Abstract: According to one or more example embodiments, a computer-implemented method for at least partially suppressing a field emitted by a magnetic resonance device during an examination, the emitted field being at least one of an electric field or a magnetic field, the method comprises measuring a signature of the emitted field with a sensor facility; providing a trained function which is configured, based on a signature to generate a field information item relating to the emitted field upon which the respective signature is based; creating a field information item for the emitted field by inputting the signature into the trained function; and suppressing the emitted field by generating a counterfield with a transmitting facility based on the created field information item, the counterfield being at least one of an electric counterfield or a magnetic counterfield, wherein the counterfield is generated such that the counterfield least partially suppresses the emitted field.

Abstract: The disclosure relates to magnetic resonance imaging triggered by a prospective acquisition correction sequence. The technique comprises determining repetition time and an acquisition window time of a single-shot fast spin echo sequence; determining the maximum imaging layer number N in each physiological movement cycle on the basis of the acquisition window time and the repetition time, where N is a positive integer greater than or equal to 2; and enabling the single-shot fast spin echo sequence to obtain M layers of imaging data within at least one acquisition window time when the prospective acquisition correction sequence generates a trigger signal, where M is a positive integer greater than or equal to 2 and less than or equal to N. According to the present disclosure, a plurality of layers of imaging data are obtained within a single acquisition window time, thereby increasing a scanning speed and shortening imaging time.

Abstract: A stream of virtual topograms, in particular live virtual topograms, is predicted. Sets of surface data of an outer surface of a subject are continuously received. Based on each received set of surface data a (live) virtual topogram is continuously generated by a trained machine learning algorithm (MLA). Thereto, a representation of body landmarks is updated based on each received set of surface data by a trained body marker detector (BMD), of the trained MLA, and the (live) virtual topogram is predicted based on the updated spatial marker map and on the corresponding set of surface data by a trained topogram generator (TG) of the trained MLA.

Abstract: A method is for providing a uniform resource locator. In an embodiment, the method includes receiving a medical data record via an interface, the medical data record being related to a patient; determining the uniform resource locater related to the medical data record via a computation unit, the uniform resource locator including an authorization token based on the medical data record, the medical data record being accessible by following the uniform resource locator; and providing the uniform resource locator with the interface to the patient. By providing a uniform resource locator of an embodiment including an authorization token, access to the medical data record can be granted fast and efficient, e.g. by a patient forwarding the uniform resource locator to another entity (e.g. a physician), while at the same time non-authorized entities cannot access the medical data record due to their lack of the suitable authorization token.

Abstract: A plurality of diffusion-weighted measurement datasets (including at least one initial and one further measurement dataset) are recorded following a common excitation and thus within a repetition time (TR) using segmented recording in the readout direction.

Abstract: One or more example embodiments relates to a computer-implemented method for providing medical imaging decision support data, the method comprising receiving natural language data, the natural language data comprising patient-specific clinical information; generating structured information by applying a large language model to the natural language data, the structured information comprising the patient-specific clinical information in a structured format; calculating the medical imaging decision support data based on the structured information; and providing the medical imaging decision support data.

Abstract: A system comprises: an input module configured to obtain imaging data of a tissue part of an organ of a patient at a time point; a computing module configured to compute at least one spatial mapping between the imaging data and corresponding reference imaging data, wherein the at least one spatial mapping is determined based on a point distance measure and a feature-based measure, wherein the at least one spatial mapping incorporates an adaptive spatial support, and wherein the reference imaging data is obtained from a database or obtained by the input module corresponding to a different time point and/or to a different patient than the imaging data; a registration module configured to process the obtained imaging data and the reference imaging data using the at least one computed spatial mapping and to generate registration information; and an output module configured to output the registration information.

Abstract: In an MR data recording method, different desired phase differences between spins of a first spin species and spins of a second spin species are generated by irradiated RF excitation pulses in each case and corresponding MR datasets are recorded in respective echo trains after irradiation of the RF excitation pulses. This makes it possible to eliminate the need to shift the readout interval away from the spin-echo time, as was previously necessary to generate different phase differences and the associated extension of echo spacings in order to generate the different phase differences between the spin species. Thus, the disadvantages of a previously necessary extension of the echo spacing can be avoided with the method according to the disclosure.

Abstract: An imaging method is described for generating image data of an examination region of an object that is to be examined. First X-ray projection measurement data of the examination region is acquired using a first X-ray energy spectrum and at least second X-ray projection measurement data of the examination region is acquired using a second X-ray energy spectrum which is different from the first X-ray energy spectrum. A priori image data is reconstructed based on at least the first X-ray projection measurement data and a location-dependent distribution of X-ray attenuation values. A basis material decomposition is performed based on the first X-ray projection measurement data and the at least second X-ray projection measurement data. A location-dependent weighting of the basis materials is determined as a function of the location-dependent distribution of the X-ray attenuation values. An image for the examination region is determined by reconstructing virtual basis-material-weighted image data.

