High quality reconstruction algorithm for cardiac magnetic resonance images based on multiscale low rank modeling_Journal of Biomedical Engineering

Authors：

HENG Yang ¹ , CHEN Feng ² , XU Jianfeng ³ ,  TANG Min ^1,4,5

1. Department of Information Engineering, School of Information Science and Technology, Nantong University, Nantong, Jiangsu 226007, P.R.China;
2. Department of Automation, School of Electrical Engineering, Nantong University, Nantong, Jiangsu 226007, P.R.China;
3. Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226007, P.R.China;
4. Tongke School of Microelectronics, Nantong, Jiangsu 226007, P.R.China;
5. Nantong University-Nantong Joint Research Center for Intelligent Information Technology, Nantong, Jiangsu 226007, P.R.China;

Corresponding author：

TANG Min, Email: tangmnt@163.com

Keywords：

cardiac magnetic resonance; multiscale low rank modeling; fast imaging; compressed sensing

DOI：

10.7507/1001-5515.201803024

Video：

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Abstract Full text Figures/Tables Video References Cited by

Taking advantages of the sparsity or compressibility inherent in real world signals, compressed sensing (CS) can collect compressed data at the sampling rate much lower than that needed in Shannon’s theorem. The combination of CS and low rank modeling is used to medical imaging techniques to increase the scanning speed of cardiac magnetic resonance (CMR), alleviate the patients’ suffering and improve the images quality. The alternating direction method of multipliers (ADMM) algorithm is proposed for multiscale low rank matrix decomposition of CMR images. The algorithm performance is evaluated quantitatively by the peak signal to noise ratio (PSNR) and relative l₂ norm error (RLNE), with the human visual system and the local region magnification as the qualitative comparison. Compared to L + S, kt FOCUSS, k-t SPARSE SENSE algorithms, experimental results demonstrate that the proposed algorithm can achieve the best performance indices, and maintain the most detail features and edge contours. The proposed algorithm can encourage the development of fast imaging techniques, and improve the diagnoses values of CMR in clinical applications.

Citation： HENG Yang, CHEN Feng, XU Jianfeng, TANG Min. High quality reconstruction algorithm for cardiac magnetic resonance images based on multiscale low rank modeling. Journal of Biomedical Engineering, 2019, 36(4): 573-580. doi: 10.7507/1001-5515.201803024 Copy

1.	杨正汉, 冯逢, 王霄英. 磁共振成像技术指南——检查规范、临床策略及新技术应用. 北京: 人民军医出版社, 2013: 550-557.
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6.	Candes E J, Tao T. The power of convex relaxation: near-optimal matrix completion. IEEE Trans Inf Theory, 2010, 56(5): 2053-2080.
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8.	Axel L, Otazo R. Accelerated MRI for the assessment of cardiac function. Brit J Radiol, 2016, 89(1063): 20150655.
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10.	陈思吉, 杨晓梅, 吕雪霜. 基于稀疏和低秩先验分离的快速动态 MRI 重建. 计算机应用研究, 2016, 33(10): 3196-3200.
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13.	陈长伟, 朱俊. 基于低秩和稀疏性先验知识的压缩感知图像重构. 计算机应用研究, 2017, 34(3): 949-952.
14.	Dong Weisheng, Shi Guangming, Li Xin, et al. Compressive sensing via nonlocal low-rank regularization. IEEE Trans Image Proc, 2014, 23(8): 3618-3632.
15.	Sun L, Chen J, Zhang X P, et al. Patch-based nonlocal dynamic MRI reconstruction with low-rank prior//2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). Xiamen: IEEE, 2015: 1-6.
16.	Dankova M, Rajmic P, Jirik R. Acceleration of perfusion MRI using locally low-rank plus sparse model//International Conference on Latent Variable Analysis and Signal Separation. Liberec, Czech Republic: Springer International Publishing, 2015: 514-521.
17.	Yu Yeyang, Jin Jin, Liu Feng, et al. Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform. PLoS One, 2014, 9(6): e98441.
18.	Roohi S F, Zonoobi D, Kassim A A, et al. Dynamic MRI reconstruction using low rank plus sparse tensor decomposition//2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016: 1769-1773.
19.	Zhang Xiaoyong, Xie Guoxi, Shi Caiyun, et al. Accelerating PS model-based dynamic cardiac MRI using compressed sensing. Magn Reson Imaging, 2016, 34(2): 81-90.
20.	Ong F, Lustig M. Beyond low rank plus sparse: multiscale low rank matrix decomposition. IEEE J Sel Top Signal Process, 2016, 10(4): 672-687.
21.	Roohi S F, Zonoobi D, Kassim A A. Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI. Pattern Recognit, 2017, 63(3): 667-679.
22.	Jung H, Sung K, Nayak K S, et al. k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med, 2009, 61(1): 103-116.
23.	Jung H, Ye J C. Motion estimated and compensated compressed sensing dynamic magnetic resonance imaging: what we can learn from video compression techniques. Int J Imaging Syst Technol, 2010, 20(2, SI): 81-98.
24.	Kim D, Dyvorne H A, Otazo R, et al. Accelerated phase-contrast cine MRI using k-t sparse-sense. Magn Reson Med, 2012, 67(4): 1054-1064.
25.	Feng Li, Grimm R, Block K T, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med, 2014, 72(3): 707-717.

