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.
@article{arxiv.2302.10343,
title = {Non-rigid Medical Image Registration using Physics-informed Neural Networks},
author = {Zhe Min and Zachary M. C. Baum and Shaheer U. Saeed and Mark Emberton and Dean C. Barratt and Zeike A. Taylor and Yipeng Hu},
journal= {arXiv preprint arXiv:2302.10343},
year = {2023}
}