A number of factors can degrade the resolution and contrast of OCT images, such as: (1) changes of the OCT pointspread
function (PSF) resulting from wavelength dependent scattering and absorption of light along the imaging depth
(2) speckle noise, as well as (3) motion artifacts. We propose a new Super Resolution OCT (SR OCT) imaging
framework that takes advantage of a Stochastically Fully Connected Conditional Random Field (SF-CRF) model to
generate a Super Resolved OCT (SR OCT) image of higher quality from a set of Low-Resolution OCT (LR OCT)
images. The proposed SF-CRF SR OCT imaging is able to simultaneously compensate for all of the factors mentioned
above, that degrade the OCT image quality, using a unified computational framework. The proposed SF-CRF SR OCT
imaging framework was tested on a set of simulated LR human retinal OCT images generated from a high resolution,
high contrast retinal image, and on a set of in-vivo, high resolution, high contrast rat retinal OCT images. The
reconstructed SR OCT images show considerably higher spatial resolution, less speckle noise and higher contrast
compared to other tested methods. Visual assessment of the results demonstrated the usefulness of the proposed
approach in better preservation of fine details and structures of the imaged sample, retaining biological tissue boundaries
while reducing speckle noise using a unified computational framework. Quantitative evaluation using both Contrast to
Noise Ratio (CNR) and Edge Preservation (EP) parameter also showed superior performance of the proposed SF-CRF
SR OCT approach compared to other image processing approaches.
The lateral resolution of a Spectral Domain Optical Coherence Tomography (SD-OCT) image is limited by the focusing properties of the OCT imaging probe optics, the wavelength range which SD-OCT system operates at, spherical and chromatic aberrations induced by the imaging optics, the optical properties of the imaged object, and in the special case of in-vivo retinal imaging by the optics of the eye. This limitation often results in challenges with resolving fine details and structures of the imaged sample outside of the Depth-Of-Focus (DOF) range. We propose a novel technique for generating Laterally Resolved OCT (LR-OCT) images using OCT measurements acquired with intentional imbrications. The proposed, novel method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model to compensate for the artifacts and noise when reconstructing a LR-OCT image from imbricated OCT measurement. The proposed lateral resolution enhancement method was tested on synthetic OCT measurement as well as on a human cornea SDOCT image to evaluate the usefulness of the proposed approach in lateral resolution enhancement. Experimental results show that applying this method to OCT images, noticeably improves the sharpness of morphological features in the OCT image and in lateral direction, thus demonstrating better delineation of fine dot shape details in the synthetic OCT test, as well as better delineation of the keratocyte cells in the human corneal OCT test image.
The axial resolution of Spectral Domain Optical Coherence Tomography (SD-OCT) images degrades with scanning depth due to the limited number of pixels and the pixel size of the camera, any aberrations in the spectrometer optics and wavelength dependent scattering and absorption in the imaged object [1]. Here we propose a novel algorithm which compensates for the blurring effect of these factors of the depth-dependent axial Point Spread Function (PSF) in SDOCT images. The proposed method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model. The aim is to compensate for the depth-dependent axial blur in SD-OCT images and simultaneously suppress the speckle noise which is inherent to all OCT images. Applying the proposed depth-dependent axial resolution enhancement technique to an OCT image of cucumber considerably improved the axial resolution of the image especially at higher imaging depths and allowed for better visualization of cellular membrane and nuclei. Comparing the result of our proposed method with the conventional Lucy-Richardson deconvolution algorithm clearly demonstrates the efficiency of our proposed technique in better visualization and preservation of fine details and structures in the imaged sample, as well as better speckle noise suppression. This illustrates the potential usefulness of our proposed technique as a suitable replacement for the hardware approaches which are often very costly and complicated.
We discuss and demonstrate the dependence of noise on the signal in time-domain optical coherence tomography (TDOCT). We then derive a depth-dependent matched filter to maximize the signal-to-noise ratio at every pixel in a depth scan (A-scan). We use an empirical estimate of the second order statistics of the noise in OCT images of vascular tissue to implement a depth-dependent filter that is matched to these images. The application of our filter results in an average increase of signal-to-noise ratio of about 7 dB compared to a simple averaging operation. Our filter is not specific to time-domain OCT, but it is applicable to other types of OCT systems.
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