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Revisiting the Drasdo Model: Implications for Structure-Function Analysis of the Macular Region
Author(s) -
Giovanni Montesano,
Giovanni Ometto,
Ruth Hogg,
Luca Rossetti,
David F. GarwayHeath,
David P. Crabb
Publication year - 2020
Publication title -
translational vision science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.508
H-Index - 21
ISSN - 2164-2591
DOI - 10.1167/tvst.9.10.15
Subject(s) - optical coherence tomography , scaling , retinal , retina , mathematics , artificial intelligence , computer science , physics , ophthalmology , medicine , optics , geometry
Purpose To provide a consistent implementation of a retinal ganglion cell (RGC) displacement model proposed by Drasdo et al. for macular structure-function analysis, customizable by axial length (AL). Methods The effect of axial length on the shape of the inner retina was measured on 235 optical coherence tomography (OCT) scans from healthy eyes, to provide evidence for geometric scaling of structures with eye size. Following this assumption, we applied the Drasdo model to map perimetric stimuli on the radially displaced RGCs using two different methods: Method 1 only displaced the center of the stimuli; Method 2 applied the displacement to every point on the edge of the stimuli. We compared the accuracy of the two methods by calculating, for each stimulus, the number of expected RGC receptive fields and the number RGCs calculated from the histology map, expected to be equivalent. The same calculation was repeated on RGC density maps derived from 28 OCT scans from 28 young healthy subjects (age < 40 years) to confirm our results on clinically available measurements. Results The size of the retinal structures significantly increased with AL ( P < 0.001) and was well predicted by geometric scaling. Method 1 systematically underestimated the RGC counts by as much as 60%. No bias was observed with Method 2. Conclusions The Drasdo model can effectively account for AL assuming geometric scaling. Method 2 should be used for structure-function analyses. Translational Relevance We developed a free web App in Shiny R to make our results available for researchers.

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