Abstract
Magnetic resonance elastography (MRE) is a medical imaging modality that non-invasively quantifies tissue stiffness (elasticity) and is commonly used for diagnosing liver fibrosis. Constructing an elasticity map of tissue requires solving an inverse problem involving a partial differential equation (PDE). Current numerical techniques to solve the inverse problem are noise-sensitive and require explicit specification of physical relationships. In this work, we apply physics-informed neural networks to solve the inverse problem of tissue elasticity reconstruction. Our method does not rely on numerical differentiation and can be extended to learn relevant correlations from anatomical images while respecting physical constraints. We evaluate our approach on simulated data and in vivo data from a cohort of patients with non-alcoholic fatty liver disease (NAFLD). Compared to numerical baselines, our method is more robust to noise and more accurate on realistic data, and its performance is further enhanced by incorporating anatomical information.
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Acknowledgements & Data Use
This work was supported by the Pennsylvania Department of Health (grant number 41000873310), National Institutes of Health (grant number R01HL141813), the National Science Foundation (grant number 1839332) and Tripod+X. This work used the Bridges-2 system, which is supported by NSF award number OAC-1928147 at the Pittsburgh Supercomputing Center (PSC).
The patient MRE data was acquired by Amir A. Borhani, MD while he was at University of Pittsburgh. We thank him for his collaboration and guidance during this project.
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Ragoza, M., Batmanghelich, K. (2023). Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_32
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