TY - JOUR
T1 - Improved model-based magnetic resonance spectroscopic imaging
AU - Jacob, Mathews
AU - Zhu, Xiaoping
AU - Ebel, Andreas
AU - Schuff, Norbert
AU - Liang, Zhi Pei
N1 - Manuscript received February 14, 2007; revised March 26, 2007. This work was supported by the Beckman Foundation. Asterisk indicates corresponding author. *M. Jacob is with the Biomedical Engineering Department, University of Rochester, Rochester, NY 14622 USA. X. Zhu, A. Ebel, and N. Schuff are with the Veterans Association Medical Center, University of California, San Fransisco, CA 94121 USA. Z.-P. Liang is with the Beckman Institute, University of Illinois, Urbana–Champaign, IL 61820 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2007.898583
PY - 2007/10
Y1 - 2007/10
N2 - Model-based techniques have the potential to reduce the artifacts and improve resolution in magnetic resonance spectroscopic imaging, without sacrificing the signal-to-noise ratio. However, the current approaches have a few drawbacks that limit their performance in practical applications. Specifically, the classical schemes use less flexible image models that lead to model misfit, thus resulting in artifacts. Moreover, the performance of the current approaches is negatively affected by the magnetic field inhomogeneity and spatial mismatch between the anatomical references and spectroscopic imaging data. In this paper, we propose efficient solutions to overcome these problems. We introduce a more flexible image model that represents the signal as a linear combination of compartmental and local basis functions. The former set represents the signal variations within the compartments, while the latter captures the local perturbations resulting from lesions or segmentation errors. Since the combined set is redundant, we obtain the reconstructions using sparsity penalized optimization. To compensate for the artifacts resulting from field inhomogeneity, we estimate the field map using alternate scans and use it in the reconstruction. We model the spatial mismatch as an affine transformation, whose parameters are estimated from the spectroscopy data.
AB - Model-based techniques have the potential to reduce the artifacts and improve resolution in magnetic resonance spectroscopic imaging, without sacrificing the signal-to-noise ratio. However, the current approaches have a few drawbacks that limit their performance in practical applications. Specifically, the classical schemes use less flexible image models that lead to model misfit, thus resulting in artifacts. Moreover, the performance of the current approaches is negatively affected by the magnetic field inhomogeneity and spatial mismatch between the anatomical references and spectroscopic imaging data. In this paper, we propose efficient solutions to overcome these problems. We introduce a more flexible image model that represents the signal as a linear combination of compartmental and local basis functions. The former set represents the signal variations within the compartments, while the latter captures the local perturbations resulting from lesions or segmentation errors. Since the combined set is redundant, we obtain the reconstructions using sparsity penalized optimization. To compensate for the artifacts resulting from field inhomogeneity, we estimate the field map using alternate scans and use it in the reconstruction. We model the spatial mismatch as an affine transformation, whose parameters are estimated from the spectroscopy data.
KW - Constrained reconstruction
KW - Inhomogeneity compensation
KW - Prior information
KW - Spectroscopic imaging
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U2 - 10.1109/TMI.2007.898583
DO - 10.1109/TMI.2007.898583
M3 - Article
C2 - 17948722
AN - SCOPUS:34948849170
SN - 0278-0062
VL - 26
SP - 1305
EP - 1318
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 10
ER -