Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations

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.

Faster Calculations of Optical Trapping Using Neural Networks Trained by T-Matrix Data: An Application to Micro- and Nanoplastics

“…NNs have been shown to be an efficient approach to improve the speed of optical force calculation 6 for spheres 7 and more complex geometries, like ellipsoids 8 or red blood cells. 9 NNs adjust their solutions to specific problems by training on data 10 and they have been used to improve the speed of conventional algorithms in a great variety of topics ranging from epidemics containment 11 to enhancing microscopy, 12 efficient tracking particles, 13 and optical tweezers. 6 Recently, NNs have also been used for the calculation of the scattering properties of spheroidal aerosols with prospects for applications to cosmic dust studies.…”