pFISTA-SENSE-ResNet for Parallel MRI Reconstruction ( [Chinese] )
Tieyuan Lu1, Xinlin Zhang1, Yihui Huang1, Di Guo2, Feng Huang3, Qin Xu3, Yuhan Hu1, Lin Ou-Yang4,5, Jianzhong Lin6, Zhiping Yan 7, Xiaobo Qu1**
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
2School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen 361024, China.
3Neusoft Medical System, Shanghai 200241, China.;
4Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Zhangzhou 363000, China..
5Institute of Medical Imaging of Medical College of Xiamen University, Zhangzhou 363000, China.;
6Magnetic Resonance Center, Zhongshan Hospital Xiamen University, Xiamen 361004, China..
7Department of Radiology, Fujian Medical University Xiamen Humanity Hospital, Xiamen 361000, China..
Citation: Tieyuan Lu, Xinlin Zhang, Yihui Huang, Di Guo, Feng Huang, Qin Xu, Yuhan Hu, Lin Ou-Yang, Jianzhong Lin, Zhiping Yan, Xiaobo Qu*, pFISTA-SENSE-ResNet for Parallel MRI Reconstruction,Journal of Magnetic Resonance, DOI: 10.1016/j.jmr.2020.106790, 2020.
Access to full text:https://authors.elsevier.com/a/1bW4K3u0yjN8GV
Magnetic resonance imaging(MRI)has been widely applied in clinical diagnosis. However, it is limited by its long data acquisition time. Although the imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstructed images with a fast computation speed remains a challenge. Recently, deep learning methods have attracted a lot of attention for encouraging reconstruction results, but they are lack of proper interpretability for neural networks. In this work, in order to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. Experimental results on a public knee dataset indicate that, as compared with the state-of-the-art deep learning-based and optimization-based methods, the proposed network achieves lower error in reconstruction and is more robust under different samplings.
KEYWORDS: Magnetic resonance imaging, image reconstruction, deep learning, sparse learning, network interpretability.
As key problems in MRI reconstruction, better image quality and faster reconstruction are still great challenges and worthy of investigation. To achieve a lower reconstruction error with compressed sensing (CS) methods, several data-free sparse transforms, including the fixed ones and the adaptive ones have been used. Recently, the database-based deep learning method has been applied in many applications, including biological magnetic resonance spectroscopy, medical image analysis, and accelerating MRI image reconstruction, and obtained outstanding performances with the powerful deep convolution neural network (CNN). However, comparing with optimization-based iterative algorithms, CNN appears to be a black box in the image reconstruction process. To improve the interpretability, some optimization-based algorithms have been unrolled and yielded as deep learning networks, such as: VN and MoDL. However, the single convolution layer used as the regular term in VN limits the reconstruction quality. MoDL incorporates the conjugate gradient optimization into the network to solve the data consistency subproblem which slows down the reconstruction speed.
We unroll the iteration of pFISTA-SENSE as the basic structure of our proposed network, and the iteration block can be written as:
whereis the sensitivity map of thecoil, is the Fourier transform, is the undersampling matrix,is the acquired k-space data of thecoil, is the step size,anddenote conjugate transpose and transpose respectively, is a pointwise soft-thresholding function, .and are the forward operation and the backward operation, both of which are made up of CNN blocks.
The structure of the proposed network is shown in Figure 1.
3. Main Results
he proposed method is compared with data-free pFISTA-SENSE and two data-based deep learning methods: VN and MoDL. Figure 2 shows the reconstruction result of a public knee dataset under acceleration factor of 7. Image recovered by pFISTA-SENSE-ResNet is closer to the fully sampled images than others. First, the result of pFISTA-SENSE-ResNet (Figure 2(e)) is less blurred than pFISTA-SENSE (Figure 2(b)), and the undersampling artifacts are well suppressed compared with MoDL (Figure 2(d)), as the yellow arrow pointing in Figure 2(d). Second, small details are recovered more faithfully than other methods, such as the red arrows pointing in Figure 2. Besides, less error is observed at the reconstruction results of pFISTA-SENSE-ResNet according to the error maps.
This work was supported in part by National Key R&D Program of China (2017YFC0108703), National Natural Science Foundation of China (61971361, 61871341, and 61811530021), Natural Science Foundation of Fujian Province of China (2018J06018), Fundamental Research Funds for the Central Universities (20720180056), and Xiamen University Nanqiang Outstanding Talents Programme. The authors would like thank the GPU donated by NVIDIA Corporation. The authors would like thank Yonggui Yang, Gang Guo of the Department of Radiology of the Second Affiliated Hospital of Xiamen Medical College, and Lijun Bao of Department of Electronic Science of Xiamen University for revising the paper. The authors would like thank to the reviewers for their valuable suggestions on this paper.
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