Toshiba Medical Systems Corporation

Kabushiki Kaisha Toshiba

University of Chicago

Canon Medical Systems Corporation

Adam Petschke

Yu Zou

Xiaolan Wang

Yuexing Zhang

Zhou Yu

Patrick Jean La Riviere

David Rigie

Chunguang Cao

Gin-Chung Wang

A method and apparatus is provided to generate material-component images from spectral computed tomography (CT) projection data, using material decomposition in both the sinogram and image domains. From material components in the sinogram domain, monoenergetic sinograms are generated, and then monoenergetic images are reconstructed from the monoenergetic sinograms. Next, the monoenergetic images are decomposed into material-component images. Material decomposition in the sinogram domain enables beam-hardening corrections, and material decomposition in the image domain enhances image quality using prior information regarding the images including (e.g., smoothness and volume constraints) and using image-domain calibrations. Additionally, the method can be improved using scatter correction and detector-response and energy-spectrum calibrations. Further, iterations of the method can be performed by feeding back the material-component images to improve the scatter correction.

Combined sinogram- and image-domain material decomposition for spectral computed tomography (CT)

A method and apparatus is provided to decompose spectral computed tomography (CT) projection data into material components using singles-counts and total-counts projection data. The singles-counts projection data more accurately solves the material decomposition problem, but can produce multiple results only one of which is correct. The total-counts projection data generates a unique result, but is less precise. The total-counts projection data is used to disambiguate the multiple results of the singles-counts projection data providing a unique results that is also precise. The unique and precise material decomposition can be achieved by limiting a search region for the singles-counts result to a neighborhood surrounding the total-counts result, choosing a singles-counts result that is closest to the total-counts result, choosing a singles-counts result that minimizes a total-counts cost function, or using a combined cost function that includes a singles-counts projection data and energy-integrated projection data.

Apparatus and method for material decomposition of spectrally resolved projection data using singles counts

A hybrid CT dataset is obtained from a combination of an integrating detector and a photon-counting detector. The hybrid CT dataset contains sparse spectral energy data and dense energy integration data. The dense panchromatic data sets inherit the resolution properties of the integrating detector while the sparse spectral data sets inherit the spectral information of the photon-counting detector. Subsequently, the sparse spectral energy data sets are pansharpened based upon at least one dense panchromatic data set that lacks spectral information according to a pansharpening algorithm.

Sinogram (data) domain pansharpening method and system for spectral CT

An apparatus and method of processing X-ray projection data including spectral computed tomography (CT) projection data. The spectral CT data is decomposed into material projection lengths using a material-decomposition method that includes an initial-estimate method to provide an initial projection-lengths estimate. The initial-estimate method can include first reconstructing an image using non-spectral CT data, and then subdividing the reconstructed image into materials according to the relative attenuation density of areas within the reconstructed image (e.g., high attenuation areas correspond to a high attenuation material such as bone). The projection length estimates of each material are then obtained by forward projecting the corresponding subdivision of the reconstructed image. Also, the initial estimate can be obtained/refined by selecting the projection lengths with smallest cost-function value from randomly chosen projection lengths within a sample space. The initial estimate can also be conditioned on the X-ray flux at the X-ray source.

More efficient method and apparatus for detector response correction and material decomposition of projection data obtained using photon-counting detectors

A method and apparatus is provided to reconstruct a collective image of a multiple method/geometry imaging system (e.g., a hybrid computed tomography system having energy-integrating detectors arranged in a third-generation geometry and photon-counting detectors arranged in a fourth generation geometry), wherein a splitting-based iterative algorithm using modified dual variables is used in the image reconstruction. Whereas a separate image for each method/geometry of the multiple method/geometry imaging system can be obtained by solving the distinct system-matrix equation corresponding to each respective method/geometry, the collective image is obtained by simultaneously solving a collective optimization problem including all respective system-matrix. The collective image is obtained more efficiently using variable splitting to subdivide the optimization into subproblems that are solved in an iterative fashion. For some applications, the collective image can be further improved by including a beam-hardening correction step.

Computed tomography using simultaneous image reconstruction with measurements having multiple distinct system matrices

An apparatus and method of processing X-ray projection data obtained using photon-counting detectors and having multiple spectral components. The processing of the projection data includes correcting for nonlinear detector response, where the detector response model includes: pileup, ballistic deficit effects, polar effects, and characteristic X-ray escape. The processing of the projection data also includes a material decomposition mapping the projection data from spectral components into material components corresponding to high-Z and low-Z materials. The material decomposition includes a noise balancing process where the allocation of spectral components between a high-energy and a low-energy combination of spectral components is adjusted such that both high- and low-energy components have signal-to-noise ratios of similar magnitude. For computed tomography (CT) applications, material decomposition can be followed by image reconstruction and then image post-processing and presentation. For non-CT applications, material decomposition can be followed by image post-processing and presentation.

