  An accelerated model-based information theoretic approach is presented to
perform the task of Magnetic Resonance (MR) thermal image reconstruction from a
limited number of observed samples on k-space. The key idea of the proposed
approach is to utilize information theoretic techniques to optimally detect
samples of k-space that are information rich with respect to a model of the
thermal data acquisition. These highly informative k-space samples are then
used to refine the mathematical model and reconstruct the image. The
information theoretic reconstruction is demonstrated retrospectively in data
acquired during MR guided Laser Induced Thermal Therapy (MRgLITT) procedures.
The approach demonstrates that locations of high-information content with
respect to a model based reconstruction of MR thermometry may be quantitatively
identified. The predicted locations of high-information content are sorted and
retrospectively extracted from the fully sampled k-space measurements data set.
The effect of interactively increasing the predicted number of data points used
in the subsampled reconstruction is quantified using the L2-norm of the
distance between the subsampled and fully sampled reconstruction. Performance
of the proposed approach is also compared with clinically available subsampling
techniques (rectilinear subsampling and variable-density Poisson disk
undersampling). It is shown that the proposed subsampling scheme results in
accurate reconstructions using small fraction of k-space points and suggest
that the reconstruction technique may be useful in improving the efficiency of
the thermometry data temporal resolution.
