Reza Madankan1, Wolfgang Stefan1, Christopher MacLellan1, Samuel Fahrenholtz1, Drew Mitchell1, R.J. Stafford1, John Hazle1, and David Fuentes1
Compressive
sensing and sparse image reconstruction has received significant attention and
has demonstrated potential in reduction of acquisition times. However, in many
methods, under-sampling strategies are heuristically chosen and empirically
validated. This often leads to a relatively larger number of k-space samples than needed for a
particular application. The presented work develops a mathematically rigorous
and quantitative methodology for k-space
under-sampling with respect to model-based reconstruction of MR thermometry. The key idea of the proposed approach is to detect the useful samples of k-space in order to refine the model, and then the refined
mathematical model is utilized to reconstruct the image.
