DeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissue
This work addresses a critical bottleneck for clinicians and researchers in medical imaging by enhancing the accuracy of inverse problem solutions in optoacoustic imaging, though it is incremental as it builds on existing surrogate model methods.
The paper tackled the problem of inaccurate derivatives in neural surrogate models for light transport in tissue, which hinder high-fidelity reconstructions in optoacoustic imaging, by using Sobolev training to improve derivative accuracy and reduce generalization error for in-distribution and out-of-distribution samples.
In optoacoustic imaging, recovering the absorption coefficients of tissue by inverting the light transport remains a challenging problem. Improvements in solving this problem can greatly benefit the clinical value of optoacoustic imaging. Existing variational inversion methods require an accurate and differentiable model of this light transport. As neural surrogate models allow fast and differentiable simulations of complex physical processes, they are considered promising candidates to be used in solving such inverse problems. However, there are in general no guarantees that the derivatives of these surrogate models accurately match those of the underlying physical operator. As accurate derivatives are central to solving inverse problems, errors in the model derivative can considerably hinder high fidelity reconstructions. To overcome this limitation, we present a surrogate model for light transport in tissue that uses Sobolev training to improve the accuracy of the model derivatives. Additionally, the form of Sobolev training we used is suitable for high-dimensional models in general. Our results demonstrate that Sobolev training for a light transport surrogate model not only improves derivative accuracy but also reduces generalization error for in-distribution and out-of-distribution samples. These improvements promise to considerably enhance the utility of the surrogate model in downstream tasks, especially in solving inverse problems.