Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Resolution MRI Priors

We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables...

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Veröffentlicht in:IEEE transactions on medical imaging Jg. 29; H. 6; S. 1297 - 1309
Hauptverfasser: Eslami, Ramin, Jacob, Mathews
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
B_{0} inhomogeneity compensation
Biopolymers - metabolism
Brain - anatomy & histology
Brain - metabolism
Data processing
ell _{1} -minimization
fat leakage
field map
High-resolution imaging
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image reconstruction
Image resolution
Integrated approach
Magnetic fields
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
magnetic resonance spectroscopic imaging (MRSI)
Magnetic Resonance Spectroscopy - methods
Pattern Recognition, Automated - methods
Polynomials
Reproducibility of Results
Robustness
Sensitivity and Specificity
sparsity
Spectroscopy
Studies
Subtraction Technique
total variation
ISSN:0278-0062, 1558-254X, 1558-254X
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Beschreibung
Zusammenfassung:We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2010.2046673