Project detail

Deep learning strategy for 3D holographic incoherent-source quantitative phase imaging

Duration: 01.03.2024 — 28.02.2025

Funding resources

Brno University of Technology - Vnitřní projekty VUT

- whole funder (2024-01-01 - 2025-12-31)

On the project

Quantitative phase imaging (QPI) allows 2D label-free live cell observations, yet achieving fast and accurate 3D reconstruction remains a challenge. While holographic tomography (HT) can partially satisfy this need, it requires a complex mechanical design. This project aims to leverage supervised deep learning for 3D reconstruction from simulated holographic incoherent-source QPI (hiQPI) z-stacked phase images. Obtaining z-stack images is less mechanically demanding, making this one of many potential stepping stones to fast and accurate 3D QPI.

Mark

CEITEC VUT-J-24-8641

Default language

Czech

People responsible

Chmelík Radim, prof. RNDr., Ph.D. - fellow researcher
Michálková Ivana, Ing. - principal person responsible

Units

Central European Institute of Technology BUT
- responsible department (2024-01-31 - 2024-03-04)
Experimental Biophotonics
- beneficiary (2024-01-01 - 2024-12-31)

Responsibility: Michálková Ivana, Ing.