Document Type

Article

Publication Date

3-2026

Publisher

Association for Research in Vision and Ophthalmology

Source Publication

Translational vision Science & Technology

Source ISSN

2164-2591

Abstract

Purpose: There has been a marked increase in use of a noninvasive functional imaging technique called optoretinography (ORG). As more groups use ORGs, it is crucial to have a consistent methodology, and understand what analysis parameters influence repeatability. In this work, we present an open-source software library called ƒ(Cell) designed to facilitate reproducible and repeatable analyses of ORG data.

Methods: We designed ƒ(Cell) as a Python software library that can co-register and analyze ORG datasets, while also enabling process auditing. To validate the software, we used our previously obtained normative optoretinography datasets as well as datasets from six other groups. We performed a variance decomposition analysis of all ƒ(Cell) parameters.

Results: Using ƒ(Cell), we successfully read and analyzed all seven datasets, despite varying data structures, file formats, and imaging protocols. Our full factorial analysis of 12,960 parameter permutations across 16 normative individuals resulted in 3 significant parameters (P < 0.001) with at least a small effect (ƞ2 > 0.01): segmentation radius, normalization, and relativization method, with ƞ2 = 0.213, 0.037, and 0.012, respectively.

Conclusions: In a full factorial analysis of analysis parameters, we found that the mean subtraction relativization method, a segmentation radius matched to the spacing of the cells of interest, and score-based normalization resulted in the lowest coefficient of variance. Overall, we found that ƒ(Cell) enables consistent, reproducible, and auditable analyses of intensity-based ORG (iORG) datasets.

Translational Relevance: This work is designed to facilitate reproducible analyses of ORG datasets to aid in the use of ORG in clinical trials.

Comments

Published version. Translational Vision Science & Technology, Vol. 15, No. 3 (March 2026). DOI. © 2026 the Authors. Used with permission.

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