Abstract: Method of data processing for Computed Tomography from a spectral image data set of an imaged zone, an anatomical image data set of the imaged zone, and an anatomical model, comprising: a. Assigning an initial set of values to a regularization scheme, 5 b. Performing a noise reduction scheme on the spectral image data set, wherein said noise reduction scheme is a function of the regularization scheme, of the spectral image data set and of the anatomical image data set, in order to obtain a processed spectral image data set, c. Performing a segmentation of structures of interest using the anatomical data set, the processed spectral image data set, and the anatomical model, in order to obtain a segmentation result, d. Updating the regularization scheme based on the segmentation result, e.

Abstract: 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 (120) includes a spectral analyzer (202) 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 (204) that refines the spectral analysis data, generating refined spectral analysis data.

Abstract: An imaging system (102) includes a radiation source (112) configured to emit X-ray radiation, a detector array (114) configured to detect X-ray radiation and generate a signal indicative thereof, an a reconstructor (116) configured to reconstruct the signal and generate non-spectral image data. The imaging system further includes a processor (124) configured to process the non-spectral image data using a deep learning regression algorithm to estimate spectral data from a group consisting of spectral basis components and a spectral image.

Abstract: A system (300) includes a memory (324) configured to store an inflammation map generator module (328). The system further includes a processor (322) configured to: receive at least one of spectral projection data or spectral volumetric image data, decompose the at least one of spectral projection data or spectral volumetric image data using a two-basis decomposition to generate a set of vectors for each basis represented in the at least one of spectral projection data or spectral volumetric image data, compute a concentration of each basis within a voxel from the set of vectors for each basis, and determine a concentration of at least one of fat or inflammation within the voxel from the concentration of each basis. The system further includes a display configured to display the determined concentration of the at least one of fat or inflammation.

Abstract: An image data processor (116) includes a high resolution restorer (218) configured to restore a voxel neighborhood of a voxel in first image data to a higher resolution, generating restored higher resolution image data, based on a corresponding voxel neighborhood of second higher resolution image data, wherein the second higher resolution image data has higher resolution than the first image data.

Abstract: The present invention relates to image processing devices and related methods. The image processing device (10) comprises a data input (11) for receiving spectral computed tomography volumetric image data organized in voxels. The image data comprises a contrast-enhanced volumetric image of a cardiac region in a subject's body and a baseline volumetric image of that cardiac region, e.g. a virtual non-contrast image, wherein the contrast-enhanced volumetric image conveys anatomical information regarding coronary artery anatomy of the subject. The device comprises a flow simulator (12) for generating, or receiving as input, a three-dimensional coronary tree model based on the volumetric image data and for simulating a coronary flow based on the three-dimensional coronary tree model.

Abstract: Method of data processing for Computed Tomography from a spectral image data set of an imaged zone, an anatomical image data set of the imaged zone, and an anatomical model, comprising: a. Assigning an initial set of values to a regularization scheme, 5 b. Performing a noise reduction scheme on the spectral image data set, wherein said noise reduction scheme is a function of the regularization scheme, of the spectral image data set and of the anatomical image data set, in order to obtain a processed spectral image data set, c. Performing a segmentation of structures of interest using the anatomical data set, the processed spectral image data set, and the anatomical model, in order to obtain a segmentation result, d. Updating the regularization scheme based on the segmentation result, e.

Abstract: A method includes generating spectral projection data of a subject, including at least first spectral projection data corresponding to a first energy range and second spectral projection data corresponding to a second energy range, wherein the first and the second energy range are different. The method further includes constructing an image from a combination of the spectral projection data, and constructing a set of basis images for the 5 energy ranges and from the spectral projection data. The method further includes constructing a multi-dimensional histogram from the set of basis images, wherein the multi-dimensional histogram includes at least two axes, a first corresponding to a first basis component and a second corresponding to a second basis component, and the multi-dimensional histogram includes a set of clusters, including one cluster for each material 10 represented in the spectral projection data.

Abstract: A method includes obtaining volumetric image data that includes a coronary vessel of a subject. The method further includes identifying the coronary vessel in the volumetric image data. The method further includes identifying a presence of a collateral flow for the identified coronary vessel. The method further includes determining a boundary condition of the collateral flow. The method further includes constructing a boundary condition parametric model that includes a term that represents the boundary condition of the collateral flow. The method further includes determining a fractional flow reserve index for the coronary vessel with the boundary condition parametric model.

Abstract: The present invention relates to an image processing device (10) comprising a data input (11) for receiving volumetric image data comprising a plurality of registered volumetric images of an imaged object, a noise modeler (12) for generating a noise model indicative of a spatial distribution of noise in each of the plurality of registered volumetric images, a feature detector (13) for detecting a plurality of image features taking the volumetric image data into account, and a marker generator (14) for generating a plurality of references indicating feature positions of a subset of the plurality of detected image features, in which said subset corresponds to the detected image features that are classified as difficult to discern on a reference volumetric image in the plurality of registered volumetric images based on a classification and/or a visibility criterium, wherein the classification and/or the visibility criterium takes the or each noise model into account.

Abstract: The present invention relates to X-ray image data analysis of a part of a cardiovascular system of a patient in order to estimate a level of inflammation in the part of the cardiovascular system. X-ray image data is received, a segmented model of the part of the cardiovascular system is generated and predetermined features related to inflammation are extracted from the segmented model. The extracted features are used as input to an inflammation function for calculating inflammation values of which each represents a level of inflammation in the part of the cardiovascular system. The image data analysis can improve the estimation of inflammation. Furthermore, the inflammation values can be presented to a user together with suggestions for performing actions. This can for example enable a prediction of plaque development as well as future acute coronary syndrome events.

Abstract: 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.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.

Abstract: 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.

Abstract: An imaging system (100) includes a sub-resolution luminal narrowing detector (112) 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.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve index. The system further includes a processor (120) configured to execute the biophysical simulator component (126) to determine the fractional flow reserve index with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve index.

Abstract: The present invention relates to an image processing device (10) comprising a data input (11) for receiving spectral computed tomography volumetric image data organized in voxels, comprising multispectral information for each voxel. The device further comprises a calcium surface feature analyzer (12) for estimating, for each voxel, based on said multispectral information, a first value indicative of a maximum probability that a local neighborhood around the voxel corresponds to a calcium surface structure and a second value indicative of an orientation of the calcium structure surface. The device also comprises a probability processor (13) for calculating a probability map indicative of a probability, for each voxel, that the voxel represents a bone or hard plaque structure, taking at least the first and second value and the multispectral information into account.

Abstract: The present invention relates to an apparatus for multi material decomposition of an object. It is described to provide (310) at least one image of an object. The at least one image is derived from at least one spectral X-ray image of the object, and the at least one image comprises a Photoelectric total attenuation coefficient image and a Compton scattering total attenuation coefficient image. A plurality of Photoelectric attenuation coefficients are provided (320) for a plurality of materials, each Photoelectric attenuation coefficient being associated with a corresponding material. A plurality of Compton scattering attenuation coefficients are provided (330) for the plurality of materials, each Compton scattering attenuation coefficient being associated with a corresponding material. A total volume constraint is set (340) at an image location in the at least one image as a function of the sum of individual volumes of the plurality of materials at the image location.

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.