# Imaging Structure and Function of Photoreceptors

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2022 · $577,031

## Abstract

Project Summary/Abstract
Human vision starts when photoreceptors collect and respond to light. Normal photoreceptor function is
essential for normal vision, yet techniques to assess these processes in vivo are limited. New optical modalities
that are rapid, specific, and non‐invasive promise to greatly expand our capability to monitor more accurately
and completely photoreceptors.
This study takes advantage of unique adaptive‐optics OCT instrumentation developed in my laboratory in
conjunction with custom algorithms for sub‐cellular image registration and phase‐sensitive detection to
measure anatomical and physiological properties of individual photoreceptors. We will use this technique to
investigate three specific aims that quantify the spectral sensitivity profiles of photoreceptors, the expression of
photoreceptor spectral types in color vision deficiencies, and the temporal dynamics of photoreceptor loss in
retinitis pigmentosa patients. The long term goal of this research is to establish high resolution, high specificity
optical techniques as valid tools for probing structure and physiologic processes of the retina at the cellular
scale. The resulting ability to study cells in vivo will improve early detection of and treatment monitoring for
diseases that impact the retina.

## Key facts

- **NIH application ID:** 10522431
- **Project number:** 2R01EY018339-15
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Donald T Miller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $577,031
- **Award type:** 2
- **Project period:** 2007-09-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10522431

## Citation

> US National Institutes of Health, RePORTER application 10522431, Imaging Structure and Function of Photoreceptors (2R01EY018339-15). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10522431. Licensed CC0.

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