An image processing apparatus, an image display system, an image processing method, and a program by which it is possible to display an optimum three dimensional image when display is switched from a two dimensional tomographic image to a three dimensional image are provided. The processor (14) outputs a tomographic image display signal representing a two dimensional tomographic image included in a first tomographic image group based on first imaging data obtained by imaging a subject, extracts a second tomographic image group having a smaller interval between tomographic images than the first tomographic image group, on the basis of second imaging data acquired in imaging corresponding to the imaging for acquiring the first imaging data, if a display switching signal indicating switching from display of the two dimensional tomographic image to display of a three dimensional image is acquired, and outputs a three dimensional image display signal representing a three dimensional image generated on the basis of the extracted second tomographic image group.

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.

The present disclosure provides a system and method for motion field generation and image correction. The method may include obtaining a plurality of first sets of magnetic resonance (MR) image data of an object generated based on a plurality of first sets of imaging sequences. The method may include obtaining a motion curve of the object. The method may include obtaining position emission tomography (PET) image data of the object generated in a scanning time period. The method may include generating one or more target motion fields corresponding to the scanning time period based on the plurality of first sets of MR image data and the motion curve. The method may include generating one or more corrected PET images by correcting, based on the one or more target motion fields, the PET image data.

A computer-implemented method for providing a result dataset, comprising: acquisition of at least one projection mapping pair of an object under examination by a medical biplane imaging device, wherein the at least one projection mapping pair contains a first and a second projection mapping of the object under examination, that map the object under examination simultaneously in a first and a second detection plane, wherein the first and second detection plane are arranged non-parallel to one another, determination of a correction model for the correction of an artifact and/or a movement, wherein the artifact and/or the movement is mapped simultaneously in the at least one first and the at least one second projection mapping, wherein the at least one projection mapping pair specifies a consistency condition for the determination of the correction model, reconstruction of the result dataset at least from the at least one first projection mapping and on the basis of the correction model, provision of the result dataset.

A method for the artifact correction of three-dimensional volume image data of an object is disclosed. In an embodiment, the method includes receiving first volume image data via a first interface, the first volume image data being based on projection measurement data acquired via a computed tomography device, the computed tomography device including a system axis, and the first volume image data including an artifact including high-frequency first portions in a direction of a system axis and including second portions, being low-frequency relative to the high-frequency first portions, in a plane perpendicular to the system axis; ascertaining, via a computing unit, artifact-corrected second volume image data by applying a trained function to the first volume image data received; and outputting the artifact-corrected second volume image data via a second interface.

The following relates generally to accelerated magnetic resonance imaging (MRI) reconstruction. In some embodiments, a MRI machine learning algorithm is trained based on reference MRI data in non-Cartesian k-space. During the training, at each iteration of a plurality of iterations: (i) a non-Cartesian sampling trajectory ω may be optimized under the physical constraints, and/or (ii) an image reconstructor may be jointly iteratively optimized. Examples of the image reconstructor include a convolutional neural network (CNN) denoiser, a model-based deep learning (MoDL) image reconstructor, iterative image reconstructor, a regularizer, and an invertible neural network.

A method for magnetic resonance imaging performs chemical shift encoded imaging to produce complex dual-echo images which are then applied (with imaging parameters) as input to a deep neural network to produce as output water-only and fat-only images. The deep neural network can be trained with ground truth water/fat images derived from chemical shift encoded images using a conventional water-fat separation algorithm such as projected power approach, IDEAL, or VARPRO. The chemical shift encoded imaging comprises performing an image acquisition with the MRI scanner via a spoiled-gradient echo sequence or a spin-echo sequence.

Techniques of prospectively compensating for motion of a subject being imaged by an MRI system, the MRI system comprising a plurality of magnetics components including at least one gradient coil and at least one radio-frequency (RF) coil, the techniques comprising: obtaining first spatial frequency data and second spatial frequency data by operating the MRI system in accordance with a pulse sequence, wherein the pulse sequence is associated with a sampling path that includes at least two non-contiguous portions each for sampling a central region of k-space; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; correcting the pulse sequence using the determined transformation to obtain a corrected pulse sequence; and obtaining additional spatial frequency data in accordance with the corrected pulse sequence.

The invention refers to an apparatus that allows to improve the image quality of nuclear images, e.g. PET images. The apparatus (110) comprises a providing unit (111) for providing nuclear image data of a region of interest, a providing unit (112) for providing a motion signal indicative of a motion of the region of interest, a determination unit (113) for determining different motion states of the region of interest based on the motion signal, a determination unit (114) for determining for each motion state nuclear image data corresponding to the motion state, a reconstruction unit (115) for reconstructing an absorption map for each motion state based on the corresponding nuclear image data of the respective motion state, and a reconstruction unit (116) for reconstructing one or more nuclear images of the region of interest based on the nuclear image data and the absorption maps reconstructed for each motion state.