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Corneal thickness (CoT) is an important tool in the evaluation process for several disorders and in the assessment of intraocular pressure. We present a method enabling high-precision measurement of CoT based on secondary speckle tracking and processing of the information by machine-learning (ML) algorithms. The proposed configuration includes capturing by fast camera the laser beam speckle patterns backscattered from the corneal–scleral border, followed by ML processing of the image. The technique was tested on a series of phantoms having different thicknesses as well as in clinical trials on human eyes. The results show high accuracy in determination of eye CoT, and implementation is speedy in comparison with other known measurement methods.
This work presets a novel use of the nonlinear image decomposition technique called K-factor that reshapes the three dimensional (3D) point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The experimentally obtained PSF of a Z-stack raw data that is acquired by a widefield microscope has a more elaborate shape that is given by the Gibson and Lanni model. This shape increases the computational complexity associated with the localization routine, when used in localization microscopy techniques. Furthermore, due to its nature, this PSF spreads over a larger volume, making the problem of overlapping emitters detection more pronounced. The ability to use Gaussian fitting with high accuracy on 3D data can facilitate the computational complexity, hence reduce the processing time required for the generation of the 3D superresolved image. In addition it allows the detection of overlapping PSFs and reduces the effects of the penetration of out of focus PSFs into in focused PSFs, therefore enables the increase in the activated fluorophore density by ~50%. The algorithm was tested both on simulated data and experimentally, where it yielded an increase in the localization accuracy by ~60% with compare to regular Gaussian fitting, and improved the minimal resolvable distance between overlapping PSFs by ~50%, making it extremely applicable to the field of 3D biomedical imaging,
Superresolved digital in-line holographic microscopy for high-resolution lensless biological imaging
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Digital imaging systems and human vision systems have limited capability for separating spatial features, thereby limiting imaging resolution. Reasons for this limitation are related to the effects of diffraction, i.e. the finite dimensions of the imaging optics, the geometry of the sensing array and its sensitivity, and the axial position of the object which may be out of focus. In this course, we will examine novel photonic approaches to imaging beyond the diffraction limit with an emphasis on practical methods to overcome these limitations.
We will use the eye to model optical extended depth of focus concepts based on the “interference” effect. Implementation on conventional refractive devices such as spectacles, contact lenses and intraocular lenses will help demonstrate practical considerations for development. Extended depth of focus technology is capable of simultaneously correcting various refractive errors such as myopia, hyperopia, presbyopia, regular/irregular astigmatism, as well as their combinations.
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