dorsal/arxiv
View SchemaDeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissue
| Authors | Philipp Haim, Vasilis Ntziachristos, Torsten Enßlin, Dominik Jüstel |
|---|---|
| Categories | |
| ArXiv ID | 2601.09439vv1 |
| URL | https://arxiv.org/abs/2601.09439 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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.
{
"annotation_id": "a48c988a-1a0a-48c3-83f3-c66fe934fef7",
"date_created": "2026-02-17T05:53:20.337000Z",
"date_modified": "2026-02-17T05:53:20.337000Z",
"file_hash": "07fd0ca69a6cd6120e07e843dd5277cb69d12cfcfb5698a655cb94eeded7e9be",
"private": false,
"record": {
"abstract": "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.",
"arxiv_id": "2601.09439",
"authors": [
"Philipp Haim",
"Vasilis Ntziachristos",
"Torsten En\u00dflin",
"Dominik J\u00fcstel"
],
"categories": [
"cs.LG",
"physics.med-ph"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "DeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissue",
"url": "https://arxiv.org/abs/2601.09439",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "150e7e66-448d-4f41-bb44-f8e4cdbcfb63",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}