Biventricular Surface Reconstruction From Cine Mri Contours Using Point Completion Networks

Many important cardiac biomarkers used in clinical practice describe cardiac anatomy and function in three dimensions (3D). However, common cardiac magnetic resonance imaging (MRI) protocols often only generate two-dimensional (2D) image slices of the underlying 3D anatomy and are susceptible to various types of motion artifacts causing slice misalignment. In this paper, we propose a deep learning method acting directly on point clouds to reconstruct a dense 3D biventricular heart model from misaligned 2D cardiac MR image contours. The method is able to reduce mild, medium, and strong slice misalignments (mean translation $\sim 3.5$ mm; mean rotation $\sim 2.5^{\circ})$ to a Chamfer distance below image resolution (1.25 mm) with high robustness (standard deviation 0.18 mm) on a statistical shape model dataset. It also manages to reconstruct smooth 3D shapes with accurate left ventricular volumes from cine MR images of the UK Biobank study.