Machine learning based compartment models with permeability for white matter microstructure imaging

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

Volumn 8675 LNCS, Issue PART 3, 2014, Pages 257-264

  Nedjati Gilani, Gemma L ^a   Schneider, Torben ^b   Hall, Matt G ^a   Wheeler Kingshott, Claudia A M ^b   Alexander, Daniel C ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN; LEARNING SYSTEMS; MATHEMATICAL MODELS; MONTE CARLO METHODS;

COMPARTMENT MODEL; COMPUTATIONAL MODEL; HUMAN CENTRAL NERVOUS SYSTEMS; MICRO-STRUCTURE PARAMETERS; MICROSTRUCTURE IMAGING; RESIDENCE TIME; SENSITIVE DATAS; WATER MOLECULE;

MICROSTRUCTURE;

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; CYTOLOGY; DIFFUSION TENSOR IMAGING; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; MYELINATED NERVE; PERMEABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; ULTRASTRUCTURE;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMPUTER SIMULATION; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; PERMEABILITY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84909633234     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10443-0_33     Document Type: Conference Paper

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