Abstract: A method and system for non-invasive hemodynamic assessment of coronary artery stenosis based on medical image data is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient. Patient-specific boundary conditions of a computational model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements of the coronary arteries. Blood flow and pressure in the coronary arteries are simulated using the computational model of coronary circulation and the patient-specific boundary conditions and coronary autoregulation is modeled during the simulation of blood flow and pressure in the coronary arteries. A wave-free period is identified in a simulated cardiac cycle, and an instantaneous wave-Free Ratio (iFR) value is calculated for a stenosis region based on simulated pressure values in the wave-free period.

Abstract: A method and system for simulating blood flow in a vessel of a patient to estimate hemodynamic quantities of interest using enhanced blood flow computations based on invasive physiological measurements of the patient is disclosed. Non-invasive patient data including medical image data is received and a patient-specific anatomical model the patient's vessels is generated. Invasive physiological measurements of the patient are received and a computational blood flow model is personalized using the invasive physiological measurements. Blood flow is simulated in the patient-specific anatomical model and one or more hemodynamic quantities of interest are computed using the personalized computational blood flow model.

Abstract: An angiographic examination method for the representation of flow properties of vessels of an object under examination is presented. To determine blood-flow parameters in 3-D with high temporal resolution, at least one 4-D DSA sequence is acquired for the generation of measurement-based 4-D DSA data sets. A model-based method determines time-dependent volume data sets, which, in terms of time, lie between the time-dependent volume data sets of the measurement-based 4-D DSA method.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Abstract: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Abstract: A three-dimensional model dataset of a blood vessel system of a patient including at least one the vessel system is determined from a number of projection images, which have been recorded from different recording angles, of the blood vessel system The projection images are divided up into image areas each containing at least one pixel. A feature vector is determined for each of the image areas. Classification information, which describes how the respective image area belongs or does not belong to a vessel segment of the blood vessel system defined in accordance with anatomical specification data, is defined for each of the image areas by applying a classification function to the feature vector assigned to the image area. The classification function has been trained by training data records annotated with classification information obtained from at least one person other than the patient.

Abstract: A method for ascertaining a fluid-dynamic characteristic value of a resilient vascular tree, through which a fluid flows in a pulsating manner, is provided. At least one 2D projection, respectively, of the resilient vascular tree is generated by a projection device from different angles of projection, and a digital 3D reconstruction of the vascular tree is generated by an analysis device based on of the 2D projections. A geometry of at least one vessel of the resilient vascular tree is estimated based on the 3D reconstruction, and at least one fluid state in the resilient vascular tree is ascertained from the geometry and predetermined resilient properties of the resilient vascular tree. The at least one fluid-dynamic characteristic value is calculated as a function of the at least one fluid state.

Abstract: A method and system for non-invasive hemodynamic assessment of coronary artery stenosis based on medical image data is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient. Patient-specific boundary conditions of a computational model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements of the coronary arteries. Blood flow and pressure in the coronary arteries are simulated using the computational model of coronary circulation and the patient-specific boundary conditions and coronary autoregulation is modeled during the simulation of blood flow and pressure in the coronary arteries. A wave-free period is identified in a simulated cardiac cycle, and an instantaneous wave-Free Ratio (iFR) value is calculated for a stenosis region based on simulated pressure values in the wave-free period.

Abstract: An X-ray imaging apparatus has at least one X-ray image system rotatable about an examination volume. The X-ray image system is controlled such that during a continuous rotation of the system, at least one 2D projection image is recorded. An image generation facility generates the 2D projection image from the measured data. The X-ray source includes an X-ray focus which can be changed in terms of position, which, during the recording of the 2D projection image, moves counter to the direction of rotation of the X-ray image system such that its spatial position in a fixed coordinate system does not change. The X-ray detector records several 2D partial images, from which the 2D projection image is calculated with the rotational movement of the X-ray detector being at least approximately compensated. The 2D projection images have significantly reduced image blur.

Abstract: In a method to determine a patient-specific injection profile for injection of a therapeutic active substance into a vascular system of a patient, which can be implemented by an image analysis device a model of the current flow of a bodily fluid located in the vascular system is determined using a provided 3D image data set of a sub-region of the vascular system, by segmentation of the 3D image data. Inflows and outflows are determined, as well as an inflow curve of the fluid in the sub-region. A virtual two-dimensional representation of a contrast agent injected into the sub-region is determined. A provided 2D image data set, as a real two-dimensional depiction of the sub-region, is compared with the virtual representation and/or the model is adapted to individualize the model. The injection profile is determined by determination of at least one parameter of the planned injection using the individualized model.

