Graduate School of Science, Engineering and Technology, Istanbul Technical University, Istanbul, Turkey.
Electronics and Communication Engineering Department, Istanbul Technical University, Istanbul, Turkey.
Comput Methods Programs Biomed. 2021 Aug;207:106151. doi: 10.1016/j.cmpb.2021.106151. Epub 2021 May 11.
Background and Objective: Recent studies in deep learning reveal that the U-Net stands out among the diverse set of deep models as an effective network structure, especially for imaging inverse problems. Initially, the U-Net model was developed to solve segmentation problems for biomedical images while using an annotated dataset. In this paper, we will study a novel application of the U-Net structure for the important inverse problem of MRI reconstruction. Deep networks are particularly efficient for the speed-up of the MR image reconstruction process by decreasing the data acquisition time, and they can significantly reduce the aliasing artifacts caused by the undersampling in the k-space. Our aim is to develop a novel and efficient cascaded U-Net framework for reconstructing MR images from undersampled k-space data. The new framework should have improved reconstruction performance when compared to competing methodologies.
In this paper, a novel cascaded framework utilizing the U-Net as a sub-block is being proposed. The introduced U-Net cascade structure is applied to the magnetic resonance image reconstruction problem. The connection between the cascaded U-Nets is realized in the form of a recently developed projection-based updated data consistency layer. The novel structure is implemented in the PyTorch environment, which is one of the standards for deep learning implementations. The recently created fastMRI dataset which forms an important benchmark for MRI reconstruction is used for training and testing purposes.
We present simulation results comparing the novel method with a variety of competitive deep networks. The new cascaded U-Net structures PSNR performance stands on average 1.28 dB higher than the baseline U-Net. The improvement, when compared to the standard CNN, is on average 3.32 dB.
The proposed cascaded U-Net configuration results in an improved reconstruction performance when compared to the CNN, the cascaded CNN, and also the singular U-Net structures, where the singular U-Net forms the baseline reconstruction method from the fastMRI package. The use of the projection-based updated data consistency layer also leads to improved quantitative (including SSIM, PSNR, and NMSE results) and qualitative results when compared to the use of the conventional data consistency layer.
深度学习的最新研究表明，U-Net 在各种深度模型中脱颖而出，成为一种有效的网络结构，特别是对于成像反问题。最初，U-Net 模型是为了解决生物医学图像的分割问题而开发的，同时使用了带注释的数据集。在本文中，我们将研究 U-Net 结构在重要的 MRI 重建反问题中的新应用。深度网络对于通过减少数据采集时间来加速磁共振图像重建过程特别有效，并且它们可以显著减少欠采样在 k 空间中引起的混叠伪影。我们的目标是开发一种新颖有效的级联 U-Net 框架，用于从欠采样 k 空间数据重建磁共振图像。与竞争方法相比，新框架应具有更好的重建性能。
本文提出了一种利用 U-Net 作为子块的新型级联框架。所提出的级联 U-Net 结构应用于磁共振图像重建问题。级联 U-Nets 之间的连接以最近开发的基于投影的更新数据一致性层的形式实现。新结构在 PyTorch 环境中实现，这是深度学习实现的标准之一。使用最近创建的 fastMRI 数据集进行训练和测试，该数据集是 MRI 重建的重要基准。
我们展示了将新方法与各种竞争深度网络进行比较的模拟结果。与基线 U-Net 相比，新的级联 U-Net 结构的 PSNR 性能平均提高了 1.28dB。与标准 CNN 相比，平均提高了 3.32dB。
与 CNN、级联 CNN 以及单一的 U-Net 结构相比，所提出的级联 U-Net 配置可提高重建性能，其中单一的 U-Net 构成了 fastMRI 包中基本的重建方法。与使用传统数据一致性层相比，使用基于投影的更新数据一致性层还可导致定量（包括 SSIM、PSNR 和 NMSE 结果）和定性结果的改善。
