Visual Analytics for Planning Left Atrial Appendage Occlusion: A Case Study on In-silico Hemodynamic Assessment
No Thumbnail Available
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Clinicians are encouraged to integrate both structural and functional information when making medical decisions, but often lack the necessary visualization and analysis tools. For patients with atrial fibrillation undergoing left atrial appendage occluder (LAAO) implantation, interventional cardiologists must consider the variable anatomy of the left atria and blood flow patterns to select the optimal LAAO device settings. While commercial tools exist to assist with LAAO implantation, they only offer morphological information related to the left atria (LA). Conversely, advanced visual analytics tools have been developed to understand blood flow patterns in cerebral aneurysms. This preliminary study aims to investigate the potential of adapting these visual analytics tools to LAAO applica for the analysis of hemodynamics in the LA obtained from fluid simulations. The resulting platform, LAAOVis, enables the comparison of different LAAO device configurations to identify the most optimal ones for a specific patient. Domain experts have assessed the potential of LAAOVis and identified additional features that would enhance its suitability for planning LAAO device interventions.
Description
CCS Concepts: Applied computing → Life and medical sciences; Human-centered computing → Visualization; Computing methodologies → Modelling and simulation
@inproceedings{10.2312:evs.20241065,
booktitle = {EuroVis 2024 - Short Papers},
editor = {Tominski, Christian and Waldner, Manuela and Wang, Bei},
title = {{Visual Analytics for Planning Left Atrial Appendage Occlusion: A Case Study on In-silico Hemodynamic Assessment}},
author = {Nuhic, Jasna and Albors, Carlos and Mill, Jordi and Olivares, Andy L. and Camara, Oscar and Meuschke, Monique},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-251-6},
DOI = {10.2312/evs.20241065}
}