ML models for 4D cine imaging

Motivation: The motivation behind this study is to severely shorten scan times for children undergoing cardiac examination. Goal(s): The goal is to be able to create a 4D dataset from a short, free breathing real-time 2D stack of images. Approach: We apply three machine learning models to the 2D stack. The first reconstructs the undersampled image data, the second corrects respiratory artefacts caused by free-breathing and the third model super resolves the images. Results: The image quality is vastly improved after applying the machine learning models. The ventricular volumes are also in good agreement with the reference volumes. Impact: Severely reduced scan times for comprehensive cardiac examination in CHD without the use of breath-holds. Machine Learning methods may be able to also be used for other imaging sequences, also resulting in faster image acquisition.