Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method
| Autor: | Weiheng Shen, Gang Cao, Cong Jin, Yonggui Zhu, Fanqiang Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science Noise reduction Physics::Medical Physics Imaging phantom Article 03 medical and health sciences symbols.namesake 0302 clinical medicine Approximation error Medical imaging lcsh:Social sciences (General) lcsh:Science (General) Multidisciplinary business.industry Pattern recognition K-space data Total variation denoising MRI reconstruction Noise 030104 developmental biology Compressed sensing Gaussian noise symbols lcsh:H1-99 Artificial intelligence business 030217 neurology & neurosurgery Mathematics lcsh:Q1-390 |
| Zdroj: | Heliyon, Vol 6, Iss 3, Pp e03680-(2020) Heliyon |
| ISSN: | 2405-8440 |
| Popis: | A modified total variation MRI image denoising method is proposed in this paper. First, the proposed method removes the noise in K-space in compressed sensing MRI reconstruction. Then, the removed K-space data is used as a partial frequency observation in compressed sensing MRI model. The proposed method shows better results than RecPF method, LDP method, TVCMRI method, and FCSA method in sparse MRI reconstruction. The proposed method is tested against Shepp-Logan phantom and real MR images corrupted by noise of different intensity level, and it gives better Signal-to-Noise Ratio (SNR), the relative error (ReErr), and the structural similarity (SSIM) than RecPF, LDP, TVCMRI, and FCSA. Medical imaging; Mathematics; Total variation denoising; K-space data; MRI reconstruction; Compressed sensing |
| Databáze: | OpenAIRE |
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