Abstract: The system includes a computed tomography gantry, a cart, a rail system, a rotary bearing and a lifting apparatus. In a translational operating state of the system, the cart is mounted to be movable along the rail system in a first translational motion along the rail system. The computed tomography gantry, the rotary bearing and the lifting apparatus are each held in the cart such that they follow the first translational motion of the cart. In the transitional operating state of the system, the cart is mounted to be movable, via the lifting apparatus, along a vertical axis of rotation relative to the rail system in a lifting motion, wherein the computed tomography gantry is held in the cart and follows the lifting motion of the cart relative to the rail system.

Abstract: The disclosure relates to techniques for saturation band MRI scanning. The techniques include obtaining the position of a saturation band of the saturation band MRI, obtaining the position of the region of interest to be imaged, taking the direction from the saturation band to the region of interest as a first direction, determining the direction of the slice selection gradient, and starting saturation band MRI scanning.

Abstract: For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.

Abstract: A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets.

Abstract: Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.

Abstract: Disclosed is a computer-implemented method for detecting one or more anatomic landmarks in medical image data. In an embodiment, the method includes receiving a medical image data set depicting a body part of a patient; and determining a first set of anatomic landmarks from a first representation of the medical image data set at a first resolution by applying a first trained function to the first representation of the medical image data set. Based on that, a second set of anatomic landmarks is determined from a second representation of the medical image data set at a second resolution, the second resolution being higher than the first resolution, by applying a second trained function different than the first trained function to the second representation of the medical image data set and using the first set of landmarks by the second trained function.

Abstract: For magnetic resonance (MR) reconstruction using artificial intelligence (AI), the AI-based reconstruction for MR imaging systems is offloaded to one or more servers. A remote server performs AI-based reconstruction. A library of recent, old, custom, and/or publicly available AI-based reconstruction processes may be rapidly deployed and available to the server, which has the memory and processing resources for AI-based reconstruction. Load balancing of the data and/or between servers may improve performance.

Abstract: A method for noise reduction in a low-dose X-ray image includes preprocessing for determining input data, at least one trained function for determining noise-reduced output data from the input data, and postprocessing for determining a result image from the output data. At least one result parameter specifying at least one desired result attribute of the result image is received or determined. The at least one result attribute is obtained by modifying the preprocessing to set a noise value of at least one first noise parameter. The noise value is determined from the result parameter. The noise value may be selected to differ from a reference value of the first noise parameter. Alternatively or additionally, the at least one result attribute is obtained by setting, according to the result parameter, the at least one trained function to one of a plurality of predefined noise values of at least one second noise parameter.

Abstract: A method of operating a magnetic resonance imaging (MRI) apparatus includes exciting a body coil of the MRI apparatus to emit a radio-frequency signal, determining a center frequency of a resonance curve of the body coil, and calculating a magnet target frequency based on the determined center frequency. A magnet is ramped to the magnet target frequency.

Abstract: Angiographic recordings are to be made more informative. To this end, a method for spatiotemporal fusion of time-resolved angiographic data sets is proposed. Respective 4D reconstructions are obtained from angiographic 3D data sets acquired from contrast agents administered at different sites. In both 4D reconstructions, a common vascular region is identified. For each contrast agent bolus, the corresponding time point or time course in the common vascular region is determined. Finally, the two 4D reconstructions are synchronized and fused.

Abstract: A magnetic resonance tomography unit includes a magnet unit having a magnetic controller for generating a homogenous magnetic field. The magnetic controller is configured to change the homogenous magnetic field in a short, predetermined time within image acquisition of an object under examination, such that a Larmor frequency for a predetermined layer of the object under examination remains in a predetermined frequency range. A layer in the object under examination is selected and a value for the homogenous magnetic field, in which the Larmor frequencies of the nuclear spins of the layer lie in a predetermined frequency band, is determined by a control unit taking into account a predetermined magnetic field gradient. The established value for the homogenous magnetic field and the predetermined magnetic field gradient is set by the magnetic controller, and an excitation pulse, frequencies of which only lie in the predetermined frequency band, is emitted.

Abstract: A system for positioning a medical object at a desired depth includes a light guiding facility and a processing unit. The processing unit is configured to receive planning information that specifies a desired depth for an arrangement of a predefined section of a medical object in an examination object with respect to an entry point of the medical object in the examination object. The medical object has a mark that has a predefined relative positioning with respect to the predefined section. The processing unit is configured to control the light guiding facility for emitting a light distribution as a function of the planning information and the predefined relative positioning such that the light distribution illuminates the mark if the predefined section is arranged at the desired depth.

Abstract: Systems and methods for automatically determining an image quality assessment of a rendered medical image are provided. A rendered medical image is received. One or more measures of interest are extracted from the rendered medical image. An image quality assessment of the rendered medical image is determined using a machine learning based image quality assessment network based on the one or more measures of interest. The image quality assessment of the rendered medical image is output.

Abstract: DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

Abstract: The present disclosure relates to techniques for monitoring an operating state of a birdcage coil in a magnetic resonance system. The birdcage coil comprises a pair of end rings, and multiple legs arranged between the pair of end rings and connected to the pair of end rings. The technique enables a determination that an open circuit has occurred in at least one of the multiple legs.

Abstract: One or more example embodiments of the present invention relates to a detector module for an X-ray detector comprising a sensor layer in a stacked construction configured to convert incident X-ray radiation into electrical signals; a readout layer configured to read out the electrical signals from the sensor layer; and a heating layer, the heating layer including a plurality of heating elements spatially distributed in the heating layer and configured separately from one another for heating the sensor layer, and wherein the readout layer has for each heating element an associated activatable adapting unit via which each heating element is contacted for feeding in power and which is configured to adapt the power fed to each heating element.

Abstract: Methods and devices for reconstructing Magnetic Resonance Imaging, MRI, images based on MRI data that asymmetrically samples K-space in accordance with a partial Fourier acquisition scheme may us a processing pipeline. The processing pipeline for such reconstruction may be flexibly configured depending on one or more settings of the partial Fourier acquisition scheme. The processing pipeline may include a trained function, e.g., implemented as a neural network, to solve one or more tasks such as deblurring, super-resolution, and/or denoising.

Abstract: At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Abstract: A magnetic resonance (MR) local coil and a magnetic resonance apparatus are disclosed. The MR local coil includes an antenna unit having at least one antenna for receiving and/or transmitting high frequency (HF) signals; a connection cable for connecting the MR local coil to a magnetic resonance apparatus; and a two-dimensional, (e.g., ribbon-shaped), transmission element for transmitting energy, (e.g., electrical energy), and/or signals, (e.g., electrical and/or optical signals), between the connection cable and the antenna unit. In this case, the transmission element is at least in part arranged about an axis of rotation in a spiral manner.

Abstract: Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.