Abstract #2668

Predicting Image Quality of Under-Sampled Data Reconstruction in the Presence of Noise

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.