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

TL;DR

This paper introduces a physics-informed neural network approach for non-rigid medical image registration, specifically modeling prostate motion during ultrasound procedures, combining biomechanical modeling with deep learning for improved accuracy and generalization.

Contribution

It proposes a novel PINN-based registration method using PointNet for feature extraction, enhancing generalization across different patients in prostate motion modeling.

Findings

Effective in patient-specific registration

Generalizes well across multiple patients

Provides biomechanically plausible transformations

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.