Pre-reconstruction calibration, data correction, and material decomposition method and apparatus for photon-counting spectrally-resolving X-ray detectors and X-ray imaging

An apparatus and method of reconstructing a computed tomography (CT) image using multiple datasets of projective measurements, wherein the method of image reconstruction favors spatial correlations among the images respectively reconstructed from each of the corresponding multiple datasets. The multiple data sets each contain projective measurements of the same object taken in close temporal proximity, but taken with different detector type or configurations (e.g., different spectral components in spectral CT or different detector types in hybrid 3- and 4-generation CT scanners). Reconstructed images minimizing a vectorial total variation norm satisfies the criteria of favoring images exhibiting spatial correlations among the reconstructed images and favoring a sparse gradient-magnitude image (i.e., edge enhancing image) for each reconstructed image.

Spectral X-ray computed tomography reconstruction using a vectorial total variation

Calibrating a photon-counting detector can include receiving a reference signal, by circuitry, where the reference signal is measured by a reference detector that measures an output from an X-ray tube. Determining circuitry can then determine, for a detector channel of the photon-counting detector, a mapping between a first true count rate on the detector channel without an object and the reference signal in accordance with a linear relationship between the reference signal and the first true count rate, based on a measured count rate on the detector channel and a predefined relationship between the first true count rate and the measured count rate.

Reference calibration in photon counting based spectral CT

An apparatus is provided to reconstruct an image using combined third-generation energy-integrating computed tomography projection data and fourth-generation spectrally resolved computed tomography projection data. The apparatus includes processing circuitry configured to obtain first projection data representing projection data from an energy-integrating detector; obtain second projection data representing projection data from a photon-counting spectrally discriminating detector; and reconstruct a first combined-system basis image and a second combined-system basis image by solving a combined-system matrix equation using the first projection data and the second projection data.

Reconstruction of computed tomography images using combined third-generation and fourth-generation projection data

A hybrid CT dataset is obtained from a combination of a integrating detector and a photon-counting detector. The hybrid CT dataset contains low-resolution photon-counting data and high-resolution integrating data. High-resolution panchromatic images are generated from the high-resolution integrating data, and low-resolution spectral images are generated from the low-resolution photon-counting data. The high-resolution panchromatic images inherit the resolution properties of the integrating detector while the low-resolution spectral images inherit the spectral information of the photon-counting detector. Subsequently, the low resolution spectral images are pansharpened based upon at least one high resolution panchromatic image that lacks spectral information according to a pansharpening algorithm.

Image domain pansharpening method and system for spectral CT with large pixel energy discriminating detectors

A method is provided for determining, in a fourth-generation computed tomography (CT) scanner, a positional offset of a center of a ring of fixed energy-discriminating detectors with respect to an iso-center of a third-generation X-ray source/detector system. The method includes obtaining a plurality of offset images, each offset image being obtained from a scan executed with the ring of fixed energy-discriminating detectors positioned in a known offset location with respect to the iso-center; performing a current scan executed with the ring positioned in an unknown offset location with respect to the iso-center, to obtain a current image; calculating, for each offset image, a corresponding error value between the offset image and the current image; and determining the positional offset of the center of the ring to be an offset location corresponding to an offset image having the smallest error value.

[object Object]

A method and an apparatus for determining primary and secondary escape probabilities for a large photon-counting detector without pile-up. A model for the detector with no pile-up is formulated and used for spectrum correction in a computed tomography scanner. The method includes computing primary K-escape and secondary K-escape probabilities occurring at a certain depth within the photon-counting detector. Further, a no pile-up model for the photon-counting detector is formulated by determining a response function, based on the computed primary and secondary K-escape probabilities and geometry of the photon-counting detector. The method includes obtaining a measured CT scan of an object and further performs spectrum correction by determining the incident input spectrum based on the response function and the measured spectrum of the large photon-counting detector.

Characteristic X-ray escape correction in photon-counting detectors

A method is provided for determining, for a fourth-generation computed tomography (CT) scanner that includes a third-generation X-ray source and detector system and a plurality fixed, sparse photon-counting detectors (PCDs), a position of each PCD of the plurality of PCDs. The method includes determining, for a given PCD, a set of view angles at which the PCD will cast a shadow on the third-generation detector; determining, for each view, by analyzing projection data obtained from a reference scan, a corresponding shadow location on the third-generation detector caused by the given PCD; generating, for each view, a line connecting a position of the X-ray source at the view angle, and a corresponding shadow location of the set of shadow locations; determining locations of all intersection points of the set of lines; and determining a PCD centerline of the given PCD based on the locations of the intersection points.

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Adam Petschke