This paper proposes a new method for optimizing feature sharing in deep neural network-based, rapid, multicontrast magnetic resonance imaging (MC-MRI). Using the shareable information of MC images for accelerated MC-MRI reconstruction, current algorithms stack the MC images or features without optimizing the sharing protocols, leading to suboptimal reconstruction results. In this paper, we propose a novel feature aggregation and selection scheme in a deep neural network to better leverage the MC features and improve the reconstruction results. First, we propose to extract and use the shareable information by mapping the MC images into multiresolution feature maps with multilevel layers of the neural network. In this way, the extracted features capture complementary image properties, including local patterns from the shallow layers and semantic information from the deep layers. Then, an explicit selection module is designed to compile the extracted features optimally. That is, larger weights are learned to incorporate the constructive, shareable features; and smaller weights are assigned to the unshareable information. We conduct comparative studies on publicly available T2-weighted and T2-weighted fluid attenuated inversion recovery brain images, and the results show that the proposed network consistently outperforms existing algorithms. In addition, the proposed method can recover the images with high fidelity under 16 times acceleration. The ablation studies are conducted to evaluate the effectiveness of the proposed feature aggregation and selection mechanism. The results and the visualization of the weighted features show that the proposed method does effectively improve the usage of the useful features and suppress useless information, leading to overall enhanced reconstruction results. Additionally, the selection module can zero-out repeated and redundant features and improve network efficiency.

中文翻译：

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通过可共享的特征聚合和选择优化多对比 MRI 重建

本文提出了一种在基于深度神经网络的快速多对比磁共振成像 (MC-MRI) 中优化特征共享的新方法。利用 MC 图像的可共享信息进行加速 MC-MRI 重建，当前算法在没有优化共享协议的情况下堆叠 MC 图像或特征，导致重建结果不理想。在本文中，我们在深度神经网络中提出了一种新颖的特征聚合和选择方案，以更好地利用 MC 特征并改善重建结果。首先，我们建议通过将 MC 图像映射到具有多层神经网络的多分辨率特征图来提取和使用可共享信息。通过这种方式，提取的特征捕获互补的图像属性，包括来自浅层的局部模式和来自深层的语义信息。然后，设计一个显式选择模块来优化编译提取的特征。也就是说，学习更大的权重以包含建设性的、可共享的特征；并且较小的权重被分配给不可共享的信息。我们对公开可用的 T2 加权和 T2 加权流体衰减反转恢复脑图像进行了比较研究，结果表明，所提出的网络始终优于现有算法。此外，该方法可以在 16 倍加速度下恢复高保真图像。进行消融研究以评估所提出的特征聚合和选择机制的有效性。结果和加权特征的可视化表明，该方法确实有效地提高了有用特征的使用率并抑制了无用信息，从而导致整体增强的重建结果。此外，选择模块可以将重复和冗余的特征归零，提高网络效率。