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

2025-05-14

AI Summary

4D Flow MRI faces key challenges in left atrial (LA) hemodynamic analysis, including limited sensitivity to low-velocity flow, insufficient spatial resolution, inter-center acquisition heterogeneity, and absence of dedicated computational frameworks. Method: We developed the first open-source, LA-specific quantitative 4D Flow MRI analysis platform. It incorporates a few-shot trainable robust segmentation model (Dice > 0.9, HD95 < 3 mm); the first systematic 3D time-resolved quantification of intra-LA energy dissipation rate, vorticity fields, and relative pressure distribution; and a vendor- and protocol-agnostic standardized post-processing pipeline. Built on a Python/C++ hybrid architecture, the platform integrates efficient computation with clinical usability. Contribution/Results: Validated across multicenter datasets, it achieves high-accuracy LA segmentation and stable hemodynamic parameter extraction. This enables rigorous investigation of LA hemodynamic biomarkers for prognostic assessment, providing a reproducible, scalable foundation for translational cardiovascular research.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Analyzing low-velocity hemodynamics in left atrium using 4D Flow MRI
• Lack of standardized computational frameworks for 4D Flow MRI analysis
• Assessing prognostic biomarkers like energy and vorticity in atrial disorders

Innovation

• Open-source computational framework for 4D Flow MRI
• Robust automated segmentation with high accuracy
• Comprehensive assessment of hemodynamic parameters