Retinal magnification factors (RMFs) allow the conversion of angles to lengths in retinal images. In this work, we propose paraxial and non-paraxial RMF calculation methods that incorporate the individual topography and separation of the anterior and posterior surfaces of the cornea and crystalline lens, assuming homogeneous ocular media. Across 34 eyes, the two RMF methods differ by 0.1% on average, due to surface tilt, decenter, and lack of rotational symmetry in the non-paraxial modeling, which results in up to 2.2% RMF variation with retinal meridian. Differences with widely used individualized RMF calculation methods are smallest for eyes with ∼24 mm axial length, and as large as 7.5% in a 29.7 mm long eye (15D myope). To better model the capture of retinal images, we propose the tracing of chief rays, instead of the scaling of posterior nodal or principal distances often used in RMF definitions. We also report that RMF scale change is approximately proportional to both refractive error and axial separation between the ophthalmoscope’s exit pupil and the eye’s entrance pupil, resulting in RMF changes as large as 13% for a 1cm displacement in a 15D myopic eye. Our biometry data shows weak correlation and statistical significance between surface radii and refractive error, as well as axial length, whether considering all eyes in the study, or just the high myopes, defined as those with refractive error sphere equivalent ≤ -4D. In contrast, vitreous thicknesses show a strong correlation (r ≤ -0.92) and significance (p ≤ 10−13) with refractive error when considering all eyes or just high myopes (r ≤ -0.95; p ≤ 10−5). We also found that potential RMF change with depth of cycloplegia and/or residual accommodation is smaller than 0.2%. Finally, we propose the reporting of individual ocular biometry data and a detailed RMF calculation method description in scientific publications to facilitate the comparison of retinal imaging biomarker data across studies.

An off-axis monocular pupil tracker designed for eventual integration in ophthalmoscopes for eye movement stabilization is described and demonstrated. The instrument consists of light-emitting diodes, a camera, a field-programmable gate array (FPGA) and a central processing unit (CPU). The raw camera image undergoes background subtraction, field-flattening, 1-dimensional low-pass filtering, thresholding and robust pupil edge detection on an FPGA pixel stream, followed by least-squares fitting of the pupil edge pixel coordinates to an ellipse in the CPU. Experimental data suggest that the proposed algorithms require raw images with a minimum of ∼32 gray levels to achieve sub-pixel pupil center accuracy. Tests with two different cameras operating at 575, 1250 and 5400 frames per second trained on a model pupil achieved 0.5-1.5 μm pupil center estimation precision with 0.6-2.1 ms combined image download, FPGA and CPU processing latency. Pupil tracking data from a fixating human subject show that the tracker operation only requires the adjustment of a single parameter, namely an image intensity threshold. The latency of the proposed pupil tracker is limited by camera download time (latency) and sensitivity (precision).

The rapid oscillation of galvanometric resonant optical scanners introduces linear astigmatism that degrades transverse resolution, and in confocal systems, also reduces signal [V. Akondi et al., Optica 7, 1506, 2020]. Here, we demonstrate correction of this aberration by tilting reflective or refractive optical elements for a single vergence or a vergence range, with and without the use of an adaptive wavefront corrector such as a deformable mirror. The approach, based on nodal aberration theory, can generate any desired third order aberration that results from tilting or decentering optical surfaces.