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.

A method for removing artifacts from an image reconstructed from scanner data according to embodiments includes: performing a forward projection p to update an estimated object image; determining a transfer function f.sub.θ that represents the effect of scatter and beam hardening; modifying the forward projection p using the transfer function f.sub.θ to provide a modified forward projection p′; and performing an iterative image reconstruction process using the modified forward projection p′ to generate a reconstructed image.

A computer-implemented process for electromagnetic imaging, the process including the steps of: accessing scattering data representing at least a two-dimensional array of measurements of electromagnetic wave scattering by internal features of an object, wherein the object is generally symmetrical with respect to a plane of symmetry through the object, and each said measurement represents scattering of electromagnetic waves emitted by a corresponding antenna of an array of antennas disposed about the object as measured by a corresponding antenna of the array of antennas; and processing the scattering data to generate image data representing a spatial distribution of at least one internal feature of the object, wherein the generation of the image data does not involve tomographic reconstruction but is in accordance with statistical metrics of similarity between pairs of corresponding regions within the object on either side of the plane of symmetry.

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.

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.

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.

Methods for simulating, and correcting for, doubly scattered annihilation gamma-ray photons in both time-of-flight (TOF) and non-TOF positron emission tomography scan data are disclosed.

A computer-implemented method for scatter correction includes receiving a nuclear imaging data set, generating a scatter-estimation from the nuclear imaging data set using a ring-specific singles countrate, and generating a clinical image incorporating the scatter-estimation.

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).

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.