ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise
  Optimization in Medical Image Registration
Haiqiao Wang Zhuoyuan Wang Dong Ni Yi Wang
Deformable image registration plays a crucial role in medical imaging, aiding
in disease diagnosis and image-guided interventions. Traditional iterative
methods are slow, while deep learning (DL) accelerates solutions but faces
usability and precision challenges. This study introduces a pyramid network
with the enhanced motion decomposition Transformer (ModeTv2) operator,
showcasing superior pairwise optimization (PO) akin to traditional methods. We
re-implement ModeT operator with CUDA extensions to enhance its computational
efficiency. We further propose RegHead module which refines deformation fields,
improves the realism of deformation and reduces parameters. By adopting the PO,
the proposed network balances accuracy, efficiency, and generalizability.
Extensive experiments on two public brain MRI datasets and one abdominal CT
dataset demonstrate the network's suitability for PO, providing a DL model with
enhanced usability and interpretability. The code is publicly available.