Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping

Eric Barnhill

doi:10.1016/j.media.2016.05.012

Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping

2017-01-01

49

OA

AI

DOI：10.1016/j.media.2016.05.012

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Fine-featured elastograms may provide additional information of radiological interest in the context of in vivo elastography. Here a new image processing pipeline called ESP (Elastography Software Pipeline) is developed to create Magnetic Resonance Elastography (MRE) maps of viscoelastic parameters (complex modulus magnitude [G*] and loss angle phi) that preserve fine-scale information through nonlinear, multi-scale extensions of typical MRE post-processing techniques. Methods: A new MRE image processing pipeline was developed that incorporates wavelet-domain denoising, image-driven noise estimation, and feature detection. ESP was first validated using simulated data, including viscoelastic Finite Element Method (FEM) simulations, at multiple noise levels. ESP images were compared with MDEV pipeline images, both in the FEM models and in three ten-subject cohorts of brain, thigh, and liver acquisitions. ESP and MDEV mean values were compared to 2D local frequency estimation (LFE) mean values for the same cohorts as a benchmark. Finally, the proportion of spectral energy at fine frequencies was quantified using the Reduced Energy Ratio (RER) for both ESP and MDEV. Results: Blind estimates of added noise (sigma) were within 5.3% +/- 2.6% of prescribed, and the same technique estimated sigma in the in vivo cohorts at 1.7 +/- 0.8%. A 5 x 5 x 5 truncated Gabor filter bank effectively detects local spatial frequencies at wavelengths lambda <= 10px. For FEM inversions, mean [Gal of hard target, soft target, and background remained within 8% of prescribed up to sigma = 20%, and mean phi results were within 10%, excepting hard target phi, which required redrawing around a ring artefact to achieve similar accuracy. Inspection of FEM [Gal images showed some spatial distortion around hard target boundaries and inspection of phi images showed ring artefacts around the same target. For the in vivo cohorts, ESP results showed mean correlation of R = 0.83 with MDEV and liver stiffness estimates within 7% of 2D-LFE results. Finally, ESP showed statistically significant increase in fine feature spectral energy as measured with RER for both [G*] [ (p < 1 x 10(-9)) and phi (p < I x 10(-3)). Conclusion: Information at finer frequencies can be recovered in ESP elastograms in typical experimental conditions, however scatter-and boundary-related artefacts may cause the fine features to have inaccurate values. In in vivo cohorts, ESP delivers an increase in fine feature spectral energy, and better performance with longer wavelengths, than MDEV while showing similar stability and robustness. (C) 2016 Published by Elsevier B.V.

Keyword:

Elastography

Magnetic resonance elastography

Wave inversion

Complex dualtree wavelet

Denoising

Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping

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