The present invention relates to a device (1) for fractional flow reserve determination. The device (1) comprises a model generator (10) configured to generate a three-dimensional model (3DM) of a portion of an imaged vascular vessel tree (VVT) surrounding a stenosed vessel segment (SVS), based on a partial segmentation of the imaged vascular vessel tree (VVT). Further, the device comprises an image processor (20) configured to calculate a blood flow (Q) through the stenosed vessel segment (SVS) based on an analysis of a time-series of X-ray images of the vascular vessel tree (VVT). Still further, the device comprises a fractional-flow-reserve determiner (30) configured to determine a fractional flow reserve (FFR) based on the three-dimensional model (3DM) and the calculated blood flow.

A route selection assistance system, a recording medium on which a route selection assistance program is recorded, a route selection assistance method, and a diagnosis method that enable easy selection of a route of a living body lumen for delivering a medical instrument to a site within a living body via the living body lumen. A route selection assistance system includes: a receiving section configured to receive an input of site information specifying a target site; an image obtaining section configured to obtain image information on a living body of a target patient; a route extracting section configured to extract a plurality of routes of a living body lumen; a ranking assigning section configured to assign rankings to the plurality of routes extracted by the route extracting section; and an output section configured to output the plurality of routes extracted and the rankings assigned by the ranking assigning section.

The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.

Provided are an apparatus and method for blood pressure measurement using an electroacoustic transducer in combination with a piezoelectric ultrasonic transducer. The apparatus and method can provide continuous, noninvasive blood pressure monitoring.

A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.

A method for providing a comparison dataset is disclosed. The method includes providing a time-resolved first dataset that maps a first contrast medium flow in a region of interest of an examination object in a first period of time; providing a time-resolved second dataset that maps a second contrast medium flow in the region of interest in a second period of time after the first period of time; spatially registering the first and second datasets; identifying a mapping of at least one vessel section of the region of interest in the first and second datasets; temporally registering the first and second datasets; identifying a difference between the first and second contrast medium flows by a comparison of the registered first and second datasets; and providing the comparison dataset based on the registered first and second datasets, wherein the comparison dataset has at least one parameter characterizing the difference.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure. Then, an embed feature is obtained based on both the local feature and the global feature and input into to the node. The method is able to integrate local and global consideration factors of the sampling points into the GNN to improve the prediction accuracy.

This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.

A method for generating a subtraction image for digital subtraction angiography to reduce noise and movement artifacts. Obtaining a plurality of mask images of an object takes place before administering a contrast agent into the object and obtaining a map of the object after administering a contrast agent into the object. A first sum image is obtained from the plurality of mask images in that the plurality of mask images is summed in each case multiplied by an individual weighting. The individual weightings for each of the plurality of mask images are automatically determined by an optimization method, and the subtraction image is ascertained by subtraction of the sum image from the map.