Cardiac Image Registration

Abstract

Long procedure time and somewhat suboptimal results hinder the widespread use of catheter ablation of complex arrhythmias such as atrial fibrillation (AF). Due to lack of contrast differentiation between the area of interest and surrounding structures in a moving organ like heart, there is a lack of proper intraprocedural guidance using current imaging techniques for ablation. Cardiac image registration is currently under investigation and is in clinical use for AF ablation. Cardiac image registration, which involves integration of two images in the context of left atrium (LA), is intermodal, with the acquired image and the real-time reference image residing in different image spaces, and involves optimization, where one image space is transformed into the other. Unlike rigid body registration, cardiac image registration is unique and challenging due to cardiac motion during the cardiac cycle and due to respiration. This review addresses the basic principles of the emerging technique of registration and the inherent limitations as they relate to cardiac imaging and registration

Multi-View 3-D Fusion Echocardiography: Enhancing Clinical Feasibility with a Novel Processing Technique

“…3-D fusion by registration has been successfully applied to fuse 3-D data sets by several groups (Hill et al 2001;Grau et al 2007;Sra 2008;Gooding et al 2010;Szmigielski et al 2010;Rajpoot et al 2011b;Bersvendsen et al 2016;Danudibroto et al 2016;Peressutti et al 2017). However, on its own, registration is of limited value, because contributing data sets need to demonstrate a significant degree of overlap.…”

Automated Assessment of Blood Flow in the Cardiovascular System Using 4D Flow MRI

“…A great number of registration methods have been implemented by extending and improving this relatively simple framework. These techniques have been evaluated in medical imaging in general [53,54], and specifically in cardiovascular imaging [55,56]. In cardiovascular image analysis, registration has been used for a variety of tasks, such as comparison of images acquired at different times or by different imaging modalities [57], motion artifact correction [58,59], motion and strain quantification [60,61], segmentation [62,63], and generation of statistical shape models [64,65].…”