Method for providing a large-area x-ray image of an object mounted by means of an object platform and providing additional synthetic x-ray projections, comprising Recording of a plurality of x-ray projections by means of an x-ray device, wherein the plurality of x-ray projections are recorded during a linear movement of the object platform and/or the x-ray device; preparation of at least one tomographic volume from the plurality of x-ray projections; preparation of at least one first synthetic forward projection from the at least one tomographic volume, wherein the at least one synthetic forward projection yields a large-area x-ray image; representation of the large-area x-ray image on a suitable display device; selection and/or marking of an area of interest within the large-area x-ray image; preparation of at least one second synthetic forward projection comprising the area of interest; representation of the at least one second synthetic forward projection on a suitable display device; characterised in that the at least one second synthetic forward projection is prepared in a projection geometry that corresponds to the current orientation of the x-ray device in space.

Systems and methods for generating attenuation maps for reconstructing medical images are disclosed. In some examples, measurement data, such as positron emission tomography (PET) data or single-photon emission computed tomography (SPECT) data, is received for a subject. A machine learning process is applied to the measurement data to generate initial synthetic images for multiple values of an imaging parameter. Further, patient data is received and classified to determine an object imaged with the subject. A second medical image is selected that includes the object, and a region-of-interest (ROI) of the initial synthetic images is determined. Further, based on the ROI an anatomical mask is generated for each initial synthetic image. A second image-to-image network process is applied to the patient data, the second medical image, a portion of each initial synthetic image that includes the ROI, and the corresponding anatomical mask to generate a final synthetic image.

Systems and methods for generating attenuation maps for reconstructing medical images are disclosed. In some examples, measurement data, such as positron emission tomography (PET) data or single-photon emission computed tomography (SPECT) data, is received for a subject. A machine learning process is applied to the measurement data to generate initial synthetic images for multiple values of an imaging parameter. Further, patient data is received and classified to determine an object imaged with the subject. A second medical image is selected that includes the object, and a region-of-interest (ROI) of the initial synthetic images is determined. Further, based on the ROI an anatomical mask is generated for each initial synthetic image. A second image-to-image network process is applied to the patient data, the second medical image, a portion of each initial synthetic image that includes the ROI, and the corresponding anatomical mask to generate a final synthetic image.

A charged particle beam (CPB) imaging apparatus intermittently acquires dark frames while the CPB is blanked and updates a dark reference with the intermittently acquired dark frames. The dark reference is used to compensate additive noise components in acquired sample frames, especially additive noise that increases during multiple image acquisitions over extended time periods.

The present disclosure provides various systems and methods for magnetic resonance imaging. In one aspect, a method for magnetic resonance imaging can include receiving k-space data sets acquired by radiofrequency (RF) coils. Each of the k-space data sets can correspond to a different one of the RF coils. Each of the k-space data sets can be truncated and/or under sampled. The method can further include generating partial images of a field of view based on the k-space data sets and generating an initial image based on the partial images. The initial image can be full image of the field of view. The method can further include applying an iterative image reconstruction technique to generate an updated image based on the initial image.

A three-dimensional image processing device for generating image data of a three-dimensional image used for an analysis of a diagnosis target, the three-dimensional image processing device including: an image processing unit configured to execute alignment between a three-dimensional image showing an analysis target captured at a first timing and a three-dimensional image showing the analysis target captured at a second timing different from the first timing, in which each of the three-dimensional images includes an image of an object in a preset space including the analysis target, the diagnosis target is present in the space, the analysis target is a support unit, and the alignment includes: first registration processing of performing a rigid transformation on one three-dimensional image of the two three-dimensional images to reduce a difference between the one three-dimensional image and the other three-dimensional image of the two three-dimensional images; and second registration processing of performing the rigid transformation on the one three-dimensional image to reduce a difference between an image of the analysis target shown in the other three-dimensional image and the image of the analysis target shown in the one three-dimensional image after execution of the first registration processing.

A three-dimensional image processing device for generating image data of a three-dimensional image used for an analysis of a diagnosis target, the three-dimensional image processing device including: an image processing unit configured to execute alignment between a three-dimensional image showing an analysis target captured at a first timing and a three-dimensional image showing the analysis target captured at a second timing different from the first timing, in which each of the three-dimensional images includes an image of an object in a preset space including the analysis target, the diagnosis target is present in the space, the analysis target is a support unit, and the alignment includes: first registration processing of performing a rigid transformation on one three-dimensional image of the two three-dimensional images to reduce a difference between the one three-dimensional image and the other three-dimensional image of the two three-dimensional images; and second registration processing of performing the rigid transformation on the one three-dimensional image to reduce a difference between an image of the analysis target shown in the other three-dimensional image and the image of the analysis target shown in the one three-dimensional image after execution of the first registration processing.

Various systems and methods are provided for processing computed tomography (CT) data in a CT imaging system. The CT imaging system comprising an X-ray source configured to emit X-rays, an X-ray detector configured to generate sampled digital detector data and a digital processor configured to process the sampled digital detector data. The method comprises filtering, in the digital processor, the sampled digital detector data to generate filtered detector data and resampling, in the digital processor, the filtered detector data to generate resampled detector data. The resampling comprises a reduction in data size of the filtered detector data. The filtering is performed on at least part of the sampled digital detector data according to a filtering setting and the resampling is performed on at least part of the filtered digital detector data according to a resampling setting, wherein the filtering setting and resampling setting are decoupled.

An imaging device and method of eliminating electromagnetic interference of magnetic resonance are provided. The imaging device includes: means for acquiring magnetic resonance imaging signals, acquiring a first electromagnetic interference signal in an electromagnetic interference affected environment, and acquiring second electromagnetic interference signals in the electromagnetic interference affected environment; means for superposing the magnetic resonance imaging signals with the first electromagnetic interference signal to obtain interfered imaging signals, respectively, taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, taking corresponding magnetic resonance imaging signals as output, and training and obtaining an electromagnetic interference eliminating model; means for inputting a real-time magnetic resonance imaging signal and a real-time electromagnetic interference signal into the electromagnetic interference eliminating model, to obtain a predicted magnetic resonance imaging signal that eliminates electromagnetic interference; means for performing image reconstruction on the predicted magnetic resonance imaging signal to obtain a magnetic resonance image.

The present invention provides a metal artifact reduction method in an image domain for spectral CT, and belongs to the technical field of CT imaging. During imaging of a target (e.g., a patient) with a metal implant, metal artifacts can be observed in virtual monoenergetic images obtained by current devices, in particular, it is more obvious at a low energy (low keV). According to the method, by extraction of non-artifact regions (or low-artifact regions) in the virtual monoenergetic images in which artifacts exist and decomposition of basis materials, a relational model between basis materials and artifact-free (or low-artifact) images is constructed, then pixel (voxel) CT values in the artifact-free monoenergetic images corresponding to artifact regions in the above images with artifacts are substituted into the above relational model, and new decomposition of the basis materials is acquired, so that virtual monoenergetic images after artifact correction under arbitrary energy are synthesized.