Automatic aorta segmentation and valve landmark detection in C-arm CT for transcatheter aortic valve implantation.

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.

System to guide transcatheter aortic valve implantations based on interventional C-arm CT imaging.

Transcatheter aortic valve implantation is an emerging technique to be applied in patients with aortic valve defects. Angiographic and fluoroscopic X-ray imaging with a C-arm system is crucial in these minimally invasive procedures. We describe a prototypical system based on the ability to acquire a 3D C-arm CT image during transcatheter aortic valve implantations. It supports the physician in measuring critical anatomical parameters, finding an optimum C-arm angulation, and guiding the positioning and deployment of the prosthesis by 3D overlay with fluoroscopic images. To yield high acceptance by the physicians in the operating room, our approach is fast, fully integrated into an angiographic C-arm system, and designed to minimize the necessary user interaction. We evaluate the accuracy of our system on 20 clinical cases.