Optical coherence tomography (OCT) with unprecedented submicrometer axial resolution achieved by use of a photonic crystal fiber in combination with a compact sub-10-fs Ti:sapphire laser (Femtolasers Produktions) is demonstrated for what the authors believe is the first time. The emission spectrum ranges from 550 to 950 nm (lambda(c)=725 nm , P(out)=27 mW) , resulting in a free-space axial OCT resolution of ~0.75 mum , corresponding to ~0.5 mum in biological tissue. Submicrometer-resolution OCT is demonstrated in vitro on human colorectal adenocarcinoma cells HT-29. This novel light source has great potential for development of spectroscopic OCT because its spectrum covers the absorption bands of several biological chromophores.

Noncontact, depth-resolved, optical probing of retinal response to visual stimulation with a <10-m spatial resolution, achieved by using functional ultrahigh-resolution optical coherence tomography (fUHROCT), is demonstrated in isolated rabbit retinas. The method takes advantage of the fact that physiological changes in dark-adapted retinas caused by light stimulation can result in local variation of the tissue reflectivity. fUHROCT scans were acquired from isolated retinas synchronously with electrical recordings before, during, and after light stimulation. Pronounced stimulusrelated changes in the retinal reflectivity profile were observed in the inner͞outer segments of the photoreceptor layer and the plexiform layers. Control experiments (e.g., dark adaptation vs. light stimulation), pharmacological inhibition of photoreceptor function, and synaptic transmission to the inner retina confirmed that the origin of the observed optical changes is the altered physiological state of the retina evoked by the light stimulus. We have demonstrated that fUHROCT allows for simultaneous, noninvasive probing of both retinal morphology and function, which could significantly improve the early diagnosis of various ophthalmic pathologies and could lead to better understanding of pathogenesis.electroretinogram ͉ functional optical coherence tomography ͉ inner plexiform layer ͉ photoreceptors ͉ retinal imaging T he vertebrate retina consists of several distinct layers: nuclear layers containing cell bodies can be differentiated from plexiform layers with axons and dendrites forming the neuronal network that preprocesses light-evoked signals before transmission to the brain. Early stages of retinal disorders are often confined to one of these layers and are manifested by both morphological abnormalities and impaired physiological responses. Detection of such pathologies requires high-resolution imaging methods. Various imaging modalities such as fundus photography, ultrasound imaging, and optical coherence tomography (OCT) are clinically used for imaging retinal morphology. OCT is an emerging imaging technique that allows for noncontact, in vivo visualization of biological tissue morphology with a micrometer-scale resolution at imaging depths of 1-2 mm (1-3). Currently, electrophysiological tests such as electroretinography (ERG) (4) and multifocal ERG (5) are used for clinical assessment of retinal function.More then 25 years ago, it was observed that the isolated retina when stimulated with visible light changes the amount of transmitted near-infrared light (NIR) (6, 7). Photoreceptors (PRs) were determined to be the main source of this effect, and in the following years, this method was used for investigation and quantitative evaluation of the activation of the PR G protein transducin and the time course of transduction events (8-10 and reviewed in ref. 11). In the last few years, other physiological processes at the cellular and subcellular level such as membrane depolarization (12), cell swelling (13), and altered metabolism...

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired in-vivo with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.