Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning

IEEE Transactions on Medical Imaging

Volumn 27, Issue 9, 2008, Pages 1356-1369

  Peng, Jyh Ying ^a,b   Aston, John A D ^a,c   Gunn, Roger N ^d,e   Liou, Cheng Yuan ^b   Ashburner, John ^f  

^a INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c UNIVERSITY OF WARWICK   (United Kingdom)

^d GLAXOSMITHKLINE   (United Kingdom)

^e UNIVERSITY OF OXFORD   (United Kingdom)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Basis pursuit; Compartmental models; DEPICT; Nonnegative least squares; Time course analysis

Indexed keywords

BAYESIAN NETWORKS; COMPUTER NETWORKS; DIAGNOSTIC RADIOGRAPHY; DYNAMIC ANALYSIS; MEDICAL IMAGING; PARAMETER ESTIMATION; POSITRONS; TOMOGRAPHY;

BASIS PURSUIT; COMPARTMENTAL MODELS; DEPICT; NONNEGATIVE LEAST SQUARES; TIME COURSE ANALYSIS;

POSITRON EMISSION TOMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BRAIN; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; POSITRON EMISSION TOMOGRAPHY; REPRODUCIBILITY; SCINTISCANNING; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; POSITRON-EMISSION TOMOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 50549097376     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2008.922185     Document Type: Article

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