Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications
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2103.06393.pdf
Description
Accepted version
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702.99 KB
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Author(s)
Giannakopoulos, Ilias I
Guryev, Georgy D
Serralles, Jose EC
Georgakis, Ioannis P
Daniel, Luca
White, Jacob K
Lattanzi, Riccardo
Date Issued
2022
Journal
IEEE Transactions on Antennas and Propagation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Giannakopoulos, Ilias I, Guryev, Georgy D, Serralles, Jose EC, Georgakis, Ioannis P, Daniel, Luca et al. 2022. "Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications." IEEE Transactions on Antennas and Propagation, 70 (1).
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Author's final manuscript
Abstract
In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body. We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm3 voxel resolution, could be performed in ~ 33 seconds in a GPU, after compressing the associated coupling matrix from ~ 80 TB to ~ 43 MB.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1109/TAP.2021.3090835
