The accuracy and precision of two non-invasive, magnetic resonance-guided focused ultrasound-based thermal diffusivity estimation methods

Abstract

Purpose: The use of correct tissue thermal diffusivity values is necessary for making accurate thermal modelling predictions during magnetic resonance-guided focused ultrasound (MRgFUS) treatment planning. This study evaluates the accuracy and precision of two non-invasive thermal diffusivity estimation methods, a Gaussian temperature method and a Gaussian specific absorption rate (SAR) method. Materials and methods: Both methods utilise MRgFUS temperature data obtained during cooling following a short (<25 s) heating pulse. The Gaussian SAR method can also use temperatures obtained during heating. Experiments were performed at low heating levels (ΔT∼10 °C) in ex vivo pork muscle and in vivo rabbit back muscle. The non-invasive MRgFUS thermal diffusivity estimates were compared with measurements from two standard invasive methods. Results: Both non-invasive methods accurately estimated thermal diffusivity when using MR temperature cooling data (overall ex vivo error <6%, in vivo <12%). Including heating data in the Gaussian SAR method further reduced errors (ex vivo error <2%, in vivo <3%). The significantly lower standard deviation values (p < 0.03) of the Gaussian SAR method indicated that it had better precision than the Gaussian temperature method. Conclusions: With repeated sonications, either MR-based method could provide accurate thermal diffusivity values for MRgFUS therapies. Fitting to more data simultaneously likely made the Gaussian SAR method less susceptible to noise, and using heating data helped it converge more consistently to the FUS fitting parameters and thermal diffusivity. These effects led to the improved precision of the Gaussian SAR method.

• High intensity focused ultrasound
• magnetic resonance temperature imaging
• mathematical modelling
• thermal properties
• treatment planning

Acknowledgements

The authors would like to thank Dennis Parker, Nick Todd, and Josh de Bever for their contributions to this work.