Abstract #3232

Calibration-free Parallel Imaging Using Randomly Undersampled Multichannel Blind Deconvolution (MALBEC)

Jingyuan Lyu¹, Ukash Nakarmi¹, Yihang Zhou¹, Chaoyi Zhang¹, and Leslie Ying^1,2

¹Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, ²Biomedical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States

This abstract presents a novel reconstruction method for parallel imaging that does not require auto-calibration data. The method formulates the image reconstruction problem as a multichannel blind deconvolution problem in k-space where the data are randomly undersampled in all channels. Under this formulation, the k-spaces of the desired image and coil sensitivities are jointly recovered by finding a rank-1 matrix subject to the data consistent constraint. Experimental results demonstrate that the proposed method is able to achieve better reconstruction results than the state-of-the-art calibration-less parallel imaging methods.

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