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Related Experiment Video

Updated: Jul 6, 2026

Systematic Bronchoscopy: the Four Landmarks Approach

Systematic Bronchoscopy: the Four Landmarks Approach

Published on: June 23, 2023

Only one fitting for bironchigrams.

A Cordero-Dávila, J M Nu Ez-Alfonso, E Luna-Aguilar

|March 28, 2008

Summary

This summary is machine-generated.

This study introduces a new algorithm for Ronchi tests using a square grid pattern. It accurately calculates optical path difference from bironchigram dot coordinates with a single nonlinear least-squares fit, even with Gaussian errors.

Related Experiment Videos

Last Updated: Jul 6, 2026

Systematic Bronchoscopy: the Four Landmarks Approach

Systematic Bronchoscopy: the Four Landmarks Approach

Published on: June 23, 2023

Area of Science:

Optics
Optical Testing
Metrology

Background:

Ronchi tests are common for optical surface analysis.
Accurate optical path difference (OPD) calculation is crucial for optical system quality control.
Existing methods may involve multiple fitting steps or assumptions about error distributions.

Purpose of the Study:

To develop an efficient algorithm for calculating optical path difference (OPD) from Ronchi test data.
To address the impact of Gaussian errors on pattern point coordinates.
To simplify the data analysis process in optical testing.

Main Methods:

Utilized a square grid pattern in the Ronchi test, termed a bironchigram.
Assumed Gaussian errors affect the bironchigram point coordinates.
Applied a single nonlinear least-squares fit to the dot coordinates for OPD calculation.

Main Results:

Successfully deduced the relevant equations for OPD calculation.
Demonstrated the algorithm's effectiveness through experimental results.
The single-fit approach provides a streamlined and potentially more accurate analysis.

Conclusions:

The presented algorithm offers an effective method for OPD calculation in Ronchi tests.
The approach is robust to Gaussian errors in bironchigram data.
This method simplifies optical testing procedures and enhances data analysis accuracy.