Super-resolution in diffusion-weighted imaging

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

Volumn 6892 LNCS, Issue PART 2, 2011, Pages 124-132

  Scherrer, Benoit ^a   Gholipour, Ali ^a   Warfield, Simon K ^a  

^a BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

Diffusion imaging; orthogonal acquisitions; super resolution

Indexed keywords

ACQUISITION MODELS; DIFFUSION IMAGING; DIFFUSION WEIGHTED IMAGING; HIGH RESOLUTION IMAGE; MAXIMUM A POSTERIORI; NON-INVASIVE; OPTIMIZATION PROCEDURES; PATIENT MOTIONS; Q-SPACE; SPATIAL HOMOGENEITY; SUPER RESOLUTION; SUPER-RESOLUTION RECONSTRUCTION;

DIFFUSION; MEDICAL COMPUTING; OPTICAL RESOLVING POWER;

MEDICAL IMAGING;

ALGORITHM; ANISOTROPY; ARTICLE; BRAIN; BRAIN MAPPING; COMPUTER LANGUAGE; COMPUTER PROGRAM; COMPUTER SIMULATION; DIFFUSION; DIFFUSION WEIGHTED IMAGING; HUMAN; IMAGE PROCESSING; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ANISOTROPY; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; DIFFUSION; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; MODELS, THEORETICAL; PROGRAMMING LANGUAGES; SOFTWARE;

EID: 82255167057     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23629-7_16     Document Type: Conference Paper

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