A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a predictor (126) configured to determine a baseline coronary state and a predicted coronary state from contrast enhanced cardiac computed tomography volumetric image data and a model of an effect of one or more substances on characteristics effecting the coronary state. The system further includes a processor (120) configured to execute the predictor to determine the baseline coronary state and the predicted coronary state from the contrast enhanced cardiac computed tomography volumetric image data and the model of the effect of one or more of the substances on the characteristics effecting the coronary state. The system further includes a display configured to display the baseline coronary state and the predicted coronary state.

A pressure measuring device 30 installs on a tube 11 for transferring a medium (e.g., blood in a extracorporeal blood circulator) so as to measure a pressure of the medium inside the tube 11. The pressure measuring device 30 includes a main body portion 31 mountable to the tube 11, an image acquisition unit 32 disposed in the main body portion 31 so as to acquire image information on a pressure receiver that is deformed in response to the received pressure of the medium inside the tube 11, and a control unit 100 that converts the image information acquired by the image acquisition unit into pressure information about the pressure.

A virtual stress test may be performed for a patient by creating an electronic model of a region of interest of the patient's anatomy, such as one or more coronary arteries, determining pressure drops through the region of interest, based on computational fluid dynamics, at two different activity levels of the patient, and determining a range of pressure drops for a range of activity levels based on flow rates associated with the two activity levels. Based on the range of pressure drops, it can be determined if the patient has a clinically-significant pressure drop, indicative of an obstructive stenosis, at an activity level relevant to the patient's metabolic requirements.

An apparatus for vascular modeling is disclosed. The apparatus receives medical images from an imaging device that include representations of a coronary vessel tree of a subject recorded at a different viewing angles. The apparatus determines, from a first of the medical images, a first centerline set and first vessel diameters for sample points along the first centerline set, and determines, from a second of the medical images, a second centerline set and second vessel diameters for sample points along the second centerline set. The apparatus determines a correspondence between the first centerline set and the second centerline set, and determines diameters for a combined centerline set based on the correspondence of sample points along the first and second centerline sets. The apparatus provides the combined centerline set for estimating blood flow resistance values of the coronary vessel tree of the subject to determine at least one potential stenosis.

Various methods and systems are described for obtaining at least one CTA perfusion functional map from Time Resolved Helical CTA (TRH-CTA) image data. At least one processor may be configured to preprocess the TRH-CTA helical image data to generate preprocessed TRH-CTA helical image data; generate time density curve data for a plurality of voxels from the preprocessed TRH-CTA helical image data for an axial imaging slice, where the time density curve data comprise intensity values for different phases of the preprocessed TRH-CTA helical image data arranged sequentially in time; generate at least one perfusion functional map for the axial imaging slice by at least one of: (1) applying at least one mapping function to different phases of the time density curve data corresponding to the axial imaging slice; (2) applying a deconvolution method to the time density curve data; and (3) applying a non-deconvolution method to the time density curve data; and perform spatial filtering on the perfusion functional map. A display may be used to display at least one filtered perfusion functional map.

The invention provides a method for obtaining a parameter relating to flow from a vessel. The method begins by obtaining ultrasound data, which includes Doppler ultrasound data, from an imaging plane and identifying a vessel cross section within the imaging plane based on the ultrasound data. A shape of the identified vessel cross section is then determined and a vessel axis extending along the length of the vessel is determined based on the shape of the identified vessel cross section, with the assumption of a circular cross section on a plane perpendicular to the vessel axis. A Doppler angle is determined between the vessel axis and the imaging plane and the parameter relating to flow derived based on the Doppler angle, the vessel axis and the Doppler ultrasound data.

A method for acquiring a blood flow volume and a blood flow velocity of a coronary artery is provided. The method comprises: acquiring image information of a coronary artery to obtain geometrical feature data of the coronary artery; obtaining, according to the geometrical feature data of the coronary artery, the total volume V of a reference lumen of the coronary artery; and calculating a blood flow volume Q at coronary artery ostia according to the following formula:

[00001]$Q=K*V^{\frac{3}{4}},$

wherein when the unit of Q is mm.sup.3/s, and when the unit of V is mm.sup.3, a value range of K is 5-9.5. A blood flow volume and a blood flow velocity of a coronary artery are obtained by means of image information of the coronary artery. Compared with a method, in the prior art, for estimating a blood flow volume of a coronary artery by means of the size of a cardiac muscle, this method is simpler, and can provide a more accurate boundary condition for image-based hemodynamic calculation.

Methods and systems are provided for detecting coronary lesions in 3D cardiac computed tomography and angiography (CCTA) images using deep neural networks. In an exemplary embodiment, a method for detecting coronary lesions in 3D CCTA images comprises, acquiring a 3D CCTA image of a coronary tree, mapping the 3D CCTA image to a multi-label segmentation map with a trained deep neural network, generating a plurality of 1D parametric curves for a branch of the coronary tree using the multi-label segmentation map, determining a location of a lesion in the branch of the coronary tree using the plurality of 1D parametric curves, and determining a severity score for the lesion based on the plurality of 1D parametric curves.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

A method for acquiring a blood flow volume and a blood flow velocity of a coronary artery is provided. The method comprises: acquiring image information of a coronary artery to obtain geometrical feature data of the coronary artery; obtaining, according to the geometrical feature data of the coronary artery, the total volume V of a reference lumen of the coronary artery; and calculating a blood flow volume Q at coronary artery ostia according to the following formula: $Q=K*V^{\frac{3}{4}},$
wherein when the unit of Q is mm.sup.3/s, and when the unit of V is mm.sup.3, a value range of K is 5-9.5. A blood flow volume and a blood flow velocity of a coronary artery are obtained by means of image information of the coronary artery. Compared with a method, in the prior art, for estimating a blood flow volume of a coronary artery by means of the size of a cardiac muscle, this method is simpler, and can provide a more accurate boundary condition for image-based hemodynamic calculation.