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arXiv · 2302.10343

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

Zhe Min·Zachary M. C. Baum·Shaheer U. Saeed·Mark Emberton·Dean C. Barratt·Zeike A. Taylor·Yipeng Hu
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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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Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed, Mark Emberton, Dean C. Barratt, Zeike A. Taylor, Yipeng Hu. 2023-02-20. Non-rigid Medical Image Registration using Physics-informed Neural Networks. https://arxiv.org/abs/2302.10343

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