A method is for providing a medical image of a patient, acquired via a computed tomography apparatus. An embodiment of the method includes acquiring first projection data of a first measurement region; acquiring second projection data of a second measurement region; registering a reference image to the at least one respiration-correlated image of the patient, wherein the reference image corresponds to the at least one functional image of the patient or is reconstructed under a second reconstruction rule from the second projection data, to produce a deformation model; applying the deformation model to the at least one functional image of the patient; combining the at least one functional image of the patient, deformed by the applying of the deformation model, with the at least one respiration-correlated image of the patient, to produce the medical image of the patient; and providing the medical image of the patient.

Tomographic images are obtained by processing a tilt series of 2D images by aligning and combining images withing a group of neighbor images. The tilt series generally includes sparsely sampled images. Images of the tilt series at tilt angles associated with the sparsely sample images are selected as reference frames, grouped with neighbor images, and the group of images aligned. The aligned images are combined to produce replacement frames and a replacement frame tilt series that can be used for tomographic reconstruction.

A computer-implemented method is for providing a difference image data record. In an embodiment, the method includes a determination of a first real image data record of an examination volume in respect of a first X-ray energy, and a determination of a multi-energetic real image data record of the examination volume in respect of a first X-ray energy and a second X-ray energy, the second X-ray energy differing from the first X-ray energy. The method further includes the determination of the difference image data record of the examination volume by applying a trained function to input data, wherein the input data is based upon the first real image data record and the multi-energetic real image data record, as well as the provision of the difference image data record.

The present invention relates to a method for the evaluation of tissue gadolinium deposition that offers advantages compared with known methods. Comparison of different gadolinium-based contrast agents (GBCAs) based on retention, organ distribution, washout and safety is facilitated using the methods of the present invention.

A method is for computed tomography imaging. In an embodiment, the method includes provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method; suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; and analyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type. A corresponding device, a control device for a computed tomography system or a diagnosis system, and a diagnosis system and a computed tomography system are also disclosed.

A computed tomography (CT) image processing apparatus and a CT image processing method are provided. The CT image processing apparatus may generate a virtual monochromatic image (VMI) by applying a weight to each of first, second, and third images corresponding to three different energy ranges. The CT image processing apparatus may set a region of interest (ROI) on a CT image, determine a VMI at an energy level at which a CNR of the ROI is at a maximum among a plurality of VMIs, and display the determined VMI.

An exemplary system, method and computer-accessible medium for generating an image(s) of a portion(s) of a patient(s) can be provided, which can include, for example, receiving first information associated with a combination of positron emission tomography (PET) information and magnetic resonance imaging (MRI) information, generating second information by applying a convolutional neural network(s) (CNN) to the first information, and generating the image(s) based on the second information. The PET information can be fluorodeoxyglucose PET information. The CNN(s) can include a plurality of convolution layers and a plurality of parametric activation functions. The parametric activation functions can include, e.g., a plurality of parametric rectified linear units. Each of the convolution layers can include, e.g., a plurality of filter kernels. The PET information can be reconstructed using a maximum likelihood estimation (MLE) procedure to generate a MLE image.

Among other things, one or more techniques and/or systems for generating a three-dimensional combined image is provided. A three-dimensional test image of a test item is combined with a three-dimensional article image of an article that is undergoing a radiation examination to generate the three-dimensional combined image. A first selection region of the three-dimensional article image is selected. The three-dimensional test image of the test item is inserted within the first selection region. Although the test item is not actually comprised within the article under examination, the three-dimensional combined image is intended to cause the test item to appear to be comprised within the article.

A tomographic image processing apparatus including a display, an input interface configured to receive an external input, a storage storing an input tomographic image of an object, and at least one processor configured to control the display to display the input tomographic image, determine a material combination to be separated from the input tomographic image, and control the display to display material separation information corresponding to the determined material combination for a region of interest selected in the input tomographic image based on the external input. The input tomographic image is a spectral tomographic image having a plurality of tomographic images respectively corresponding to a plurality of energy levels.

A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.