Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

TL;DR

This paper introduces Active Polynomials, a novel method for estimating left ventricular wall motion from echocardiograms, aiding early myocardial infarction detection and visualization, supported by a new public echo database.

Contribution

The study presents a new active polynomial approach for robust LV wall motion estimation and introduces the first public echocardiogram database for MI detection benchmarking.

Findings

High accuracy, sensitivity, and precision in MI detection on the HMC-QU dataset.

Effective motion estimation even with poor echo quality and low temporal resolution.

Enhanced visualization tools for clinicians and technicians.

Abstract

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over the HMC-QU dataset show that the proposed approach can achieve high accuracy, sensitivity and precision in MI detection even though the echo quality is quite poor, and the temporal resolution is low.