High quality reconstruction algorithm for cardiac magnetic resonance images based on multiscale low rank modeling.
10.7507/1001-5515.201803024
- Author:
Yang HENG
1
;
Feng CHEN
2
;
Jianfeng XU
3
;
Min TANG
4
,
5
,
6
Author Information
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. Department of Information Engineering, School of Information Science and Technology, Nantong University, Nantong, Jiangsu 226007, P.R.China
5. Tongke School of Microelectronics, Nantong, Jiangsu 226007, P.R.China
6. Nantong University-Nantong Joint Research Center for Intelligent Information Technology, Nantong, Jiangsu 226007, P.R.China.tangmnt@163.com.
- Publication Type:Journal Article
- Keywords:
cardiac magnetic resonance;
compressed sensing;
fast imaging;
multiscale low rank modeling
- MeSH:
Algorithms;
Heart;
diagnostic imaging;
Humans;
Magnetic Resonance Imaging;
Signal-To-Noise Ratio
- From:
Journal of Biomedical Engineering
2019;36(4):573-580
- CountryChina
- Language:Chinese
-
Abstract:
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 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.
