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

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 ( divide G* divide ) for varying: (1) ground truth liver divide G* divide ; (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 divide G* divide . Main results. The simulated MRE accuracy for a given ground truth divide G* divide 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 divide G* divide could contain errors on the scale of the difference between liver fibrosis stages, e.g. 54% error for ground truth divide G* divide = 1 kPa. Optimum imaging resolutions were identified for given ground truth divide G* divide 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 divide G* divide of 2 and 3 kPa (i.e. early-stage liver fibrosis) it was predicted that the optimum isotropic voxel size is 4-6 mm.