Since the mapping relationship between definitized intra-interventional 2D X-ray and undefined pre-interventional 3D Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the 2D X-ray and 3D CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary 2D X-ray and 3D CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.
翻译:由于分解的2DX射线和未定义的干预前3D成形成像仪(CT)之间的绘图关系不确定,因此通常使用辅助定位装置或身体标记(如医用植入器)来确定这种关系,但是,由于复杂的现实情况,在临床中无法广泛使用这种方法;为了确定绘图关系,在没有辅助设备或标记的情况下对人体进行初始后估计,拟议方法采用图像分割和深度特征匹配,直接匹配2DX射线和3DCT成像。 因此,经过良好训练的网络可以直接预测任意的2DX射线和3DCT之间的空间通信。 实验结果显示,在将我们的方法与常规方法相结合时,实现的准确性和速度能够满足基本的临床干预需要,并为干预内部登记提供新的方向。