A robust computational framework for estimating 3D Bi-Atrial chamber wall thickness.

Area of Science:

• Cardiology
• Medical Imaging
• Computational Biology

Background:

• Atrial fibrillation (AF) is a common cardiac arrhythmia.
• Atrial wall thickness (AWT) is crucial for understanding AF mechanisms and clinical management.
• Current AWT estimation methods are limited, often manual and restricted to specific locations or single atria.

Purpose of the Study:

• To develop and validate a computational pipeline for automatic 3D AWT estimation in both left and right atria.
• To overcome limitations of existing manual and single-chamber AWT measurement techniques.
• To provide a robust tool for enhanced clinical diagnosis and treatment guidance in AF patients.

Main Methods:

• Utilized a novel machine learning approach for segmenting atrial walls from MRI data.
• Employed a multi-planar convex hull approach to separate epicardial and endocardial surfaces.
• Solved coupled partial differential equations, integrating a Laplace solution with surface trajectory functions, for accurate bi-atrial AWT calculation.

Main Results:

• Successfully reconstructed and visualized 3D AWT across bi-atrial chambers.
• Achieved a relative error of 8% in AWT estimation.
• Demonstrated superior performance, outperforming existing algorithms by over 7%.

Conclusions:

• The developed computational pipeline offers robust and automatic 3D AWT estimation for both atria.
• This approach has the potential to significantly improve clinical diagnosis, patient stratification, and guidance for AF ablation treatments.
• The method provides valuable insights into atrial structure relevant to AF pathophysiology.