To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

PROPELLER sequence is useful because of its robustness to patient motion. Longer acquisition than FSE is a major drawback limiting its wider application in clinical practice. Here, we propose an accelerated T1 weighted PROPELLER of the brain using deep learning based parallel imaging (PI) reconstruction. Our method can unfold highly undersampled aliased images (PI factor = 7), enabling 2.3 times faster acquisition than full-sampling. A preliminary reader study with prospectively undersampled data showed that the proposed method significantly outperformed a conventional SENSE reconstruction in terms of streak artifact.