Fully automated quantification of LV regional wall motion from echocardiograms to detect myocardial infarction

Abstract

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Ultromics Ltd Background Myocardial wall motion analysis from echocardiography allows precise assessment of cardiac contractile function. Strain, which assesses myocardial deformation, has been shown to enable earlier detection of myocardial disease [1]. Current analysis software packages [2] use semi-automated methods to compute strain, which frequently require manual endocardial delineation and iterative contour adjustment based on tracking results, respectively, causing significant variability. Purpose We present a fully automated pipeline for tracking left ventricular (LV) wall motion to quantify global and segmental longitudinal strain from 2D echocardiograms, and go on to validate the pipeline with an openly available myocardial infarction (MI) dataset. Methods We applied our existing deep learning-based automated contouring method [3] to delineate the endocardial border in the A4C, A2C and A3C views and combined this with spline-based elastic image registration to track LV motion through time. We sampled points from a region of interest initiated from the endocardial border at the end-diastolic (ED) frame, and tracked subsequent motion by recomputing updated positions of all sample points based on each frame‘s displacement field, enabling us to both track the myocardium throughout the cardiac cycle and calculate longitudinal strain relative to the ED frame. The automated endocardial contour was used to regularise the process. The pipeline was independently tested on the HMC-QU dataset [4] which was downloaded from Kaggle and consists of a single cardiac cycle from the A4C view from 160 patients who were diagnosed with an acute MI and underwent echocardiography either prior to percutaneous coronary intervention or within 24 hours of undergoing the procedure; the dataset includes the labels of ED and end-systolic (ES) frames, as well as the presence of an MI in 6 segments excluding the apical cap (Fig 1a), as determined by the consensus of cardiologists from HMC Hospital in Qatar. The Wilcoxon signed-rank test was used to compare peak strain between the MI and non-MI segments; ROC curves were computed to compare the performance of the automatically derived peak longitudinal strain against the MI labels. Results Fig 1b shows ROC curves of peak segmental longitudinal strain for detecting MI, with the best performance in the mid-anterolateral segment (AUC 0.84), and a lower performance for basal segments than mid and apical segments, consistent with known variation in clinical practice [5]. Fig 2 shows that peak longitudinal strain computed from our pipeline was statistically significantly more positive in segments with an MI. Conclusions We present a fully automated pipeline for calculating segmental strain across a cardiac cycle to identify infarcted segments without any observer variability. Clinical application of this method has the potential to identify and monitor regional myocardial function and benefit patient management. Abstract Figure. Fig1. ROC of peak longitudinal strains Abstract Figure. Fig2.Boxplot of peak longitudinal strain