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Updated: Jun 17, 2025
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
Published on: October 27, 2023
MatchMorph: A real-time pre- and intra-operative deformable image registration framework for MRI-guided surgery
Xinhao Bai1, Hongpeng Wang1, Yanding Qin1
1College of Artificial Intelligence, Nankai University, Tianjin, 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, 518083, China.
Purpose:
The technological advancements in surgical robots compatible with magnetic resonance imaging (MRI) have created an indispensable demand for real-time deformable image registration (DIR) of pre- and intra-operative MRI, but there is a lack of relevant methods. Challenges arise from dimensionality mismatch, resolution discrepancy, non-rigid deformation and requirement for real-time registration.
Methods:
In this paper, we propose a real-time DIR framework called MatchMorph, specifically designed for the registration of low-resolution local intraoperative MRI and high-resolution global preoperative MRI. Firstly, a super-resolution network based on global inference is developed to enhance the resolution of intraoperative MRI to the same as preoperative MRI, thus resolving the resolution discrepancy. Secondly, a fast-matching algorithm is designed to identify the optimal position of the intraoperative MRI within the corresponding preoperative MRI to address the dimensionality mismatch. Further, a cross-attention-based dual-stream DIR network is constructed to manipulate the deformation between pre- and intra-operative MRI, real-timely.
Results:
We conducted comprehensive experiments on publicly available datasets IXI and OASIS to evaluate the performance of the proposed MatchMorph framework. Compared to the state-of-the-art (SOTA) network TransMorph, the designed dual-stream DIR network of MatchMorph achieved superior performance with a 1.306 mm smaller HD and a 0.07 mm smaller ASD score on the IXI dataset. Furthermore, the MatchMorph framework demonstrates an inference speed of approximately 280 ms.
Conclusions:
The qualitative and quantitative registration results obtained from high-resolution global preoperative MRI and simulated low-resolution local intraoperative MRI validated the effectiveness and efficiency of the proposed MatchMorph framework.
