Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method

• Main Authors: Yonggui Zhu, Weiheng Shen, Fanqiang Cheng, Cong Jin, Gang Cao
• Format: Article
• Language: English
• Published: Elsevier 2020-03-01
• Series: Heliyon
• Subjects: Medical imaging; Mathematics; Total variation denoising; K-space data; MRI reconstruction; Compressed sensing
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405844020305259
• ISSN: 2405-8440

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.