Evaluation of image quality and global cardiac function for deep learning accelerated cardiac Cine

Cardiac bSSFP Cine is widely used clinically; however, it is time consuming and requires multiple breath-holds. Deep learning-based accelerated Cine (DLCine) is a novel technique combining accelerated variable density sampling and deep learning regularized reconstruction that allows much higher acceleration compared to conventional Cine with parallel imaging. The purpose of this work was to compare image quality and global cardiac function utilizing DLCine versus conventional Cine by three expert readers. The results demonstrate that DLCine can be used to reduce the scan time while maintaining image quality and providing accurate global cardiac function measurement.