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The incorporation of adaptive optics to scanning ophthalmoscopes (AOSOs) has allowed for in vivo, noninvasive
imaging of the human rod and cone photoreceptor mosaics. Light safety restrictions and power limitations of
the current low-coherence light sources available for imaging result in each individual raw image having a low
signal to noise ratio (SNR). To date, the only approach used to increase the SNR has been to collect large number
of raw images (N >50), to register them to remove the distortions due to involuntary eye motion, and then
to average them. The large amplitude of involuntary eye motion with respect to the AOSO field of view (FOV)
dictates that an even larger number of images need to be collected at each retinal location to ensure adequate
SNR over the feature of interest. Compensating for eye motion during image acquisition to keep the feature of
interest within the FOV could reduce the number of raw frames required per retinal feature, therefore significantly
reduce the imaging time, storage requirements, post-processing times and, more importantly, subject's exposure
to light. In this paper, we present a particular implementation of an AOSO, termed the adaptive optics scanning
light ophthalmoscope (AOSLO) equipped with a simple eye tracking system capable of compensating for eye
drift by estimating the eye motion from the raw frames and by using a tip-tilt mirror to compensate for it in
a closed-loop. Multiple control strategies were evaluated to minimize the image distortion introduced by the
tracker itself. Also, linear, quadratic and Kalman filter motion prediction algorithms were implemented and
tested and tested using both simulated motion (sinusoidal motion with varying frequencies) and human subjects.
The residual displacement of the retinal features was used to compare the performance of the different correction
strategies and prediction methods.
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