Predicting 3D Cardiac Deformations with Point Cloud Autoencoders

Abstract

Mechanical contraction and relaxation of the heart play an important role in evaluating healthy and diseased cardiac function. Mechanical patterns consist of complex non-linear 3D deformations that vary considerably between subjects and are difficult to observe on 2D images, which impacts the prediction accuracy of cardiac outcomes. In this work, we aim to capture 3D biventricular deformations at the end-diastolic (ED) and end-systolic (ES) phases of the cardiac cycle with a novel geometric deep learning approach. Our network consists of an encoder-decoder structure that works directly with light-weight point cloud data. We initially train our network on pairs of ED and ES point clouds stemming from a mixed population of subjects with the aim of accurately predicting ED outputs from ES inputs as well as ES outputs from ED inputs. We validate our network’s performance using the Chamfer distance (CD) and find that ED and ES predictions can be achieved with an average CD of 1.66 ± 0.62 mm on a dataset derived from the UK Biobank cohort with an underlying voxel size of $1.8 \times 1.8 \times 8.0$ mm [8]. We derive structural and functional clinical metrics such as myocardial mass, ventricular volume, ejection fraction, and stroke volume from the predictions and find an average mean deviation from their respective gold standards of 1.6% and comparable standard deviations. Finally, we show our method’s ability to capture deformation differences between specific subpopulations in the dataset.