The present disclosure relates to a method for vascular imaging and determining dynamic vascular parameters of blood flow. According to an embodiment, the present disclosure relates to an apparatus and method of determining dynamic vascular parameters of blood flow, comprising acquiring two-dimensional projection images of a vascular region of interest at a predetermined frequency, the vascular region of interest being downstream of a site of vascular administration of a radio-opaque medium, identifying, within the acquired two-dimensional projection images, heterogeneities of the radio-opaque medium, and determining the dynamic vascular parameters of the blood flow based on spatial movements of the identified heterogeneities of the radio-opaque medium. In an embodiment, the predetermined frequency is greater than 100 Hz.

Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.

A method for determining a significance of a stenosis in a currently examined blood vessel, the method comprising: providing a pre-trained reasoning module (130) that has been trained to output a value of a stenosis significance parameter by means of a training data set comprising a plurality of records of prior clinically examined stenosis cases, each training record comprising data related to dimensional parameters, blood flow parameters and clinical measurement parameters of the prior clinically examined blood vessel containing the stenosis; inputting, to the pre-trained reasoning module (130), an examination record comprising data related to the dimensional parameters of the currently examined blood vessel containing the stenosis and instructing the reasoning module (130) to output the value of the stenosis significance parameter based on the examination record.

An apparatus for classifying a brain tissue area as functional or non-functional by a stimulation of the brain includes a receiver unit for receiving information about a performed stimulation, a recording device for recording images that represent the brain tissue area, a detection unit for detecting a change in perfusion in the brain tissue area, and a classification unit configured to determine with the information whether there is a correlation between the performed stimulation and the detected change in perfusion, and to classify the brain tissue area as functional or as non-functional. The recording device is an endomicroscope for recording endomicroscopic images of the brain tissue area with a spatial resolution better than 20 μm and a frame rate of at least 0.4 frames per second. The detection unit is configured to detect a change in perfusion based on the positions of certain tissue structures in the recorded images.

Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

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.

Vessel perfusion and myocardial blush are determined by analyzing fluorescence signals obtained in a static region-of-interest (ROI) in a collection of fluorescence images of myocardial tissue. The blush value is determined from the total intensity of the intensity values of image elements located within the smallest contiguous range of image intensity values containing a predefined fraction of a total measured image intensity of all image elements within the ROI. Vessel (arterial) peak intensity is determined from image elements located within the ROI that have the smallest contiguous range of highest measured image intensity values and contain a predefined fraction of a total measured image intensity of all image elements within the ROI. Cardiac function can be established by comparing the time differential between the time of peak intensity in a blood vessel and that in a region of neighboring myocardial tissue both pre and post procedure.

Methods and systems for alignment of a subject for medical imaging are disclosed, and involve providing a reference image of an anatomical region of the subject, the anatomical region comprising a target tissue, processing the reference image to generate an alignment reference image, displaying the alignment reference image concurrently with real-time video of the anatomical region, and aligning the real-time video with the alignment reference image to overlay the real-time video with the alignment reference image. Following such alignment, the subject may be imaged using, for example, fluorescence imaging, wherein the fluorescence imaging may be performed by an image acquisition assembly aligned in accordance with the alignment.

A method of processing cross-sectional post contrast images. The method comprises obtaining a first cross-sectional post contrast image of at least part of a body (70). The first cross-sectional post contrast image represents a first time point. A second cross-sectional post contrast image of the at least part of the body is also obtained (72). The second cross-sectional post contrast image represents a second time point which is different from the first time point. A first difference image is generated using the first and second cross-sectional post contrast images (74). The first difference image highlights any differences between the first and second cross-sectional post contrast images.

An ultrasound imaging system is for determining stroke volume and/or cardiac output. The imaging system may include a transducer unit for acquiring ultrasound data of a heart of a subject (or an input for receiving the acquired ultrasound data), and a controller. The controller is adapted to implement a two-step procedure, the first step being an initial assessment step, and the second being an imaging step having two possible modes depending upon the outcome of the assessment. In the initial assessment procedure, it is determined whether regurgitant ventricular flow is present. This is performed using Doppler processing techniques applied to an initial ultrasound data set. If regurgitant flow does not exist, stroke volume is determined using segmentation of 3D ultrasound image data to identify and measure the volume of the left or right ventricle at each of end systole and end-diastole, the difference between them giving a measure of stroke volume. If regurgitant flow does exist, stroke volume is determined using Doppler techniques applied to ultrasound data continuously collected throughout a cardiac cycle.