title: Linear Dynamic Sparse Modelling for functional MR imaging.
abstract: The reconstruction quality of a functional MRI sequence is determined by reconstruction algorithms as well as the information obtained from measurements. In this paper, we propose a Linear Dynamic Sparse Modelling method which is composed of measurement design and reconstruction processes to improve the image quality from both aspects. This method models an fMRI sequence as a linear dynamic sparse model which is based on a key assumption that variations of functional MR images are sparse over time in the wavelet domain. The Hierarchical Bayesian Kalman filter which follows the model is employed to implement the reconstruction process. To accomplish the measurement design process, we propose an Informative Measurement Design (IMD) method. The IMD method addresses the measurement design problem of selecting k feasible measurements such that the mutual information between the unknown image and measurements is maximised, where k is a given budget and the mutual information is extracted from the linear dynamic sparse model. The experimental results demonstrated that our proposed method succeeded in boosting the quality of functional MR images.
authors.affiliation: Data Science Institute, Imperial College London, London, UK. shu.yan09@imperial.ac.uk.
authors.forename: Shulin
authors.initials: S
authors.lastname: Yan
authors.affiliation: Data Science Institute, Imperial College London, London, UK.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
authors.affiliation: Data Science Institute, Imperial College London, London, UK.
authors.forename: Chao
authors.initials: C
authors.lastname: Wu
authors.affiliation: Data Science Institute, Imperial College London, London, UK.
authors.forename: Yike
authors.initials: Y
authors.lastname: Guo
keywords: Kalman filter; Linear Dynamic Sparse Modelling; Mutual information; Sparse Bayesian Learning