High quality reconstruction algorithm for cardiac magnetic resonance images based on multiscale low rank modeling / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 573-580, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-774169
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Diagnostic Imaging
/
Magnetic Resonance Imaging
/
Signal-To-Noise Ratio
/
Heart
Type of study:
Diagnostic study
/
Prognostic study
/
Qualitative research
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2019
Type:
Article
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