Abstract
We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity and stability of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells and microbeads, benchmarking against the conventional method using background subtractions.
Highlights
Quantitative phase imaging (QPI) has rapidly emerged as a prominent imaging modality in life sciences and medicine owing to its non-invasive, label-free, and quantitative visualization at the individual single-cell level [1]
2.1 Calibration-free 2D Quantitative Phase Imaging For experimental verification, we tested our method with eukaryotic cells (HeLa, NIH-3T3, HEK-293T, MDA-231, and COS7 cells)
We have verified the robust performance of our method by successfully operating 2D QPI on various eukaryotic cells and optical diffraction tomography where the 3D refractive index of a biological sample is reconstructed from the 2D aberration-corrected optical fields
Summary
Quantitative phase imaging (QPI) has rapidly emerged as a prominent imaging modality in life sciences and medicine owing to its non-invasive, label-free, and quantitative visualization at the individual single-cell level [1]. In practice, the presence of optical aberration induces a deviation in the measured phase image from the correct one [Fig. 1(b)]. Other straightforward methods to circumvent optical aberration in QPI are background subtraction [19] or sample translation [20].
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