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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