CardiacField: Computational Echocardiography for Universal Screening

Abstract

Abstract Cardiovascular diseases, the worldwide leading cause of death, are preventable and treatable. Early diagnosis and monitoring using ultrasound, x-ray or MRI are crucial clinical tools. Routine imaging is, however, currently cost prohibitive. Here we show that computational imaging enables a 3 order of magnitude reduction in the cost of tomographic echocardiography while also radically improving image quality and diagnostic utility. This advance relies on decompressive inference using artificial neural networks. Our system, CardiacField, generates 3D images of the heart from 2D echocardiograms using commodity clinical instruments. CardiacField automatically segments and quantifies the volume of the left ventricle (LV) and right ventricle (RV) without manual calibration. CardiacField estimates the left ventricular ejection fraction (LVEF) with 33% higher accuracy than state-of-the-art video-based methods, and the right ventricular ejection fraction (RVEF) with a similar accuracy, which is not available in existing 2DE methods. This technology will enable routine world-wide tomographic heart screening, such that patients will get instant feedback on lifestyle changes that improve heart health. CardiacField also illustrates the value of a conceptual shift in diagnostic imaging from direct physical model inversion to Bayesian inference. While clinicians tend to prefer linear inference algorithms for their conceptual simplicity, as discussed in this paper, neural inference will save lives.