Abstract: A method for modeling blood flow through a flow diverter includes receiving a medical image containing blood vessels. Vessel geometry is extracted from the received medical image. Inlets and outlets are tagged within the extracted vessel geometry. A desired flow diverter is selected. A model of the selected flow diverter is generated within the imaged blood vessel, the model representing the flow diverter as a tube having a porous surface characterized by a viscous resistance and an inertial resistance. A course of blood flow though the flow diverter is predicted based on the generated model, the extracted vessel geometry, and the tagged inlets and outlets.

Abstract: A method for computing a color-coded analysis image of an examination area of an examination object from a temporal sequence of fluoroscopic images of the examination area comprising a vascular system containing arteries and/or veins is provided. An acquisition time instant has been assigned to each of the fluoroscopic images representing a given distribution of a material embolizing some of the vascular system. The fluoroscopic image spatially corresponds to an analysis image pixel by pixel. A computer receives the fluoroscopic images with a color attribute assigned to each pixel of the analysis image at an image point and a time instant. If a pixel differs from a pixel at a preceding time instant, the color attribute assumes a color attribute of the time instant and the difference. If a pixel corresponds to a background color of the analysis image, the color attribute assumes a background color.

Abstract: An interventional medical diagnosis and/or therapy system is provided. The system provides an interventional imaging system and method which allows for an intervention, to be conducted in accordance with an intervention plan, to be supported and monitored by ongoing imaging, in particular radioscopy, with which, at the same time the effort in calibration and registration is kept low, and which functions without an additional location system. The interventional imaging system includes an imaging device to record intervention data of a body, at least two position markings, capable of being recorded with the imaging device, for the marking of an intervention instrument, a display apparatus to reproduce recorded intervention data and position markings, a navigation facility connected to the display apparatus to load pre-intervention data of the body, in which an intervention location of the body is contained, and for the mutual registration of the pre-intervention data with the intervention data.

Abstract: A flow diverter is automatically detected from medical imaging data. The appearance of the flow diverter as represented in the data as well as the geometry or shape of the flow diverter is used in the detection. Using scoring for appearance relative to the centerline and cross-section of the centerline, the flow diverter is detected for increasing visualization.

Abstract: A method and apparatus for generating at least one functional data set of a perfused region of the human or animal body are proposed. A first image data set is supplied comprising at least two images of the perfused region recorded at different times before and after an injection of contrast agent into a first artery supplying the region. A second image data set is supplied comprising at least two images of the perfused region recorded at different times before and after an injection of contrast agent into a second artery supplying the region. A first functional data set is generated by pixel-based calculation of at least one perfusion parameter from the first image data set. A second functional data set is generated by pixel-based calculation of at least one perfusion parameter from the second image data set.

Abstract: A determination method for reinitialization of a temporal sequence of fluoroscopic images of an examination region of an examination object is provided. The examination region comprises a vascular system including arteries and/or veins. An acquisition time is assigned to each of the images representing a given distribution of a substance in the examination region at the acquisition time. A computer receives the temporal sequence of the images, determines an evaluation image corresponding spatially on a pixel-by-pixel basis to the images, and calculates a differential value between a pixel of the evaluation image at a time and a pixel at a preceding time during a time characteristic of the sequence. A reinitialization of the temporal sequence of the images is performed at a specific time and thereafter the determination method is started over and/or repeated. The specific time is determined as a function of at least one previously calculated differential value.

Abstract: A method for simulating a blood flow in a vascular segment of a patient is proposed. A 3D image dataset of an examination region is recorded by a radiographic diagnostic device for generating a 3D vascular model. Contrast agent propagation in the examination region is captured by a dynamic 2D angiography method for generating a real 2D angiography recording. A CFD simulation of the blood flow is performed in the 3D vascular model based on a blood flow parameter for generating a virtual 2D angiography recording. A degree of correspondence between the real and the virtual 2D angiography recordings is determined from identical angulation and adjusted recording geometry of the patient and compared with predefinable tolerance values. The CFD simulation is iteratively optimized while changing the blood flow parameter as a function of the comparison. The degree of correspondence is outputted when the optimum CFD simulation is achieved.