Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: | https://hdl.handle.net/1721.1/143112 |
| _version_ | 1811070693108875264 |
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| author | Giannakopoulos, Ilias I Guryev, Georgy D Serralles, Jose EC Georgakis, Ioannis P Daniel, Luca White, Jacob K Lattanzi, Riccardo |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Giannakopoulos, Ilias I Guryev, Georgy D Serralles, Jose EC Georgakis, Ioannis P Daniel, Luca White, Jacob K Lattanzi, Riccardo |
| author_sort | Giannakopoulos, Ilias I |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:40:03Z |
| format | Article |
| id | mit-1721.1/143112 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:40:03Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1431122023-03-29T19:30:57Z Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications Giannakopoulos, Ilias I Guryev, Georgy D Serralles, Jose EC Georgakis, Ioannis P Daniel, Luca White, Jacob K Lattanzi, Riccardo Massachusetts Institute of Technology. Research Laboratory of Electronics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2022-06-13T19:18:23Z 2022-06-13T19:18:23Z 2022 2022-06-13T19:13:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/143112 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). en 10.1109/TAP.2021.3090835 IEEE Transactions on Antennas and Propagation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Giannakopoulos, Ilias I Guryev, Georgy D Serralles, Jose EC Georgakis, Ioannis P Daniel, Luca White, Jacob K Lattanzi, Riccardo Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title | Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title_full | Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title_fullStr | Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title_full_unstemmed | Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title_short | Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications |
| title_sort | compression of volume surface integral equation matrices via tucker decomposition for magnetic resonance applications |
| url | https://hdl.handle.net/1721.1/143112 |
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