In silico evaluation and optimisation of magnetic resonance elastography of the liver

McGrath, Deirdre M.; Bradley, Christopher R.; Francis, Susan T.

Abstract

Objective. Magnetic resonance elastography (MRE) is widely adopted as a biomarker of liver fibrosis. However, in vivo MRE accuracy is difficult to assess. Approach. Finite element model (FEM) simulation was employed to evaluate liver MRE accuracy and inform methodological optimisation. MRE data was simulated in a 3D FEM of the human torso including the liver, and compared with spin-echo echo-planar imaging MRE acquisitions. The simulated MRE results were compared with the ground truth magnitude of the complex shear modulus (|G∗|) for varying: (1) ground truth liver |G∗|; (2) simulated imaging resolution; (3) added noise; (4) data smoothing. Motion and strain-based signal-to-noise (SNR) metrics were evaluated on the simulated data as a means to select higher-quality voxels for preparation of acquired MRE summary statistics of |G∗|. Main results. The simulated MRE accuracy for a given ground truth |G∗| was found to be a function of imaging resolution, motion-SNR and smoothing. At typical imaging resolutions, it was found that due to under-sampling of the MRE wave-field, combined with motion-related noise, the reconstructed simulated |G∗| could contain errors on the scale of the difference between liver fibrosis stages, e.g. 54% error for ground truth |G∗| = 1 kPa. Optimum imaging resolutions were identified for given ground truth |G∗| and motion-SNR levels. Significance. This study provides important knowledge on the accuracy and optimisation of liver MRE. For example, for motion-SNR ≤ 5, to distinguish between liver |G∗| of 2 and 3 kPa (i.e. early-stage liver fibrosis) it was predicted that the optimum isotropic voxel size is 4-6 mm.

Citation

McGrath, D. M., Bradley, C. R., & Francis, S. T. (2021). In silico evaluation and optimisation of magnetic resonance elastography of the liver. Physics in Medicine and Biology, 66(22), Article 225005. https://doi.org/10.1088/1361-6560/ac3263

• Journal Article Type: Article
• Acceptance Date: Oct 22, 2021
• Online Publication Date: Nov 10, 2021
• Publication Date: Nov 10, 2021
• Deposit Date: Nov 22, 2021
• Publicly Available Date: Nov 22, 2021
• Journal: Physics in Medicine and Biology
• Print ISSN: 0031-9155
• Electronic ISSN: 1361-6560
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 66
• Issue: 22
• Article Number: 225005
• DOI: https://doi.org/10.1088/1361-6560/ac3263
• Keywords: Radiology Nuclear Medicine and imaging; Radiological and Ultrasound Technology
• Public URL: https://nottingham-repository.worktribe.com/output/6782356
• Publisher URL: https://iopscience.iop.org/article/10.1088/1361-6560/ac3263