Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
This provides an incremental improvement for cardiac imaging by automating a manual process, potentially saving time for radiologists.
The authors tackled the problem of automatically generating cardiac long and short axis views from 4D Flow MRI data by localizing six landmarks, achieving projections of equivalent quality to those created by an experienced radiologist in a blinded test.
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.