Abstract：
OBJECTIVE: 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.
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.
摘要：
目的：与磁共振成像（MRI）兼容的手术机器人的技术进步对术前和术中MRI的实时可变形图像配准（DIR）产生了不可或缺的需求，但是缺乏相关方法。挑战来自维度不匹配，分辨率差异，非刚性变形和实时配准的要求。
方法：在本文中，我们提出了一个叫做MatchMorph的实时DIR框架，专为低分辨率局部术中MRI和高分辨率全局术前MRI的配准而设计。首先,开发了一种基于全局推理的超分辨率网络，以将术中MRI的分辨率提高到术前MRI的分辨率。从而解决了分辨率差异。其次,设计了一种快速匹配算法,用于确定术中MRI在相应术前MRI中的最佳位置,以解决维度不匹配问题.Further,构建了一个基于交叉注意力的双流DIR网络来操纵术前和术中MRI之间的变形，真正及时。
结果：我们对公开可用的数据集IXI和OASIS进行了全面的实验，以评估所提出的MatchMorph框架的性能。与最先进的（SOTA）网络TransMorph相比，MatchMorph设计的双流DIR网络在IXI数据集上具有1.306mm小的HD和0.07mm小的ASD评分,实现了卓越的性能.此外，MatchMorph框架演示了大约280毫秒的推理速度。
结论：从高分辨率全局术前MRI和模拟的低分辨率局部术中MRI获得的定性和定量配准结果验证了所提出的MatchMorph框架的有效性和效率。
方法：在本文中，我们提出了一个叫做MatchMorph的实时DIR框架，专为低分辨率局部术中MRI和高分辨率全局术前MRI的配准而设计。首先,开发了一种基于全局推理的超分辨率网络，以将术中MRI的分辨率提高到术前MRI的分辨率。从而解决了分辨率差异。其次,设计了一种快速匹配算法,用于确定术中MRI在相应术前MRI中的最佳位置,以解决维度不匹配问题.Further,构建了一个基于交叉注意力的双流DIR网络来操纵术前和术中MRI之间的变形，真正及时。
结果：我们对公开可用的数据集IXI和OASIS进行了全面的实验，以评估所提出的MatchMorph框架的性能。与最先进的（SOTA）网络TransMorph相比，MatchMorph设计的双流DIR网络在IXI数据集上具有1.306mm小的HD和0.07mm小的ASD评分,实现了卓越的性能.此外，MatchMorph框架演示了大约280毫秒的推理速度。
结论：从高分辨率全局术前MRI和模拟的低分辨率局部术中MRI获得的定性和定量配准结果验证了所提出的MatchMorph框架的有效性和效率。
