A Rigid Registration Method in TEVAR
This addresses the challenge of rigid registration in thoracic endovascular aortic repair for clinicians, offering a marker-free solution, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of uncertain mapping between intra-interventional X-ray and pre-interventional CT images in TEVAR by proposing a method using image segmentation and deep feature matching to predict spatial correspondence without auxiliary devices. The result shows that combining this approach with conventional methods achieves accuracy and speed meeting basic clinical needs.
Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional 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 X-ray and CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary X-ray and 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.