1Department of Communication Engineering, Xiamen University, Xiamen 361005, China;
2Department of Electronic Science, Xiamen University, Xiamen 361005, China;
3Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
4School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen 361024, China
5Department of Radiology, Xiamen 2nd Hospital, Xiamen 361021, China
Citations: 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.
Access to full text ( https://doi.org/10.1016/j.mri.2017.07.009)
Contact : quxiaobo<|at|>xmu.edu.cn
Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans.
KEYWORDS: Magnetic resonance imaging; image reconstruction; sparse representation; multi-contrast; misalignment.
In this sparse MRI reconstruction method using multi-contrast image guided graph representation, multi-contrast images are firstly registered to each other to resist misalignment; then, graph representation is learned from a fully sampled-and-registered multi-contrast image by viewing image patches as vertices and their differences as edges, via a shortest-path-visit to makes pixels ranges smoother, and finally form the graph representation (multi-contrast image guided graph-based redundant wavelet transform, MGBRWT). The MGBRWT is used as the sparse representation to do sparse reconstruction of other contrast MRI images. The flowchart of sparse reconstruction of using MGBRWT is illustrated in Fig.1.
Fig. 1 Flowchart of this work.
1. Sparse MRI reconstruction methods comparisons on in vivo data.
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.
2. The advantages of multi-contrast image as prior
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.
A sparse MRI image reconstruction method by incorporating prior information from multi-contrast reference images is proposed in this work. The prior information is embedded into the graph wavelet-based image sparse representation learnt from the reference image and this information updated with better accuracy by iteratively registering the reference image to the target one. Results on realistic MRI images implies that incorporating multi-contrast images can significantly improve the reconstruction and the iteration between registration and reconstruction is necessary when the acquired is very limited. The proposed approach would be meaningful for highly accelerated MRI imaging
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