Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA; Department of Medical Imaging, University of Arizona, Tucson, AZ, USA.
Department of Medical Imaging, University of Arizona, Tucson, AZ, USA.
Magn Reson Imaging. 2020 Nov;73:152-162. doi: 10.1016/j.mri.2020.08.013. Epub 2020 Sep 1.
A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T mapping results are obtained on brain and abdomen datasets and in vivo T mapping results are obtained on brain and knee datasets. Quantitative results for the T mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T mapping and in vivo brain results for T mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.
提出了一种将加速的径向数据采集与多尺度残差网络（MS-ResNet）相结合的深度学习磁共振参数映射框架，用于图像重建。所提出的监督学习策略使用具有径向欠采样伪影的多对比度图像的输入图像块和无伪影的多对比度图像的目标图像块作为输入。在预处理过程中使用子空间滤波对输入块进行去噪。针对每个解剖结构和弛豫参数，训练一个单独的网络。在脑和腹部数据集上获得 T 映射结果，在脑和膝关节数据集上获得 T 映射结果。膝关节 T 映射的定量结果表明，使用完全采样或欠采样数据训练的 MS-ResNet 优于传统的基于模型的压缩感知方法。这是非常重要的，因为在许多应用中不可能获得完全采样的训练数据。T 映射的脑和腹部的体内结果和 T 映射的脑的体内结果表明，MS-ResNet 可以从高度加速的径向数据采集恢复高质量的对比度加权图像和参数图，同时将重建时间减少两个数量级。所提出的方法能够从高度加速的径向数据采集恢复高质量的对比度加权图像和参数图。所提出的方法快速的图像重建使其成为常规临床应用的良好候选。
