Jingyuan Lyu1,
Yuchou Chang2, Leslie Ying1
1Department
of Biomedical Engineering, Department of Electrical Engineering, The State
University of New York at Buffalo, Buffalo, NY, United States; 2Department
of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee,
Milwaukee, WI, United States
In GRAPPA, the computational time increases with the number of channels and the amount of ACS data. To address this issue, different from the existing approaches that compress the large number of physical channels to fewer virtual channels, we propose to use random projections to reduce the dimension of the problem in the calibration step. Experimental results show that randomly projecting the data onto a lower-dimensional subspace yields results comparable to those of traditional GRAPPA, but is computationally less expensive.