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

Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed, Mark Emberton, Dean C. Barratt +2 more
2/20/2023
eess.IVcs.CVphysics.med-ph

No official code found. Best implementation: ZheMin-1992/Registration_PINNs (13 stars, last commit 2023-04-21).

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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Code Implementations(1)

ZheMin-1992/Registration_PINNsCommunity100%
132PythonFeb 13, 20233 years ago

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Cite this paper

@article{min2023nonrigid,
  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},
  year   = {2023},
  eprint = {2302.10343},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.10343v1}
}

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