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Abstract #0075

Joint Bayesian Compressed Sensing with Prior Estimate

Berkin Bilgic1, Elfar Adalsteinsson1, 2

1EECS, Massachusetts Institute of Technology, Cambridge, MA, United States; 2Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States

In clinical MRI, it is routine to acquire images with different contrasts for increased diagnostic power. Yet depending on the imaging sequences, acquiring certain contrasts is relatively faster. Here, a Bayesian compressed sensing (CS) algorithm that uses a fully-sampled image as prior information to help reconstruct images from undersampled k-space is presented. This method substantially improves the reconstruction quality, and allows joint reconstruction of multi-contrast images.