Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach
Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach
复制标题
感兴趣区域欠采样 MRI 重建：深度卷积神经网络方法
DOI：
10.1016/j.mri.2019.07.010
复制
发表时间：
2019-11-01
影响因子：
2.5
通讯作者：
Paisley, John
中科院分区：
文献类型：
--
作者：
Sun, Liyan;Fan, Zhiwen;Paisley, John
压缩传感可利用欠采样 k 空间数据进行快速磁共振成像 (MRI) 重建。然而，在大多数现有的MRI重建模型中，对整个MR图像进行目标重建，而没有考虑特定的组织区域。这可能无法强调用于诊断的重要组织和感兴趣区域（ROI）组织的重建准确性。在一些基于ROI的MRI重建模型中，ROI掩模是由人类专家提前提取的，当MRI数据集太大时，这很费力。在本文中，我们提出了一种用于 ROI MRI 重建的深度神经网络架构，称为 ROIRecNet，以提高欠采样 MRI 中 ROI 区域的重建精度。在该模型中，我们通过将初始重建的 MRI 从预训练的 MRI 重建网络 (RecNet) 馈送到预训练的 MRI 分割网络 (ROINet) 来获得 ROI 掩模。然后，我们使用生成的 ROI 掩码通过二进制加权 l(2) 损失函数对 RecNet 进行微调。由此产生的 ROIRecNet 可以更加关注投资回报率。我们在 MRBrainS13 数据集上测试模型，以不同的脑组织作为 ROI。实验表明，所提出的ROIRecNet可以显着提高感兴趣区域的重建质量。
Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with undersampled k-space data. However, in most existing MRI reconstruction models, the whole MR image is targeted and reconstructed without taking specific tissue regions into consideration. This may fails to emphasize the reconstruction accuracy on important and region-of-interest (ROI) tissues for diagnosis. In some ROI-based MRI reconstruction models, the ROI mask is extracted by human experts in advance, which is laborious when the MRI datasets are too large. In this paper, we propose a deep neural network architecture for ROI MRI reconstruction called ROIRecNet to improve reconstruction accuracy of the ROI regions in under-sampled MRI. In the model, we obtain the ROI masks by feeding an initially reconstructed MRI from a pre-trained MRI reconstruction network (RecNet) to a pre-trained MRI segmentation network (ROINet). Then we fine-tune the RecNet with a binary weighted l(2) loss function using the produced ROI mask. The resulting ROIRecNet can offer more focus on the ROI. We test the model on the MRBrainS13 dataset with different brain tissues being ROIs. The experiment shows the proposed ROIRecNet can significantly improve the reconstruction quality of the region of interest.