Dynamic tracking of onion-like carbon nanoparticles in cancer cells using limited-angle holographic tomography with self-supervised learning.

This research presents a novel approach for the dynamic monitoring of onion-like carbon nanoparticles inside colorectal cancer cells. Onion-like carbon nanoparticles are widely used in photothermal cancer therapy, and precise 3D tracking of their distribution is crucial. We proposed a limited-angle digital holographic tomography technique with unsupervised learning to achieve rapid and accurate monitoring. A key innovation is our internal learning neural network. This network addresses the information limitations of limited-angle measurements by directly mapping coordinates to measured data and reconstructing phase information at unmeasured angles without external training data. We validated the network using standard SiO microspheres. Subsequently, we reconstructed the 3D refractive index of onion-like carbon nanoparticles within cancer cells at various time points. Morphological parameters of the nanoparticles were quantitatively analyzed to understand their temporal evolution, offering initial insights into the underlying mechanisms. This methodology provides a new perspective for efficiently tracking nanoparticles within cancer cells.