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Abstract #5370

Highly accelerated cardiac cine acquisition with deep learning reconstruction and protocol optimization at 0.5T

Sajith Rajamani1, Xucheng Zhu2, Albin V Kuriakose1, Mayuri Limbachiya3, Rajagopalan Sundaresan1, Ashok Kumar P Reddy1, Arjun Narula3, Anja Brau2, and Ramesh Venkatesan1
1GE Healthcare, Bangalore, India, 2GE Healthcare, Menlo Park, CA, United States, 3Narula Diagnostics, Rohtak, India

Synopsis

Motivation: To Study Cardiac Cine FIESTA with high acceleration factors and Deep learning reconstruction at 0.5T

Goal(s): Functional assessment of ventricles using highly accelerated cine fiesta acquired in 3 Heart beats at 0.5T field strength.

Approach: DLCine uses a variable density sampling scheme and deep learning reconstruction algorithm to accelerate FIESTA Cine scans. This data was compared to the conventional cine fiesta accelerated by parallel imaging.

Results: We observed DL cine method has 2.4 times higher SNR compared to the conventional acceleration method such as parallel imaging and scan time is reduced by 70%

Impact: Acquisition of cardiac short axis scan in 3 heart beats using DL Cine for ventricular functional assessment at 0.5T

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