Mohamed Esmail Karar1, Denis R Merk2, Volkmar Falk3, Oliver Burgert4. 1. Faculty of Electronic Engineering, Menoufia University, 32952 Minuf, Egypt. Electronic address: mekarar@ieee.org. 2. Department of Cardiothoracic Surgery, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany. Electronic address: drmerk@me.com. 3. Division of Cardiovascular Surgery, University Hospital Zurich, 8091 Zurich, Switzerland. Electronic address: volkmar.falk@usz.ch. 4. Department of Medical Informatics, Reutlingen University, 72762 Reutlingen, Germany. Electronic address: Oliver.Burgert@Reutlingen-University.DE.
Abstract
BACKGROUND AND PURPOSE: Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper, a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy. METHODS: The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, landmarks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent. Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation. RESULTS: Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets. CONCLUSION: The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.
BACKGROUND AND PURPOSE: Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper, a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy. METHODS: The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, landmarks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent. Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation. RESULTS: Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets. CONCLUSION: The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.