Automated 4D flow MRI pipeline for the quantification of advanced hemodynamic parameters in the left atrium.
Authors
Affiliations (13)
Affiliations (13)
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
- Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand.
- University Hospital Centre Zagreb, Zagreb, Croatia.
- Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.
- Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Institut d'investigacions biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Department of Engineering Science and Biomedical Engineering, University of Auckland, Auckland, New Zealand.
- Institució Catalana de Recerca i Estudis Avançats, (ICREA), Barcelona, Spain.
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain. [email protected].
Abstract
The left atrium (LA) plays a pivotal role in modulating left ventricular filling, yet its hemodynamics remain poorly understood due to the limitations of conventional ultrasound analysis. Four-dimensional flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics, but its analysis is hindered by the inherently low velocities within the chamber and the modest spatial resolution of 4D Flow MRI. Heterogeneity in acquisition protocols and MRI vendors, and the lack of standardized computational frameworks further complicates the creation of large, comparable datasets needed to assess the prognostic value of hemodynamic markers provided by 4D Flow MRI. To address these challenges, we introduce a computational framework tailored to the analysis of 4D Flow MRI in the LA, enabling the qualitative and quantitative analysis of advanced hemodynamic parameters (e.g., kinetic energy, vorticity, and pressure). We applied this framework to a diverse cohort spanning different degrees of left ventricular diastolic dysfunction to investigate the prognostic potential of these metrics. Our framework proved robustness across multicenter data of varying quality, producing high-accuracy automated segmentations. Notably, our findings show that 4D Flow MRI-derived parameters provide superior differentiation between healthy and pathological states than those available to conventional hemodynamic analysis tools.