Computationally efficient cardiac views projection using 3D Convolutional Neural Networks

TL;DR

This paper introduces a fast, automated method using 3D CNNs to localize landmarks in cardiac MRI data, enabling efficient generation of diagnostic cardiac views comparable to expert manual annotations.

Contribution

It presents a novel 3D CNN-based approach for automatic cardiac landmark localization, improving efficiency and consistency in generating cardiac views from MRI data.

Findings

Automated landmarks are comparable to expert annotations.

The method reduces processing time for cardiac view generation.

Automated projections meet diagnostic quality standards.

Abstract

4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.