Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.Comment: 23 page

Left atrial wall segmentation using clinically correlated metrics

© Springer International Publishing AG 2017. The thickness of the left atrium wall may be an important parameter in atrial fibrillation disease mechanisms and subsequent treatment by catheter ablation. We have previously developed a simple, threshold-based, direct wall thickness measure from CT that has been found to correlate with clinical out- comes. In this paper, we describe the application of this method to the seg- mentation of the left atrium wall in the 2016 STACOM Left Atrium Wall Thickness Challenge. Our original method sought to partially automate the way a clinical researcher manually measures left atrial wall thickness to increase precision and repeatability. We have adapted our method to create a segmented volume instead of individual measurements in order to meet the challenge goals. We apply the method to the ten contrast-enhanced CT images provided