# Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications

arXiv: https://arxiv.org/abs/2103.06393 · HTML: https://mlsanity.com/paper/2103.06393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images](https://mlsanity.com/paper/2604.16947/) (2026)

## Abstract (arXiv)

In this work, we propose a method for the compression of the coupling matrix in volumehyp 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 matrixhyp 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 matrixhyp 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.