Disclosed are techniques for automated analysis and assessments of contrast medium absorption phases in contrast medium based medical images. A target image set includes a plurality of medical images acquired to image a plurality of contrast medium absorption phases. For the images of the target image set, a set of contrast medium absorption phase probabilities are determined corresponding to likelihoods that a given image corresponds to a given contrast medium absorption phase. The determined sets of contrast medium absorption phases are compared against a reference set of contrast medium absorption phases to determine differences to determine a set of matching scores indicative of how closely the contrast medium absorption phases of the target image set align with the plurality of contrast medium absorption phases as compared to the reference set of contrast medium absorption phases.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

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.

Systems for obtaining an image of a target are provided including at least one multi-wavelength illumination module configured to illuminate a target using two or more different wavelengths, each penetrating the target at different depths; a multi-wavelength camera configured to detect the two or more different wavelengths illuminating the target on corresponding different channels and acquire corresponding images of the target based on the detected two or more different wavelengths illuminating the target; a control module configured synchronize illumination of the target by the at least one multi-wavelength illumination module and detection of the two or more different wavelengths by the camera; an analysis module configured to receive the acquired images of the target and analyze the acquired images to provide analysis results; and an image visualization module modify the acquired images based on the analysis results to provide a final improved image in real-time.

A stenosis assessment method and device based on the intracranial digital subtraction angiographic (DSA) imaging, including acquiring the intracranial DSA imaging and extracting two planar images containing the target blood vessel from the DSA imaging, wherein the two planar images have different shooting angles. According to the two planar images, a 3D model of the target vessel is established. Based on the established 3D model of the target vessel and the DSA imaging, the hemodynamic simulation of the target vessel is performed. The disclosure realizes the functional assessment of intracranial vascular stenosis, improves the diagnostic accuracy, and provides certain assistance for neurologists to determine intervention means. The disclosure of noninvasive FFR technology in the assessment of intracranial vascular stenosis can only rely on angiography for functional assessment, saving the medical examination cost of patients. It has more convenient operation and higher repeatability.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.

A medical image-processing apparatus of an embodiment includes a processing circuitry. The processing circuitry acquires a plurality of medical images including a predetermined region and having different time phases. The processing circuitry sets a first region of interest and a second region of interest in each of the plurality of medical images. The processing circuitry derives a temporal change in first pixel values, which are pixel values on the first region of interest, on the basis of the first pixel values and derives a temporal change in second pixel values, which are pixel values on the second region of interest, on the basis of the second pixel values. The processing circuitry sets a first time window on the basis of the temporal change in the first pixel values and sets a second time window on the basis of the temporal change in the second pixel values.