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

Abstract

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 6C for model only and 3C 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 0.7C for R ranging from 4 to 12 and T of 0.28-0.75C/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.