Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

中文翻译：

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使用全局和局部特征提取通过卷积神经网络的并行残差学习对3D脑MR图像进行去噪

磁共振（MR）图像经常在图像获取和传输过程中受到随机噪声污染，这会损害医生或自动化系统的疾病诊断。近年来，已经提出了许多具有令人印象深刻的性能的噪声去除算法。在这项工作中，受深度学习的启发，我们提出了一种称为3D-Parallel-RicianNet的降噪方法，该方法将结合全局和局部信息以消除MR图像中的噪声。具体来说，我们引入了功能强大的扩散卷积残差（DCR）模块，以扩展网络的接收范围并避免丢失全局特征。然后，为了提取更多的本地信息并降低计算复杂度，我们设计了深度可分离卷积残差（DSCR）模块，以学习图像中的通道和位置信息，这不仅大大减少了参数，而且还改善了局部去噪性能。另外，通过融合从每个DCR模块和DSCR模块提取的特征来构建并行网络，以提高效率并降低训练降噪模型的复杂度。最后，重建（REC）模块旨在通过获得的噪声偏差和给定的噪声图像来构建清晰图像。由于实际MR数据集中缺少真实的图像，因此在一个模拟的T1加权MR图像数据集上进行了定性和定量测试，并扩展了四个真实数据集。实验结果表明，所提出的3D-Parallel-RicianNet网络在峰值信噪比，结构相似性指数和熵度量方面均达到了优于几种最新方法的性能。特别是，我们的方法展示了强大的噪声抑制能力和结构保持能力。