17 results about "Material decomposition" patented technology

An X-ray computed tomography (CT) apparatus includes an X-ray tube configured to radiate X-rays; a detector configured to detect X-rays radiated by the X-ray tube and passed through a subject; a reconstruction processing unit configured to reconstruct imaging data output by the detector and generate CT image data and material decomposition image data; a first inference unit configured to perform inference on the material decomposition image data; a second inference unit configured to perform inference on the CT image data; a third inference unit configured to perform inference on composite image data; a selection unit configured to, in a case where an inference result of the first inference unit is different from an inference result of the second inference unit, select an inference result of the third inference unit; and a display control unit configured to cause the selected inference result to be displayed.

The present application relates to the technical field of spectral CT imaging, and particularly relates to a physical parameter-based material decomposition method and device, electronic equipment and medium, the method comprising: calculating at least two groups of different equivalent energy CT projection correction parameters and spectral correction parameters based on known phantom spectral CT pre-scan data and initial equivalent spectrum; correcting a material decomposition model according to the parameters to establish a mapping relationship between CT projection data and base material thickness under corresponding equivalent energy. The corresponding projection data of the to-be-measured phantom is corrected using the projection correction parameters, and the base material thickness data is obtained in combination with the mapping relationship; the base material image is generated through analysis or iterative reconstruction, and the virtual single-energy image is obtained through linear combination, thereby solving the problem of low spectral CT material quantitative analysis precision in the related art and improving the precision of spectral CT material quantitative analysis.

A method for forming radiographic images of oral anatomy of a subject exposes the subject to radiation along a radiation path. At each position on a detector array in the radiation path, the number of photons received from the radiation through the subject are counted, wherein the detector maintains a first photon count for photons having a first energy above a first threshold energy value and at least a second photon count for photons having a second energy above a higher threshold energy value. One or more materials in the mouth of the subject are identified by performing material decomposition processing, according to two or more basis materials, and distinguishing between natural oral anatomy features and fabricated materials in the mouth of the subject according to the material decomposition processing. Conditioned image content enhances the distinction between natural oral anatomy features and fabricated material according to the material decomposition.

An X-ray computed tomography (CT) apparatus includes an X-ray tube configured to radiate X-rays; a detector configured to detect X-rays radiated by the X-ray tube and passed through a subject; a reconstruction processing unit configured to reconstruct imaging data output by the detector and generate CT image data and material decomposition image data; a first inference unit configured to perform inference on the material decomposition image data; a second inference unit configured to perform inference on the CT image data; and a display control unit configured to cause a display unit to display at least one of an inference result of the first inference unit and an inference result of the second inference unit.

The present invention relates to a method (1), resp. a device, system and computer-program product, for material decomposition of spectral imaging projection data. The method comprises receiving (2) projection data acquired by a spectral imaging system and reducing (3) noise in the projection data by combining corresponding spectral values for different projection rays to obtain noise-reduced projection data. The method comprises applying (6) a first projection-domain material decomposition algorithm to the noise-reduced projection data to obtain a first set of material path length estimates, and applying (7) a second projection-domain material decomposition algorithm to the projection data to obtain a second set of material path length estimates. The second projection-domain material decomposition algorithm comprises an optimization that penalizes a deviation between the second set of material path length estimates being optimized and the first set of material path length estimates.

This disclosure provides a method for obtaining an energy threshold, a method for decomposing a matrix material, a system, an apparatus, and a medium. The method for obtaining the energy threshold includes: acquiring imaging images of at least two preset calibration phantoms corresponding to preset energy ranges based on preset imaging parameters; wherein the preset calibration phantoms include a target matrix material, and the preset energy ranges are determined according to a preset energy threshold in the preset imaging parameters; acquiring the mass decay coefficient of the preset calibration phantoms based on the imaging images; determining the decay difference between the target matrix materials based on the mass decay coefficient; and determining the target energy threshold for imaging based on the decay difference and the preset energy threshold. This disclosure calculates the decay difference based on the actually measured mass decay coefficient of the preset calibration phantom corresponding to the target matrix material, and determines the target energy threshold based on the decay difference and the preset energy threshold used during measurement, which can assist in determining the selection of the target energy threshold for any target matrix material to be decomposed.

A photon counting CT device and a material decomposition method capable of reducing the influence of variation in the amount of accumulation due to differences in the line quality of X-rays. A photon counting CT device characterized by comprising: a storage unit that, while changing the thickness of a first base material and the thickness of a second base material, acquires the energy spectrum of X-rays that have transmitted a calibration member composed of the first base material and the second base material as known materials to become a reference, and stores the X-ray amount as calibration data in advance; a correction table creation unit that, while changing the thickness of the first base material, the thickness of the second base material, and the X-ray amount, acquires the energy spectrum of X-rays that have transmitted the calibration member, and creates a correction table that indicates the relationship between the number of X-ray photons per energy bin and the X-ray amount; and an image generation unit that generates a tomographic image that has been decomposed by each material based on the calibration data and the correction table.

The present application provides a kind of material identification method, this method is based on the material decomposition of double effect, is calibrated with two or more materials, improves the accuracy of material decomposition, obtains more accurate atomic number estimation physical model by adaptive parameter determination method.The present application is divided into empirical double effect material decomposition and adaptive effective atomic number estimation two parts, wherein empirical double effect material decomposition, by the calibration of multiple standard phantoms, the determination of decomposition parameters and the estimation of electron density are realized by the polynomial combination of projection data and the constraint in image domain;Effective atomic number estimation establishes an adaptive effective atomic number estimation model by the calibration of known standard phantom, realizes the estimation of effective atomic number of imaging material.The present application can realize more accurate electron density and effective atomic number estimation for different kinds of materials at different energies through adaptive material decomposition algorithm, improves the precision of material identification.

