Abstract #2107

Accelerated MR Thermometry in the Presence of Uncertainties

Reza Madankan¹, Wolfgang Stefan¹, Christopher MacLellan¹, Samuel Fahrenholtz¹, Drew Mitchell¹, R.J. Stafford¹, John Hazle¹, and David Fuentes¹

¹Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

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.

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