Aspects relate to a method for reconstructing a digital volume tomography (DVT) imaging in the dental field including (S1) estimating the projection geometry from the sinogram of a patient imaging using motion artifact compensation (MAC); (S2) reconstructing a first volume using the estimated projection geometry; (S3) detecting the metal regions in the sinogram and in the first volume using the first volume and the estimated projection geometry; (S4) correcting the metal regions in the sinogram and generating a corrected sinogram; (S5) reconstructing a second volume with the estimated projection geometry and the corrected sinogram; and (S6) correcting the metal regions in the second volume.

A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

The invention relates to a method and an apparatus for reducing artefacts that are caused particularly by disturbance bodies and/or metal bodies in computed tomography (CT) images by means of a regulated iteration process that can be integrated particularly into a process for processing measurement data and preferably into a superordinate process for data alignment.

A method for geometric calibration of a DVT imaging in the dental field, including: (S1) reconstruction of a first volume from a sinogram with an initial projection geometry; (S2) detection of metal areas in the sinogram; (S3) correction of the metal areas in the sinogram; (S4) reconstruction of a second volume from the corrected sinogram from step (S3) with the initial or a varied projection geometry; (S5) geometrically calibrating by varying the projection geometry and evaluating by a similarity measure between a simulated sinogram of the reconstructed second volume and the sinogram or the corrected sinogram.

According to some embodiments, a method comprises obtaining a group of reconstructed-image data; converting the group of reconstructed-image data to a derivative of Radon space, thereby generating Radon-space data, wherein the Radon-space data have a radial sampling pattern in the derivative of Radon space; and generating resampled data by inputting the Radon-space data into a first trained machine-learning model for resampling Radon-space data, wherein the resampled data have a cone-beam-projection-geometry-shaped sampling pattern in the derivative of Radon space.

The present disclosure includes systems, methods and media for rendering objects translucent and for recovery of anatomical information blocked by the objects in medical images.

A computer-implemented method for reconstructing a dental DVT image, including: (S1) providing a sinogram acquired through an extraoral dental X-ray device during a rotation of at least 180 degrees around a patient's head and an initial projection geometry; (S2) geometry calibration through varying the projection geometry and evaluating the varied projection geometry using data consistency constraints derived from a first volume generated from the sinogram using the varied projection geometry in a first reconstruction method, the first reconstruction method using first reconstruction parameters; (S3) generating a final volume with a final reconstruction method using the varied projection geometry and final reconstruction parameters from the sinogram, the first reconstruction parameters of the first reconstruction method and the final reconstruction parameters of the final reconstruction method differing in at least one reconstruction parameter.

Various systems and methods are provided for MAR in CT images. A corrupted CT image, of a region of interest (ROI) of a subject, including artifacts caused by a metal object in the subject may be acquired. A corrupted sinogram including a corrupted region of corrupted data caused by the metal object and an uncorrupted region of uncorrupted data may be generated. A mask sinogram that delineates the corrupted region of the corrupted data may be generated. A corrected sinogram including the uncorrupted region of the uncorrupted data and an inpainted region of inpainted data corresponding to the corrupted region may be generated using a denoising diffusion probabilistic model, the corrupted sinogram, and the mask sinogram. A corrected CT image, of the ROI of the subject, that includes reduced artifacts relative to the artifacts in the corrupted CT image may be generated based on the corrected sinogram.

A method and a system for training an initial artifact removal model may be provided. One or more first initial images, one or more objective feature maps corresponding to the one or more first initial images, and one or more reference images corresponding to the one or more first initial images may be obtained. A trained artifact removal model may be generated by training the initial artifact removal model using the one or more first initial images, the one or more objective feature maps, and the one or more reference images.

The invention relates to a method and an apparatus for reducing artefacts that are caused particularly by disturbance bodies and/or metal bodies in computed tomography (CT) images by means of a regulated iteration process that can be integrated particularly into a process for processing measurement data and preferably into a superordinate process for data alignment.