This invention discloses an empirical correction method for material decomposition using energy dispersive spectroscopy (EDS). The method comprises: 1) Selecting a primary calibrator based on the material of the substance of interest, with the number of its material components matching the number of energy regions in the EDS; establishing a first polynomial relationship between the EDS projection data of the primary calibrator and the primary calibrator using EDS; empirically decomposing the substance scanned by EDS based on the material decomposition coefficients of the primary calibrator to obtain the empirical decomposition value of the substance; 2) Selecting a series of samples with different concentrations as secondary calibrators; empirically decomposing the secondary calibrators using the material decomposition coefficients; obtaining the empirical decomposition error of the secondary calibrators by referring to the actual decomposition value corresponding to the actual concentration value of the secondary calibrators; then using the second polynomial relationship between the empirical decomposition value of the secondary calibrators and the empirical decomposition error to obtain the error of the empirical decomposition value of the scanned substance, and correcting the empirical decomposition value of the substance.

An X-ray imaging system, such as a computed tomography (CT) computed tomography (CT) imaging system is provided for material decomposition calibration and intended for use with a calibration phantom. The X-ray imaging system comprises an X-ray source configured to emit X-rays and an X-ray detector arranged in the X-ray beam path configured to generate detector data. The calibration phantom is located in the X-ray beam path. The X-ray imaging system further comprises an X-ray beam limiting device including at least one calibration element in the X-ray beam path. The X-ray imaging system also comprises image processing circuitry configured to acquire projection data for a set of projections based on the detector data, and to determine pathlengths through at least one material of the at least one calibration element and at least one material of the calibration phantom, at least partly based on acquired projection data, for performing material decomposition calibration.

The invention relates to the technical field of energy spectrum CT imaging, in particular to a substance decomposition method and device based on physical parameters, electronic equipment and a medium, and the method comprises the steps: calculating at least two groups of CT projection correction parameters and energy spectrum correction parameters of different equivalent energy based on energy spectrum CT pre-scanning data and an initial equivalent energy spectrum of a known motif; and correcting the material decomposition model according to the data, and establishing a mapping relation between CT projection data and the base material thickness under corresponding equivalent energy. Correcting the corresponding projection data of the to-be-measured die body by using the projection correction parameter, and obtaining the thickness data of the base material in combination with the mapping relationship; according to the method, the basic substance image is generated through analysis or iterative reconstruction, and the virtual single-energy image is obtained after linear combination, so that the problem of low energy spectrum CT substance quantitative analysis precision in related technologies is solved, and the energy spectrum CT substance quantitative analysis precision is improved.

A method for material decomposition of an object based on spectral X-ray scan data for the object and based on application of a frequency split approach. The method comprises using two AI models in parallel to perform the material decomposition analysis based on input spectral X-ray data, wherein the models are configured such that one exhibits higher bias and lower variance (lower noise) than the other. The input spectral X-ray data is fed to both models. The output material composition data from the low bias model is low-pass filtered and the output material composition data from the low variance model is high pass filtered. The outputs from the two models are linearly combined, either before the filtering or after. The resulting combined material decomposition data has both lower bias and lower noise compared to the output generated if just one AI model were to be used.

The application discloses a rotary kiln cylinder material state axial trend identification method and processing strategy. In the rotary kiln operation process, the cylinder wall vibration signals of each point are obtained in real time through a plurality of vibration collection points arranged along the kiln cylinder outer wall axis. Each vibration collection point is provided with a solid conduction vibration sensor. The collection end of the vibration sensor is in rolling contact with the kiln cylinder outer wall through a shock transmission roller. The real-time working condition parameters of the rotary kiln are obtained, and the working condition parameters at least include kiln rotating speed, feeding amount and motor load. The vibration signals of each collection point are preprocessed and feature extracted to obtain the vibration feature vectors corresponding to each point. Then, the vibration feature vectors of each point and the real-time working condition parameters are input into an axial material state mapping model, and the material state trend curve in the kiln length direction is output by the mapping model. The material state trend curve at least includes a material decomposition rate curve. The drift of the reaction completion point and the precursors of the ring formation can be found.

This application provides a deep learning-based material decomposition method based on a physical model, comprising: constructing a physical model to map the X-ray response of a CT detector under different physical parameters, and creating a dataset based on the physical model; constructing and training a neural network based on the dataset to fit the CT detector response under different physical parameters; setting up several calibration experiments to determine the physical parameters of the CT detector by reducing the error between the neural network prediction value and the experimental response value; during imaging, calculating the thickness information of the detected material through the neural network based on the physical parameters and actual response of the CT detector. This application constructs a neural network to map the correlation between various physical parameters during the material decomposition process of the CT detector, and obtains the physical parameters of the CT detector through a small number of calibration experiments. Therefore, during material decomposition, the thickness information of the detected material can be inferred from the known physical parameters and response energy spectrum, and the method has good robustness.

A system and method for projection based spectral X-ray imaging, such as computed tomography (CT) imaging, the system and method comprising performing projection based material decomposition based on spectral X-ray data to generate a set of material basis sinograms, and performing a weighted combination of at least part of at least two material basis sinograms of the set of material basis sinograms into a reconstructed image based on material weighting in the projection domain.