Liver tumor ablation procedures require accurate placement of the needle applicator at the tumor centroid. The lower-cost and real-time nature of ultrasound (US) has advantages over computed tomography (CT) for applicator guidance, however, in some patients, liver tumors may be occult on US and tumor mimics can make lesion identification challenging. Image registration techniques can aid in interpreting anatomical details and identifying tumors, but their clinical application has been hindered by the tradeoff between alignment accuracy and runtime performance, particularly when compensating for liver motion due to patient breathing or movement. Therefore, we propose a 2D-3D US registration approach to enable intra-procedural alignment that mitigates errors caused by liver motion. Specifically, our approach can correlate imbalanced 2D and 3D US image features and use continuous 6D rotation representations to enhance the model's training stability. The dataset was divided into 2388, 196 and 193 image pairs for training, validation and testing, respectively. Our approach achieved a mean Euclidean distance error of 2.28 mm $\pm$ 1.81 mm and a mean geodesic angular error of 2.99$^{\circ}$ $\pm$ 1.95$^{\circ}$, with a runtime of 0.22 seconds per 2D-3D US image pair. These results demonstrate that our approach can achieve accurate alignment and clinically acceptable runtime, indicating potential for clinical translation.

本研究解决了在肝脏肿瘤消融过程中，由于肝脏运动导致的超声图像配准精度不足的问题。提出了一种2D-3D超声图像配准的新方法，该方法通过连续6D旋转表示增强模型训练的稳定性。研究结果显示，该方法在准确性和实时性能方面均达到了临床可接受的标准，显示出良好的临床转化潜力。

基于深度回归的2D-3D超声图像配准用于肝脏运动校正在局部肿瘤热消融中的应用