TY - JOUR
T1 - Artificial neural networks for magnetic resonance elastography stiffness estimation in inhomogeneous materials
AU - Scott, Jonathan M.
AU - Arani, Arvin
AU - Manduca, Armando
AU - McGee, Kiaran P.
AU - Trzasko, Joshua D.
AU - Huston, John
AU - Ehman, Richard L.
AU - Murphy, Matthew C.
N1 - Funding Information:
Research reported in this publication was supported by the N ational Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award numbers EB001981 , EB024450 , EB027064 .
Funding Information:
Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award numbers EB001981, EB024450, EB027064.
Publisher Copyright:
© 2020
PY - 2020/7
Y1 - 2020/7
N2 - Purpose: To test the hypothesis that removing the assumption of material homogeneity will improve the spatial accuracy of stiffness estimates made by Magnetic Resonance Elastography (MRE). Methods: An artificial neural network was trained using synthetic wave data computed using a coupled harmonic oscillator model. Material properties were allowed to vary in a piecewise smooth pattern. This neural network inversion (Inhomogeneous Learned Inversion (ILI)) was compared against a previous homogeneous neural network inversion (Homogeneous Learned Inversion (HLI)) and conventional direct inversion (DI) in simulation, phantom, and in-vivo experiments. Results: In simulation experiments, ILI was more accurate than HLI and DI in predicting the stiffness of an inclusion in noise-free, low-noise, and high-noise data. In the phantom experiment, ILI delineated inclusions ≤ 2.25 cm in diameter more clearly than HLI and DI, and provided a higher contrast-to-noise ratio for all inclusions. In a series of stiff brain tumors, ILI shows sharper stiffness transitions at the edges of tumors than the other inversions evaluated. Conclusion: ILI is an artificial neural network based framework for MRE inversion that does not assume homogeneity in material stiffness. Preliminary results suggest that it provides more accurate stiffness estimates and better contrast in small inclusions and at large stiffness gradients than existing algorithms that assume local homogeneity. These results support the need for continued exploration of learning-based approaches to MRE inversion, particularly for applications where high resolution is required.
AB - Purpose: To test the hypothesis that removing the assumption of material homogeneity will improve the spatial accuracy of stiffness estimates made by Magnetic Resonance Elastography (MRE). Methods: An artificial neural network was trained using synthetic wave data computed using a coupled harmonic oscillator model. Material properties were allowed to vary in a piecewise smooth pattern. This neural network inversion (Inhomogeneous Learned Inversion (ILI)) was compared against a previous homogeneous neural network inversion (Homogeneous Learned Inversion (HLI)) and conventional direct inversion (DI) in simulation, phantom, and in-vivo experiments. Results: In simulation experiments, ILI was more accurate than HLI and DI in predicting the stiffness of an inclusion in noise-free, low-noise, and high-noise data. In the phantom experiment, ILI delineated inclusions ≤ 2.25 cm in diameter more clearly than HLI and DI, and provided a higher contrast-to-noise ratio for all inclusions. In a series of stiff brain tumors, ILI shows sharper stiffness transitions at the edges of tumors than the other inversions evaluated. Conclusion: ILI is an artificial neural network based framework for MRE inversion that does not assume homogeneity in material stiffness. Preliminary results suggest that it provides more accurate stiffness estimates and better contrast in small inclusions and at large stiffness gradients than existing algorithms that assume local homogeneity. These results support the need for continued exploration of learning-based approaches to MRE inversion, particularly for applications where high resolution is required.
KW - Artificial neural networks
KW - Inversion
KW - Magnetic resonance elastography
KW - Stiffness
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U2 - 10.1016/j.media.2020.101710
DO - 10.1016/j.media.2020.101710
M3 - Article
C2 - 32442867
AN - SCOPUS:85084788528
VL - 63
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
M1 - 101710
ER -