Abstract: A counter-swarm device comprises a mortar tube with an opening on one end, multiple streamers positioned in the mortar tube, a cone positioned below the multiple streamers in the mortar tube, the cone pointing towards the mortar-tube opening, a kick charge positioned below the cone. The cone is configured to disperse the multiple streamers upon discharge of the kick charge.

Abstract: A wireless passive marker device (1) to be tracked and a respective tracking system (3) are provided which make use of a sensing unit (10) comprising a resonator element (11) with piezoelectric properties and a coil element (13), whereby an externally applied excitation field having a particular frequency is applied to act on the sensing unit (10) and wherein the sensing unit (10) responds to the externally applied excitation field by the resonator element (11) performing persisting mechanical oscillations in resonant mode, the persisting mechanical oscillations resulting in a piezoelectric voltage causing the coil element (13) to generate a magnetic field that may then be detected by the tracking system (3) and used for determining the position of the marker device (1) and/or sensing a physical property in the surrounding environment of the marker device (1).

Abstract: A wireless passive marker device (1) to be tracked and a respective tracking system (3) are provided which make use of a sensing unit (10) comprising a resonator element (11) with piezoelectric properties and a coil element (13), whereby an externally applied excitation field having a particular frequency is applied to act on the sensing unit (10) and wherein the sensing unit (10) responds to the externally applied excitation field by the resonator element (11) performing persisting mechanical oscillations in resonant mode, the persisting mechanical oscillations resulting in a piezoelectric voltage causing the coil element (13) to generate a magnetic field that may then be detected by the tracking system (3) and used for determining the position of the marker device (1) and/or sensing a physical property in the surrounding environment of the marker device (1).

Abstract: A system for receiving signals from a magneto-mechanical oscillator includes a main coil array adapted to receive a response signal of the magneto-mechanical oscillator and to transmit an excitation signal to the magneto-mechanical oscillator, and an additional coil for receiving a signal of the magneto-mechanical oscillator. A localizer is adapted to localize the additional coil and comprises a controller for controlling the main coil array and the additional coil such that a received localization signal is generated, a sensitivity provider for providing sensitivity information, and a processor for determining a position and/or orientation of the additional coil based on the provided sensitivity information and based on the received localization signal. A kit is provided for upgrading a system with a main coil array, by adding one or more additional coils and providing software for locating the one or more additional coils with the use of a pilot tone transmission.

Abstract: A magnetic resonance examination system comprising a main magnet for applying a uniform static magnetic field. An active shim system applies shim magnetic fields to correct for inhomogeneities of the static magnetic field. A shim driver system activates the active shim system on the basis of B0-shim settings. A trained machine-learning module is trained to return the B0-shim settings from one or more actual load parameters. The magnetic resonance examination system may further comprise an RF transmit system with RF antenna elements and an RF driver system to activate the RF antenna elements for applying a (B1) radio frequency field having a predetermined spatial distribution. An RF shim system to control the RF driver system to apply shim radio frequency fields to correct for deviation of the radio frequency field's spatial distribution from the predetermined spatial distribution on the basis of RF-shim settings.

Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and multiple neural networks. The multiple neural networks comprise a noise estimation neural network (122) and at least one image quantification neural network (124). The noise estimation neural network is configured to output a noise estimate (128) of a medical image (126, 126?) in response to receiving the medical image as input. The at least one image quantification neural network is configured to output an image attribute (130) of the medical image in response to receiving the medical image as input. The medical system further comprises a computational system (104).

Abstract: The invention relates to a method of MR imaging of an object positioned in the examination volume of an MR system (1). It is an object of the invention to provide a deep learning-based denoising approach that overcomes the Rician bias problem. As a solution, the invention proposes a method comprising the following steps: a) subjecting the object to an imaging sequence comprising RF pulses and switched magnetic field gradients, whereby MR signals are generated, b) acquiring the MR signals, c) reconstructing a complex-valued MR image from the acquired MR signals, d) denoising the MR image using a deep learning algorithm that operates on the real and the imaginary parts of the MR image, and c) computing a magnitude MR image from the denoised complex-valued MR image. According to an aspect of the invention, the deep learning algorithm uses a set of denoising models that are trained using different loss functions.

Abstract: The invention provides for a medical imaging system (100, 300) comprising: a memory (110) for storing machine executable instructions (120) and a processor (104) for controlling the medical imaging system.

Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (116) and a super resolution neural network (118). The super resolution neural network is configured to receive an initial magnetic resonance image (114, 114?) descriptive of a subject (318), having a first resolution, and containing an image distortion artifact. The image distortion artifact is a Gibbs ringing image artifact. The super resolution neural network is configured to output an enhanced magnetic resonance image in response to receiving the initial magnetic resonance image. The enhanced magnetic resonance image has a second resolution, that is higher than the first resolution, and has a reduction or removal of the image distortion artifact.

Abstract: The invention provides for a medical instrument (100, 300, 400, 500) comprising a magnetic resonance imaging system (102). The medical instrument further comprises a subject support (120) with a support surface (121) configured for supporting at least a portion of the subject within an imaging zone (108). The subject support comprises a radar array (125) embedded below the support surface. The medical instrument further comprises a radar system (124) for acquiring a radar signal (144) from the subject. The medical instrument further comprises a motion detection system (122) configured for acquiring a movement signal (146).

Abstract: A method of setting an RF operating frequency of an MRI system (1) uses a first reference frequency signal, obtained from a geo-satellite positioning system, as a stable long term frequency reference. A second frequency source (24) is calibrated using the first frequency reference signal and the second frequency reference source (24) is then used as the master clock for the MRI system (1), for setting the RF operating frequency.

