Cross-grating phase microscopy (CGM): In-silico experiment (insilex) algorithm, noise and accuracy

TL;DR

This paper introduces a numerical algorithm for simulating cross-grating phase microscopy (CGM), allowing detailed analysis of how various experimental parameters affect measurement accuracy, noise, and precision in CGM applications.

Contribution

The paper presents a novel in silico simulation algorithm for CGM that helps optimize experimental parameters and understand measurement limitations.

Findings

Quantified effects of grating-camera distance on noise

Analyzed impact of pixel size on measurement accuracy

Assessed influence of light intensity and numerical aperture

Abstract

Cross-grating phase microscopy (CGM) is a quantitative phase microscopy technique based on the association of a 2-dimensional diffraction grating (cross-grating) and a regular camera sensor, separated by a millimetric distance. This simple association enables the high-resolution imaging of the complex electric field amplitude of a light beam (intensity and phase) from a single image acquisition. While CGM has been used for metrology applications in cell biology and nanophotonics this last decade, there has been few studies on its basics, especially for the microscopy community. In this article, we provide a numerical algorithm that enables the in silico (i.e. computer-simulated) data acquisition, to easily vary and observe the effects of all the CGM experimental parameters using computer means. In the frame on this article, we illustrate the interest of this numerical algorithm by using it to explain and quantify the effects of several important CGM parameters (grating-camera distance, pixel size, light intensity, numerical apertures, etc) on the noise, precision and trueness of CGM measurements. This work is aimed to push the limits of CGM toward advanced applications in biomicroscopy and nanophotonics.