Systems and methods are disclosed for performing operations comprising: accessing a current structural estimate of a region of interest; generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement; and generating an update to the current structural estimate of the region of interest as a function of the first simulated X-ray measurement and the first real X-ray measurement, the update being generated invariant on the current structural estimate.

An improved x-ray cone-beam CT image reconstruction by end-to-end training of a multi-layered neural network is proposed, which employs cone-beam CT images of many patients as input training data, and precalculated scattering projection images of the same patients as output training data. After the training is completed, scattering projection images for a new patient are estimated by inputting a cone-beam CT image of the new patient into the trained multi-layered neural network. Subsequently, scatter-free projection images for the new patient are obtained by subtracting the estimated scattering projection images from measured projection images, beam angle by beam angle. A scatter-free cone-beam CT image is reconstructed from the scatter-free projection images.

Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

The invention is directed to a method for correcting scattered radiation in a computed tomography apparatus, wherein x-ray radiation emanating from an x-ray radiation source is divided into a plurality of partial beams by a grid structure such that irradiated regions and non-irradiated regions alternate, wherein a grid position of the grid structure is changed parallel to a detector surface. In a changed grid position, previously non-irradiated regions are irradiated and previously irradiated regions are not irradiated, wherein at least one radiograph of the test object is captured for each of the grid positions, wherein the radiographs captured at different grid positions are used to generate a bright field radiograph from the respectively irradiated regions and a dark field radiograph from the respectively non-irradiated regions and wherein a corrected radiograph is generated on the basis of the bright field radiograph and the dark field radiograph.

A medical apparatus of embodiments includes processing circuitry. The processing circuitry is configured to input third projection data to a first trained model to generate fourth projection data, the first trained model being generated through learning using first projection data collected by a first X-ray detector included in a first scanner and relatively greatly affected by scattered rays as learning data of an input side and using second projection data relatively less affected by scattered rays as learning data of an output side, the first trained model being configured to generate, on the basis of the third projection data collected by a second X-ray detector included in a second scanner, the fourth projection data in which the influence of scattered rays in the third projection data has been reduced. The first projection data is collected by the first X-ray detector in a case where a collimator provided in a first X-ray source included in the first scanner has a first opening width. The second projection data is collected by the first X-ray detector in a case where the collimator has an opening width smaller than the first opening width.

Nondestructive evaluation (NDE) of objects can elucidate impacts of various process parameters and qualification of the object. Computed tomography (CT) enables rapid NDE and characterization of objects. However, CT presents challenges because of artifacts produced by standard reconstruction algorithms. Beam-hardening artifacts especially complicate and adversely impact the process of detecting defects. By leveraging computer-aided design (CAD) models, CT simulations, and a deep-neutral network high-quality CT reconstructions that are affected by noise and beam-hardening can be simulated and used to improve reconstructions. The systems and methods of the present disclosure can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art.

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.

Apparatus and methods for constructing a tomographic image of a sample are disclosed. The apparatus comprises at least one source configured to emit electromagnetic radiation at a first wavelength, at least one angularly-selective detector configured to detect the electromagnetic radiation at a second wavelength after the electromagnetic radiation has interacted with the sample, and a controller configured to construct a tomographic image of the sample based on information gathered using the at least one detector. The controller obtains information indicative of an intensity of the electromagnetic radiation detected at a second position by the at least one detector while the source is in a first position. Then, the source and detector positions are interchanged, and the controller obtains information indicative of an intensity of the electromagnetic radiation detected at the first position by the at least one detector while the electromagnetic radiation is emitted from the second position. By utilising the non-reciprocity of the broken ray transform, the controller can determine a first coefficient relating to an attenuation of the electromagnetic radiation in the sample at the first wavelength, a second coefficient relating to an attenuation of the electromagnetic radiation in the sample at the second wavelength, and a third coefficient relating to a material property that influences the intensity of the electromagnetic radiation measured by the at least one detector, and construct a tomographic image of the sample based on the determined first, second and third coefficients.

A method of estimating energy-based scatter content in PET list-mode data is provided.

An image reconstruction system generates de-noised, attenuation corrected, and scatter corrected images using AI processing. The system receives a low-dose PET image and applies a machine learning algorithm via a convolutional neural network to the low-dose PET image to generate an output image. The output image includes correction for scatter and attenuation associated with the image being low-dose. The system provides the output image to a computing device comprising a user interface.