Abstract: The present invention relates to multispectral imaging. In order to improve an identification of relevant multispectral material transitions (in particular caused by injected contrast agent), an apparatus is proposed to use the local maxima of the variances and/or covariances of the intensities of the multi-channel images to locate material transitions. In comparison to gradient vectors, the local variance is not directed and not prone to noise. An alternative apparatus is proposed to use the local covariance deficits of the intensities of the multi-channel images to locate material transitions. The proposed alternative approach is independent of spatial drifts across the image volume.

Abstract: A method for visualization may include: obtaining data of a first perfusion measure of myocardial tissues of a patient; obtaining data of a geometry of a coronary artery of the patient; obtaining data of a second perfusion measure of the coronary artery; obtaining data of a flow impediment measure along the coronary artery based on the data of the second perfusion measure of the coronary artery; and visualizing, on a single image, the first perfusion measure of the myocardial tissues and the coronary artery, the coronary artery being overlaid with the first perfusion measure on the single image, the visualized coronary artery representing the geometry of the coronary artery and the flow impediment measure along the coronary artery.

Abstract: The present disclosure provides means for improved medical image post-processing. It utilizes an image data post-processing mechanism (122) comprising an encoder (122A) configured to encode input image data with input image properties and a decoder (122B) configured to decode the input image data to provide output image data with output image properties different to the input image properties. The post-processing mechanism (122) comprises a first post-processing setting applied to the encoder and/or the decoder and assigned to first output image properties. Further, the post-processing mechanism (122) is configured to predict a second post-processing setting applicable to the encoder and/or the decoder and assigned to second output image properties. The post-processing mechanism (122) is further configured to provide an image proposal comprising the input image data post-processed with the predicted second post-processing setting.

Abstract: A system (SY) for determining a relative importance of each of a plurality of image features (Fn) of a vascular medical image impacting an overall diagnostic metric computed for the image from an automatically-generated diagnostic rule. A medical kin image database (MIDB) includes a plurality of vascular medical images (M1 . . . k). A rule generating unit (RGU) analyzes the plurality of C vascular medical images and automatically generates at least one diagnostic rule corresponding to a common diagnosis of a subset of the plurality of vascular medical images based on a plurality of image features common to the subset of vascular medical images. An image providing unit (IPU) provides a current vascular medical image (CVMI) including the plurality of image features. A diagnostic metric computation unit (DMCU) computes an overall diagnostic metric for the current vascular medical image by applying the at least one automatically-generated diagnostic rule to the current vascular medical image.

Abstract: An imaging system (IS), comprising an image acquisition unit (AQ) for acquisition of image data (I1) of an object (OB). The image acquisition is based on an imaging signal imitable by the unit (AQ) to interact with the object. The image acquisition unit (AQ) is adjustable to operate at different acquisition parameters that determine a property of the imaging signal. A predictor component (PC) predicts, based at least on the acquired image data (I1), one or more properties of the object. An acquisition parameter adjuster (PA) adjusts, based on the predicted object properties, the acquisition parameter at which the image acquisition unit (AQ) is to acquire follow-up image data (I2).

Abstract: A system and method for prioritizing a set of medical images to be evaluated using a machine learning model, including: training the machine learning model using a training data set, wherein the machine learning model receives input medical images and outputs a medical condition shown in the input medical images; running the trained machine learning model on the set of medical images to be evaluated to produce a medical condition output for each of the set of medical images; calculating a likelihood score for each medical condition outputs based upon a determined statistical parameters for the different outputs of the machine learning model; and determining the order of the set of input images to be evaluated based upon the calculated likelihood score and a severity of the medical condition outputs.

Abstract: A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126), and a reference location (128), and a processor (120) configured to the biophysical simulator and simulate a reference FFR value at a predetermined location along a segmented coronary vessel indicated by the reference location. A computer readable storage medium encoded with computer readable instructions, which, when executed by a processor of a computing system, causes the processor to simulate a reference FFR value at a predetermined location along a segmented coronary vessel indicated by a predetermined reference location. A method including simulating a reference FFR value at a predetermined location along a segmented coronary vessel indicated by a predetermined reference location.

Abstract: 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.

Abstract: 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.

Abstract: A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a including a biophysical simulator (126) with a segmentor (202) and a boundary condition determiner (206). The computing system further includes a processor (120) configured to execute the biophysical simulator to compute a fractional flow reserve index with cardiac imaging data and at least one of an adapted coronary tree segmentation and an adapted boundary condition.

Abstract: 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.

