Abstract: A method for reconstructing spatial information, the method includes: (i) Obtaining an under-sampled frequency domain representation (FDR) of the spatial information. The under-sampled FDR was obtained by sampling an FDR of the spatial information with a sampling mask. (ii) Feeding the under-sampled FDR and the sampling mask to a machine learning process. (iii) Reconstructing the spatial information by the machine learning process. The machine learning process was trained using a training data set that includes training under-sampled FRDs of training spatial information, and one or more training sampling masks that were used to sample FDRs of training spatial information.

Abstract: One embodiment of the present invention includes a computer-implemented method that includes receiving spectral computed tomography (CT) volumetric image data. The spectral CT volumetric image data include data for at least two different energies and/or energy ranges. The spectral CT volumetric image data is processed with a machine learning engine configured to map spectrally enhanced features extracted from the spectral CT volumetric image data onto fractional flow reserve (FFR) values to determine a FFR value. The FFR value is then visually presented.

Abstract: The invention refers to an apparatus for determining decomposed spectral image data with an improved accuracy. The apparatus (110) comprises a spectral image data providing unit (111) for providing spectral image data, a spectral image data decomposition unit (112) for calculating a basis decomposition for the spectral image data, a virtual non-contrast image generation unit for generating a virtual non-contrast image based on the decomposed spectral image data, and a contrast agent residual identification unit (113) for identifying a residual region comprising a contrast agent residual in the virtual non-contrast image based on an expected structural characteristic of the contrast agent residual, wherein the spectral image data decomposition unit (114) is configured to calculate a new basis decomposition in the residual region.

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 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: The present invention relates to an apparatus (10) for generating photon counting spectral image data, comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to receive non-photon counting X-ray spectral energy data. The processing unit is configured to implement a deep learning regression algorithm to generate photon counting X-ray spectral data, and the generation comprises utilization of the non-photon counting X-ray spectral energy data. The output unit is configured to output the photon counting X-ray spectral data.

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: 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: 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: 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: A computer-implemented system for supporting an imaging operation and related methods. The system comprises one or more input interfaces (IN) for receiving input data including an input image procured by an imaging apparatus (IA) of a target location (ROI) in a conduit (VS) of an object (OB). The conduit includes a target substance (CA) and the target substance (CA) is propagatabale in the conduit towards the target location. A pretrained-machine learning component (MLC2) is configured to process the input data to obtain output data indicative of an arrival of the said target substance at said target location. An output interface (OUT) outputs said output data.

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: 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: 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: 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 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 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 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.