High-resolution imaging and T1 mapping is needed to achieve useful clinical information optimally in cardiac MRI. However, prolonged acquisition time can lead to poor or non-diagnostic image quality. In this study, we investigated the use of a deep learning-based reconstruction algorithm to highly accelerate T1map acquisition for cardiac MRI. Adaptive-CS-Net, a deep neural network previously introduced at the 2019 fastMRI challenge, was expanded and integrated into the Compressed-SENSE Artificial Intelligence (CS-AI) reconstruction. The purpose of this study was to compare the image quality of high-resolution T1map between reference and accelerated methods: SENSE, Compressed-SENSE, and CS-AI.
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