Abstract: The apparatus has a control unit, a connection unit and a vibration unit. The connection unit has a control-element-side region, an edge region and a flexible region, wherein the control-element-side region is flexibly mounted relative to the edge region by the flexible region. The control unit has a contact region, wherein the contact region is fixed relative to the control-element-side region. The control unit is configured to generate a contact signal based on contact with the contact region. The vibration unit is configured to induce a vibration of the contact region relative to the edge region during contact with the contact region.

Abstract: One or more embodiments of the present invention relates to an energy supply circuit for a CT system. The energy supply circuit comprises a stationary energy distribution device, a co-rotating bias voltage supply device, a standard energy supply path having an energy transmission device between the stationary energy distribution device and the co-rotating bias voltage supply device and an alternatively connectable service energy supply path having a voltage protection device. A computed tomography system is also described. Further, a production method for producing an energy supply circuit for a CT system is described. In addition, a method for operating a CT system is described.

Abstract: An analysis method is for automatically determining radiological result data from radiology data sets. In an embodiment, the method includes provisioning a first radiology data set at least based on X-ray data of a first X-ray energy spectrum; provisioning at least one second radiology data set at least based on X-ray data of a second X-ray energy spectrum; provisioning an analysis unit including a neural network, to analyze radiology data sets including an input layer with a plurality of cells, a number of intermediate layers and an output layer representing a radiological result; analyzing the first radiology data set and the at least one second radiology data set, at least subsets of the first radiology data set and the at least one second radiology data set being assigned to different cells of the input layer for joint processing in the neural network; and acquiring result data on the output layer.

Abstract: One or more example embodiments of the present invention relates to a method for generating a resultant image dataset of a patient based on spatial distributions of materials in the patient.

Abstract: A trained deep learning network is for determining a cardiac phase in magnet resonance imaging. In an embodiment, the trained deep learning network includes an input layer; an output layer; and a number of hidden layers between input layer and output layer, the layers processing input data entered into the input layer. In an embodiment, the deep learning network is designed and trained to output a probability or some other label of a certain cardiac phase at a certain time from entered input data. A method for determining a cardiac phase in magnet resonance imaging; a related device; a training method for the deep learning network; a control device and a related magnetic resonance imaging system are also disclosed.

Abstract: A method is for generating a medical result image using a current image, a target image and a reference image. All images depict at least partially the same body region of a patient. In an embodiment, the method includes defining at least one image segment within the target image; registering the reference image with the target image by establishing a registration matrix for each image segment within the target image, the respective registration matrix being specific for the respective image segment; detecting a position of a surgical instrument in the current image, the position corresponding to an image segment of the target image; and generating the medical result image by fusing the current image and the reference image using the registration matrix assigned to the image segment according to the position of the surgical instrument within the current image.

Abstract: In a method for improving the contrast of magnetization-transfer-prepared magnetic resonance imaging (MRI), an acquisition scheme comprising a plurality of inversion-recovery (IR)-imaging modules in an interleaved arrangement is selected, a number of magnetization-transfer (MT)-preparation modules is selected, a pulse sequence is generated by arranging at least one MT-preparation module of the number of MT-preparation modules between two successive IR-preparation modules of the interleaved IR-imaging modules or in front of the first IR-preparation module of a group of interleaved IR-imaging modules, and the pulse sequence for an MRI examination is applied or saved. Each IR-imaging module may include an IR-preparation module and a slice acquisition module.

Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: A method to automatically align magnetic resonance (MR) scans for diagnostic scan planning includes acquiring a three-dimensional (3D) localizer image of an anatomical object. One or more initial landmarks are identified in the 3D localizer image using a landmarking engine. One or more main axes associated with the anatomical object are identified based on the one or more initial landmarks. The 3D localizer image is registered to a canonical space based on the main axes associated to yield a registered 3D localizer image. The landmarking engine is applied to the registered 3D localizer image to yield one or more updated landmarks. A plurality of reference points for performing a MR scan are computed based on the one or more updated landmarks.