The present system employs optical coherence tomography (OCT) or optical coherence microscopy (OCM) systems, including ultra-high resolution Gabor-domain optical coherence microscopy (GD-OCM), into a 3D flow imaging technique. This technique models the repeated scans as Gaussian latent variables, with the common variance representing both static tissue structure and dynamic blood flow, and the anisotropic unique variance representing tissue motion in specific frames. Since the motion generated variance is independent from that of the structure or the flow, by iteratively maximizing the combined log-likelihood probability of these two variances modeled through exploratory factor analysis, the unique variance (or the tissue motion) may be largely excluded. In the common variance, the dynamic blood flow may be separated from the static tissue structure, by integrating the factors that represent the relatively low levels of correlation. Compared to a direct differentiation of OCT or OCM scans, the present flow imaging algorithm improves the visualization of capillaries with reduced motion artifacts.

The invention described provides a way to use video image to estimate the human artery blood pressure reducing or completely eliminating the need for human contact (non invasive). Since video images can be stored and transmitted, the estimation of the blood pressure can be performed locally, remotely and in real time or offline.

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

In some embodiments of the present invention, a method of reducing artifacts includes obtaining OCT/OCTA data from an OCT/OCTA imager; preprocessing OCTA/OCT volume data; extracting features from the preprocessed OCTA/OCT volume data; classifying the OCTA/OCT volume data to provide a probability determination data; determining a percentage data from the probability data determination; and reducing artifacts in response to the percentage data.

An embodiment in accordance with the present invention provides a method for non-invasively determining the functional severity of coronary artery stenosis. The method includes gathering patient-specific data related to concentration of a contrast agent within a coronary artery of a patient using a coronary computed tomography angiography scan (CCTA). The patient-specific data is used to calculate a patient-specific transluminal attenuation gradient for the coronary artery of the patient. The patient specific transluminal attenuation gradient is used to determine an estimate of a coronary flow velocity, pressure gradient, loss coefficient, coronary flow reserve, and/or fractional flow reserve for the patient. Coronary flow velocity, pressure gradient, loss coefficient, coronary flow reserve, and fractional flow reserve can then be used to estimate the functional severity of coronary artery stenosis.

Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

System and method for measuring dyslipidemia condition of a subject using thermal imaging is disclosed. The disclosed system and method includes thermal sensors for capturing thermal images and/or videos of a body part; and a processing engine to detect a predefined region of the body part in each frame of the captured images and/or videos. The processing engine segments one or more portions from the predefined region in each frame of the captured images and/or videos to identify a ROI comprising arteries in the segmented portions. Based on the identified region of interest, the engine extracts pixel values, representing biosignals, from each frame of the captured images and/or videos to determine parameters associated with a rate of atherosclerotic, levels of lipids and lipoproteins, and hemodynamic factors of the subject. Further a risk score for the dyslipidemia condition based on the determined parameters using computational models is measured.

Provided is an image processing apparatus, including: an information acquiring unit configured to acquire three-dimensional polarization tomographic information and three-dimensional motion contrast information of an object to be inspected using tomographic signals of light beams having polarizations different from each other, which are obtained by splitting combined light obtained by combining return light from the object to be inspected irradiated with measuring light and reference light corresponding to the measuring light; an extracting unit configured to extract a specific region of the object to be inspected using the three-dimensional polarization tomographic information; and an image generating unit configured to generate a motion contrast enface image of the extracted specific region using the three-dimensional motion contrast information.

Z maps combined with a standardized stimulus in the form of a targeted arterial partial pressures of carbon dioxide provide suprisingly enhanced images for the assessment of pathological CVR. For example, the z-map assessment of patients with known steno-occlusive diseases of the cervico-cerebral vasculature showed an enhanced resolution of the presence, localization, and severity of the pathological CVR. Z-map have been found to be useful to reduce the confounding effects of test-to-test, subject-to-subject, and platform-to-platform variability for comparison of CVR images showing the importance of combining this analysis with the standardized stimulus.