A system and method for assessing blood flow include: an ocular lens; a light source; a digital video camera; a biosensor; a trigger; and a computer. The ocular lens is for viewing a fundus of an eye. The light source is for illuminating the fundus. The digital video camera is for imaging the fundus. The biosensor is for sensing a pulse waveform. The computer is configured for: recording input frames and pulse waveform data in response to an input from the trigger; defining a low-pass frequency and a high-pass frequency from the pulse waveform data; stabilizing the input frames; enhancing contrast of the input frames; separating the input frames into sub-channels; conducting eulerian video magnification for color amplification using the inputs of image sampling rate, the low-pass frequency, the high-pass frequency, and an amplification factor; reconstructing the sub-channels into output frames; and combining the output frames with the input frames.

An image processing apparatus includes: an acquisition unit that acquires first angiographic image data of a subject eye, and second angiographic image data of the subject eye generated after the first angiographic image data; a first generation unit that calculates a first blood vessel area density from the first angiographic image data to generate first blood vessel area density map data based on the first blood vessel area density, and that calculates a second blood vessel area density from the second angiographic image data to generate second blood vessel area density map data based on the second blood vessel area density; a second generation unit that generates comparison image data for comparing the first blood vessel area density map data to the second blood vessel area density map data; and an output unit that outputs the comparison image data.

Imaging various regions of the eye is important for both clinical diagnostic and treatment purposes as well as for scientific research. Diagnosis of a number of clinical conditions relies on imaging of the various tissues of the eye. The subject technology describes a method and apparatus for imaging of the back and/or front of the eye using multiple illumination modalities, which permits the collection of one or more of reflectance, spectroscopic, fluorescence, and laser speckle contrast images.

This invention relates to, in part, methods and compositions that are useful for the diagnosis, treatment, or prevention of a blinding eye disease, including in the discovery of drugs that are efficacious against these diseases. Diseases include, for example, age related macular degeneration and reticular pseudodrusen disease, and the methods described herein include, for example, the method named delayed near infrared analysis (DNIRA).

The present invention relates to systems for low-energy (e.g., 1.0 nJ-7.0 nJ) photoacoustic microscopy and methods for employing such systems. In certain embodiments, such systems employ a low-energy nanosecond pulsed laser beam (NPLB), at least two amplifiers, and a data acquisition system with at least three channels to generate at least three digital signals (e.g., which are averaged and normalized to the energy of the NPLB). In other embodiments, provided herein are systems for combined use of photoacoustic microscopy, dye-based microscopy (e.g., with fluorescein), and optical coherence tomography.

A blood flow measurement apparatus of an aspect example includes a scanning optical system, a front image acquiring unit, a region-of-interest determining processor, a scan controller, and a blood flow information acquiring unit. The scanning optical system applies OCT scanning to a fundus of a subject's eye. The front image acquiring unit acquires a front image of the fundus. The region-of-interest determining processor determines a region of interest that intersects a blood vessel by analyzing the front image based on a condition set in advance. The scan controller controls the scanning optical system to apply a repetitive OCT scan to the region of interest. The blood flow information acquiring unit acquires blood flow information based on data collected by the repetitive OCT scan.

Predicting a non perfusion area.

An enhancement image processing section performs enhancement image processing on a fundus image of a subject eye to enhance vascular portions (304). A prediction processing section predicts a non perfusion area in the fundus image that has been subjected to the enhancement image processing (306 to 312). A generation section generates a non perfusion area candidate image (314).

Systems and methods for use in measuring sympathetic nervous system activity or blood vessel autoregulation corrected for sympathetic activity. The choroid in the human eye is imaged and, using the resulting image, the vascular perfusion density (VPD) in the choroid is measured. VPD provides a measurement that is directly related to sympathetic nervous system activity. The effect of stimuli on sympathetic nervous system activity can be measured by comparing pre-stimuli VPD measurements with post-stimuli measurements. Quantifying VPD can be performed by determining pixel density within specific areas of the choroid image. Altered sympathetic nervous system activity can be detected in a subject by comparing that subject's VPD measurements to baseline VPD measurements from healthy individuals. Blood vessel autoregulation can be measured by imaging changes in other blood vessels in the eye and correcting with choroid VPD measurements of sympathetic activity.

The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.

Relationships between morphological changes to an eye due to intraocular pressure changes and blood perfusion and nerve function changes in the retina are determined by colocalizing retinal perfusion data, optic nerve head (ONH) mechanical deformation data, visual field data and nerve fiber data. Perfusion and nerve function changes from intraocular pressure (IOP) changes are determined by colocalizing retinal perfusion data with ONH mechanical deformation data, visual field data and nerve fiber data. Optical coherence tomography-angiography (OCT-A) can be used to generate retinal perfusion data, mechanical deformation data for an imaged volume, and nerve fiber data. A three-dimensional model (e.g., connectivity map or connectivity model) of the vasculature and nerve fibers can be generated from the OCT-A imaging data and used to predict changes in blood perfusion and nerve function in various areas of the retina due to IOP-induced mechanical deformations.