Multi-diffusion-tensor fitting via spherical deconvolution: A unifying framework

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

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 674-681

  Schultz, Thomas ^a   Westin, Carl Fredrik ^b   Kindlmann, Gordon ^a  

^a THE UNIVERSITY OF CHICAGO   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION MAGNETIC RESONANCE IMAGING; DIFFUSION TENSOR; FIBER CROSSINGS; MODEL FITTING; ORIENTATION DISTRIBUTION FUNCTION; PARTIAL VOLUMING; PRACTICAL USE; SINGLE DIFFUSION; SPHERICAL DECONVOLUTION; TENSOR MODEL;

DIFFUSION; DISTRIBUTION FUNCTIONS; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; RESONANCE;

TENSORS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION TENSOR IMAGING; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; NERVE FIBER; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; AXONS; BRAIN; DATA INTERPRETATION, STATISTICAL; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, NEUROLOGICAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STATISTICAL DISTRIBUTIONS;

EID: 84881135038     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_82     Document Type: Conference Paper

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