Keywords: Machine Learning/Artificial Intelligence, Myocardium, Cardiomyopathy,Data AnalysisCardiac MRI is considered reference standard for the noninvasive assessment of ventricular volumes and function. However, long multibreath-hold acquisition time can prove difficult in patients and lead to poor image quality. In this study, we investigated the use of a deep learning-based reconstruction algorithm, named Compressed SENSE Artificial Intelligence(CS-AI), to accelerate two-dimensional cine bSSFP for cardiac MRI. The purpose of this study was to compare the image quality and performance of a CS-AI-based cine sequence between reference and accelerated methods: SENSE, Compressed-SENSE, and CS-AI, and then to investigate the impact of images reconstructed by deep learning on AI segmentation model.
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