Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography

Jonathan M. Scott, Kowsalya Devi Pavuluri, Joshua D. Trzasko, Armando Manduca, Matthew L. Senjem, John Huston, Richard L. Ehman, Matthew Murphy

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Inversion algorithms used to convert acquired MR elastography wave data into material property estimates often assume that the underlying materials are locally homogeneous. Here we evaluate the impact of that assumption on stiffness estimates in gray-matter regions of interest in brain MR elastography. Methods: We describe an updated neural network inversion framework using finite-difference model–derived data to train convolutional neural network inversion algorithms. Neural network inversions trained on homogeneous simulations (homogeneous learned inversions [HLIs]) or inhomogeneous simulations (inhomogeneous learned inversions [ILIs]) are generated with a variety of kernel sizes. These inversions are evaluated in a brain MR elastography simulation experiment and in vivo in a test–retest repeatability experiment including 10 healthy volunteers. Results: In simulation and in vivo, HLI and ILI with small kernels produce similar results. As kernel size increases, the assumption of homogeneity has a larger effect, and HLI and ILI stiffness estimates show larger differences. At each inversion's optimal kernel size in simulation (7 × 7 × 7 for HLI, 11 × 11 × 11 for ILI), ILI is more sensitive to true changes in stiffness in gray-matter regions of interest in simulation. In vivo, there is no difference in the region-level repeatability of stiffness estimates between the inversions, although ILI appears to better maintain the stiffness map structure as kernel size increases, while decreasing the spatial variance in stiffness estimates. Conclusions: This study suggests that inhomogeneous inversions provide small but significant benefits even when large stiffness gradients are absent.

Original languageEnglish (US)
JournalMagnetic Resonance in Medicine
DOIs
StateAccepted/In press - 2022

Keywords

  • artificial neural networks
  • brain stiffness
  • inversion
  • magnetic resonance elastography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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