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.

Systems and methods for reconstructing medical images are disclosed. Measurement data from positron emission tomography (PET) data, and measurement data from an anatomy modality, such as magnetic resonance (MR) data or computed tomography (CT) data, is received from an image scanning system. A PET image is generated based on the PET measurement data, and an anatomy image is generated based on the anatomy measurement data. A trained neural network is applied to the PET image and the anatomy image to generate an attenuation map. The neural network may be trained based on anatomy and PET images. In some examples, the trained neural network generates an initial attenuation map based on the anatomy image, registers the initial attenuation map to the PET image, and generates an enhanced attenuation map based on the registration. Further, a corrected image is reconstructed based on the generated attenuation map and the PET image.

Disclosed herein is a method and a system for reconstructing a three-dimensional image of an object, based on stitched images of the object obtained using multiple beams.

A method for rendering metal obscured regions in a volume radiographic image reconstructs a first 3D image using a plurality of 2D projection images obtained over a scan angle range relative to the subject and identifies metal in the first 3D image or metal shadows in the plurality of 2D projection images. Then, metal obscured regions are determined in a reconstructed 3D image of the object, and an alternative reconstruction being a limited angle reconstruction is performed for the metal obscured regions and displayed to the user with an indication of the spatial relationship to a corresponding metal obscured region.

A method for reconstructing an image may include obtaining scan data relating to a subject. The method may also include determining a first field of view (FOV) and determining a second FOV. The method may further include reconstructing a first image based on a first portion of the scan data corresponding to the first field of view, and reconstructing a second image based on a second portion of the scan data corresponding to the second field of view. The method may also include generating a third image based on the first image and the second image.

Multiple interactions, such as Compton scattering, inside a PET detector are used to predict an incident photon's direction for identifying true coincidence events versus scatter/random coincidence events by creating a cone shaped shell projection defining a range of possible flight directions for the incident photon. The disclosed techniques can be used as prior information to improve the image reconstruction process. The disclosed techniques can be implemented in a LYSO/SiPM-based layer stacked detector, which can precisely register multiple interactions' 3D position.

A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

A computer-implemented method for automated classification of 3D image data of teeth includes a computer receiving one or more of 3D image data sets where a set defines an image volume of voxels representing 3D tooth structures within the image volume associated with a 3D coordinate system. The computer pre-processes each of the data sets and provides each of the pre-processed data sets to the input of a trained deep neural network. The neural network classifies each of the voxels within a 3D image data set on the basis of a plurality of candidate tooth labels of the dentition. Classifying a 3D image data set includes generating for at least part of the voxels of the data set a candidate tooth label activation value associated with a candidate tooth label defining the likelihood that the labelled data point represents a tooth type as indicated by the candidate tooth label.

The disclosure relates to a system and method for medical imaging. The method may include: move, by a motion controller, a phantom along an axis of a scanner to a plurality of phantom positions; acquire, by a scanner of the imaging device, a first set of PET data relating to the phantom at the plurality of phantom positions; and store the first set of PET data as an electrical file. The length of an axis of the phantom may be shorter than the length of an axis of the scanner, and at least one of the plurality of phantom positions may be inside a bore of the scanner.