL; NERVE FIBERS; PHANTOMS, IMAGING; POPULATION; PRINCIPAL COMPONENT ANALYSIS; RATS; RATS, SPRAGUE-DAWLEY;

EID: 79955474992     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.01.053     Document Type: Article

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