Disclosed is a dual-energy ray imaging method and system. The method comprises: calculating the mass thicknesses of the materials in the overlapped area of two materials by using a calibrated surface fitting method, and then decomposing a pair of original high-energy and low-energy data for this pixel into two high-low-energy data sets corresponding to the two materials, and finally calculating and acquiring the composition result of different materials for each pixel. The disclosure is especially advantageous in that the problem of error recognition of materials due to the two overlapped materials can be eliminated and the stratified imaging of multiple materials can be achieved, thereby improving the accuracy of the substance recognition and reducing the rate of false positive and false negative which is very important to the applications in the field of security check and anti-smuggling.

An apparatus for determining material property, includes: an interface configured to obtain a first HU value associated with a first image of an object, and to obtain a second HU value associated with a second image of the object, wherein the first image is created using a first energy having a first energy level, and wherein the second image is created using a second energy having a second energy level that is different from the first energy level; and a processing unit configured to determine a weighted property value for the object based at least in part on the first HU value and the second HU value.

Extended field-of-view imaging is enabled by combined imaging with a kilovolt (“kV”) x-ray source and a megavolt (“MV”) x-ray source, in which at least one of the corresponding x-ray detectors is laterally offset from the target isocenter by an amount such that the x-ray detector does not have a view of the target isocenter. This scan geometry enables the reconstruction of non-truncated images without resorting to the more expensive solution of outfitting the imaging or radiotherapy system with enlarged x-ray detectors.

A machine-learning-based monochromatic CT image reconstruction method is described for quantitative CT imaging. The neural network is configured to learn a nonlinear mapping function from a training data set to map a CT image, which is reconstructed from a single spectral current-integrating projection data set, to monochromatic projections at a pre-specified energy level, realizing monochromatic CT imaging and overcoming beam hardening.

An apparatus, method and/or system are configured to determine, by a trained artificial neural network (ANN), a monochromatic projection data set based, at least in part, on a measured CT image. The measured CT image may be reconstructed based, at least in part, on measured projection data. The measured projection data may be polychromatic. The apparatus, method and/or system may be further configured to reconstruct a monochromatic CT image based, at least in part, on the monochromatic projection data set.

Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.

A system includes memory (420) with instructions for at least one of processing spectral CT projection data to mitigate at least one of noise of the spectral CT projection data or a noise induced bias of the spectral CT projection data or generating a decomposition algorithm that mitigates the noise induced bias of the spectral CT projection data. The system further includes a processor (418) that executes the instructions and at least one of processes the spectral CT projection data or generates the decomposition algorithm and decomposes the spectral CT projection data to basis materials. The system further includes a reconstructor (434) that reconstructs the basis materials, thereby generating spectral images.

A method for image reconstruction is disclosed, based upon a first plurality of spectral raw data sets. The method includes forming a second plurality of virtual raw data sets; reconstructing an auxiliary image data set on the basis of a virtual raw data set. A first material is selected from a material group which comprises a plurality of materials. Material-specific maps are generated for a number of second materials of the material group. A determination of material line integrals take place, for the second materials with forward projection of the respective material-specific map. Subsequently, synthetic projection data sets are determined for each material. Finally, a reconstruction of at least one image data set takes place on the basis of the synthetic projection data sets for a number of materials of the material group. An image reconstruction device and a computed tomography system are also disclosed.

The present invention relates to a device (100) for iterative reconstruction of images recorded by at least two imaging methods, the device comprising: an extraction module (10), which is configured to extract a first set of patches from a first image recorded by a first imaging method and to extract a second set of patches from a second image recorded by a second imaging method; a generation module (20), which is configured to generate a set of reference patches based on a merging of a first limited number of atoms for the first set of patches and of a second limited number of atoms for the second set of patches; and a regularization module (30), which is configured to perform a regularization of the first image or the second image by means of the generated set of reference patches.

A computing system (116) includes a reconstruction processor (114) configured to execute computer readable instructions, which cause the reconstruction processor to: receive, in electronic format, non-spectral projection data, reconstruct the non-spectral projection data to generate a non-spectral image, retrieve a non-spectral to spectral voxel value map for a basis material of interest from a set of non-spectral to spectral voxel value maps, generate a spectral iterative reconstruction start image based on the non-spectral image and the non-spectral to spectral voxel value map, and reconstruct a spectral image, in electronic format, for the material basis of interest from the non-spectral projection data with a spectral iterative reconstruction algorithm and the spectral iterative reconstruction start image.

An image processing apparatus comprises a filtering unit for performing recursive filtering on a first signal component and a second signal component that are obtained by emitting radiation at a plurality of levels of energy toward an object, and a generation unit for generating a moving image based on the first signal component and the second signal component on which the recursive filtering is performed. A filter coefficient of the recursive filtering performed on the first signal component and a filter coefficient of the recursive filtering performed on the second signal component differ from each other.