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Year : 2020  |  Volume : 16  |  Issue : 5  |  Page : 1171-1176

Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion

1 Physical Examination Office, Health Commission of Shandong Province, Jinan, Shandong, China
2 Department of Infrastructure Management, Qilu Hospital of Shandong University, Jinan, Shandong, China
3 Department of Intervention Medicine, The Second Hospital of Shandong University, Jinan, Shandong, China
4 School of Mathematics, Shandong University, Jinan, Shandong, China
5 School of Mathematics and Physics, Qingdao University of Science and Technology, Shandong, China
6 College of Basic Medicine, Jining Medical University, Shenghua, China
7 Department of Radiology, Peking University Third Hospital; Beijing Key Laboratory of Magnetic Resonance Imaging Equipment and Technique, Beijing, China

Correspondence Address:
Shujun Fu
School of Mathematics, Shandong University, Jinan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_656_20

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Background: Targetable drug delivery is an important method for the treatment of liver tumors. For the quantitative analysis of drug diffusion, the establishment of a method for information collection and characterization of extracellular space is developed by imaging analysis of magnetic resonance imaging (MRI) sequences. In this paper, we smoothed out interferential part in scanned digital MRI images. Materials and Methods: Making full use of priors of low rank, nonlocal self-similarity, and regularized sparsity-promoting entropy, a block-matching regularized entropy minimization algorithm is proposed. Sparsity-promoting entropy function produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternating direction method of multipliers algorithm is proposed to iteratively solve the problem above. Results and Conclusions: Experiments on simulated and real images reveal that the proposed method obtains better image restorations compared with some state-of-the-art methods, where most information is recovered and few artifacts are produced.

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