CZCRetinalProsthesis

Introduction

The project aims at modeling the electric stimulation elicited by a subretinal-implanted photovoltaic retinal prosthesis, given a light field input from outside the eye. The model would help people better understand prosthetic vision, and would be informative for optimizing the image processing workflow.

Background

Subretinal implanted photodiode arrays with micrometer-level pixels are considered as a promising approach to restore vision. The system comprises an array implanted behind the inner nuclear layer (INL) and a pair of laser projection goggles that selectively elicit a set of pixels with near infrared (NIR) laser beams [1]. The microscopic image of Figure 1 shows the front surface (facing the inner nuclear layer and "outside") of a photovoltaic array implant. Figure 2 schematically illustrates the workflow of the whole system. However, the projected light field in the human optics has yet been carefully studied. Together with the unique point spread function (SPF) associated with a photovoltaic pixel [2], this results in the primeval understanding of the prosthetic vision.

Figure 1: The front surface of a photovoltaic retinal prosthesis implant. Each hexagon is a pixel.

Figure 2: The implant and the projection goggles capturing views. http://www.pixium-vision.com/en

A MATLAB toolbox is available for modeling the front end of the human visual system, including the scene construction, the human optics and the retinal images. However, a major modification necessary is the refractive index of the crystalline lens for 880nm wavelength, which, to the best of my knowledge, has not been reported [3][4][5][6][9]. The mosaic sampling by cones would be replaced by the electric field elicited by the hexagonal photovoltaic pixels [2]. The technical path of the project would be to some extent similar to the one described in [7] and [8].

Based on the model, researchers may further explore the “matching space” for prosthetic vision, to make intuitive sense of the restoration quality. With the matching space, one may also study the image processing workflow on the goggles, to optimize for the best visual quality with limited computational power.

References

[1] Boinagrov, David, et al. "Photovoltaic pixels for neural stimulation: circuit models and performance." IEEE transactions on biomedical circuits and systems 10.1 (2016): 85-97.

[2] Flores, Thomas, et al. "Optimization of return electrodes in neurostimulating arrays." Journal of neural engineering 13.3 (2016): 036010.

[3] Palmer, D. A., and J. Sivak. "Crystalline lens dispersion." JOSA 71.6 (1981): 780-782.

[4] Smith, George, Barbara K. Pierscionek, and David A. Atchison. "The optical modelling of the human lens." Ophthalmic and Physiological Optics 11.4 (1991): 359-369.

[5] Liang, Junzhong, and David R. Williams. "Aberrations and retinal image quality of the normal human eye." JOSA A 14.11 (1997): 2873-2883.

[6] Jones, Catherine E., et al. "Refractive index distribution and optical properties of the isolated human lens measured using magnetic resonance imaging (MRI)." Vision research 45.18 (2005): 2352-2366.

[7] Golden, James R., et al. "Simulation of visual perception and learning with a retinal prosthesis." bioRxiv (2018): 206409.

[8] Cottaris, Nicolas, et al. "A computational observer model of spatial contrast sensitivity: Effects of wavefront-based optics, cone mosaic structure, and inference engine." bioRxiv (2018): 378323.

[9] Thibos, Larry N., et al. "The chromatic eye: a new reduced-eye model of ocular chromatic aberration in humans." Applied optics 31.19 (1992): 3594-3600.

[10] Berendschot, Tos TJM, Jan van de Kraats, and Dirk van Norren. "Wavelength dependence of the Stiles–Crawford effect explained by perception of backscattered light from the choroid." JOSA A 18.7 (2001): 1445-1451.

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