Diffusion tensor imaging (DTI) is widely used clinically but typically requires acquiring diffusion-weighted images (DWIs) along many diffusion-encoding directions for robust model fitting, resulting in lengthy acquisitions. Here, we propose a joint denoising and q-space angular super-resolution method called “DeepDTI” achieved using data-driven supervised deep learning that minimizes the data requirement for DTI to the theoretical minimum of one b=0 image and six DWIs. Metrics derived from DeepDTI’s results are equivalent to those obtained from three b=0 and 19 to 26 DWI volumes for different scalar and orientational DTI metrics, and superior to those derived from state-of-the-art denoising methods.
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