Luan Vu1, Jon Furuyama1, Thomas Goldstein2, Stanley Osher2, Yung-Ya Lin1
1Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA; 2Applied Mathematics, UCLA, Los Angeles, CA, USA
The ability to successfully reconstruct images without having to collect the entire k-space is valuable for parameter optimization and can lead to a higher success rate for the detection of early tumors. Other k-space trajectories can be used to further improve the performance of the algorithm, such as concentrating the kept data points towards the center of k-space, while deleting more points around the edges (results not shown). As a result, strategic k-space sampling (beyond purely random) can be employed to further accelerate data acquisition.