Transcranial ultrasound simulation with uncertainty estimation.

Area of Science:

• Biomedical Engineering
• Acoustics
• Medical Imaging

Background:

• Transcranial ultrasound simulations are vital for predicting exposure parameters in brain therapies.
• Estimating acoustic properties of the skull and brain from CT images introduces significant uncertainty.

Purpose of the Study:

• To develop a computationally efficient method for predicting uncertainty in transcranial ultrasound simulations.
• To assess the accuracy of linear uncertainty propagation against Monte Carlo methods for acoustic field prediction.

Main Methods:

• Linear uncertainty propagation was employed to estimate the impact of acoustic property variations.
• A representative transcranial simulation using a focused bowl transducer at 500 kHz was analyzed.
• Results were compared with unbiased uncertainty estimates derived from Monte Carlo simulations.

Main Results:

• Linear uncertainty propagation provides a computationally efficient approach to predict acoustic field uncertainty.
• The method demonstrated good agreement with Monte Carlo-based uncertainty estimates.
• Accurate prediction of uncertainty is feasible for transcranial ultrasound simulations.

Conclusions:

• Linear uncertainty propagation is a viable and efficient tool for assessing uncertainty in transcranial ultrasound simulations.
• This method can improve the reliability of simulated acoustic fields for brain therapies.
• Efficient uncertainty quantification enhances the predictive power of ultrasound simulations.