多对比度图像graph稀疏学习下的磁共振图像稀疏重建（英文版）

赖宗英1,2, 屈小波2,*, 鲁恒发2, 彭玺3, 郭迪4, 杨钰2, 郭岗5, 陈忠2,*

 

1厦门大学，通信工程系，中国，厦门，361005;

2厦门大学，电子科学系，福建等离子体与磁共振重点研究实验室，中国，厦门，361005;

3中国科学院深圳先进技术研究院医工所劳特伯生物医学成像研究中心，中国，深圳，518055;

4厦门理工学院，计算机与信息工程学院，中国，厦门，361024;

5厦门第二医院，放射科，中国，厦门，361021。

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.

全文： ( https://doi.org/10.1016/j.mri.2017.07.009)
代码： ( https://csrc.xmu.edu.cn/index_cn/project/CS_MRI_GraphWavelet_Multicontrast/Toolbox_GBRWT_Multicontrast_MRI.zip )

联系人: 屈小波 quxiaobo<|at|>xmu.edu.cn

摘要:

随着磁共振应用的广泛应用，在保证磁共振图像质量的前提下加速磁共振成像速度非常重要。通过欠采样的k空间数据并结合稀疏重建能有效的加快磁共振的成像速度。然而，欠采率太高会导致图像细节丢失、图像模糊。为了进一步提高欠采率并加快成像速度，我们利用磁共振成像通常一次成像过程需要采集多对比度图像的特点，以其中一种对比度的图像作为参考图像，训练自适应于当前成像目标的graph稀疏表示。稀疏表示的能力越强，稀疏重建的图像质量越好，从而能降低采样率、加快成像速度。同时，考虑到多对比度图像之间的不配准问题会减弱参考图像训练的稀疏表示对重建目标的实用性，我们构建了图像配准与稀疏重建的二次规划模型。实验结果表明，相同采样率下，本文方法相对于其他方法能得到更好的重建图像质量。同时，由于考虑了图像配准，本文方法对成像目标有一定运动的情况下也有一定的鲁棒性。

关键词: 磁共振成像，图像重建，稀疏表示， 多对比度，图像配准                                                                                                                     

方法:

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.

 

实验结果:

1.活体数据的多对比度图像稀疏重建结果对比。.

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[41] 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[48], TLMRI[15], DLMRI[9] 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.

2. 多对比度图像作为参考图像相对于无参考图像的优势

 

Fig. 5 Proposed method vs. original GBRWT-based MRI reconstruction[16]. The target and reference images are same with that shown in Fig. 2 in the full paper.

 

 

结论:

本文构建了一种结合多对比度图像为参考图像的磁共振图像稀疏重建方法。通过从参考图像学习graph稀疏表示用以目标图像的欠采样重建，并且通过迭代优化多对比度图像的配准及稀疏重建来实现多对比度图像稀疏重建的最优化。实验结果表明，当采样率很低时，多对比度图像作为参考图像能改善稀疏重建的结果，此研究对加速磁共振成像是有意义的。同时，多对比度图像配准及稀疏重建的迭代优化使得本文方法对实验条件要求降低，允许成像目标有一定的运动的情况下的磁共振成像。

 

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