Monte Carlo investigation on quantifying the retinal pigment epithelium melanin concentration by photoacoustic ophthalmoscopy

Abstract

The retinal pigment epithelium (RPE) melanin plays an important role in maintaining normal visual functions. A decrease in the RPE melanin concentration with aging is believed to be associated with several blinding diseases, including age-related macular degeneration. Quantifying the RPE melanin noninvasively is therefore important in evaluating the retinal health and aging conditions. Photoacoustic ophthalmoscopy (PAOM), as an optical absorption-based imaging technology, can potentially be applied to measure variations in the RPE melanin if the relationship between the detected photoacoustic (PA) signal amplitudes and the RPE melanin concentrations can be established. In this work, we tested the feasibility of using PA signals from retinal blood vessels as references to measure RPE melanin variation using Monte Carlo (MC) simulation. The influences from PAOM axial resolution, the depth and diameter of the retinal blood vessel, and the RPE thickness were examined. We proposed a calibration scheme by relating detected PA signals to the RPE melanin concentrations, and we found that the scheme is robust to these tested parameters. This study suggests that PAOM has the capability of quantitatively measuring the RPE melanin in vivo.

Fig. 1

Schematic of the eye model. (a) The geometry of the eye, optical illumination, recorded region of interest (ROI), and the coordinates. (b) Magnified view of the anatomical layers.

Fig. 2

Simulated optical absorption distribution and photoacoustic (PA) profile. (a) A representative B-scan image of energy deposition using 2.1 billion photon packets. The retinal pigment epithelium (RPE) melanin concentration is $300\mathrm{mmol}/\mathrm{L}$. (b) Representative A-lines of optical absorption at 5 different lateral positions with respect to the vessel center. (c) Simulated impulse response of a 75-MHz bandwidth ultrasonic detection system. (d) Simulated PA A-lines by convolving the energy deposition (b) with the impulse response (c).

Fig. 3

Simulated depth-resolved profiles of energy deposition in VR and RR. Simulation results with different RPE melanin concentrations are shown in the same plot. (a) Vessel region (VR) and RR. It is a top–down view of simulation field. The red square is the retinal blood vessel while the gray background represents the RPE. (b) Depth-resolved profiles of energy deposition in VR. (c) A magnified view of the dashed square (b). (d) Depth-resolved profiles of energy deposition in RR.

Fig. 4

Relative optical energy deposition in posterior eye layers with varied RPE melanin concentration. ChB, ChMIn, and ChMOut stand for choroidal blood layer, inner suprachoroid and outer suprachoroid, respectively. (a) Relative energy deposition in VR. (b) Relative energy deposition in RR. The vertical axes are plotted in logarithmic scale.

Fig. 5

The influence of photoacoustic ophthalmoscopy (PAOM) axial resolution on the PA signal amplitude ratio. The simulated RPE thickness is $10\mu \mathrm{m}$. (a) Percentage of RPE contribution to PA signal generated at RPE-choroid. (b) Calibration curves of amplitude ratio with different RPE melanin concentrations. PAOM cannot resolve RPE layer until axial resolution is as small as $10\mu \mathrm{m}$. 570-nm excitation wavelength is used.

Fig. 6

The influence of illumination wavelength. The axial resolution of PAOM is $20\mu \mathrm{m}$.

Fig. 7

The influence of retinal parameters variations on PA signal amplitude ratio. (a) Influence of the retinal blood vessel diameter. (b) Influence of the retinal blood vessel depth with regard to retina surface. (c) Influence of the RPE thickness.