A method for processing digital subtraction angiography (DSA) or computed tomography (CT) images for reduced radiation exposure to a DSA or CT subject includes receiving, as input, a plurality of captured DSA or CT image frames of a contrast agent flowing through a volume of interest in a subject. The method further includes fitting a mathematical model to measured contrast agent density of individual voxels of the captured DSA or CT image frames to produce a mathematical model of contrast agent flow across the captured DSA or CT image frames. The method further includes sampling the mathematical model of contrast agent flow for the individual voxels to produce reconstructed DSA or CT image frames. The method further includes outputting at least one of the reconstructed CT or DSA image frames.

A method of quantifying a 3D fractional moving blood volume (3D-FMBV) in a tissue volume of a subject using an ultrasound system, including acquiring images of the tissue volume from a power Doppler scan of the tissue volume; applying image enhancement settings to the images; segmenting an organ, tissue or region thereof from the image data; determining geometric partitions of the segments based on distance from the transducer head of the ultrasound system; and computing a 3D-FMBV using a 3D-FMBV analysis algorithm from the partitions.

An ultrasound imaging system including an ultrasound probe having an array of ultrasonic transducers configured to emit generated ultrasound signals into a patient, receive reflected ultrasound signals from the patient, and convert the reflected ultrasound signals into corresponding electrical signals of the ultrasound signals for processing into ultrasound images. The system includes a console having a processor to execute logic that, when executed, causes operations including capturing first and second ultrasound images of a target insertion area of a patient at a first time, generating and causing rendering of a notification indicating results of a comparison of the first ultrasound image and the second ultrasound image. The operations can also include determining, via a machine learning model, whether the second ultrasound image corresponds to the second ultrasound image by at least a threshold amount, and providing a visual indication of a result of applying the trained machine learning model.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.

Systems and methods for determining modified fractional flow reserve values of vascular lesions are provided. Patient physiologic data, including coronary vascular information, is measured. According to the physiologic data, a coronary vascular model is generated. Lesions of interest within the coronary vascular system of the patient are identified for modified fractional flow reserve value determination. The coronary vascular model is modified to generate modified blood flow information for determining the modified fractional flow reserve value.

Large vessel occlusion (LVO) detection and treatment prediction is provided via receiving a patient image depicting a medical scan of a patient; in response to identifying, via a first model, an LVO in the patient image, determining, via the first model whether the LVO is acute or chronic; in response to determining that the LVO is acute, determining via a second model different from the first model, a predicted treatment outcome for an immediate medical intervention for the LVO; and responsive to determining a successful treatment outcome for the LVO, assigning the immediate medical intervention to the patient.

A method for determining blood vessel parameters is provided. The method may include obtaining a blood vessel image of a target blood vessel. The method may also include generating a blood vessel model of the target blood vessel based on the blood vessel image. The blood vessel model is a grid model. The method may further include determining at least one blood vessel parameter of the target blood vessel based at least on the blood vessel model.

Apparatus and methods are described for use with an endoluminal device that includes one or more radiopaque portions and that moves through a lumen of a subject. A sequence of radiographic images of a portion of the subject's body, in which the lumen is disposed, is acquired, during movement of the endoluminal device through the lumen. Locations at which the one or more radiopaque portions of the endoluminal device were imaged during the movement of the endoluminal device through the lumen are identified, by analyzing the sequence of radiographic images. A set of locations at which the one or more radiopaque portions were disposed during the movement of the endoluminal device through the lumen is defined, and an endoluminal path of the device through the lumen is estimated based upon the set of locations. Other applications are also described.

The present disclosure relates to methods and systems for image analysis. The method may include obtaining a plurality of image sequences relating to a blood vessel. At least one of the plurality of image sequences may be acquired using a black blood imaging sequence. The method may also include determining a plurality of aligned image sequences by aligning, based on a reference image sequence of the plurality of image sequences, the plurality of image sequences, determining a target region based on the plurality of aligned image sequences, and determining one or more target parameters based on the target region.