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Abstract #2634

Non-Iterative Bayesian Reconstruction Algorithm for Undersampled MRI Data

Gengsheng Lawrence Zeng1, Edward V.R. DiBella1

1Radiology, University of Utah, Salt Lake City, UT, United States

A non-iterative Bayesian reconstruction algorithm is derived to reconstruct dynamic undersampled MRI images. The k-space is radially sampled and 24 lines are acquired at each time frame. The Bayesian constraint uses the combination of immediately-before, current, and immediately-after data (referred to as the secondary data) to assist the image reconstruction. Unlike the ad hoc HYPR-type methods, the proposed algorithm is analytically derived and is able to track the object motion. The secondary data must be pre-filtered with a ramp filter before a small fraction of it is added to the current data for image reconstruction, with a modified ramp filter.