title: Compressed-sensing dynamic MR imaging with partially known support.
abstract: Compressed Sensing (CS) has recently been applied to dynamic MRI to improve the acquisition speed. Existing methods exploit the information that the dynamic images are sparse in the spatial and temporal-frequency (y-f) domain. In this paper, we propose to use the additional prior information in CS reconstruction that the support of y-f space is partially known from the motion pattern of dynamic MR images. The reconstruction is then formulated as a truncated ℓ(1) minimization problem. Experimental results show that the dynamic image reconstruction quality of the proposed method is superior to that of existing methods when the same number of measurements is used.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI 53201 USA. liangd@uwm.edu
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
authors.forename: Leslie
authors.initials: L
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001698:Biomedical Engineering / Q000379:methods; D044962:Data Compression / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors