Literature DB >> 22437980

Image-guided transapical aortic valve implantation: sensorless tracking of stenotic valve landmarks in live fluoroscopic images.

Denis R Merk1, Mohamed Esmail Karar, Claire Chalopin, David Holzhey, Volkmar Falk, Friedrich W Mohr, Oliver Burgert.   

Abstract

OBJECTIVE: Aortic valve stenosis is one of the most frequently acquired valvular heart diseases, accounting for almost 70% of valvular cardiac surgery. Transapical transcatheter aortic valve implantation has recently become a suitable minimally invasive technique for high-risk and elderly patients with severe aortic stenosis. In this article, we aim to automatically define a target area of valve implantation, namely, the area between the coronary ostia and the lowest points of two aortic valve cusps. Therefore, we present a new image-based tracking method of these aortic landmarks to assist in the placement of aortic valve prosthesis under live 2D fluoroscopy guidance.
METHODS: We propose a rigid intensity-based image registration technique for tracking valve landmarks in 2D fluoroscopic image sequences, based on a real-time alignment of a contrast image including the initialized manual valve landmarks to each image of sequence. The contrast image is automatically detected to visualize aortic valve features when the aortic root is filled with a contrast agent.
RESULTS: Our registration-based tracking method has been retrospectively applied to 10 fluoroscopic image sequences from routine transapical aortic valve implantation procedures. Most of all tested fluoroscopic images showed a successful tracking of valve landmarks, especially for the images without contrast agent injections.
CONCLUSIONS: A new intraoperative image-based method has been developed for tracking aortic valve landmarks in live 2D fluoroscopic images to assist transapical aortic valve implantations and to increase the overall safety of surgery as well.

Entities:  

Year:  2011        PMID: 22437980     DOI: 10.1097/IMI.0b013e31822c6a77

Source DB:  PubMed          Journal:  Innovations (Phila)        ISSN: 1556-9845


  3 in total

1.  Editorial comment.

Authors:  Volkmar Falk
Journal:  Int J Cardiovasc Imaging       Date:  2012-06-30       Impact factor: 2.357

2.  Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans.

Authors:  Mohamed Esmail Karar; Ezz El-Din Hemdan; Marwa A Shouman
Journal:  Complex Intell Systems       Date:  2020-09-22

3.  Deep learning-based approach for detecting COVID-19 in chest X-rays.

Authors:  M Emin Sahin
Journal:  Biomed Signal Process Control       Date:  2022-07-14       Impact factor: 5.076

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.