Provided are improved referenceless multi-material beam hardening correction methods, with an emphasis on maintaining data quality for real-world imaging of geologic materials with a view towards automation. A referenceless post reconstruction (RPC) correction technique is provided that applies the corrections in integrated attenuation space. A container-only pre-correction technique also is provided to allow automation of the segmentation process required for beam hardening correction methods.

A processor acquires at least one radiographic image of a subject including a plurality of compositions and acquires a composition ratio of the subject. The processor sets an attenuation coefficient of radiation used in a case in which the radiographic image is acquired for each pixel of the radiographic image according to the composition ratio. The processor performs image processing on the radiographic image using the set attenuation coefficient.

There is provided a method of image reconstruction based on energy-resolved image data from a photon-counting multi-bin detector or an intermediate storage. The method comprises processing (S1) the energy-resolved image data by performing at least two separate basis decompositions using different number of basis functions for modeling linear attenuation, wherein a first basis decomposition is performed using a first smaller set of basis functions to obtain at least one first basis image representation, and wherein a second basis decomposition is performed using a second larger set of basis functions to obtain at least one second basis image representation. The method also comprises reconstructing a first image based on said at least one first basis image representation obtained from the first basis decomposition, and combining the first image with information representative of said at least one second basis image representation.

A method of processing x-ray images comprises training an artificial neural network to process multi-spectral x-ray projections to determine composition information about an object in terms of equivalent thickness of at least one basis material. The method further comprises providing a multi-spectral x-ray projection of an object, wherein the multi-spectral x-ray projection of the object contains energy content information describing the energy content of the multi-spectral x-ray projection. The multi-spectral x-ray projection is then processed with the artificial neural network to determine composition information about the object, and then the composition information about the object is provided.

Methods and systems are provided for dual energy imaging. In one embodiment, a method for a dual energy imaging system comprises determining a first tube potential and a second tube potential according to a size of a subject, and controlling the dual energy imaging system with the first tube potential and the second tube potential to generate lower energy x-rays and higher energy x-rays respectively to image the subject. In this way, image quality may be increased while minimizing dose during dual energy imaging of a particular imaging subject.

A computed tomography (CT) apparatus and a method for identifying photon-counting detectors that are polarized due to high flux, thereby rendering the photon-counting detector inoperable. The data obtained from the photon-counting detectors that are determined to be polarized is skipped during an image pre-reconstruction phase. The data is further assigned a weight of zero during an image reconstruction phase in order to avoid imaging artifacts in the reconstructed CT image.

A method and corresponding arrangement for reconstructing an image based on spectral image data acquired for at least two different effective energies includes: obtaining a first set of spectral image data related to an object to be imaged and a second set of spectral image data related to a calibration phantom including at least one reference material; performing basis decomposition based on the first set of spectral image data, providing estimated basis images of the object to be imaged with respect to associated basis functions; performing basis decomposition based on the second set of spectral image data, providing calibrated estimates of reference basis coefficients corresponding to the at least one reference material; and determining image values representing the object based on a system model of an imaging system to be emulated, the estimated basis images and their associated basis functions, and the calibrated estimates of reference basis coefficients.

The present invention relates to a method and a system for producing X-ray images from an object. According to this invention, a shift-and-add method is used for generating a stack of linear tomography planes each associated with a different area inside the object. A set of shift values is defined from the consideration of ensuring that said stack of linear tomography planes fills in the tomographic volume with a spatial density adequate to the application. If so required by the application, some focal planes can be selectively processed for sharpness reduction in some areas to control the depth-of-field. A focus stacking method is used to synthesize a single 2D X-ray image from UPI the tomographic stack of images. A depth map of in-focus areas from the linear tomography stack can be used for creating a 3D object model.

Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.

A method for artifact correction in computed tomography, the method comprising: (1) acquiring a plurality of data sets associated with different X-ray energies (i.e., D.sub.1, D.sub.2, D.sub.3 . . . D.sub.n); (2) generating a plurality of preliminary images from the different energy data sets acquired in Step (1) (i.e., I.sub.1, I.sub.2, I.sub.3 . . . I.sub.n); (3) using a mathematical function to operate on the preliminary images generated in Step (2) to identify the sources of the image artifact (i.e., the artifact source image, or ASI, where ASI=f(I.sub.1, I.sub.2, I.sub.3 . . . I.sub.n)); (4) forward projecting the ASI to produce ASD=fp(ASI); (5) selecting and combining the original data sets D.sub.1, D.sub.2, D.sub.3 . . . D.sub.n in order to produce a new subset of the data associated with the artifact, whereby to produce the artifact reduced data, or ARD, where ARD=f(ASD, D.sub.1, D.sub.2, D.sub.3 . . . D.sub.n); (6) generating a repaired data set (RpD) to keep low-energy data in artifact-free data and introduce high-energy data in regions impacted by the artifact, where RpD=f(ARD, D.sub.1, D.sub.2, D.sub.3 . . . D.sub.n); and (7) generating a final reduced artifact image (RAI) from the repaired data, RAI=bp(RpD), where the function bp is any function which generates an image from data.