研究目的
To describe a standardized flood-illuminated adaptive optics imaging protocol suitable for the clinical setting and to assess sampling methods for measuring cone density.
研究成果
Different sampling methods significantly affect cone density measurements, with peak density best matching histological data in nasal/temporal quadrants but overestimating in superior/inferior quadrants. The arcuate mean method showed the highest repeatability. Automated imaging and sampling are feasible but limited by image quality and algorithm accuracy. Improvements in image acquisition and processing are needed for broader clinical utility.
研究不足
Image quality was inadequate for automated cone identification in nearly half of the subjects, limiting the accuracy of measurements. The study excluded cones within the foveal exclusion zone (average 1.9° from fovea). Repeatability varied with sampling method, and there were issues with overidentification of cones in poor-quality regions. The rtx1 camera and current algorithms may not be sufficient for all clinical applications, especially in older patients or those with retinal disease.
1:Experimental Design and Method Selection:
The study used a flood-illuminated adaptive optics camera (rtx1 from Imagine Eyes) to image the macula. Three sampling methods were evaluated: fixed-interval, arcuate mean, and peak density methods for cone density measurement. Automated cone identification was performed using a custom MATLAB algorithm.
2:Sample Selection and Data Sources:
74 healthy subjects aged 14-69 years were imaged, with the dominant eye tested. Seven subjects were excluded due to montaging issues, and further exclusions based on image quality.
3:List of Experimental Equipment and Materials:
rtx1 flood-illuminated AO camera (Imagine Eyes), IOLMaster 500 (Carl Zeiss Meditec AG) for axial length measurement, MATLAB software for cone detection, i2k Retina software (DualAlign, LLC) for montaging.
4:Experimental Procedures and Operational Workflow:
A series of 25 images were acquired with 50% overlap to cover a 128x128 field. Images were registered and combined using vendor software, montaged, and cones were identified automatically. Density maps were created using Voronoi diagrams. Sampling was done at various eccentricities.
5:Data Analysis Methods:
Cone density was calculated and compared across methods. Repeatability was assessed using coefficient of variation and repeatability coefficient. Statistical analysis included comparisons with histological data.
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