Model predictive filtering MR thermometry: Effects of model inaccuracies, k-space reduction factor, and temperature increase rate.

Purpose: Evaluate effects of model parameter inaccuracies (thermal conductivity, k, and ultrasound power deposition density, Q), k-space reduction factor (R), and rate of temperature increase ( T˙) in a thermal model-based reconstruction for MR-thermometry during focused-ultrasound heating.
Methods: Simulations and ex vivo experiments were performed to investigate the accuracy of the thermal model and the model predictive filtering (MPF) algorithm for varying R and T˙, and their sensitivity to errors in k and Q. Ex vivo data was acquired with a segmented EPI pulse sequence to achieve large field-of-view (192 × 162 × 96 mm) four-dimensional temperature maps with high spatiotemporal resolution (1.5 × 1.5 × 2.0 mm, 1.7 s).
Results: In the simulations, 50% errors in k and Q resulted in maximum temperature root mean square errors (RMSE) of 6 °C for model only and 3 °C for MPF. Using recently developed methods, estimates of k and Q were accurate to within 3%. The RMSE between MPF and true temperature increased with R and T˙. In the ex vivo study the RMSE remained below 0.7 °C for R ranging from 4 to 12 and T˙ of 0.28-0.75 °C/s.
Conclusion: Errors in MPF temperatures occur due to errors in k and Q. These MPF temperature errors increase with increase in R and T˙, but are smaller than those obtained using the thermal model alone.
(© 2015 Wiley Periodicals, Inc.)