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- Open Access
Dynamical model parameter adjustments in model predictive filtering MR thermometry
© Odéen and Parker; licensee BioMed Central Ltd. 2015
Published: 30 June 2015
In magnetic resonance guided focused ultrasound (MRgFUS) brain applications the fully insonified field-of-view (FOV) is ideally monitored. This can be achieved by k-space subsampling and using a dedicated reconstruction method, such as the previously described model predictive filtering (MPF) method. MPF utilizes the Pennes Bioheat transfer equation (PBTE) and tissue thermal and acoustic properties determined from a low-power pre-treatment heating (which ideally does not deliver any thermal dose, i.e. ΔT<2°C). The accuracy of the determined tissue parameters, and hence of the MPF reconstruction, depends on the low power heating. In this work we investigate dynamical adjustment of model parameters during heating for improved MPF temperature measurement accuracy.
MR and US parameters used for the 5 Low Power heatings (to estimate Q and k), for the fully sampled “truth,” and for the subsampled MPF heatings.
Low Power Heating
Fully Sampled “Truth”
3. Final adjustment Here the average values of Q and k achieved from all time-frames in 2) are used in the reconstruction. Since the average values of Q/k are used, the data cannot be reconstructed until all data is acquired, hindering real-time reconstruction.
Temperature measurement accuracy was evaluated by investigating a local (hottest voxel) and a global (all voxels with ΔT>20°C) root-mean-square-error (RMSE).
Results and conclusions
Mean and standard deviation (STD) of the RMSE for three repeated 40W heatings, for the three implementations of the MPF algorithm.
1) No adjustment
2) Best current estimate
3) Final adjustment
This work was supported by The Focused Ultrasound Foundation, The Ben B. and Iris M. Margolis Foundation, Siemens Healthcare, and NIH grants R01s EB013433, CA134599, and CA172787.
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