TY - JOUR
T1 - Model-Based Chemical Exchange Saturation Transfer MRI for Robust z-Spectrum Analysis
AU - Lee, Hoonjae
AU - Chung, Julius Juhyun
AU - Lee, Joonyeol
AU - Kim, Seong Gi
AU - Han, Jae Ho
AU - Park, Jaeseok
N1 - Funding Information:
Manuscript received December 19, 2018; revised January 27, 2019; accepted February 3, 2019. Date of publication February 12, 2019; date of current version January 31, 2020. This work was supported in part by the National Research Foundation of Korea under Grant IBS-R015-D1, Grant NRF-2016M3C7A1913844, Grant NRF-2017R1A2B4012581, and Grant NRF-2018M3C7A1056887. (Corresponding author: Jaeseok Park.) H. Lee is with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea, and also with the Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea.
Publisher Copyright:
© 1982-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - This paper introduces a novel, model-based chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), in which asymmetric spectra of interest are directly estimated from complete or incomplete measurements by incorporating subspace-based spectral signal decomposition into the measurement model of CEST MRI for a robust z-spectrum analysis. Spectral signals are decomposed into symmetric and asymmetric components. The symmetric component, which varies smoothly, is delineated by the linear superposition of a finite set of vectors in a basis trained from the simulated (Lorentzian) signal vectors augmented with data-driven signal vectors, while the asymmetric component is to be inherently lower than or equal to zero due to saturation transfer phenomena. Spectral decomposition is performed directly on the measured spectral data by solving a constrained optimization problem that employs the linearized spectral decomposition model for the symmetric component and the weighted Frobenius norm regularization for the asymmetric component while utilizing additional spatial sparsity and low-rank priors. The simulations and in vivo experiments were performed to demonstrate the feasibility of the proposed method as a reliable molecular MRI.
AB - This paper introduces a novel, model-based chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), in which asymmetric spectra of interest are directly estimated from complete or incomplete measurements by incorporating subspace-based spectral signal decomposition into the measurement model of CEST MRI for a robust z-spectrum analysis. Spectral signals are decomposed into symmetric and asymmetric components. The symmetric component, which varies smoothly, is delineated by the linear superposition of a finite set of vectors in a basis trained from the simulated (Lorentzian) signal vectors augmented with data-driven signal vectors, while the asymmetric component is to be inherently lower than or equal to zero due to saturation transfer phenomena. Spectral decomposition is performed directly on the measured spectral data by solving a constrained optimization problem that employs the linearized spectral decomposition model for the symmetric component and the weighted Frobenius norm regularization for the asymmetric component while utilizing additional spatial sparsity and low-rank priors. The simulations and in vivo experiments were performed to demonstrate the feasibility of the proposed method as a reliable molecular MRI.
KW - Magnetic resonance imaging
KW - chemical exchange saturation transfer
KW - compressed sensing
KW - fast imaging
KW - z-spectrum
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U2 - 10.1109/TMI.2019.2898672
DO - 10.1109/TMI.2019.2898672
M3 - Article
C2 - 30762539
AN - SCOPUS:85078865511
VL - 39
SP - 283
EP - 293
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 2
M1 - 8640851
ER -