SignificanceIt has been hypothesized that abnormal microcirculation in the retina might predict the risk of ischemic damages in the brain. Direct comparison between the retinal and the cerebral microcirculation using similar animal preparation and under similar experimental conditions would help test this hypothesis.AimWe investigated capillary red-blood-cell (RBC) flux changes under controlled conditions and bilateral-carotid-artery-stenosis (BCAS)-induced hypoperfusion, and then compared them with our previous measurements performed in the brain.ApproachWe measured capillary RBC flux in mouse retina with two-photon microscopy using a fluorescence-labeled RBC-passage approach. Key physiological parameters were monitored during experiments to ensure stable physiology.ResultsWe found that under the controlled conditions, capillary RBC flux in the retina was much higher than in the brain (i.e., cerebral cortical gray matter and subcortical white matter), and that BCAS induced a much larger decrease in capillary RBC flux in the retina than in the brain.ConclusionsWe demonstrated a two-photon microscopy-based technique to efficiently measure capillary RBC flux in the retina. Since cerebral subcortical white matter often exhibits early pathological developments due to global hypoperfusion, our results suggest that retinal microcirculation may be utilized as an early marker of brain diseases involving global hypoperfusion.
Impaired oxygen delivery and/or consumption in the retinal tissue underlies the pathophysiology of many retinal diseases. However, the essential tools for measuring oxygen concentration in retinal capillaries and studying oxygen transport to retinal tissue are still lacking. We show that two-photon phosphorescence lifetime microscopy can be used to map absolute partial pressures of oxygen (pO2) in the retinal capillary plexus. Measurements were performed at various retinal depths in anesthetized mice under systemic normoxic and hyperoxic conditions. We used a newly developed two-photon phosphorescent oxygen probe, based on a two-photon absorbing platinum tetraphthalimidoporphyrin, and commercially available optics without correction for optical aberrations of the eye. The transverse and axial distances within the tissue volume were calibrated using a model of the eye’s optical system. We believe this is the first demonstration of in vivo depth-resolved imaging of pO2 in retinal capillaries. Application of this method has the potential to advance our understanding of oxygen delivery on the microvascular scale and help elucidate mechanisms underlying various retinal diseases.
The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.
The visibility and continuity of the inner segment outer segment (ISOS) junction layer of the photoreceptors on spectral domain optical coherence tomography images is known to be related to visual acuity in patients with age-related macular degeneration (AMD). Automatic detection and segmentation of lesions and pathologies in retinal images is crucial for the screening, diagnosis, and follow-up of patients with retinal diseases. One of the challenges of using the classical level-set algorithms for segmentation involves the placement of the initial contour. Manually defining the contour or randomly placing it in the image may lead to segmentation of erroneous structures. It is important to be able to automatically define the contour by using information provided by image features. We explored a level-set method which is based on the classical Chan-Vese model and which utilizes image feature information for automatic contour placement for the segmentation of pathologies in fluorescein angiograms and en face retinal images of the ISOS layer. This was accomplished by exploiting a priori knowledge of the shape and intensity distribution allowing the use of projection profiles to detect the presence of pathologies that are characterized by intensity differences with surrounding areas in retinal images. We first tested our method by applying it to fluorescein angiograms. We then applied our method to en face retinal images of patients with AMD. The experimental results included demonstrate that the proposed method provided a quick and improved outcome as compared to the classical Chan-Vese method in which the initial contour is randomly placed, thus indicating the potential to provide a more accurate and detailed view of changes in pathologies due to disease progression and treatment.
KEYWORDS: Oxygen, Arteries, Veins, Blood vessels, Phosphorescence, Optic nerve, Monte Carlo methods, Medical imaging, Visualization, Information science
Phosphorescence lifetime measurement based on a frequency domain approach is used to estimate oxygen tension in
large retinal blood vessels. The classical least squares (LS) estimation was initially used to determine oxygen tension
indirectly from intermediate variables. A spatial regularized least squares (RLS) method was later proposed to reduce the
high variance of oxygen tension estimated by LS method. In this paper, we provide a solution using a modified RLS
(MRLS) approach that utilizes prior knowledge about retinal vessels oxygenation based on expected oxygen tension
values in retinal arteries and veins. The performance of MRLS method was evaluated in simulated and experimental
data by determining the bias, variance, and mean absolute error (MAE) of oxygen tension measurements and comparing
these parameters with those derived with the use of LS and RLS methods.
Automated counting of photoreceptor cells in high-resolution retinal images generated by adaptive optics (AO) imaging
systems is important due to its potential for screening and diagnosis of diseases that affect human vision. A drawback in
recently reported photoreceptor cell counting methods is that they require user input of cell structure parameters. This
paper introduces a method that overcomes this shortcoming by using content-adaptive filtering (CAF). In this method,
image frequency content is initially analyzed to design a customized filter with a passband to emphasize cell structures
suitable for subsequent processing. The McClellan transform is used to design a bandpass filter with a circularly
symmetric frequency response since retinal cells have no preferred orientation. The automated filter design eliminates
the need for manual determination of cell structure parameters, such as cell spacing. Following the preprocessing step,
cell counting is performed on the binarized filtered image by finding regional points of high intensity. Photoreceptor cell
count estimates using this automated procedure were found to be comparable to manual counts (gold standard). The new
counting method when applied to test images showed overall improved performance compared with previously reported
methods requiring user-supplied input. The performance of the method was also examined with retinal images with
variable cell spacing.
We have developed a new method to segment and analyze retinal layers in optical coherence tomography (OCT) images
with the intent of monitoring changes in thickness of retinal layers due to disease. OCT is an imaging modality that
obtains cross-sectional images of the retina, which makes it possible to measure thickness of individual layers. In this
paper we present a method that identifies six key layers in OCT images. OCT images present challenges to conventional
edge detection algorithms, including that due to the presence of speckle noise which affects the sharpness of inter-layer
boundaries significantly. We use a directional filter bank, which has a wedge shaped passband that helps reduce noise
while maintaining edge sharpness, in contrast to previous methods that use Gaussian filter or median filter variants that
reduce the edge sharpness resulting in poor edge-detection performance. This filter is utilized in a spatially variant
setting which uses additional information from the intersecting scans. The validity of extracted edge cues is determined
according to the amount of gray-level transition across the edge, strength, continuity, relative location and polarity.
These cues are processed according to the retinal model that we have developed and the processing yields edge contours.
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