Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, People's Republic of China.
Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, People's Republic of China.
Phys Med Biol. 2024 Oct 4;69(20). doi: 10.1088/1361-6560/ad7e78.
Given the different noise distribution information of global and local magnetic resonance (MR) images, this study aims to extend the current work on convolutional neural networks that preserve global structure and local details in MR image denoising tasks.This study proposed a parallel and serial network for denoising 3D MR images, called 3D-PSNet. We use the residual depthwise separable convolution block to learn the local information of the feature map, reduce the network parameters, and thus improve the training speed and parameter efficiency. In addition, we consider the feature extraction of the global image and utilize residual dilated convolution to process the feature map to expand the receptive field of the network and avoid the loss of global information. Finally, we combine both of them to form a parallel network. What's more, we integrate reinforced residual convolution blocks with dense connections to form serial network branches, which can remove redundant information and refine features to further obtain accurate noise information.The peak signal-to-noise ratio, structural similarity index measure, and root mean square error metrics of 3D-PSNet are as high as 47.79%, 99.81%, and 0.40%, respectively, achieving competitive denoising effect on three public datasets. The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.The proposed 3D-PSNet takes advantage of multi-scale receptive fields, local feature extraction and residual dense connections to more effectively restore the global structure and local fine features in MR images, and is expected to help doctors quickly and accurately diagnose patients' conditions.
鉴于全局和局部磁共振（MR）图像的不同噪声分布信息，本研究旨在扩展当前关于卷积神经网络的工作，在 MR 图像去噪任务中保留全局结构和局部细节。本研究提出了一种用于 3D MR 图像去噪的并行和串行网络，称为 3D-PSNet。我们使用残差深度可分离卷积块来学习特征图的局部信息，减少网络参数，从而提高训练速度和参数效率。此外，我们考虑全局图像的特征提取，并利用残差扩张卷积来处理特征图，以扩展网络的感受野并避免全局信息的丢失。最后，我们将它们组合在一起形成一个并行网络。此外，我们还将强化残差卷积块与密集连接相结合，形成串行网络分支，以去除冗余信息并细化特征，从而进一步获取准确的噪声信息。3D-PSNet 的峰值信噪比、结构相似性指数测量和均方根误差指标分别高达 47.79%、99.81%和 0.40%，在三个公共数据集上均取得了有竞争力的去噪效果。消融实验表明，所有设计的模块在两个数据集的所有评估指标上都具有有效性。所提出的 3D-PSNet 利用多尺度感受野、局部特征提取和残差密集连接，更有效地恢复了 MR 图像中的全局结构和局部精细特征，有望帮助医生快速准确地诊断患者的病情。
