Abstract: A method for determining a patient movement during a medical imaging measurement with an imaging apparatus, a computer-implemented method for providing trained functions, an imaging apparatus, and a computer program product are provided. The method for determining the patient movement provides that reference image data of a body region, such as a head region, of a patient is acquired and the patient movement is determined with the aid of the reference image data.

Abstract: In a method for determining a type of characteristic eye movement of the eyes of a patient, the patient is provided with an optokinetic stimulus for the characteristic eye movement, a medical image of the brain of a patient also including the eyes of the patient is generated. An amount of a displacement of an optic nerve of the patient may be determined based on the medical image and a type of a characteristic eye movement may be determined based on the velocity of eye movement for the determined amount of displacement of the optic nerve. In a method for training an artificial intelligence based algorithm for determining an amount of a displacement of an optic nerve of a patient, the algorithm may be trained based on at least one medical image.

Abstract: A method is for noise reduction in image recordings. In an embodiment, the method includes providing an input image; de-noising the input image and producing a de-noised input image; and adapting noise texture of pixels of the de-noised input image via an adaptation method, noise amplitude of the de-noised input image being largely retained and the noise texture of the pixels of the de-noised input image being adapted to correspond largely to a defined noise texture. A corresponding device, a production method for an adaptation device, such an adaptation device, and a control facility and a computed tomography system are also disclosed.

Abstract: A pilot tone signal generator, a magnetic resonance tomograph, a method for transmission of a synchronization signal, and a computer program product are disclosed. The pilot tone signal generator includes a receive unit for receipt of a synchronization signal of a system control unit of a magnetic resonance tomograph. The synchronization signal may include a clock signal, and the pilot tone signal generator is configured to emit a pilot tone signal as a function of the synchronization signal.

Abstract: Risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient are predicted. Data associated with a region of interest comprising the target region is received. The received data includes a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. A trained machine-learning algorithm is applied to the received data. The trained machine-learning algorithm generates at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

Abstract: A stand is for a medical imaging system. In an embodiment, the stand includes an X-ray tube; a driver to adjust the X-ray tube in at least three spatial degrees of freedom; at least one environmental sensor to acquire at least one environmental parameter; and a controller to control an adjustment movement of the X-ray tube by generating control signals for the drive unit and to adapt the adjustment movement based on the at least one environmental parameter.

Abstract: A method is for the automatic regulation of radiation doses of a person for an examination with a medical X-ray device, which has a first dose regulation. In an embodiment, the method includes a specification of X-ray parameters of an X-ray examination based upon patient information and a specification of a dose regulation as a function of X-ray parameters as well as a limit value for deterministic radiation damage and/or a guide value for stochastic effective doses.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Brain tumor or other tissue classification and/or segmentation is provided based on from multi-parametric MRI. MRI spectroscopy, such as in combination with structural and/or diffusion MRI measurements, are used to classify. A machine-learned model or classifier distinguishes between the types of tissue in response to input of the multi-parametric MRI. To deal with limited training data for tumors, a patch-based system may be used. To better assist physicians in interpreting results, a confidence map may be generated using the machine-learned classifier.

Abstract: A method for generating a perfusion weighted image using arterial spin labeling (ASL) with segmented acquisitions includes dividing an anatomical area of interest into a plurality of slices and performing a multi-band (MB) echo planar imaging (EPI) acquisition process using a magnetic resonance imaging (MRI) system to acquire a control image dataset representative of the plurality of slices using a central-to-peripheral or peripheral-to-central slice acquisition order. An ASL preparation process is performed using the MRI system to magnetically label protons in arterial blood water in an area upstream from the anatomical area of interest. Following a post-labeling delay time period, the MB EPI acquisition process is performed to a labeled image dataset corresponding to the slices using the central-to-peripheral or peripheral-to-central slice acquisition order. A perfusion weighted image of the anatomical area is generated by subtracting the labeled image dataset from the control image dataset.

Abstract: Accelerated acquisition of scan data by means of magnetic resonance to enable short echo times so that scan data of substances can also be acquired with a transversal relaxation time.

Abstract: A reconstruction module may include at least one parameter interface to receive a set of measurement sequence parameters and a memory interface to a memory having a trained mathematical model stored in the memory. The trained mathematical model determines a results dataset containing at least one iterative denoising parameter and at least one edge enhancement parameter for at least one received measurement sequence parameter. A control of the reconstruction module controls a reconstruction of the medical images using a reconstruction algorithm based on the determined parameters for parameterizing an iterative denoising function and an edge enhancement function.

Abstract: In a method for an actuation of a magnetic resonance device for capturing image data from an examination region of an examination object, at least one first control sequence is provisioned, the magnetic resonance device is actuated according to the at least one first control sequence to capture first data from the examination object, the first data is analyzed with respect to a property to generate a result, and, based on the result, a selective performance of one of: a selection of a further control sequence, and termination of the actuation of the magnetic resonance device, is performed.

Abstract: Body coil tuning control device having: DC-DC converter with input connected to a DC power supply of an MRI system and output connected to input of an LDO, output of the LDO connected to first connection of a first resistor group; a first opamp with non-inverting input connected to first connection of the first resistor group, inverting input connected to the second connection of the first resistor group, and output connected to gate of a MOSFET array; and a negative feedback circuit connected between the output and the non-inverting input of the first opamp. The MOSFET array has a drain connected to the second connection of the first resistor group and a source connected to the input of the body coil of the MRI system. After the output signal of the first opamp is input to the gate of the MOSFET array, the source outputs a constant preset current.

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: A skull clamping device for fixing and aligning a head of a patient for a medical intervention includes a first ring element and a second ring element arranged concentrically. The second ring element is arranged within the first ring element and is connected hereto by a second axis, about which the second ring element is mounted rotatably within the first ring element. The first ring element is mounted rotatably about a first axis that is orthogonal to the second axis. The skull clamping device also includes a motor-driven drive that drives rotation of the first ring element and/or the second ring element about the respective axis, at least two pins arranged in an innermost ring element, two telescope bars that hold the first ring element and the second ring element, and a control unit for the motor-driven drive.

Abstract: A system and method for a non-contrast enhanced magnetic resonance imaging technique using a temporal maximum intensity projection reconstructed from multiple temporal subsets of data acquired the acquisition window. The method includes applying a radiofrequency pulse to the subject, waiting a quiescent interval, performing a radial acquisition with a golden-angle view angle increment over a duration corresponding to a cardiac cycle of the subject to generate acquisition data, reconstructing a plurality of images across a plurality of temporal phases from the acquisition data and generating a temporal maximum intensity projection image by tracking an intensity of each pixel across the plurality of images and selecting the pixel having a maximum intensity value across the plurality of images.

Abstract: The invention relates to an anaesthesia procedure and associated anaesthetic equipment (3). In said procedure, a first respiratory gas (G1) is supplied to an anaesthetized patient (15) through a ventilator (3) with unidirectional flow or rapidly alternating flow direction. At least one adjustment parameter (F) of the ventilator (3) is controlled or regulated in such a manner that a current position (LA) of at least one body part (36) of the patient (15) is adjusted to a specified desired position (LS).