引用: Zongying Lai, Xiaobo Qu, Hengfa Lu, Xi Peng, Di Guo, Yu Yang, Gang Guo, Zhong Chen. Sparse MRI reconstruction using multi-contrast image guided graph representation, Magnetic Resonance Imaging, 43:95-104, 2017.
联系人: 屈小波 quxiaobo<|at|>xmu.edu.cn
关键词: 磁共振成像，图像重建，稀疏表示， 多对比度，图像配准
本文方法主要包含两个部分，一是多对比度图像配准，用于训练graph稀疏表示的全采样参考图像要先配准到待重建的目标图像上，以保证训练得到的稀疏表示能自适应于目标图像的稀疏重建。二是稀疏训练及稀疏重建， 利用参考图像(多对比度图像)的图像块构建加权的graph结构，其中每个图像块为顶点、图像块之间的相似性为权重，通过最短路径访问找到图像像素的光滑排序，构成本文的graph稀疏表示（multi-contrast image guided graph-based redundant wavelet transform, MGBRWT）。最后，利用MGBRWT稀疏表示做目标图像的稀疏重建。图像配准及目标图像的稀疏重建被构建成一个二次规划模型，通过配准与稀疏的迭代求解得到重建图像的最优化结果。本文方法的实验流程如图1所示。
Fig. 1 Flowchart of this work.
Fig. 2 MRI reconstruction with pseudo radial sampling. The (a-c) are the ground-truth, pseudo radial under-sampling with 25% data, and reference, respectively. (d-f) are the reconstructed images using BCS[42,43], PANO and the proposed method; (g-i) magnitude errors using BCS, PANO and the proposed method. Note: (d-f) achieved RLNEs 0.071, 0.056 and 0.048, respectively; (d-f) achieved MSSIMs 0.9508, 0.9581 and 0.9673, respectively.
Fig. 3 Reconstruction with more MRI slices. The (a) and (b) are reconstructed RLNEs and MSSIMs with 20% under-sampled data.
Fig. 4 Reconstructed images using other typical methods. (a) The fully sampled target image; (b) is the unregistered reference image in another contrast; (c) is the registered reference image; (d) denotes the under-sampling pattern; (e-h) are reconstructed images using the l 21-norm, TLMRI, DLMRI and the proposed method with the registered another contrast image in (c) as the reference image. (i-l) are reconstructed errors of l 21-norm, TLMRI, DLMRI reconstructions and the proposed method.
Fig. 5 Proposed method vs. original GBRWT-based MRI reconstruction. The target and reference images are same with that shown in Fig. 2 in the full paper.
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