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Abstract #4571

High resolution myocardial T1mapping with a deep learning constrained Compressed SENSE reconstruction

Takashige Yoshida1, Kohei Yuda2, Masami Yoneyama3, Jihun Kwon3, and Marc Van Cauteren4
1radiology, Tokyo metropolitan police hospital, Tokyo, Japan, 2Tokyo metropolitan police hospital, Tokyo, Japan, 3Philips Japan, Tokyo, Japan, 4Philips Healthcare, Best, Netherlands


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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