Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification

Understand

We present a novel approach to transcranial ultrasound computed tomography that utilizes normalizing flows to improve the speed of imaging and provide Bayesian uncertainty quantification.

• Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image.
• We make use of a physics-informed summary statistic to incorporate the known ultrasound physics with the goal of compressing large incoming observations.
• This compression enables efficient training of the normalizing flow and standardizes the size of the data regardless of imaging configurations.