A Tensor-Based Catheter and Wire Detection and Tracking Framework and Its Clinical Applications.

OBJECTIVE: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. METHOD: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. RESULTS: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. CONCLUSION: A novel and fully automatic detection framework and its clinical applications are developed. SIGNIFICANCE: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.

Real-time registration of 3D echo to x-ray fluoroscopy based on cascading classifiers and image registration.

Three-dimensional (3D) transesophageal echocardiography (TEE) is one of the most significant advances in cardiac imaging. Although TEE provides real-time 3D visualization of heart tissues and blood vessels and has no ionizing radiation, x-ray fluoroscopy still dominates in guidance of cardiac interventions due to TEE having a limited field of view and poor visualization of surgical instruments. Therefore, fusing 3D echo with live x-ray images can provide a better guidance solution. This paper proposes a novel framework for image fusion by detecting the pose of the TEE probe in x-ray images in real-time. The framework does not require any manual initialization. Instead it uses a cascade classifier to compute the position and in-plane rotation angle of the TEE probe. The remaining degrees of freedom are determined by fast marching against a template library. The proposed framework is validated on phantoms and patient data. The target registration error for the phantom was 2.1 mm. In addition, 10 patient datasets, seven of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures, were used to test the clinical feasibility as well as accuracy. A mean registration error of 2.6 mm was achieved, which is well within typical clinical requirements.