Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge

IEEE Transactions on Medical Imaging

Volumn 28, Issue 10, 2009, Pages 1534-1547

  Kelm, Bernd Michael ^a   Menze, Bjoern H ^b   Nix, Oliver ^c   Zechmann, Christian M ^c   Hamprecht, Fred A ^d  

^a SIEMENS AG   (Germany)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^d Heidelberg Collaboratory for Image Processing   (Germany)

Author keywords

Block iterated conditional modes; Dynamic contrast enhanced imaging; Kinetic parameter maps; Markov random field; Nonlinear least squares

Indexed keywords

BIAS AND VARIANCE; BLOCK ITERATED CONDITIONAL MODES; COMPUTATION TIME; CONTRAST AGENT; DATA SETS; DCE-MRI; DYNAMIC CONTRAST; DYNAMIC CONTRAST ENHANCED MRI; DYNAMIC CONTRAST-ENHANCED IMAGING; EDGE PRESERVING; ESTIMATION BIAS; GAUSSIANS; GENERALIZED GAUSSIAN MARKOV RANDOM FIELDS; HIGHLY NONLINEAR; ICM ALGORITHMS; IN-VIVO; ITERATED CONDITIONAL MODES; LEAST SQUARES ESTIMATE; MARKOV RANDOM FIELD; MAXIMUM A POSTERIORI; MICROVASCULAR; MR IMAGES; NONLINEAR LEAST SQUARES; OBSERVED DATA; PARAMETER ESTIMATE; PHARMACOKINETIC MODEL; ROOT MEAN SQUARE ERRORS; SIMULATED DATA; SINGLE VOXEL; SPARSE SOLVERS; SPATIAL PRIORS; SPATIALLY RESOLVED; TIME CURVES;

ALGORITHMS; COMPUTATION THEORY; CURVE FITTING; EDGE DETECTION; HIDDEN MARKOV MODELS; IMAGE RESOLUTION; KINETIC PARAMETERS;

PARAMETER ESTIMATION;

ALGORITHM; ARTICLE; COMPUTER SIMULATION; HUMAN; KINETICS; MALE; METHODOLOGY; MICROVASCULATURE; MONTE CARLO METHOD; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROBABILITY; PROSTATE; REGRESSION ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; KINETICS; LEAST-SQUARES ANALYSIS; MAGNETIC RESONANCE IMAGING; MALE; MARKOV CHAINS; MICROVESSELS; MODELS, STATISTICAL; MONTE CARLO METHOD; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; PROSTATE;

EID: 70349860091     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2009.2019957     Document Type: Article

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