The apparatus for an X-ray data generation according to an embodiment of the inventive concept includes a processor that receives 3D data to generate output data and a buffer, and the processor includes an extraction unit that extracts raw object data from the 3D data and projects the raw object data onto a 2D plane to generate first object data, an augmentation unit that performs data augmentation on the first object data to generate second object data, a composition unit that synthesizes the second object data and background data to generate composite data, and a post-processing unit that performs post-processing on the composite data to generate the output data, and the buffer stores a plurality of parameters related to generation of the first object data, the second object data, the composite data, and the output data.

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

The present invention relates to spectral correction. A spectral correction apparatus is described that is configured to identify a voltage fluctuation in the X-ray tube and to parameterize the high voltage fluctuation to correct the effective X-ray spectrum per individual frame.

An x-ray system and method can improve speed of imaging and/or reduce radiation dosage compared to conventional imaging technique, such as CT. The system can identify a volume of interest within a subject. The system can include scatter removal algorithms and/or a beam selection device. Material decomposition of the imaged subject can be based on the dual energy decomposition method which can be iterative to solve the energy response function equation system. X-rayx-rayx-rayx-rayx-rayX-rayX-rayX-ray

Provided are a method and apparatus for correcting scattering artifacts in industrial three-dimensional (3D) cone beam computed tomography (CT) that may prepare raw data acquired from a subject through computed tomography (CT) and a primary signal acquired from shape prior information of the subject, may estimate a scatter kernel based on the raw data and the primary signal, may acquire result data by removing, from the raw data, a scatter signal estimated based on the scatter kernel, and may generate an image from the result data.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Disclosed herein is an X-ray apparatus for acquiring a medical image, and a method of using said X-ray apparatus, said method comprising the steps of: acquiring an original radiation image of a target object and capturing condition information of the object; acquiring a scatter radiation image related to the original radiation image by inputting the original radiation image and the capturing condition information to a learning network model configured to estimate scatter radiation; and acquiring a scatter radiation-processed medical image from the original radiation image on the basis of the original radiation image and the scatter radiation image, wherein the learning network model configured to estimate scatter radiation is a learning network model taught using a plurality of scatter radiation images and a plurality of pieces of capturing condition information related to each of the plurality of scatter radiation images.

An N-M tomography system comprising: a carrier for the subject of an examination procedure; a plurality of detector heads; a carrier for the detector heads; and a detector positioning arrangement operable to position the detector heads during performance of a scan without interference or collision between adjacent detector heads to establish a variable bore size and configuration for the examination. Additionally, collimated detectors providing variable spatial resolution for SPECT imaging and which can also be used for PET imaging, whereby one set of detectors can be selectably used for either modality, or for both simultaneously.

The invention comprises a method and apparatus for tracking and/or imaging impact of a particle beam treating a tumor using one or more imaging systems positionable about the tumor, such as a positron emission tracking and/or imaging system, where resulting tracking/imaging data: dynamically determines a treatment beam position, tracks a history of treatment beam positions, guides the treatment beam, and/or images a tumor before, during, and/or after treatment with the charged particle beam.