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ModeTv2: GPU-accelerated motion decomposition transformer for pairwise optimization in medical image registration.

Haiqiao Wang¹, Zhuoyuan Wang¹, Dong Ni²

¹School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China; Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China.

Medical Image Analysis

|November 23, 2025

Summary

This study introduces ModeTv2, a deep learning network for medical image registration, enhancing accuracy and efficiency. It offers improved usability for precise disease diagnosis and image-guided interventions.

Keywords:

Deformable image registration GPU acceleration Motion decomposition Pairwise optimization

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Deformable image registration is vital for medical diagnosis and interventions.
Traditional methods are slow; deep learning (DL) offers speed but faces usability and precision issues.

Purpose of the Study:

Introduce a novel DL network, ModeTv2, for enhanced deformable image registration.
Improve computational efficiency and accuracy in medical image analysis.

Main Methods:

Developed a pyramid network incorporating the enhanced motion decomposition Transformer (ModeTv2) operator.
Re-implemented the ModeT operator with CUDA for computational efficiency.
Proposed the RegHead module to refine deformation fields and improve realism.

Main Results:

ModeTv2 demonstrated superior pairwise optimization (PO) comparable to traditional methods.
The network achieved a balance of accuracy, efficiency, and generalizability across multiple datasets.
Experiments on brain MRI and abdominal CT datasets confirmed the network's suitability for PO.

Conclusions:

ModeTv2 offers a DL solution for deformable image registration with enhanced usability.
The proposed network provides a viable alternative to traditional methods, improving speed and precision in medical imaging.