Abstract: A hospital bed may include a bed frame, a first bed board and an auxiliary component. The first bed board is connected to the bed frame. The auxiliary component may include a connecting frame, a first moving and supporting mechanism and a second bed board. The connecting frame is detachably connected to the first bed board. The first moving and supporting mechanism is connected to the connecting frame. The second bed board is connected to the first moving and supporting mechanism and can carry an object along the bearing direction. The second bed board is arranged on one side of the first bed board along the bearing direction. The first moving and supporting mechanism can drive the second bed board to move relative to the first bed board along an adjustment direction, where the adjustment direction is perpendicular to the bearing direction.

Abstract: A gantry of a computed tomography system is rotated relative to a fixed base body of the computed tomography system. During the rotation of the gantry, digital data is transmitted between the gantry and the base body from a data source via a modulator, a transmission channel and a demodulator to a data sink. A transmission signal generated by the modulator by modulating a data stream supplied to the modulator from the data source is pre-distorted by a pre-distortion facility. The pre-distorted transmission signal is supplied to the transmission channel. Alternatively, or in addition, a transmitted signal transmitted via the transmission channel is post-distorted by a post-distortion facility and the post-distorted signal is supplied to the demodulator. The pre-distortion rule and/or the post-distortion rule are repeatedly reset by a setting facility during the rotation of the gantry.

Abstract: For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning where one of the tasks (e.g., segmentation, non-image data, and/or feature extraction) is unsupervised and/or draws on a greater number of training samples than available for outcome prediction alone.

Abstract: Nuclear spins are excited in a region of interest in an object under examination by a radio-frequency pulse. During at least one phase of the radio-frequency pulse, excitation fields are transmitted while magnetic field gradients are simultaneously applied so that the magnetization of the nuclear spins moves on a trajectory through a transmission k-space. In a first phase of the at least one phase of the radio-frequency pulse, the trajectory moves at a radial distance around the center of the transmission k-space. The radial distance corresponds to the radius of a sphere superimposed with at least one radial harmonic.

Abstract: Embodiments relate to processing of medical data, in particular image data of medical images and related medical reports for automated generation of a draft report for a newly recorded medical image. The image elements relating to corresponding documentation elements for a former medical image are mapped to image elements in the newly recorded medical image, and based on this mapping, an automated generation of the draft of the medical report of the newly recorded medical image is performed.

Abstract: A computer-implemented method for providing a treatment response prediction for a patient suffering from a cancerous disease, comprises: obtaining a whole slide image of the patient showing a tissue sample relating to the cancerous disease; providing a prediction function configured to derive a treatment response prediction for one or more treatment options from whole slide images; and applying the prediction function to the whole slide image to provide the treatment response prediction.

Abstract: A computed tomography system comprises: an examination region and a static gantry, wherein the gantry includes an X-ray source and a detector ring. The detector ring encircles the examination region and includes at least two detector segments. The computed tomography system is configured such that a position of the at least two detector segments relative to one another in an axial direction of the detector ring is adjustable.

Abstract: A method for refining annotations in medical images, comprises: obtaining an initially annotated image; cropping said initially annotated image to obtain a cropped image, which retains only a part of the initially annotated image indicated by the annotation; analyzing pixel intensity distributions within the cropped image; segmenting the cropped image based on the analysis of the pixel intensity distributions to obtain a segmented image; refining the segmented image to obtain a refined segmented image; and performing similarity matching on the refined segmented image to obtain a delineation mask.

Abstract: According to one or more example embodiments, a computed tomography system includes a first part; a second part, wherein the first part is rotatable relative to the second part around an axis of rotation; a plurality of first modems on the first part which are connected in a data-technical manner to first coupling elements on the first part; and a plurality of second modems on the second part which are connected in a data-technical manner to second coupling elements on the second part.

Abstract: A gantry of a computed tomography system is rotated relative to a fixed base body of the computed tomography system. During the rotation of the gantry, digital data is transmitted between the gantry and the base body from a data source via a modulator, a transmission channel and a demodulator to a data sink. A transmission signal generated by the modulator by modulating a data stream supplied to the modulator from the data source is pre-distorted by a pre-distortion facility. The pre-distorted transmission signal is supplied to the transmission channel. Alternatively, or in addition, a transmitted signal transmitted via the transmission channel is post-distorted by a post-distortion facility and the post-distorted signal is supplied to the demodulator. The pre-distortion rule and/or the post-distortion rule are repeatedly reset by a setting facility during the rotation of the gantry.

Abstract: A system configured to segment medical imaging data, comprising an input unit configured to receive text data and the medical imaging data, wherein the received text data comprises a segmentation task formulated in natural language with regard to the medical imaging data; a text encoder unit configured to generate a text embedding based on the received text data; an imaging encoder unit configured to generate an image embedding based on the received medical imaging data; a segmentation unit configured to receive the generated text embedding and the generated image embedding and to determine a segmentation of the received medical imaging data via a function trained by an artificial intelligence algorithm; and an output interface configured to output the segmentation of the medical imaging data.