Patrick Virtue1, Martin Uecker1, Michael Elad2, Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States; 2Computer Science, Technion - Israel Institute of Technology, Haifa, Israel
The results of under-sampling reconstruction algorithms are often compared to a fully-sampled reconstruction. This comparison is overly optimistic because even if the reconstruction removes the aliasing due to under-sampling, we would still have an inherent loss of SNR due to the reduced acquisition time. We present a process to predict image quality for a given reconstruction technique and under-sampling pattern. Using this prediction as a gold standard enables a fair comparison for reconstruction results and provides an efficient means of quickly assessing reconstruction algorithms and parameters.