Abstract: A computing system (126) includes a computer readable storage medium (130) with computer executable instructions (128), including: a segmentation standardizer (120) configured to determine a standardized vascular tree from a segmented vascular tree segmented of volumetric image data and a predetermined set of pruning rules (206), and a biophysical simulator (122) configured to perform a biophysical simulation based on the standardized vascular tree. The computing system further includes a processor (124) configured to execute the segmentation standardizer to determine the standardized vascular tree from the segmented vascular tree segmented of volumetric image data and the predetermined set of pruning rules, and configured to execute the biophysical simulator to perform a biophysical simulation based on the standardized vascular tree. The computing system further includes a display configured to display at least one of the standardized vascular tree and a result of the biophysical simulation.

Abstract: The present invention relates to an apparatus (26) and a method for determining a fractional flow reserve. For this purpose, a new personalized hyperemic boundary condition model is provided. The personalized hyperemic boundary condition model is used to condition a parametric model for a simulation of a blood flow in a coronary tree (34) of a human subject. As a basis for the personalized hyperemic boundary condition model, a predefined hyperemic boundary condition model is used, which represents empirical derived hyperemic boundary condition parameters. However, these empirical hyperemic boundary condition parameters are not specific for a human subject under examination. In order to achieve a specification of the respective predefined hyperemic boundary condition model, specific human subject features are derived from a volumetric image of the coronary tree of the human subject.

Abstract: A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a including a biophysical simulator (126) with a segmentor (202) and a boundary condition determiner (206). The computing system further includes a processor (120) configured to execute the biophysical simulator to compute a fractional flow reserve index with cardiac imaging data and at least one of an adapted coronary tree segmentation and an adapted boundary condition.

Abstract: The present invention relates to a determination of a physiological functional parameter of a living being. Ultrasound image data and Doppler image data of a vessel structure are provided (101) and registered (102). The vessel structure is segmented (103) to generate (104) a representation of the vessel structure. The flow velocity inside a vessel of the vessel structure is determined (105) based on the Doppler image data. A physiological functional parameter determination model defining a value of a functional physiological parameter in dependence of a representation of a vessel structure and a flow velocity inside a vessel of the vessel structure is used (106) to determine (107) the physiological functional parameter inside the vessel of the vessel structure. The representation of the vessel structure and/or the flow velocity values can be constantly updated upon receipt of further input images to provide an estimation of the functional physiological parameter in real-time.

Abstract: 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 coronaiy 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 coronaiy 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 coronaiy state and the predicted coronaiy state.

Abstract: A system (100) for segmenting a coronary artery vessel tree (182) of a patient heart in a three dimensional (3D) cardiac image (120) includes a coronary volume definition unit (150) and a coronary artery segmentation unit (180). The coronary volume definition unit (150) sets a spatial boundary (210, 220) from internal and external surfaces of heart tissues in the 3D cardiac image based on a fitted heart model (200). The coronary artery segmentation unit (180) segments the coronary artery vessel tree (182) in the 3D cardiac image using a segmentation algorithm with a search space limited by the spatial boundary set from the internal and external surfaces of the heart tissues.

Abstract: Stenosis information is obtained by obtaining photographic image data (302) from a displayed image of a blood vessel (103, 203) containing the stenosis. Contours of the blood vessel and the stenosis are detected and dimensions are estimated from the photographic image data. A blood vessel model is reconstructed and fractional flow reserve data is calculated using the blood vessel model.

Abstract: The disclosure relates to a system for image decomposition of an anatomical projection image. The system comprises a data processing system which implements a decomposition algorithm of an projection image which is generated by irradiating a part of a subject with imaging radiation. A body portion within the irradiated part is a three-dimensional attenuation structure of an attenuation of the imaging radiation, wherein the attenuation structure represents a member of a predefined class of attenuation structures of the decomposition algorithm, thereby representing a classification of the body portion. The data processing system decomposes the projection image data using the classification of the attenuation structure. The decomposition of the projection image data substantially separates the contribution of the classified body portion to the projection image from the contribution of a further body portion of the subject to the projection image.

Abstract: A system (SY) for determining a relative importance of each of a plurality of image features (Fn) of a vascular medical image impacting an overall diagnostic metric computed for the image from an automatically-generated diagnostic rule. A medical kin image database (MIDB) includes a plurality of vascular medical images (M1 . . . k). A rule generating unit (RGU) analyzes the plurality of C vascular medical images and automatically generates at least one diagnostic rule corresponding to a common diagnosis of a subset of the plurality of vascular medical images based on a plurality of image features common to the subset of vascular medical images. An image providing unit (IPU) provides a current vascular medical image (CVMI) including the plurality of image features. A diagnostic metric computation unit (DMCU) computes an overall diagnostic metric for the current vascular medical image by applying the at least one automatically-generated diagnostic rule to the current vascular medical image.