Tognato, R;
Ciriza, D Bronte;
Maragò, OM;
Jones, PH;
(2023)
Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations.
Biomedical Optics Express
, 14
(7)
pp. 3748-3762.
10.1364/BOE.488931.
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Abstract
Optically trapping red blood cells allows for the exploration of their biophysical properties, which are affected in many diseases. However, because of their nonspherical shape, the numerical calculation of the optical forces is slow, limiting the range of situations that can be explored. Here we train a neural network that improves both the accuracy and the speed of the calculation and we employ it to simulate the motion of a red blood cell under different beam configurations. We found that by fixing two beams and controlling the position of a third, it is possible to control the tilting of the cell. We anticipate this work to be a promising approach to study the trapping of complex shaped and inhomogeneous biological materials, where the possible photodamage imposes restrictions in the beam power.
| Type: | Article |
|---|---|
| Title: | Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/BOE.488931 |
| Publisher version: | https://doi.org/10.1364/BOE.488931 |
| Language: | English |
| Additional information: | © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement. Published under the terms of the Creative Commons Attribution 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Biochemical Research Methods, Optics, Radiology, Nuclear Medicine & Medical Imaging, Biochemistry & Molecular Biology, BROWNIAN-MOTION, HYDRODYNAMIC PROPERTIES, TWEEZERS, SHAPE, PARTICLES, MANIPULATION, SIMULATION, TOOL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174789 |
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