Model predictive filtering MR thermometry: Effects of model inaccuracies, k-space reduction factor, and temperature increase rate.
Model predictive filtering MR thermometry: Effects of model inaccuracies, k-space reduction factor, and temperature increase rate.
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DOI：
10.1002/mrm.25622
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发表时间：
2016-01
影响因子：
3.3
通讯作者：
Parker DL
中科院分区：
文献类型：
--
作者：
Odéen H;Todd N;Dillon C;Payne A;Parker DL
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 (Ṫ) in a thermal model-based reconstruction for MR-thermometry during focused-ultrasound heating. 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 Ṫ, 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 (192x162x96mm) 4D temperature maps with high spatio-temporal resolution (1.5x1.5x2.0mm, 1.7s). 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 Ṫ. In the ex-vivo study the RMSE remained below 0.7°C for R ranging from 4–12 and Ṫ of 0.28–0.75°C/s. Errors in MPF temperatures occur due to errors in k and Q. These MPF temperature errors increase with increase in R and Ṫ, but are smaller than those obtained using the thermal model alone.
