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) over dot) 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) over dot, 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 x 162 x 96 mm) four-dimensional temperature maps with high spatiotemporal resolution (1.5 x 1.5 x 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 degrees C for model only and 3 degrees 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) over dot. In the ex vivo study the RMSE remained below 0.7 degrees C for R ranging from 4 to 12 and (T) over dot of 0.28-0.75 degrees 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) over dot, but are smaller than those obtained using the thermal model alone. (C) 2015 Wiley Periodicals, Inc.