Diffusion tensor imaging (DTI) is a well-established tool for providing insights into brain network connectivity and detecting brain microstructure but suffers from artifacts, low SNR, low spatial resolution, and long scan times. High-resolution DTI at 7T with multiband MUSE (MB-MUSE) and noise reduction methods have shown many potentials for mitigating these challenges. In this study, we combine a deep learning reconstruction method with MB-MUSE to overcome the image quality challenges and demonstrate improved quantification of high-resolution DTI at 7T compared with MB-MUSE and MB-MUSE with low-rank denoising.
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