Fast Registration For Liver Motion Compensation In Ultrasound-Guided Navigation

In recent years, image-guided thermal ablations have become a considerable treatment method for cancer patients, including support through navigational systems. One of the most critical challenges in these systems is the registration between the intraoperative :images and the preoperative volume. The motion secondary to inspiration makes registration even more difficult. In this work, we propose a coarse-fine fast patient registration technique to solve the problem of motion compensation. In contrast to other state-of-the-art methods, we focus on improving the convergence range of registration. To this end, we make use of a Deep Learning 2D U-Net framework to extract the vessels and liver borders from intraoperative ultrasound images and employ the segmentation results as regions of interest in the registration. After an initial 3D-3D registration during breath hold, the following motion compensation is achieved using a 2D-3D registration. Our approach yields a convergence rate of over 70% with an accuracy of 1.97 +/- 1.07 mm regarding the target registration error. The 2D-3D registration is GPU-accelerated with a time cost of less than 200 ms.