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 mea...

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Bibliographic Details
Published in:IEEE transactions on medical imaging Vol. 31; no. 12; pp. 2307 - 2321
Main Authors: Yefeng Zheng, John, M., Rui Liao, Nottling, A., Boese, J., Kempfert, J., Walther, T., Brockmann, G., Comaniciu, D.
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2012
Subjects:
Algorithms
Aorta segmentation
Aortic Valve - diagnostic imaging
Aortic Valve - surgery
aortic valve landmark detection
Aortography - methods
Biomedical imaging
C-arm computed tomography (CT)
Computed tomography
Heart Valve Prosthesis Implantation - methods
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Reproducibility of Results
Robustness
Surgery
Surgery, Computer-Assisted - methods
Tomography, X-Ray Computed - methods
transcatheter aortic valve implantation
transcatheter aortic valve replacement
Valves
ISSN:0278-0062, 1558-254X, 1558-254X
Online Access:Get full text
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Summary: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.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2012.2216541