Koninklijke Philips Corporation N.v.

Yechiel Lamash

Liran Goshen

Raz Carmi

Mordechay Pinchas Freiman

Guy Gilboa

Asher Gringauz

Michael Grass

Holger Schmitt

Sven Prevrhal

Hannes Nickisch

Jonathan Lessick

Andrei Feldman

Guido Pardo-Roques

Jonathan Sapir

A method includes obtaining contrast enhanced spectral image data that includes voxels representing a tubular structure. The method further includes generating at least a contrast map based on the obtained contrast enhanced spectral image data. The method further includes generating an updated contrast map based on a spectral model. The method further includes segmenting the tubular structure based on updated contrast map. A computing system () includes a spectral analyzer () that receives contrast enhanced spectral image data and generates a spectral analysis data based thereon, wherein the spectral analysis data includes a contrast map, The computing system further includes a spectral analysis data processor () that refines the spectral analysis data, generating refined spectral analysis data.

Spectral image data processing

A method includes for non-invasively determining an instantaneous wave-free ratio metric includes receiving electronically formatted image data generated by an imaging system. The image data includes voxels with intensities representative of a vessel with a stenosis. The method further includes computing peripheral resistances of outlets of the vessel from the image data. The method further includes calculating a stenosis resistance of the stenosis between an inlet of the vessel inlet and the outlets of the vessel based on a set of boundary conditions and a computational fluid dynamics algorithm. The method further includes calculating the instantaneous wave-free ratio metric. The metric is a numerical value, based on the stenosis resistance and generating a signal indicative of the calculated instantaneous wave-free ratio metric.

iFR-CT

A method includes obtaining a boundary condition estimate. The boundary condition estimate includes at least an estimated outlet resistance of a vessel with a stenosis. The method further includes correcting the boundary condition estimate based on a severity of the stenosis, thereby creating a corrected boundary condition. The method further includes determining an FFR index based on the corrected boundary condition. The method further includes displaying the FFR index. A computing system includes a computer readable storage medium with instructions that iteratively determine at least an FFR index based on a severity of a stenosis of a vessel. The computing system further includes a computer processor that processes the instructions and generates the FFR index based on the severity of the stenosis of the vessel.

Fractional flow reserve (FFR) index with adaptive boundary condition parameters

An imaging system () includes a sub-resolution luminal narrowing detector () which detects sub-resolution narrowing of a vessel lumen in an image volume by a centerline profile analysis and computes a sub-resolution determined diameter by modifying an approximated visible lumen diameter with the detected sub-resolution narrowing.

Vessel lumen sub-resolution segmentation

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

As described herein, an unknown FFR is classified based on certain extracted features. In addition, an estimation of the unknown FFR can be determined based on certain extracted features. Furthermore, a confidence interval can be determined for the estimated FFR. In another instance, boundary conditions for determining an FFR via simulation are determined. The boundary conditions can be used to classify the unknown FFR.

Fractional flow reserve (FFR) index

In one aspect, a method for generating higher resolution volumetric image data from lower resolution volumetric image data includes receiving volumetric image data of a scanned subject, wherein the volumetric image data includes data representing a periodically moving structure of interest of the scanned subject, and wherein the volumetric image data covers multiple motion cycles of the periodically moving structure of interest. The method further includes estimating inter-image motion between neighboring images of the received volumetric image data. The method further includes registering the received volumetric image data based at least on the estimated inter-image motion. The method further includes generating the higher resolution volumetric image data based on the registered volumetric image data, a super resolution post-processing algorithm, and a point spread function of an imaging system that generated the volumetric image data. The higher resolution volumetric image data has an image resolution that is greater than the lower resolution volumetric image data.

High resolution volumetric image generation based on super resolution post-processing and point spread function

A perfusion imaging data processor () includes an agent peak characteristic-time determiner () configured to determine two or more agent peak characteristic-times respectively for two or more circulatory sub-systems represented in a same sub-set of voxels of a set of time-series data of perfusion imaging data, an agent peak argument determiner () configured to determine an agent peak argument for each of the two or more agent peak characteristic-times, an agent peak argument relation determiner () configured to determine a relationship between the agent peak arguments of the two or more agent peak characteristic-times, and a perfusion map generator () configured to generate, based on the determined relationship and the perfusion imaging data, at least one perfusion map, wherein the at least one perfusion map includes volumetric image data visually presenting at least one of a relationship or a difference between the two or more circulatory sub-systems.

Perfusion imaging

A method includes enhancing a contrast to noise ratio (CNR) of image data, generating CNR enhanced image data, wherein the CNR enhanced image data has a substantially same image quality as the image data. A computing system () includes a computer readable storage medium () encoded with computer readable instructions for enhancing a contrast to noise ratio (CNR) of image data and one or more processors (), which, when executing the computer readable instructions, causes the computing system to enhance the CNR of the image data. A method includes generating CNR enhanced image data, wherein CNR enhanced image data has a substantially same noise level, noise power spectrum, and spatial resolution of the image data.

Contrast to noise ratio (CNR) enhancer

A method for classifying tissue as normal or abnormal tissue includes obtaining segmented reconstructed volumetric image data for predetermined tissue of interest, generating a 2D voxel representation of the segmented reconstructed volumetric image data, and classifying voxels of the segmented reconstructed volumetric image data as corresponding to abnormal and normal tissue based on the 2D voxel representation.

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Haifa, Israel

Yechiel Lamash