Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A global optimization and apparatus to decompose spectral computed tomography (CT) projection data into basis materials. A cost function is defined to represent the difference between the measured projection data and calculated attenuation data using projection lengths for the basis materials with corresponding material models and a detector model to calculate the detector response. The cost function may have many local minima and only one global minima. A global optimization method is then used to obtain the projection lengths corresponding to the global minimum of the cost function. The global optimization method can be either a single-stage optimization method, or can be performed in multiple stages, e.g., a first coarse optimization stage followed by a second fine optimization stage using the final values of the first stage as the inputs into the second stage. The global optimization method can be a stochastic optimization method.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 PCI)s. The method includes determining, for a given PCD, a set of view angles at which the PCI) 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.