Department of Radiology, University of Yamanashi.
Magn Reson Med Sci. 2021 Mar 1;20(1):99-105. doi: 10.2463/mrms.tn.2019-0081. Epub 2020 Mar 6.
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.
为了加速多回波平面序列的高分辨率弥散加权成像，我们提出了一种基于减少平均和深度学习的方法。去卷积神经网络可以在不需要广泛平均的情况下减少放大的噪声，从而实现更短的扫描时间和更高的图像质量。初步实验结果表明，所提出的去噪方法优于广泛使用的块匹配和 3D 滤波等现有方法。
