We report pupillometry results corresponding to three studies. A first study aims at measuring 2D pupil geometry with high precision (below 2 microns) at high frequency (more than 450Hz). The two other studies aim at measuring 3D pupil movements, with and without a chin rest. Results of measurements over 42 subjects are presented.
The design of robust Adaptive Optics systems (AO) requires to characterize spatially and temporally
the aberrations. Thus, it is of importance to have an instrument able to measure the aberrations at
high spatial and temporal resolutions. The high spatial resolution is necessary to have an extended
modal decomposition of aberrations, to characterize finely the pupil irradiance and to quantify
aliasing and fitting errors. The high temporal resolution is necessary to analyze the evolution of
very fast phenomena contributing in the aberration dynamics, e.g. fast pupil movements, tear film
and accommodation. Because, Hardware constraints make it difficult to obtain high spatial and
temporal resolutions on a single detector, we have designed a new aberrometer comprising two
synchronized instruments, one highly spatially resolved, the second one highly temporally resolved
that allows to perform such measurements. Preliminary results have been obtained on the highly
spatially resolved instrument. The integration of the second instrument is in progress. An overview
of the first instrument results is presented in this paper.
KEYWORDS: Deconvolution, 3D image processing, 3D modeling, Point spread functions, Sensors, Retina, Retinal scanning, Image processing, 3D acquisition, Convolution
High resolution wide-field imaging of the human retina calls for a 3D deconvolution. In this communication, we report
on a regularized 3D deconvolution method, developed in a Bayesian framework in view of retinal imaging, which is fully
unsupervised, i.e., in which all the usual tuning parameters, a.k.a. "hyper-parameters", are estimated from the data. The
hyper-parameters are the noise level and all the parameters of a suitably chosen model for the object's power spectral
density (PSD). They are estimated by a maximum likelihood (ML) method prior to the deconvolution itself.
This 3D deconvolution method takes into account the 3D nature of the imaging process, can take into account the
non-homogeneous noise variance due to the mixture of photon and detector noises, and can enforce a positivity constraint
on the recovered object. The performance of the ML hyper-parameter estimation and of the deconvolution are illustrated
both on simulated 3D retinal images and on non-biological 3D experimental data.
Retinal pathologies, like ARMD or glaucoma, need to be early detected, requiring imaging instruments with resolution at
a cellular scale. However, in vivo retinal cells studies and early diagnoses are severely limited by the lack of resolution
on eye-fundus images from classical ophthalmologic instruments. We built a 2D retina imager using Adaptive Optics to
improve lateral resolution. This imager is currently used in clinical environment. We are currently developing a time
domain full-field optical coherence tomograph. The first step was to conceive the images reconstruction algorithms and
validation was realized on non-biological samples. Ex vivo retina are currently being imaged. The final step will consist
in coupling both setups to acquire high resolution retina cross-sections.
We describe here two parts of our future 3D fundus camera coupling Adaptive Optics and full-field Optical Coherence Tomography. The first part is an Adaptive Optics flood imager installed at the Quinze-Vingts Hospital, regularly used on healthy and pathological eyes. A posteriori image reconstruction is performed, increasing the final image quality and field of view. The instrument lateral resolution is better than 2 microns. The second part is a full-field Optical Coherence Tomograph, which has demonstrated capability of performing a simple kind of "4 phases" image reconstruction of non biological samples and ex situ retinas. Final aim is to couple both parts in order to achieve 3D high resolution mapping of in vivo retinas.
We report on a deconvolution method developed in a Bayesian framework for adaptive-optics corrected images of the human retina. The method takes into account the three-dimensional nature of the imaging process; it incorporates a positivity constraint and a regularization metric in order to avoid uncontrolled noise amplification. This regularization metric is designed to simultaneously smooth noise out and preserve edges, while staying convex in order to keep the solution unique. We demonstrate the effectiveness of the method, and in particular of the edge-preserving regularization, on realistic simulated data.
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