Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

One or more example embodiments of the present invention relates to a computer-implemented method for providing an outline of a lesion in digital breast tomosynthesis includes receiving input data, wherein the input data comprises a reconstructed tomosynthesis volume dataset based on projection recordings, a virtual target marker within a lesion being in the tomosynthesis volume dataset; applying a trained function to at least a part of the tomosynthesis volume dataset to establish an outline enclosing the lesion, the part of the tomosynthesis volume dataset corresponding to a region surrounding the virtual target marker in the tomosynthesis volume dataset; and providing output data, wherein the output data is an outline of a two-dimensional area or a three-dimensional volume surrounding the target marker.

An imaging system and methods including a gantry defining a bore and an imaging axis extending through the bore, and at least one support member that supports the gantry such that the imaging axis has a generally vertical orientation, where the gantry is displaceable with respect to the at least one support member in a generally vertical direction. The imaging system may be configured to obtain a vertical imaging scan (e.g., a helical x-ray CT scan), of a patient in a weight-bearing position. The gantry may be rotatable between a first position, in which the gantry is supported such that the imaging axis has a generally vertical orientation, and a second position, such that the imaging axis has a generally horizontal orientation. The gantry may be displaceable in a horizontal direction and the system may perform a horizontal scan of a patient or object positioned within the bore.

Methods and systems for deep learning based image reconstruction are disclosed herein. An example method includes receiving a set of imaging projections data, identifying a voxel to reconstruct, receiving a trained regression model, and reconstructing the voxel. The voxel is reconstructed by: projecting the voxel on each imaging projection in the set of imaging projections according to an acquisition geometry, extracting adjacent pixels around each projected voxel, feeding the regression model with the extracted adjacent pixel data to produce a reconstructed value of the voxel, and repeating the reconstruction for each voxel to be reconstructed to produce a reconstructed image.

An imaging planning apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a first value of a first index that is related to an X-ray dose and a second value of a second index that is related to an image quality, based on an X-ray imaging condition of a subject set in a predetermined examination. The processing circuitry displays an association chart indicating an association between the first index and the second index on a display unit, displays an acceptable range of the first index and the second index, the acceptable range being based on information related to a diagnostic reference level corresponding to the predetermined examination, in a manner distinguished from a range other than the acceptable range in the association chart, and also displays a mark at a position corresponding to the first value and the second value in the association chart.

A flat panel detector and an imaging system are provided. The flat panel detector includes a plurality of pixel units which include photosensitive pixel units and alignment pixel units. Each photosensitive pixel unit includes a photoelectric sensor configured to convert an incident light into an electrical signal so that a photosensitive pixel unit in which the photoelectric sensor is located has a grayscale that changes according to a real-time change of the incident light. Each alignment pixel unit is configured to have a fixed grayscale, and the fixed grayscale does not change according to the real-time change of the incident light. The alignment pixel units includes first alignment pixel units and second alignment pixel units. Each first alignment pixel unit has a first fixed grayscale, each second alignment pixel unit has a second fixed grayscale different from the first fixed grayscale.

A flat panel detector and an imaging system are provided. The flat panel detector includes a plurality of pixel units which include photosensitive pixel units and alignment pixel units. Each photosensitive pixel unit includes a photoelectric sensor configured to convert an incident light into an electrical signal so that a photosensitive pixel unit in which the photoelectric sensor is located has a grayscale that changes according to a real-time change of the incident light. Each alignment pixel unit is configured to have a fixed grayscale, and the fixed grayscale does not change according to the real-time change of the incident light. The alignment pixel units includes first alignment pixel units and second alignment pixel units. Each first alignment pixel unit has a first fixed grayscale, each second alignment pixel unit has a second fixed grayscale different from the first fixed grayscale.

Digital tomosynthesis (DT) gives better diagnostic information than 2D X-ray, rivalling CT. However, tomosynthesis reconstruction requires sophisticated algorithms and a powerful computer, and can take several minutes to complete. The present invention takes a single x-ray image of a body 50 using multiple sources. In normal tomography and tomosynthesis imaging, such overlapping cones would lead to un-reconstructable data as significant overlap, in general, can’t be deconvolved and is not soluble. However, here, for the detection and localization of dense, compact objects 40, a location of an object 40 may be determined in three spatial dimensions from a single two-dimensional image. That is, processor-intensive reconstruction of a three-dimensional volume may be avoided.

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

A radiography apparatus is provided in which delays in it do not occur due to the influence of preliminary preparation of a radiation detector. The FPD 4 receives a signal from an X-ray tube control unit 6 and then completes preliminary preparation for the detection of radiation during accelerated movement of an X-ray tube 3 or the FPD 4. That is, the accelerated movement of the X-ray tube 3 or the FPD 4 and the preliminary preparation for the detection of radiation are carried out simultaneously. This enables imaging to be started immediately after the start of constant speed movement of the X-ray tube 3 or the FPD 4 without having to wait for constant speed movement thereof to start preliminary preparation of the FPD 4 as in conventional apparatuses. As a result, delays in imaging do not affect the radiation image.