Koninklijke Philips Corporation N.v.

Hannes Nickisch

Holger Schmitt

Michael Grass

Sven Prevrhal

Liran Goshen

Mordechay Pinchas Freiman

Raoul Florent

Hanno Heyke Homann

Odile Bonnefous

Tobias Wissel

Rafael Wiemker

Juergen Weese

Tobias Klinder

Cristian Lorenz

Jochen Peters

Frank Michael Weber

Alexandra Groth

Christian Haase

Claas Bontus

Rolf Jürgen Weese

Jan Timmer

Tom Brosch

Hans Barschdorf

Yechiel Lamash

Manindranath Vembar

Eran Langzam

A computer-implemented method of measuring a blood flow parameter in a vasculature, is provided. The method includes: analyzing spectral CT projection data to isolate from the spectral CT projection data, contrast agent projection data representing the flow of the injected contrast agent; sampling the contrast agent projection data at one or more regions of interest in the vasculature to provide temporal blood flow data at the one or more regions of interest; and calculating, from the temporal blood flow data, a value of one or more blood flow parameters at the one or more regions of interest.

Measurement of blood flow parameters

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.

Local spectral-covariance or local spectral covariance deficits computation and display for highlighting of relevant material transitions in spectral CT and MR

A system (SY) for determining a relative importance of each of a plurality of image features (F) 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 (M). 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. A decision propagation unit (DPU) identifies, in the current vascular medical image, the relative importance of each of the plurality of image features on the computed overall diagnostic metric.

Resolving and steering decision foci in machine learning-based vascular imaging

A computing system () includes a computer readable storage medium () with computer executable instructions (), including a biophysical simulator (), and a reference location (), and a processor () 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.

Standardized coronary artery disease metric

The present invention relates to a device () for fractional flow reserve determination. The device () comprises a model generator () 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 () 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 () configured to determine a fractional flow reserve (FFR) based on the three-dimensional model (3DM) and the calculated blood flow.

Fractional flow reserve determination

A system () includes a computer readable storage medium () with computer executable instructions (), including: a predictor () 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 () 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.

Coronary artery health state prediction based on a model and imaging data

A computing system () includes a computer readable storage medium () with computer executable instructions (), including a including a biophysical simulator () with a segmentor () and a boundary condition determiner (). The computing system further includes a processor () 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.

Fractional flow reserve simulation parameter customization, calibration and/or training

A computing system () includes a computer readable storage medium () with computer executable instructions (), including: a segmentation standardizer () configured to determine a standardized vascular tree from a segmented vascular tree segmented of volumetric image data and a predetermined set of pruning rules (), and a biophysical simulator () configured to perform a biophysical simulation based on the standardized vascular tree. The computing system further includes a processor () 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.

Vascular tree standardization for biophysical simulation and/or an extension simulation for pruned portions

The present invention relates to an apparatus () 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 () 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. These features are used to adjust the predefined hyperemic boundary condition model resulting in a personalized hyperemic boundary condition model. As an effect, a flow simulation using the parametric model conditioned by the personalized hyperemic boundary condition model. As an effect, a flow simulation using the parametric model conditioned by the personalized hyperemic boundary condition model improves the performance of flow simulation in order to determine an enhanced fractional flow reserve.

Apparatus and method for determining a fractional flow reserve

A system () for segmenting a coronary artery vessel tree () of a patient heart in a three dimensional (3D) cardiac image () includes a coronary volume definition unit () and a coronary artery segmentation unit (). The coronary volume definition unit () sets a spatial boundary () from internal and external surfaces of heart tissues in the 3D cardiac image based on a fitted heart model (). The coronary artery segmentation unit () segments the coronary artery vessel tree () 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.

Heart model guided coronary artery segmentation

Stenosis information is obtained by obtaining photographic image data () from a displayed image of a blood vessel () 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.

Mobile FFR simulation

A method is provided for generating a deformable model () for segmenting an anatomical structure in a medical image. The anatomical structure comprises a wall. The deformable model () is generated such that it comprises, in addition to two surface meshes (), an intermediate layer mesh () for being applied in-between a first surface layer of the wall and a second surface layer of the wall. In generating the intermediate layer mesh (), the mesh topology of at least part () of the intermediate layer mesh is matched to the mesh topology of one of the surface meshes (), thereby establishing matching mesh topologies. The deformable model (), as generated, better matches the composition of such walls, thereby providing a more accurate segmentation.

Model-based segmentation of an anatomical structure

The invention relates to an apparatus for determining a fractional flow reserve (FFR) value of the coronary artery system of a living being (). A fractional flow reserve value determination unit () determines the FFR value by using an FFR value determination algorithm that is adapted to determine the FFR value based on a boundary condition and a provided representation of the coronary artery system, wherein the boundary condition is specific for the living being and determined by a boundary condition determination unit (). Since the boundary condition determination unit determines a boundary condition, which is specific for the living being, and since the fractional flow reserve value determination unit not only uses the provided representation of the coronary artery system, but also the living being specific boundary condition for determining the FFR value, the accuracy of the FFR value, which is non-invasively determined, can be improved.

Apparatus for determining a fractional flow reserve value

A system (IPS) and related method for fractional flow reserve, FFR, simulation. The simulation for a range of FFR values for a vasculature portion is based on a composite transfer function which is combined from a weighted sum of global effect transfer functions he, each representing a distinct physical effect that causes a pressure drop. The weights we are gotten from a previous training phase against pressure pi versus flow rate fi 5 sample measurements associated with respective vasculature geometries. The simulated range of FFR values is visualized in a graphics display (GD) as a function of pressure and flow rate values within respective intervals.

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Hamburg, Germany

Hannes Nickisch