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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| Published in: | IEEE transactions on medical imaging Vol. 29; no. 6; pp. 1297 - 1309 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.06.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0278-0062, 1558-254X, 1558-254X |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Eslami, Ramin Jacob, Mathews |
| Author_xml | – sequence: 1 givenname: Ramin surname: Eslami fullname: Eslami, Ramin email: reslami@ieee.org organization: Dept. of Biomed. Eng., Univ. of Rochester, Rochester, NY, USA – sequence: 2 givenname: Mathews surname: Jacob fullname: Jacob, Mathews email: mathews.jacob@rochester.edu organization: Dept. of Biomed. Eng., Univ. of Rochester, Rochester, NY, USA |
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| SubjectTerms | 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 |
| Title | Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Resolution MRI Priors |
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