title: High spatiotemporal resolution fMRI using partial separability model.
abstract: Blood oxygenation level dependent functional MRI (BOLD fMRI) requires repeatedly scanning the same region to capture neuronal activities, so the sampling data is very sparse along the temporal direction, which offers an opportunity to accelerate the fMRI. In this paper, (k-t) space data is sparsely acquired and reconstructed for BOLD fMRI using a partial separability (PS) model with a ℓ2-norm constraint. The proposed approach achieves a high temporal resolution of 200 ms without compromising spatial resolution (3.5 × 3.5 × 4.0 mm(3)). A simulation based on the EPI data with the right finger tapping experiment demonstrates that the proposed method can realize high spatiotemporal fMRI with accurate reconstruction of the activation regions from highly undersampled data. Meanwhile, preliminary in vivo experiment results also demonstrate the potential application of the proposed method in fMRI.
authors.affiliation: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Caiyun
authors.initials: C
authors.lastname: Shi
authors.affiliation: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.|Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China.
authors.forename: Guoxi
authors.initials: G
authors.lastname: Xie
authors.affiliation: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.|Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China.
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
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: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China.
authors.forename: Bensheng
authors.initials: B
authors.lastname: Qiu
authors.affiliation: Shenzhen Key Laboratory for MRI, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis
keywords: BOLD; fMRI (function magnetic resonance imaging); partial separability (PS) model; sparse sampling