Available thesis projects
Description:
The student will analyze experimental data obtained using the F-SHARP (Focus Scanning Holographic Aberration Probing) method from fluorescent neurons in the brain tissue of Danionella translucida. The goal is to process and analyze the data to extract quantitative information on the improvement of optical focusing in scattering media.
Required Knowledge:
- Understanding basic concepts of optical microscopy and laser propagation in tissues
- Programming in Matlab or Python
- Data analysis and visualization
Suggested Bibliography:
- Papadopoulos, I. N. et al. Scattering compensation by F-SHARP. Nature Photonics (2017).
- Papadopoulos, I. N. et al. Dynamic conjugate F-SHARP microscopy. Light: Science and Applications (2020).
- Denk, W. et al. Two-photon laser scanning fluorescence microscopy. Science (1990).
Description:
This project focuses on simulating laser light propagation in the superficial layers of the circulatory system (e.g., capillaries, veins). Students will apply the Monte-Carlo method to understand how light interacts with blood and tissues, aiming at applications in optical diagnostics and phototherapy.
Required Knowledge:
- Programming (Matlab or Python)
- Physics of light propagation in scattering media
- Introduction to Monte-Carlo methods
Suggested Bibliography:
- Wang, L., Jacques, S. L., & Zheng, L. MCML—Monte Carlo modeling of light transport in multi-layered tissues (1995).
- Tuchin, V. V. Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis (2015).
Description:
Students will develop a model for coherent light (laser) propagation through scattering media such as biological tissues, utilizing Fourier optics techniques and computational models. The aim is to predict light scattering and signal using machine learning (ML) and neural networks (NN), with applications in optical imaging and therapy.
Required Knowledge:
- Programming in Matlab or Python
- Basic knowledge of ML / neural networks
- Fundamentals of optics and optical propagation
Suggested Bibliography:
- Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science (2018).
- Kamilov*, U. S. & Papadopoulos*, I.N. Learning approach to optical tomography. Optica (2015).
