Physics-embedded deep learning for computational 3D phase microscopy

Lei Tian; Alex Matlock

doi:10.1117/12.2593020

20 June 2021 Physics-embedded deep learning for computational 3D phase microscopy

Lei Tian, Alex Matlock

Proceedings Volume 11786, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials V; 1178610 (2021) https://doi.org/10.1117/12.2593020
Event: SPIE Optical Metrology, 2021, Online Only

Abstract

Intensity Diffraction Tomography (IDT) is a new computational microscopy technique providing quantitative, volumetric phase imaging of biological samples over a large field-of-view. This approach uses computationally efficient inverse scattering models to recover the 3D phase objects from a set of intensity measurements taken under diverse illumination at a single focal plane. IDT is easily implemented in a standard microscope equipped with an LED array source and requires no exogeneous contrast agents, making the technology easily accessible to the biological research community. Here, I will discuss physics-embedded deep learning-based strategies for improving the imaging capabilities of IDT for handling highly scattering 3D objects.

Conference Presentation

Citation Download Citation

Lei Tian and Alex Matlock "Physics-embedded deep learning for computational 3D phase microscopy", Proc. SPIE 11786, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials V, 1178610 (20 June 2021); https://doi.org/10.1117/12.2593020

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