An image processing apparatus comprising an image producing unit 101 for producing an axial image of a body part to be imaged including an aorta and an esophagus; a map generating unit 102 for generating a map M2 for locating a region in which a probability that the aorta lies is high in the axial image; a detecting unit 103 for detecting a temporary position of the aorta based on the map M2; and a deciding unit 104 for making a decision on whether or not the temporary position of the aorta falls within the region of the aorta in the axial image based on a distribution model DM containing information representing a reference position (x.sub.e, y.sub.e) of the esophagus and information representing a range over which the aorta distributes relative to the reference position (x.sub.e, y.sub.e) of the esophagus, and on the map M2.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: perform error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

A method for processing medical images includes analyzing a medical image to detect a medical condition from a list of medical conditions, wherein the list of medical conditions includes aortic dissection, pulmonary embolism, and coronary stenosis. Responsive to determining the medical image includes a first medical condition, the method generates a first report that includes information on a detection of the first medical condition. The method identifies, a medical specialist based on availability and medical expertise and sends to the identified medical specialist, the medical image and the first report for a decision on the detection of the first medical condition. Responsive to receiving the decision from the medical specialist, the method sends to a second electronic device, the decision, the medical image, and the first report.

A vascular assessment apparatus is disclosed. The apparatus is configured to receive medical images of a coronary vessel tree of a subject from a medical imaging device and analyze the medical images to identify vessel segments within the coronary vessel tree. For each identified vessel segment, the apparatus is configured to analyze portions of the segment to determine at least one of a radius, diameter, or cross-sectional area of the vessel segment at the analyzed portions, determine resistances for the analyzed portions of the vessel segment based the radius, diameter, or the cross-sectional area at the analyzed portions, and combine the determined resistances for the analyzed portions of the vessel segment to determine a total resistance of the identified vessel segment. The example apparatus is also configured to determine flow rates at each identified vessel segment and calculate an index indicative of vascular function based on the determined flow rates.

Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

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.

The disclosure relates to a device and a method for ascertaining at least one individual fluid-dynamic characteristic parameter of a stenosis in a vascular segment having a plurality of serial stenoses, wherein angiography image data of the vascular segment is received from an angiography recording device, geometry data of the vascular segment is ascertained by an analysis device based on the angiography image data and combined into a segment model. At least one division point located between two of the stenoses respectively is ascertained by a dividing device in the segment model, the segment model is subdivided into subsegment models at each of the at least one division points, and the respective fluid-dynamic characteristic parameter is ascertained by a simulation device for at least one of the subsegment models based on respective geometry data of the subsegment model.

A method and a device for analysis and display of blood flow information in the human or animal body are described. The method includes the following steps: a) providing a digital input data set including a time series of two or three dimensional velocity vector fields, wherein each velocity vector field represents the velocity of the blood flow within a blood vessel, especially of a heart chamber or part thereof, of a certain human or animal body within a certain time frame within one heart cycle, b) calculating a gradient vector field for each time frames from the time series of velocity vector fields; c) summing the gradients over the gradient vector field or a part thereof for each time frame to a summed gradient; and d) displaying and/or analyzing the summed gradients with reference to their space directions within the blood vessel.

A method for measuring a physiological signal is provided, including the steps of: receiving a video and performing face detection for each image frame thereof; performing photo-plethysmography (PPG) calculation and analysis on first face image for first image frame to obtain first PPG information of first regions and second PPG information of corresponding second regions; determining at least one region-of-interest (ROI) and one noise reference region according to the first PPG information and the second PPG information; generating ROI information and noise reference region information based on the ROI and the noise reference region; and performing the PPG calculation and analysis on the ROI and the noise reference region of the second face image of each subsequent second image frame and generating corresponding third PPG information; and counting the PPG information of all the image frames to calculate a measured value of a physiological signal.