Amortized normalizing flows for transcranial ultrasound with uncertainty quantification
Title | Amortized normalizing flows for transcranial ultrasound with uncertainty quantification |
Publication Type | Conference |
Year of Publication | 2023 |
Authors | Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix J. Herrmann |
Conference Name | Medical Imaging with Deep Learning |
Month | 07 |
Keywords | Bayesian Estimation, invertible networks, medical imaging, MIDL, Physics and Machine Learning Hybrid, Uncertainty quantification |
Abstract | 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. |
Notes | (MIDL, Nashville) |
URL | https://slim.gatech.edu/Publications/Public/Conferences/MIDL/2023/orozco2023MIDLanf/paper.pdf |
Citation Key | orozco2023MIDLanf |