Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results than conventional two-dimensional (2D) processing. However, deepening and widening 3D networks increases training difficulty significantly due to the large number of parameters and small number of training samples. Thus, we propose a 3D convolutional neural network (CNN) for SR of magnetic resonance (MR) and computer tomography (CT) volumetric data called ParallelNet using parallel connections. We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with a few parameters. As a result, the model thoroughly learns more feature maps with larger receptive fields. In addition, to further improve accuracy, we present an efficient version of ParallelNet (called VolumeNet), which reduces the number of parameters and deepens ParallelNet using a proposed lightweight building block module called the Queue module. Unlike most lightweight CNNs based on depthwise convolutions, the Queue module is primarily constructed using separable 2D cross-channel convolutions. As a result, the number of network parameters and computational complexity can be reduced significantly while maintaining accuracy due to full channel fusion. Experimental results demonstrate that the proposed VolumeNet significantly reduces the number of model parameters and achieves high precision results compared to state-of-the-art methods in tasks of brain MR image SR, abdomen CT image SR, and reconstruction of super-resolution 7T-like images from their 3T counterparts.

中文翻译：

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VolumeNet：用于超分辨率 MR 和 CT 体积数据的轻量级并行网络

基于深度学习的超分辨率（SR）技术在计算机视觉领域普遍取得了优异的表现。最近，已经证明用于医疗体积数据的三维 (3D) SR 比传统的二维 (2D) 处理提供更好的视觉效果。然而，由于参数数量多，训练样本数量少，加深和加宽 3D 网络会显着增加训练难度。因此，我们提出了一种用于磁共振 (MR) 和计算机断层扫描 (CT) 体积数据的 3D 卷积神经网络 (CNN)，称为使用并行连接的 ParallelNet。我们基于组卷积和特征聚合构建并行连接结构，以构建尽可能宽的 3D CNN，参数较少。其结果，该模型彻底学习了更多具有更大感受野的特征图。此外，为了进一步提高准确性，我们提出了一个高效版本的 ParallelNet（称为 VolumeNet），它减少了参数数量并使用一种称为 Queue 模块的轻量级构建块模块来加深 ParallelNet。与大多数基于深度卷积的轻量级 CNN 不同，队列模块主要使用可分离的 2D 跨通道卷积构建。因此，由于全通道融合，可以在保持准确性的同时显着减少网络参数的数量和计算复杂度。实验结果表明，在脑 MR 图像 SR 任务中，与最先进的方法相比，所提出的 VolumeNet 显着减少了模型参数的数量并获得了高精度的结果，