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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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 31; H. 12; S. 2307 - 2321
Hauptverfasser:	Yefeng Zheng, John, M., Rui Liao, Nottling, A., Boese, J., Kempfert, J., Walther, T., Brockmann, G., Comaniciu, D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2012
Schlagworte:	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
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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.
AbstractList	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.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. 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.
Author	Walther, T. Comaniciu, D. Brockmann, G. John, M. Yefeng Zheng Rui Liao Nottling, A. Kempfert, J. Boese, J.
Author_xml	– sequence: 1 surname: Yefeng Zheng fullname: Yefeng Zheng email: yefeng.zheng@siemens.com organization: Imaging & Comput. Vision Technol. Field, Siemens Corp. Res., Princeton, NJ, USA – sequence: 2 givenname: M. surname: John fullname: John, M. email: matthias.mj.john@siemens.com organization: Healthcare Sector, Siemens AG, Forchheim, Germany – sequence: 3 surname: Rui Liao fullname: Rui Liao email: rui.liao@siemens.com organization: Imaging & Comput. Vision Technol. Field, Siemens Corp. Res., Princeton, NJ, USA – sequence: 4 givenname: A. surname: Nottling fullname: Nottling, A. email: alois.noetting@siemens.com organization: Healthcare Sector, Siemens AG, Forchheim, Germany – sequence: 5 givenname: J. surname: Boese fullname: Boese, J. email: jan.boese@siemens.com organization: Healthcare Sector, Siemens AG, Forchheim, Germany – sequence: 6 givenname: J. surname: Kempfert fullname: Kempfert, J. email: kempfertj@googlemail.com organization: Dept. of Cardiac Surg., Kerckhoff Klinik, Bad Nauheim, Germany – sequence: 7 givenname: T. surname: Walther fullname: Walther, T. email: t.walther@kerckhoff-klinik.de organization: Dept. of Cardiac Surg., Kerckhoff Klinik, Bad Nauheim, Germany – sequence: 8 givenname: G. surname: Brockmann fullname: Brockmann, G. email: brockmann@dhm.mhn.de organization: Dept. of Cardiovascular Surg., German Heart Center, Munich, Germany – sequence: 9 givenname: D. surname: Comaniciu fullname: Comaniciu, D. email: dorin.comaniciu@siemens.com organization: Imaging & Comput. Vision Technol. Field, Siemens Corp. Res., Princeton, NJ, USA
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Snippet	Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm...
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SubjectTerms	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
Title	Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT for Transcatheter Aortic Valve Implantation
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