Magnetic resonance imaging, despite of its significant role in today's healthcare, suffers from long image acquisition time which leads to patient discomfort and cost increment. Compressive sensing magnetic resonance imaging, where clinically acceptable images are reconstructed using partially sampled k-space data, is one possible approach to mitigate this problem. The recent evolution in compressive sensing magnetic resonance imaging field is the model based deep learning approach, which is comprised of cascaded convolutional neural network based denoizer and data consistency layer. In this paper, we propose an edge guided model based deep learning approach employing U-net module as an artifact removal unit. The proposed model contains cascaded U-net architectures with interleaved data consistency layer. To effectively retain the fine details in the reconstructed output, along with the image, edge maps of the image were also applied at the input of each stage in the cascaded structure and the edge map loss was incorporated in the objective function along with the pixel loss. Experiments were performed on MR-PD and MR-T1 images with different sampling patterns. Qualitative and quantitative comparison of the results obtained with the proposed method with other model based and deep learning methods validates the superiority of the proposed method in reconstructing high quality magnetic resonance images.

中文翻译：

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一种用于加速磁共振成像重建的边缘引导级联 U-net 方法

尽管磁共振成像在当今的医疗保健中发挥着重要作用，但其图像采集时间长，这会导致患者不适和成本增加。使用部分采样的 k 空间数据重建临床上可接受的图像的压缩传感磁共振成像是缓解此问题的一种可能方法。压缩传感磁共振成像领域的最新发展是基于模型的深度学习方法，它由基于级联卷积神经网络的降噪器和数据一致性层组成。在本文中，我们提出了一种基于边缘引导模型的深度学习方法，采用 U-net 模块作为工件去除单元。所提出的模型包含具有交错数据一致性层的级联 U-net 架构。为了有效地保留重建输出中的精细细节以及图像，图像的边缘图也应用于级联结构中每个阶段的输入，并且边缘图损失与像素损失一起被合并到目标函数中. 对具有不同采样模式的 MR-PD 和 MR-T1 图像进行了实验。使用所提出的方法获得的结果与其他基于模型和深度学习方法的定性和定量比较验证了所提出的方法在重建高质量磁共振图像方面的优越性。对具有不同采样模式的 MR-PD 和 MR-T1 图像进行了实验。使用所提出的方法获得的结果与其他基于模型和深度学习方法的定性和定量比较验证了所提出的方法在重建高质量磁共振图像方面的优越性。对具有不同采样模式的 MR-PD 和 MR-T1 图像进行了实验。使用所提出的方法获得的结果与其他基于模型和深度学习方法的定性和定量比较验证了所提出的方法在重建高质量磁共振图像方面的优越性。