Projects per year
Abstract
Heterogeneous materials such as biological tissue scatter light in random, yet deterministic, ways. Wavefront shaping can reverse the effects of scattering to enable deep-tissue microscopy. Such methods require either invasive access to the internal field or the ability to numerically compute it. However, calculating the coherent field on a scale relevant to microscopy remains excessively demanding for consumer hardware. Here we show how a recurrent neural network can mirror Maxwell's equations without training. By harnessing public machine learning infrastructure, such \emph{Scattering Network} can compute the 633nm -wavelength light field throughout a 25mm2 or 1763μm3 scattering volume. The elimination of the training phase cuts the calculation time to a minimum and, importantly, it ensures a fully deterministic solution, free of any training bias. The integration with an open-source electromagnetic solver enables any researcher with an internet connection to calculate complex light-scattering in volumes that are larger by two orders of magnitude
Original language | Undefined/Unknown |
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Place of Publication | Cornell University |
Publisher | arXiv |
Number of pages | 8 |
DOIs | |
Publication status | Published - 8 Dec 2022 |
Keywords
- physics.comp-ph
- physics.optics
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Correlative Refractive Index Light-Sheet Microscopy
Vettenburg, T. (Investigator)
1/01/20 → 1/07/27
Project: Research
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DTP 2018-2019 Training Grant
Rowan, J. (Investigator)
Engineering and Physical Sciences Research Council
1/10/18 → 30/09/23
Project: Research
Student theses
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Combining Computational & Holographic Methods to Improve the Imaging Depth of Microscopy
Valantinas, L. (Author), Vettenburg, T. (Supervisor) & MacDonald, M. (Supervisor), 2024Student thesis: Doctoral Thesis › Doctor of Philosophy