一种基于HAMMER算法的软组织形变检测新方法

A new method to detect soft-tissue deformation based on HAMMER algorithm

目的:本研究利用改进HAMMER算法,将MRI数据和超声图像进行配准和融合,对软组织形变进行检测和评价。方法:为定量评价提出算法的性能,设计超声图像的计算机仿真模块,通过对腹部MRI图像进行刚性和非刚性形变,利用形变后的MRI图像产生模拟的超声图像。在此基础上利用提出的改进HAMMER算法对形变的超声图像和原始MRI图像进行配准,并采用平均位移和互信息对形变检测和校正的结果进行定量评价。结果:通过将术前高分辨率、高对比度的三维MRI图像和术中超声图像结合,可有效反映腹部手术中软组织器官的形变。结论:初步实验结果表明,提出的改进HAMMER算法用于软组织形变检测和校正的可行性。

Objective： In this study, a new approach is presented to detect and evaluate the deformation of soft tissue through the registration and fusion of MRI data with US scans based on HAMMER algorithm. Methods： To evaluate the proposed method quantitatively, an ultrasound simulation module was developed and then ultrasound images were simulated based on deformed MRI images that were generated from acquired abdominal MRI data using rigid and non-rigid transformations. The deformed ultrasound images and original MRI images were registered by modified HAMMER algorithm, and the deformation correction was evaluated by the average displacement and mutual information. Results： The integration of preoperative high-resolution and high tissue contrast MRI 5D data with intraoDerative Ultrasound （US） ima2es would bea possible way to reflect organ deformation during abdominal surgery. Conclusion： Preliminary experimental results demonstrate the feasibility of proposed method on the detection and correction of soft tissue deformation.