Analysis of generalized rosette trajectory for compressed sensing MRI.
OBJECTIVE
The application of compressed sensing (CS) technology in magnetic resonance imaging (MRI) is to accelerate the MRI scan speed by incoherent undersampling of k-space data and nonlinear iterative reconstruction of MRI images. This paper generalizes the existing rosette trajectories to configure the sampling patterns for undersampled k-space data acquisition in MRI scans. The arch and curvature characteristics of the generalized rosette trajectories are analyzed to explore their feasibility and advantages for CS reconstruction of MRI images.


METHODS
Two key properties crucial to the CS MRI application, the scan speed and sampling incoherence of the generalized rosette trajectories, are analyzed. The analysis on the scan speed of generalized rosette trajectories is based on the transversal time derived from the curvature of the trajectories, and the sampling incoherence is based on the evaluation of the point spread function for the measurement matrix. The results of analysis are supported by extensive simulations where the performances of rosette, spiral, and radial sampling patterns at different acceleration factors are compared.


RESULTS
It is shown that compared with spiral trajectories, the arch and curvature characteristics of the generalized rosette trajectories are more feasible to meet the physical requirements of undersampled k-space data acquisition in terms of time shortness and scan area. It is further shown that the sampling pattern of the rosette trajectory has higher incoherence than that of the other trajectories and can thus achieve higher reconstruction performance. Reconstruction performances illustrate that the rosette trajectory can achieve about 10% higher peak signal-to-noise ratio than radial and spiral trajectories under the high acceleration factor R = 10.


CONCLUSIONS
The generalized rosette trajectories can be a desirable candidate for CS reconstruction of MRI.
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510009, China.
Ya
Y
Li
School of Mobile Information Engineering, Sun Yat-sen University, Zhuhai 519082, China.
Ran
R
Yang
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn VIC 3122, Australia.
Cishen
C
Zhang
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn VIC 3122, Australia and Department of Electrical and Computer Systems Engineering, Monash University, Clayton VIC 3800, Australia.
Jingxin
J
Zhang
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510009, China.
Sen
S
Jia
Department of Radiology, The Sixth Affiliated Hospital (Gastrointestinal Hospital), Sun Yat-sen University, Guangzhou 510655, China.
Zhiyang
Z
Zhou
D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical