title: A robust algorithm for high-resolution dynamic MRI based on the partially separable functions model.
abstract: A recently developed partially separable functions (PSF) model can be used to generate high-resolution dynamic magnetic resonance imaging (MRI). However, this method could not robustly reconstruct high-quality MR images because the estimation of the PSF parameters is often interfered by the noise of the sampled MR data. To improve the robustness of MRI reconstruction using the PSF model, we proposed a new algorithm to estimate the PSF parameters by jointly using robust principal component analysis and modified truncated singular value decomposition regularization methods, instead of using the least square fitting method in the original PSF model. The experiment results of in vivo cardiac MRI demonstrated that the proposed algorithm can robustly reconstruct dynamic MR images with higher signal-to-noise ratio and clearer anatomical structures in comparison with the previous PSF model.
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Feng
authors.forename: Guoxi
authors.initials: G
authors.lastname: Xie
authors.forename: Shan
authors.initials: S
authors.lastname: He
authors.forename: Bo
authors.initials: B
authors.lastname: Kou
authors.forename: Chao
authors.initials: C
authors.lastname: Zou
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Bensheng
authors.initials: B
authors.lastname: Qiu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity