Recently, deep learning approaches have been extensively investigated to reconstruct images from accelerated magnetic resonance image (MRI) acquisition. Although these approaches provide significant performance gain compared to compressed sensing MRI (CS-MRI), it is not clear how to choose a suitable network architecture to balance the trade-off between network complexity and performance. Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image. Thus, the expressivity or the representation power is determined by the number of piecewise linear regions. As an extension of this geometric understanding, this paper proposes a systematic geometric approach using bootstrapping and subnetwork aggregation using an attention module to increase the expressivity of the underlying neural network. Our method can be implemented in both k-space domain and image domain that can be trained in an end-to-end manner. Experimental results show that the proposed schemes significantly improve reconstruction performance with negligible complexity increases.

中文翻译：

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提高深度神经网络在 MR 重建中的表现力的几何方法

最近，深度学习方法已被广泛研究以从加速磁共振图像 (MRI) 采集中重建图像。尽管与压缩感知 MRI (CS-MRI) 相比，这些方法提供了显着的性能提升，但尚不清楚如何选择合适的网络架构来平衡网络复杂性和性能之间的权衡。最近，研究表明编码器-解码器卷积神经网络 (CNN) 可以解释为分段线性基类表示，其具体表示由给定输入图像的 ReLU 激活模式决定。因此，表现力或表现力由分段线性区域的数量决定。作为这种几何理解的延伸，本文提出了一种使用引导和子网聚合的系统几何方法，使用注意模块来增加底层神经网络的表现力。我们的方法可以在 k 空间域和图像域中实现，可以以端到端的方式进行训练。实验结果表明，所提出的方案显着提高了重建性能，而复杂度的增加可以忽略不计。