Computational methods for the estimation of ideal current patterns in realistic human models

Magn Reson Med. 2024 Feb;91(2):760-772. doi: 10.1002/mrm.29864. Epub 2023 Oct 6.


Purpose: To introduce a method for the estimation of the ideal current patterns (ICP) that yield optimal signal-to-noise ratio (SNR) for realistic heterogeneous tissue models in MRI.

Theory and methods: The ICP were calculated for different surfaces that resembled typical radiofrequency (RF) coil formers. We constructed numerical electromagnetic (EM) bases to accurately represent EM fields generated by RF current sources located on the current-bearing surfaces. Using these fields as excitations, we solved the volume integral equation and computed the EM fields in the sample. The fields were appropriately weighted to calculate the optimal SNR and the corresponding ICP. We demonstrated how to qualitatively use ICP to guide the design of a coil array to maximize SNR inside a head model.

Results: In agreement with previous analytic work, ICP formed large distributed loops for voxels in the middle of the sample and alternated between a single loop and a figure-eight shape for a voxel 3-cm deep in the sample's cortex. For the latter voxel, a surface quadrature loop array inspired by the shape of the ICP reached 87 . 5 % $$ 87.5\% $$ of the optimal SNR at 3T, whereas a single loop placed above the voxel reached only 55 . 7 % $$ 55.7\% $$ of the optimal SNR. At 7T, the performance of the two designs decreased to 79 . 7 % $$ 79.7\% $$ and 49 . 8 % $$ 49.8\% $$ , respectively, suggesting that loops could be suboptimal at ultra-high field MRI.

Conclusion: ICP can be calculated for human tissue models, potentially guiding the design of application-specific RF coil arrays.

Keywords: MRI; ideal current patterns; integral equation methods; radiofrequency coils; ultimate intrinsic signal-to-noise ratio.

MeSH terms

  • Electromagnetic Fields*
  • Equipment Design
  • Humans
  • Magnetic Resonance Imaging* / methods
  • Phantoms, Imaging
  • Radio Waves
  • Signal-To-Noise Ratio