基于PCAU-Net的快速多通道磁共振成像方法
收稿日期: 2022-03-29
网络出版日期: 2022-08-26
基金资助
国家自然科学基金面上项目(61372024)
Fast Multi-channel Magnetic Resonance Imaging Based on PCAU-Net
Received date: 2022-03-29
Online published: 2022-08-26
多通道磁共振成像方法采用多个接收线圈同时欠采样k空间以加快成像速度，并基于后处理算法重建图像，但在较高加速因子时，其图像重建质量仍然较差．本文提出了一种基于PCAU-Net的快速多通道磁共振成像方法，将单通道实数U型卷积神经网络拓展到多通道复数卷积神经网络，设计了一种结构不对称的U型网络结构，通过在解码部分减小网络规模以降低模型的复杂度．PCAU-Net网络在跳跃连接前增加了1×1卷积，以实现跨通道信息交互．输入和输出之间利用残差连接为误差的反向传播提供捷径．实验结果表明，使用规则和随机采样模板，在不同加速因子时，相比常规的GRAPPA重建算法和SPIRiT重建方法，本文提出的PCAU-Net方法可高质量重建出磁共振复数图像，并且相比于PCU-Net方法，PCAU-Net减少了模型参数、缩短了训练时间．
施伟成 , 金朝阳 , 叶铮 . 基于PCAU-Net的快速多通道磁共振成像方法[J]. 波谱学杂志, 2023 , 40(1) : 39 -51 . DOI: 10.11938/cjmr20222992
Multi-channel magnetic resonance imaging methods utilize multiple receiving coils to simultaneously under-sample k-space for fast magnetic resonance imaging. Post-processing algorithms were often used to restore the missing information for under-sampled images. However, the quality of reconstructed images is far from satisfying, especially at higher acceleration factor. In this backdrop, a fast multi-channel magnetic resonance imaging method based on parallel complex asymmetric U-Net (PCAU-Net) was proposed, which extends single-channel real-valued U-shaped convolutional neural network to multi-channel complex convolutional neural network. A U-shaped network with an asymmetric structure was designed to reduce the model complexity by reducing the network size in decoding. A 1×1 convolution block was added before skip-connection for PCAU-Net to share cross-channel information. The residual connection between the input and output of the network facilitates loss backpropagation. The experimental results show that by using regular and random sampling templates, the proposed PCAU-Net method can reconstruct the complex magnetic resonance images with high quality compared with the conventional generalized auto-calibrating partially parallel acquisitions (GRAPPA) reconstruction algorithm and iterative self-consistent parallel imaging reconstruction (SPIRiT) algorithm at different acceleration factors. Moreover, compared with the parallel complex U-Net (PCU-Net) method, PCAU-Net reduces model parameters and shortens the training time.
| [1] | EDELMAN R R, WARACH S. Medical progress: Magnetic resonance imaging[J]. New Engl J Med, 1993, 328(11): 785-791. |
| [2] | BUXTON R B. Introduction to functional magnetic resonance imaging: principles and techniques[M]. Cambridge university press, 2009. |
| [3] | MORRIS S A, SLESNICK T C. Magnetic resonance imaging[J]. Visual Guide to Neonatal Cardiology, 2018: 104-108. |
| [4] | LARKMAN D J, NUNES R G. Parallel magnetic resonance imaging[J]. Phys Med Biol, 2007, 52(7): R15-55. |
| [5] | CARLSON J W. An algorithm for NMR imaging reconstruction based on multiple RF receiver coils[J]. J Magn Reson (1969), 1987, 74(2): 376-380. |
| [6] | HUTCHINSON M, RAFF U. Fast MRI data acquisition using multiple detectors[J]. Magn Reson Med, 1988, 6(1): 87-91. |
| [7] | GRISWOLD M A, JAKOB P M, HEIDEMANN R M, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA)[J]. Magn Reson Med, 2002, 47(6): 1202-1210. |
| [8] | PRUESSMANN K P, WEIGER M, SCHEIDEGGER M B, et al. SENSE: sensitivity encoding for fast MRI[J]. Magn Reson Med, 1999, 42(5): 952-962. |
| [9] | LUSTIG M, PAULY J M. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space[J]. Magn Reson Med, 2010, 64(2): 457-471. |
| [10] | DENG L, YU D. Deep learning: Methods and applications[J]. Found Trends Signal, 2014, 7(3,4): 197-387. |
| [11] | LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. |
| [12] | RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]// International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241. |
| [13] | WANG S S, SU Z H, YING L, et al. Accelerating magnetic resonance imaging via deep learning[C]// 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, 2016: 514-517. |
| [14] | SCHLEMPER J, DUAN J, OUYANG C, et al. Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction[EB/OL]. arXiv preprint, arXiv:19-09.11795, 2019. |
| [15] | SCHLEMPER J, QIN C, DUAN J M, et al. Σ-net: Ensembled iterative deep neural networks for accelerated parallel MR image reconstruction[EB/OL]. arXiv preprint, arXiv:1912.05480, 2019. |
| [16] | SRIRAM A, ZBONTAR J, MURRELL T, et al. GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 14315-14322. |
| [17] | LEE D, YOO J, TAK S, et al. Deep residual learning for accelerated MRI using magnitude and phase networks[J]. IEEE Trans Biomed Eng, 2018, 65(9): 1985-1995. |
| [18] | CHENG H T, WANG S S, KE Z W, et al. A Deep recursive cascaded convolutional network for parallel MRI[J]. Chinese J Magn Reson, 2019, 36(4): 437-445. |
| [18] | 程慧涛, 王珊珊, 柯子文, 等. 基于深度递归级联卷积神经网络的并行磁共振成像方法[J]. 波谱学杂志, 2019, 36(4): 437-445. |
| [19] | TRABELSI C, BILANIUK O, ZHANG Y, et al. Deep complex networks[EB/OL]. arXiv preprint, arXiv:1705.09792, 2017. |
| [20] | DEDMARI M A, CONJETI S, ESTRADA S, et al. Complex fully convolutional neural networks for MR image reconstruction[C]// International Workshop on Machine Learning for Medical Image Reconstruction. Springer, Cham, 2018: 30-38. |
| [21] | LIANG H Y, GONG Y, KERVADEC H, et al. Laplacian pyramid-based complex neural network learning for fast MR imaging[C]// Medical Imaging with Deep Learning. PMLR, New York, 2020: 454-464. |
| [22] | WANG S S, CHENG H T, YING L, et al. DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution[J]. Magn Reson Imaging, 2020, 68: 136-147. |
| [23] | 施伟成, 王春林, 金朝阳. 基于PCU-Net网络的快速多通道磁共振成像方法[P]. 中国: CN112946545A, 2021-06-11. |
| [24] | MYRONENKO A. 3D MRI brain tumor segmentation using autoencoder regulari-zation[C]// International MICCAI Brainlesion Workshop. Springer, Cham, 2018: 311-320. |
| [25] | LIN M, CHEN Q, YAN S Q. Network in network[EB/OL]. arXiv preprint arXiv:1312.4400, 2013. |
| [26] | SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, 2015: 1-9. |
| [27] | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, 2016: 770-778. |
| [28] | WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE T Image Process, 2004, 13(4): 600-612. |
| [29] | ZHANG T, PAULY J M, VASANAWALA S S, et al. Coil compression for accelerated imaging with Cartesian sampling[J]. Magn Reson Med, 2013, 69(2): 571-582. |
/
| 〈 |
|
〉 |