Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
This work addresses the need for faster and more reliable medical imaging for clinicians, though it appears incremental as it builds on existing normalizing flow and physics-informed techniques.
The paper tackles the problem of slow and uncertain image reconstruction in transcranial ultrasound computed tomography by introducing a method that combines physics-informed and data-driven approaches, resulting in significantly improved imaging speed with calibrated uncertainty quantification.
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. The combinations of these methods results in fast uncertainty-aware image reconstruction that generalizes to a variety of transducer configurations. We evaluate our approach with in silico experiments and demonstrate that it can significantly improve the imaging speed while quantifying uncertainty. We validate the quality of our image reconstructions by comparing against the traditional physics-only method and also verify that our provided uncertainty is calibrated with the error.