Systems and methods are disclosed for simulating microvrascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more, perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

Systems and methods for estimating arterial flow information can include a processor generating a time attenuation sequence for each point of a pair of points along a segment of a coronary artery structure. The processor can determine the arterial flow velocity between the pair of points using the distance between the pair of points and the difference between average transit times associated with the pair of points. The one or more processors can determine the average transit times across the same time window. The processor can determine the arterial flow velocity between the pair of points using the distance between the pair of points and the difference between a first time duration that a number of particles take to pass by a first point of the pair of points and a second time duration that the number of particles take to pass by the other point.

According to one embodiment, a depth map used for a reflection model is generated based on a power image as a blood flow image. A reflection image is generated from the depth map according to the reflection model. By synthesizing the reflection image 70 with the power image, a weighted power image is generated. Using the same method as described above, a weighted velocity image may be generated.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.

Embodiments for assessing flow at an anatomical region of interest are disclosed. One embodiment uses pulsed contrast media injections at a known frequency along with corresponding image data to derive a measurement of blood flow velocity at the region of interest. Another embodiment uses incremental changes in known contrast media injection flow rates to match the blood flow rate relative to one of these known contrast media injection flow rates based on the presence of a particular indicia in image data. For example, this indicia can be the flow of contrast media out from a coronary artery back into the aorta or the onset of a steady state pixel density. A further embodiment uses contrast media injections that are synchronized with the cardiac cycle. For example, contrast media injections can be synchronized with the diastolic and/or systolic phases and used to measure blood flow accordingly.

The present disclosure is directed to a method and device for automatically predicting FFR based on images of vessel. The method for automatically predicting FFR based on images of a vessel. The method comprises a step of receiving the images of a vessel acquired by an imaging device. Then, a sequence of flow speeds at a sequence of positions on a centerline of the vessel is acquired by a processor. A sequence of first features at the sequence of positions on a centerline of the vessel are acquired by the processor, by fusing structure-related features and flow speeds and using a convolutional neural network. Then, a sequence of FFR at the sequence of positions is determined by the processor through using a sequence-to-sequence neural network on the basis of the sequence of first features.

Impaired intraocular blood flow within vascular beds in the human eye is associated with certain ocular diseases including, for example, glaucoma, diabetic retinopathy and age-related macular degeneration. A reliable method to quantify blood flow in the various intraocular vascular beds could provide insight into the vascular component of ocular disease pathophysiology. Using ultrahigh-speed optical coherence tomography (OCT), a new 3D angiography algorithm called split-spectrum amplitude-decorrelation angiography (SSADA) was developed for imaging microcirculation within different intraocular regions. A method to quantify SSADA results was developed and used to detect perfusion changes in early stage ocular disease. Associated embodiments relating to methods for quantitatively measuring blood flow at various intraocular vasculature sites, systems for practicing such methods, and use of such methods and systems for diagnosing certain ocular diseases are herein described.

A method of processing optical coherence tomography (OCT) scans through a subject's skin, the method comprising: receiving at least one OCT scan through the subject's skin, each scan representing an OCT signal in a slice through the subject's skin; processing each OCT scan so as to determine a set of parameters comprising at least a measure of the atrophy of the vascular structure in the epidermis; in which the processing produces a measurement of skin condition dependent upon each of the set of parameters, and the method comprises outputting the measurement of skin condition.

There is provided a method for calculation of a functional index parameter in at least one blood vessel of a patient, comprises: receiving a dataset of registered functional image data and anatomical image data, wherein the functional image data and the anatomical image data include data indicative of anatomical and functional data for at least one blood vessel of a certain patient; calculating at least one value for at least one functional index parameter for at least one of: (i) at least one blood vessel, and (ii) for the anatomical region of the at least one blood vessel, wherein the at least one value of the at least one functional index parameter is computed based on the functional image data of the dataset; and outputting the calculated at least one value for the at least one functional index parameter for the at least one blood vessel of the certain patient.