Non-rigid Medical Image Registration using Physics-informed Neural Networks

• URL: http://arxiv.org/abs/2302.10343v1
• Date: Mon, 20 Feb 2023 22:17:29 GMT
• Title: Non-rigid Medical Image Registration using Physics-informed Neural Networks
• Authors: Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed, Mark Emberton, Dean C. Barratt, Zeike A. Taylor, Yipeng Hu
• Abstract summary: Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for modelling prostate motion.
• Score: 8.7196937169933
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for modelling prostate motion commonly encountered during transrectal ultrasound guided procedures. To overcome a widely-recognised challenge in generalising PINNs to different subjects, we propose to use PointNet as the nodal-permutation-invariant feature extractor, together with a registration algorithm that aligns point sets and simultaneously takes into account the PINN-imposed biomechanics. The proposed method has been both developed and validated in both patient-specific and multi-patient manner.

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