MODEL-BASED MR PARAMETER MAPPING WITH SPARSITY CONSTRAINT.
MR parameter mapping (e.g.,T1mapping,T2mapping, or [Formula: see text] 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, which utilizes an explicit signal model and imposes a sparsity constraint on the parameter values. The proposed method enables direct estimation of the parameters of interest from highly undersampled, noisyk-space data. An algorithm is presented to solve the underlying parameter estimation problem. Its performance is analyzed using estimation-theoretic bounds. Some representative results fromT2brain mapping are also presented to illustrate the performance of the proposed method for accelerating parameter mapping.
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Bo
B
Zhao
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Fan
F
Lam
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Wenmiao
W
Lu
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Zhi-Pei
ZP
Liang
model-based reconstruction; parameter estimation; parameter mapping; sparsity