Paper Info
Title
Model-based MR parameter mapping with sparsity constraints: parameter estimation and performance bounds.
Abstract
Magnetic resonance parameter mapping (e.g., T1 mapping, T2 mapping, T*2 mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data acquisition time. This paper addresses this problem with a new model-based parameter mapping method. The proposed method utilizes a formulation that integrates the explicit signal model with sparsity constraints on the model parameters, enabling direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An efficient greedy-pursuit algorithm is described to solve the resulting constrained parameter estimation problem. Estimation-theoretic bounds are also derived to analyze the benefits of incorporating sparsity constraints and benchmark the performance of the proposed method. The theoretical properties and empirical performance of the proposed method are illustrated in a T2 mapping application example using computer simulations.
Year
DOI
Venue
2014
10.1109/TMI.2014.2322815
IEEE Trans. Med. Imaging
Keywords
Field
DocType
t2* mapping method,parameter mapping,quantitative magnetic resonance imaging,t2 mapping method,model-based reconstruction,magnetic resonance parameter mapping method,parameter estimation,model-based mr parameter mapping method,explicit signal model,greedy-pursuit algorithm,biomedical mri,cramér-rao bounds,greedy algorithms,tissue characterization,sparsity,biological tissues,t1 mapping method,sparsity constraints,medical image processing,magnetic resonance imaging,neuroimaging,algorithms,computer simulation
Mathematical optimization,Computer science,Data acquisition,Estimation theory
Journal
Volume
Issue
ISSN
33
9
1558-254X
Citations 
PageRank 
References 
13
0.75
29
Authors
3
Name
Order
Citations
PageRank
Bo Zhao1778.46
Fan Lam2509.14
Zhi-Pei Liang352264.94