An ultrasound image recognition system and a data output module are provided. The ultrasound image recognition system includes an image analyzing device, a data processing device, and a data output module. The image analyzing device is configured to receive an image having a predetermined format. The image analyzing device generates a plurality of physiological image parameters. The data processing device is connected to the data processing device. The image analyzing device provides the plurality of physiological image parameters and the image having a predetermined format to the data processing device. The data processing device provides a comparison result of physiological parameter based on the plurality of physiological image parameters and a plurality of predetermined physiological image parameters. The data processing device converts the image having a predetermined format into an image having a first format.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

Angiographic recordings are to be made more informative. To this end, a method for spatiotemporal fusion of time-resolved angiographic data sets is proposed. Respective 4D reconstructions are obtained from angiographic 3D data sets acquired from contrast agents administered at different sites. In both 4D reconstructions, a common vascular region is identified. For each contrast agent bolus, the corresponding time point or time course in the common vascular region is determined. Finally, the two 4D reconstructions are synchronized and fused.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Described herein is blood flow imaging based on an area under a time-enhancement curve predicted by a computer learning model. Image data comprising a plurality of corresponding images capturing at least a portion of one or both an increase phase and a decline phase of a contrast agent in a cardiovasculature of interest is inputted to a machine learning model to predict an area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest, the predicted area under the time-enhancement curve representing the total sum of contrast agent concentration time product within the cardiovasculature of interest. In an example, a computer implemented method for blood flow imaging comprising: obtaining image data comprising a plurality of corresponding images capturing at least a portion of one or both an increase phase and a decline phase of a contrast agent in a cardiovasculature of interest; providing the image data to a machine learning model to predict an area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest; determining a blood flow characteristic through the region of interest based on the area under the time-enhancement curve. Systems and non-transitory computer-readable media for executing the method are also described.

A dynamic image analysis apparatus includes a hardware processor. The hardware processor, acquires a dynamic image obtained by radiographing a dynamic state of a subject, analyzes the dynamic image using one or more types of analysis parameters, generates an analysis result image, determines whether the analysis parameter needs to be corrected, and notifies the analysis parameter determined to require correction.

The present disclosure provides apparatus and methods to generate a three-dimensional (3D) model of the physiology of a vessel from a single angiographic image and a series of intravascular images as well as another physical characteristic of the vessel, such as, for example, pressure.

An analysis device according to an embodiment includes processing circuitry. The processing circuitry extracts, from medical image data, the shape of a blood vessel of a subject and the shape of a plaque formed in the blood vessel. Then, while changing a first-type timing in sequence, the processing circuitry calculates a mechanical index, which is related to the plaque at the first-type timing, based on the shape of the blood vessel and the shape of the plaque at the first-type timing. Subsequently, based on the mechanical index at the first-type timing, the processing circuitry predicts the shape of the plaque at a second-type timing that is the next timing to the first-type timing. Then, the processing circuitry displays, in a display unit, the predicted shape of the plaque at the time second-type at which the plaque reaches a specific condition.

Systems and methods for automated physiological parameter estimation from ultrasound image sequences are provided. An ultrasound system includes an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient. An anatomical structure recognition module includes processing circuitry configured to receive the acquired sequence of ultrasound images from the ultrasound imaging device, and automatically recognize an anatomical structure in the received sequence of ultrasound images. A physiological parameters estimation module includes processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analyzing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.