Automated 4D flow MRI pipeline for the quantification of advanced hemodynamic parameters in the left atrium.

Journal: Scientific reports
Published Date:

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.

Authors

  • Xabier Morales
    BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
  • Ayah Elsayed
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Debbie Zhao
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Filip Loncaric
    Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Electronic address: [email protected].
  • Ainhoa Aguado
    BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
  • Mireia Masias
    BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
  • Gina Quill
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Marc Ramos
    Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.
  • Adelina Doltra
    Cardiovascular Institute, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.
  • Ana García-Álvarez
    Servicio de Cardiología, Hospital Clínic, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Spain.
  • Marta Sitges
    Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
  • David Marlevi
    Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Alistair Young
  • Martyn Nash
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Bart Bijnens
    Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain.
  • Oscar Camara
    Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain.

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