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

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.

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.

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 system for PET image reconstruction is provided. The system may obtain PET data of a subject. The PET data may be associated with a plurality of coincidence events, which includes scattering events. The system may also generate a preliminary scatter sinogram relating to the scattering events based on the PET data. The system may also generate a target scatter sinogram relating to the scattering events by applying a scatter sinogram generator based on the preliminary scatter sinogram. The target scatter sinogram may have a higher image quality than the preliminary scatter sinogram. The system may further reconstruct a target PET image of the subject based on the PET data and the target scatter sinogram.

Provided is an X-ray CT apparatus including an X-ray irradiation unit that rotates around a placement portion on which an irradiation target is placed and emits X-rays; an X-ray detection unit that detects the X-rays emitted from the X-ray irradiation unit and passed through the irradiation target; and an image reconstruction unit that reconstructs a tomographic image of the irradiation target based on image data of the X-rays detected by the X-ray detection unit, in which the image reconstruction unit calculates a scattered ray component scattered in each of a plurality of three-dimensional spaces obtained by partitioning the irradiation target by a predetermined size among the X-rays detected by the X-ray detection unit in consideration of an atom number density per unit volume in each of sections included in the plurality of three-dimensional spaces and an atomic number, and reconstructs the tomographic image in consideration of the scattered ray component.

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.

The present invention relates to scatter correction method and apparatus for dental cone-beam CT. An object of the present invention is improving quality of reconstructed images by processing the scatter correction by learning which uses Monte Carlo simulation and artificial neural network. In order to achieve this object, the scatter correction method is characterized in that the method comprises steps of: rotating X-ray source of cone-beam CT in a predetermined angle while obtaining CT images for respective angles with flat-panel detector so as to reconstruct 3-dimensional CT image; generating a 2D profile of projection image by Monte Carlo simulation for respective angles by use of the reconstructed 3-dimensional CT image; decomposing the 2D profile of projection image so as to separate primary x-ray image and scatter image, wherein the primary x-ray image is unscattered in reaching the detector and wherein the scatter image is generated only by the scatter; building and doing learning of artificial neural network, wherein the objective function of the artificial neural network is primary image and scatter image which have been generated in simulation and wherein the input of the artificial neural network is the projection image which have been obtained in reality; and storing the learning information for the artificial neural network and then applying the learning information to scatter correction.

A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

A scattering estimation method includes determining a convolution kernel for smoothing a single scattering distribution based on a scattered radiation index value (R) of a radioactive image (5) (S4) and fitting, to positron emission tomography measurement data, a scattering distribution smoothed by applying the convolution kernel to the single scattering distribution (S5).