title: Automatic aorta segmentation and valve landmark detection in C-arm CT for transcatheter aortic valve implantation.
abstract: 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.
authors.affiliation: Imaging and Computer Vision Technology Field, Siemens Corporate Research, Princeton, NJ 08540, USA. yefeng.zheng@siemens.com
authors.forename: Yefeng
authors.initials: Y
authors.lastname: Zheng
authors.forename: Matthias
authors.initials: M
authors.lastname: John
authors.forename: Rui
authors.initials: R
authors.lastname: Liao
authors.forename: Alois
authors.initials: A
authors.lastname: Nöttling
authors.forename: Jan
authors.initials: J
authors.lastname: Boese
authors.forename: Jörg
authors.initials: J
authors.lastname: Kempfert
authors.forename: Thomas
authors.initials: T
authors.lastname: Walther
authors.forename: Gernot
authors.initials: G
authors.lastname: Brockmann
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D000465:Algorithms; D001021:Aortic Valve / Q000000981:diagnostic imaging* / Q000601:surgery*; D001027:Aortography / Q000379:methods*; D019918:Heart Valve Prosthesis Implantation / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D025321:Surgery, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*