title: Accelerating PS model-based dynamic cardiac MRI using compressed sensing.
abstract: High spatiotemporal resolution MRI is a challenging topic in dynamic MRI field. Partial separability (PS) model has been successfully applied to dynamic cardiac MRI by exploiting data redundancy. However, the model requires substantial preprocessing data to accurately estimate the model parameters before image reconstruction. Since compressed sensing (CS) is a potential technique to accelerate MRI by reducing the number of acquired data, the combination of PS and CS, named as Stepped-SparsePS, was introduced to accelerate the preprocessing data acquisition of PS in this work. The proposed Stepped-SparsePS method sequentially reconstructs a set of aliased dynamic images in each channel based on PS model and then the final dynamic images from the aliased images using CS. The results from numerical simulations and in vivo experiments demonstrate that Stepped-SparsePS could significantly reduce data acquisition time while preserving high spatiotemporal resolution.
authors.affiliation: Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China; Paul C. Lauterber Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Paul C. Lauterber Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Beijing Center for Mathematics and imformation Interdisciplinary Science, Beijing, China. Electronic address: gx.xie@siat.ac.cn.
authors.forename: Guoxi
authors.initials: G
authors.lastname: Xie
authors.affiliation: Paul C. Lauterber Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
authors.forename: Caiyun
authors.initials: C
authors.lastname: Shi
authors.affiliation: Paul C. Lauterber Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
authors.forename: Shi
authors.initials: S
authors.lastname: Su
authors.affiliation: School of Information Science and Technology, Northwest University, Xian, China.
authors.forename: Yongqin
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Paul C. Lauterber Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.affiliation: Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China. Electronic address: bqiu@ustc.edu.cn.
authors.forename: Bensheng
authors.initials: B
authors.lastname: Qiu
mesh_terms: D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D005260:Female; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D055815:Young Adult
keywords: Cardiac MRI; Compressed sensing; Low rank-ness; Partial separability model; Sparsity