1. 杨正汉, 冯逢, 王霄英. 磁共振成像技术指南——检查规范、临床策略及新技术应用. 北京: 人民军医出版社, 2013: 550-557.
2. Donoho D L. Compressed sensing. IEEE Trans Inf Theory, 2006, 52(4): 1289-1306.
3. Candes E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math, 2006, 59(8): 1207-1223.
4. Candes E J, Romberg J K, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52(2): 489-509.
5. Candes E J, Recht B. Exact matrix completion via convex optimization. Found Comput Math, 2009, 9(6): 717-772.
6. Candes E J, Tao T. The power of convex relaxation: near-optimal matrix completion. IEEE Trans Inf Theory, 2010, 56(5): 2053-2080.
7. Yang A C, Kretzler M, Sudarski S, et al. Sparse reconstruction techniques in magnetic resonance imaging: methods, applications, and challenges to clinical adoption. Invest Radiol, 2016, 51(6): 349-364.
8. Axel L, Otazo R. Accelerated MRI for the assessment of cardiac function. Brit J Radiol, 2016, 89(1063): 20150655.
9. Otazo R, Candes E, Sodickson D K. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med, 2015, 73(3): 1125-1136.
10. 陈思吉, 杨晓梅, 吕雪霜. 基于稀疏和低秩先验分离的快速动态 MRI 重建. 计算机应用研究, 2016, 33(10): 3196-3200.
11. Yao Jiawen, Xu Zheng, Huang Xiaolei, et al. Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015). Munich, Germany: Springer International Publishing, 2015: 635-642.
12. Ulas C, Gomez P A, Sperl J I, et al. Spatio-temporal MRI reconstruction by enforcing local and global regularity via dynamic total variation and nuclear norm minimization// 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). Prague, Czech Republic: IEEE, 2016: 306-309.
13. 陈长伟, 朱俊. 基于低秩和稀疏性先验知识的压缩感知图像重构. 计算机应用研究, 2017, 34(3): 949-952.
14. Dong Weisheng, Shi Guangming, Li Xin, et al. Compressive sensing via nonlocal low-rank regularization. IEEE Trans Image Proc, 2014, 23(8): 3618-3632.
15. Sun L, Chen J, Zhang X P, et al. Patch-based nonlocal dynamic MRI reconstruction with low-rank prior//2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). Xiamen: IEEE, 2015: 1-6.
16. Dankova M, Rajmic P, Jirik R. Acceleration of perfusion MRI using locally low-rank plus sparse model//International Conference on Latent Variable Analysis and Signal Separation. Liberec, Czech Republic: Springer International Publishing, 2015: 514-521.
17. Yu Yeyang, Jin Jin, Liu Feng, et al. Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform. PLoS One, 2014, 9(6): e98441.
18. Roohi S F, Zonoobi D, Kassim A A, et al. Dynamic MRI reconstruction using low rank plus sparse tensor decomposition//2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016: 1769-1773.
19. Zhang Xiaoyong, Xie Guoxi, Shi Caiyun, et al. Accelerating PS model-based dynamic cardiac MRI using compressed sensing. Magn Reson Imaging, 2016, 34(2): 81-90.
20. Ong F, Lustig M. Beyond low rank plus sparse: multiscale low rank matrix decomposition. IEEE J Sel Top Signal Process, 2016, 10(4): 672-687.
21. Roohi S F, Zonoobi D, Kassim A A. Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI. Pattern Recognit, 2017, 63(3): 667-679.
22. Jung H, Sung K, Nayak K S, et al. k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med, 2009, 61(1): 103-116.
23. Jung H, Ye J C. Motion estimated and compensated compressed sensing dynamic magnetic resonance imaging: what we can learn from video compression techniques. Int J Imaging Syst Technol, 2010, 20(2, SI): 81-98.
24. Kim D, Dyvorne H A, Otazo R, et al. Accelerated phase-contrast cine MRI using k-t sparse-sense. Magn Reson Med, 2012, 67(4): 1054-1064.
25. Feng Li, Grimm R, Block K T, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med, 2014, 72(3): 707-717.

Journal of Biomedical Engineering

High quality reconstruction algorithm for cardiac magnetic resonance images based on multiscale low rank modeling

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