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Abstract #3920

Improving Motion-Robust Structural Imaging at 7T with Deep Learning-Based PROPELLER Reconstruction

Daniel V Litwiller1, Xinzeng Wang2, R Marc Lebel3, Baolian Yang4, Jeffrey McGovern4, Brian Burns5, and Suchandrima Banerjee6
1GE Healthcare, Denver, CO, United States, 2GE Healthcare, Houston, TX, United States, 3GE Healthcare, Calgary, AB, Canada, 4GE Healthcare, Waukesha, WI, United States, 5GE Healthcare, Olympia, WA, United States, 6GE Healthcare, Menlo Park, CA, United States


The high sensitivity of MRI at 7T enables brain imaging with unprecedented spatial resolution, which can be important to the assessment of a variety of neurological disorders, such as multiple sclerosis, epilepsy, and neurodegenerative disease. With sub-millimeter voxel dimensions, and prolonged acquisition times, however, sensitivity to motion and pulsatility is increased dramatically. This increased sensitivity to motion can be managed with techniques like PROPELLER. Here, we present an initial assessment of a deep learning-based image reconstruction for high-resolution, 7T PROPELLER, and evaluate its ability to improve signal-to-noise ratio, and anatomical conspicuity, without increasing scan time.

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