Huajun She1, Rong-Rong Chen1, Dong Liang2, 3, Edward DiBella4, Leslie Ying3
1Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States; 2Shenzhen Institutes of Advanced Technology, Shenzhen, China; 3Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI, United States; 4Department of Radiology, University of Utah, Salt Lake City, UT, United States
This work investigates the blind multichannel under-sampling problem where both the channel functions and signal are reconstructed simultaneously. We propose a new approach to blind compressed sensing in the context of parallel imaging where the sensing matrix is not known exactly and needs to be reconstructed. The proposed method effectively incorporates the sparseness of both the desired image and coil sensitivities in reconstruction of both the coil sensitivities and image simultaneously from randomly-undersampled, multichannel k-space data. The proposed method is compared with Sparse SENSE and L1 SPIRiT and demonstrates a significant improvement in image quality at high reduction factors.