High-resolution 3D Maps of Left Atrial Displacements using an
  Unsupervised Image Registration Neural Network
Christoforos Galazis Anil Anthony Bharath Marta Varela
Functional analysis of the left atrium (LA) plays an increasingly important
role in the prognosis and diagnosis of cardiovascular diseases.
Echocardiography-based measurements of LA dimensions and strains are useful
biomarkers, but they provide an incomplete picture of atrial deformations.
High-resolution dynamic magnetic resonance images (Cine MRI) offer the
opportunity to examine LA motion and deformation in 3D, at higher spatial
resolution and with full LA coverage. However, there are no dedicated tools to
automatically characterise LA motion in 3D. Thus, we propose a tool that
automatically segments the LA and extracts the displacement fields across the
cardiac cycle. The pipeline is able to accurately track the LA wall across the
cardiac cycle with an average Hausdorff distance of $2.51 \pm 1.3~mm$ and Dice
score of $0.96 \pm 0.02$.