Non-rigid Medical Image Registration using Physics-informed Neural Networks
This work addresses the challenge of biophysically plausible image registration for prostate procedures, offering an explainable method for clinical applications.
The authors tackled the problem of non-rigid medical image registration for prostate intervention by using physics-informed neural networks (PINNs) with a 3D linear elastic model and PointNet for generalization, achieving validation in patient-specific and multi-patient scenarios.
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.