Abstract
Characterizing cellular metabolism through noninvasive imaging of metabolic cofactors NAD(P)H and FAD has become a powerful diagnostic tool, with the Optical Redox Ratio (ORR) serving as a key quantitative metric. However, widespread adoption of ORR imaging is hindered by the lack of standardized image quality requirements. This study addresses that gap by analytically deriving expressions for the variance of ORR measurements as a function of photon shot noise and dark current contributions. Using statistical conditioning, we modeled the expected pixelwise ORR variance and verified the model through simulated image sets and experimental data. We observed that variance decreases with higher photon counts and frame averaging, aligning with theoretical predictions. The results showed strong correlation between analytical and empirical variance, even under varying noise conditions. This work provides a quantitative foundation for evaluating ORR image quality and can inform future acquisition protocols and data processing. Ongoing efforts include developing a MATLAB application for variance analysis and preparing a manuscript to disseminate these findings.
Document Type
Article
Class Name
Electrical and Systems Engineering Undergraduate Research
Language
English (en)
Date of Submission
5-2-2025
Recommended Citation
Sharafuddin, Evan; Hu, Hong; and Sorrells, Janet, "Quantifying Noise in Optical Redox Ratio Imaging" (2025). Electrical and Systems Engineering Undergraduate and Graduate Research. 45.
https://openscholarship.wustl.edu/eseundergraduate_research/45