Abstract: The invention provides for a medical imaging system (100, 300, 500) comprising a processor (104). Machine executable instructions cause the processor to: receive (200) magnetic resonance data (120) comprising discrete data portions (612) that are rotated in k-space; bin (202) the discrete data portions into predetermined motion bins (122) using a motion signal value; reconstruct (204) a reference image (124) for each of the predetermined motion bins; construct (206) a motion transform (126) between the reference images; bin (208) a chosen group (610) of the discrete data portions into a chosen time bin (128). Generate an enhanced image (130) for the chosen time bin using the chosen group fo the discrete data portions and the motion transform of each of the chosen group to correct the discrete data portions.

Abstract: Described herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an upsampling neural network (122). The upsampling neural network is configured to output an upsampled magnetic resonance image (130) with a second resolution in response to receiving a preliminary magnetic resonance image (126) with a first resolution which is lower than the second resolution. The execution of the machine executable instructions causes a computational system (104) to: receive (200) preliminary k-space data (124); reconstruct (202) the preliminary magnetic resonance image from the preliminary k-space data; receive (204) clinical k-space data (204); receive (206) the upsampled magnetic resonance image in response to inputting the preliminary magnetic resonance image into the upsampling neural network; and provide (208) a motion corrected magnetic resonance image (132) using the upsampled magnetic resonance image and the clinical k-space data.

Abstract: The invention refers to a device (100) for detecting a working status of a medical implant (130), like a stent or bone implant, wherein a micro device (131) is integrated in and/or attached to the implant and comprises a magneto-mechanical oscillator configured to transduce a magnetic excitation field into a magnetic response field, wherein the response field is indicative of a temperature change of the micro device. The device comprises a transmit/receive unit (110) adapted to generate the excitation field, detect the response field, and transduce the detected response field into an electric response signal, and a controller (120) adapted to control the transmit/receive unit and further adapted to determine a change in a temperature of the micro device based on the electric response signal, and to determine the working status of the medical implant based on the determined change in the temperature of the micro device.

Abstract: Disclosed herein is a medical system (100, 300). The execution of machine executable instructions (120) causes a processor (104) to: receive (200) measured gradient echo k-space data (122); receive (202) an off-resonance phase map (124); reconstruct (204) an initial image (126) from the measured gradient echo k-space data; calculate (206) an upsampled phase map (128) from the off-resonance phase map; calculate (208) an upsampled image (130) from the initial image; calculating (210) a modulated image (132) by modulating the upsampled image with the upsampled phase map; calculate (212) a corrected image (134) comprising iteratively. The iterative calculation comprises: calculating (214) updated k-space data by applying a data consistency algorithm (138) to a k-space representation of the modulated image and the measured gradient echo k-space data and calculating (216) an updated image (142) from the updated k-space data.

Abstract: A wireless passive marker device (1) to be tracked and a respective tracking system (3) are provided which make use of a sensing unit (10) comprising a resonator element (11) with piezoelectric properties and a coil element (13), whereby an externally applied excitation field having a particular frequency is applied to act on the sensing unit (10) and wherein the sensing unit (10) responds to the externally applied excitation field by the resonator element (11) performing persisting mechanical oscillations in resonant mode, the persisting mechanical oscillations resulting in a piezoelectric voltage causing the coil element (13) to generate a magnetic field that may then be detected by the tracking system (3) and used for determining the position of the marker device (1) and/or sensing a physical property in the surrounding environment of the marker device (1).

Abstract: Disclosed herein is a medical system (100, 300, 400) comprising a memory (110) storing a trainable machine learning module (122) trained using training data descriptive of a training data distribution (600) to output a reconstructed medical image (136) in response to receiving measured medical image data (128) as input. The medical system comprises a computational system (104). The execution of machine executable instructions (120) causes the computational system to: receive (200) the measured medical image data and determine (202) the out-of-distribution score and the in-distribution accuracy score consecutively in an order determined a sequence, detect (204) a rejection of the measured medical image data using the out-of-distribution score and/or the in-distribution accuracy score during execution of the sequence, provide (206) a warning signal (134) if the rejection of the measured medical image data is detected.

Abstract: A method of setting an RF operating frequency of an MRI system (1) uses a first reference frequency signal, obtained from a geo-satellite positioning system, as a stable long term frequency reference. A second frequency source (24) is calibrated using the first frequency reference signal and the second frequency reference source (24) is then used as the master clock for the MRI system (1), for setting the RF operating frequency.

Abstract: For delivering an image-guided radiation therapy treatment to a moving structure included in a region of a patient body a series of first images of the region of the patient body in different phases of a motion of the structure is acquired in accordance with a first imaging mode. The series of first images is associated with a series of second images of the patient body in essentially the same phases of the motion of the target structure, the second images being acquired in a second imaging mode. During the treatment, a third image is acquired using the second imaging mode during the radiation therapy treatment and a continuation of the radiation therapy treatment is planned on the basis of data relating to one of the first images selected on the basis of a comparison between the third image and the second images associated with the first images.

Abstract: A system (100) and computer-implemented method are provided for data collection for distributed machine learning of a machine learnable model. A privacy policy data (050) is provided defining computer-readable criteria for limiting a selection of medical image data (030) to a subset of the medical image data to obfuscate an identity of the at least one patient. The medical image data is selected based on the computer-readable criteria to obtain privacy policy-compliant training data (060) for transmission to another entity. The system and method enable medical data collection at clinical sites without requiring manual oversight, and enables such selections to be made automatically, e.g., based on a request for medical image data which may be received from outside of the clinical site.