A Computational Pipeline for Advanced Analysis of 4D Flow MRI in the Left Atrium

TL;DR

This paper introduces an open-source computational framework for analyzing 4D Flow MRI data of the left atrium, enabling detailed hemodynamic assessment and exploring its prognostic potential across various disorders.

Contribution

The study presents the first dedicated computational pipeline for LA 4D Flow MRI analysis, robust to diverse data sources and capable of automated high-accuracy segmentation and hemodynamic parameter extraction.

Findings

High-accuracy automated segmentation (Dice > 0.9)

Robust analysis across different data qualities

Assessment of energy, vorticity, and pressure as potential biomarkers

Abstract

The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehension of its hemodynamics is significantly limited by the constraints of conventional ultrasound analysis. 4D flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics. However, the low velocities within the LA and the limited spatial resolution of 4D Flow MRI make analyzing this chamber challenging. Furthermore, the absence of dedicated computational frameworks, combined with diverse acquisition protocols and vendors, complicates gathering large cohorts for studying the prognostic value of hemodynamic parameters provided by 4D Flow MRI. In this study, we introduce the first open-source computational framework tailored for the analysis of 4D Flow MRI in the LA, enabling comprehensive qualitative and quantitative analysis of advanced hemodynamic parameters. Our framework proves robust to data from different centers of varying quality, producing high-accuracy automated segmentations (Dice $>$ 0.9 and Hausdorff 95 $<$ 3 mm), even with limited training data. Additionally, we conducted the first comprehensive assessment of energy, vorticity, and pressure parameters in the LA across a spectrum of disorders to investigate their potential as prognostic biomarkers.