Abstract #0071

Joint Bayesian Compressed Sensing for Multi-Contrast Reconstruction

Berkin Bilgic¹, Vivek K. Goyal¹, Elfar Adalsteinsson^{1, 2}

¹EECS, MIT, Cambridge, MA, USA; ²Harvard-MIT Division of Health Sciences & Technology, MIT, Cambridge, MA, USA

Clinical MRI routinely relies on multiple acquisitions of the same region of interest with several different contrasts. We present a reconstruction algorithm based on Bayesian compressed sensing to exploit such multi-contrast acquisitions for accelerated imaging by jointly reconstructing a set of related images from undersampled k-space. Our method offers better performance than when the images are either reconstructed individually with the algorithm by Lustig et al., or jointly with a previously proposed method, M-FOCUSS.