Abstract: A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a method (100) of monitoring a component (10) of a medical device (1). The method includes retrieving information about a correction factor (40) applied during operation of the component from the medical device; deriving a wear metric indicative of wear of a portion of the component from the retrieved information about the correction factor applied during the operation of the component; estimating a time remaining until end-of-life (EOL) of the component; and outputting an alert (30) indicating estimated time remaining until EOL of the component.

Abstract: The present disclosure relates to a medical imaging method, comprising: receiving (201) a set of subject parameters descriptive of a subject; in response to inputting (203) the set of subject parameters into a trained deep neural network, DNN, receiving (205) from the trained DNN a predicted task; presenting the task to the subject; controlling (207) an MRI system (700) for acquiring fMRI data from the subject in response to the predicted task performed by the subject during the acquisition.

Abstract: A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a service work order (SWO) method (200). The method includes via a user interface (UI) (140), receiving entry of a SWO report (136); applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score (138) for the SWO report; via the UI, providing an indication (142) of the information missing from the SWO report and/or the completeness score for the SWO report; and storing the SWO report in a SWO database (111).

Abstract: A system (100) for transferring data (110) generated by a medical imaging system over a communication network (120), is provided. The system includes a processing arrangement (130, 140) configured to identify (S120) one or more protected health information, PHI, elements (110?) in the data (110); obscure (S130) the one or more PHI elements (110?) in the data (110) to provide de-identified data (110?); transmit (S140) the de-identified data (110?) over the communication network (120); and receive (S150) the de-identified data (110?) at a remote terminal (150).

Abstract: The invention provides means for determining 3D position data in an MRI system.

Abstract: The invention provides for a medical imaging system (100, 400) comprising a memory (110) storing machine executable instructions (120) and a configured artificial neural network (122). The medical imaging system further comprises a processor (104) configured for controlling the medical imaging system. Execution of the machine executable instructions causes the processor to receive (200) magnetic resonance imaging data (124), wherein the magnetic resonance imaging data is BOLD functional magnetic resonance imaging data descriptive of a time dependent BOLD signal (1100) for each of a set of voxels. Execution of the machine executable instructions further causes the processor to construct (202) a set of initial signals (126) by reconstructing the time dependent BOLD signal for each of the set of voxels using the magnetic resonance imaging data.

Abstract: An electroencephalography net (44) comprised of electrodes (34, 36) coupled together by a connector (28) comprising separate elastically (32) and plastically (30) deformable elements.

Abstract: The present disclosure relates to a medical imaging method for enabling magnetic resonance imaging of a subject (318) using a set of imaging parameters of imaging protocols, the method comprising: receiving information related to the subject; using a predefined machine learning model for suggesting at least one imaging protocol for the received information, wherein the imaging protocol comprises at least part of the set of imaging parameters and associated values; providing the imaging protocol.

Abstract: When predicting required component service in an imaging device such as a magnetic resonance (MR) imaging device (12), component parameters such as coil voltage, phase lock lost (PLL) events, etc. are sampled to monitor system components. Voltage samples are filtered according to their temporal proximity to coil plug-in and unplug events to generate a filtered data set that is analyzed by a processor (46) to determine whether to transmit a fault report. A service recommendation is received based on the transmitted report and includes a root cause diagnosis and service recommendation that is output to a user interface (50).

Abstract: The present disclosure relates to a medical imaging method, comprising: receiving (201) a set of subject parameters descriptive of a subject; in response to inputting (203) the set of subject parameters into a trained deep neural network, DNN, receiving (205) from the trained DNN a predicted task; presenting the task to the subject; controlling (207) an MRI system (700) for acquiring fMRI data from the subject in response to the predicted task performed by the subject during the acquisition

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

Abstract: A medical imaging system for acquiring medical image data from an imaging zone. The medical imaging system includes a memory for storing machine executable instructions and medical imaging system commands. The medical imaging system further includes a user interface and a processor. Execution of the machine executable instructions causes the processor to: receive scan parameter data for modifying the behavior of the medical imaging system commands; receive metadata descriptive of imaging conditions from the user interface; store configuration data descriptive of a current configuration of the medical imaging system in the memory; calculate an error probability by comparing the metadata, the configuration data, and the scan parameter data using a predefined model, wherein the error probability is descriptive of a deviation between the metadata and between the configuration data and/or the scan parameter data; perform predefined action if the error probability is above a predetermined threshold.

Abstract: The invention provides for a medical imaging system (100, 400) comprising a memory (110) storing machine executable instructions (120) and a configured artificial neural network (122). The medical imaging system further comprises a processor (104) configured for controlling the medical imaging system. Execution of the machine executable instructions causes the processor to receive (200) magnetic resonance imaging data (124), wherein the magnetic resonance imaging data is BOLD functional magnetic resonance imaging data descriptive of a time dependent BOLD signal (1100) for each of a set of voxels. Execution of the machine executable instructions further causes the processor to construct (202) a set of initial signals (126) by reconstructing the time dependent BOLD signal for each of the set of voxels using the magnetic resonance imaging data.

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

Abstract: The present disclosure relates to a medical imaging method for enabling magnetic resonance imaging of a subject (318) using a set of imaging parameters of imaging protocols, the method comprising: receiving information related to the subject; using a predefined machine learning model for suggesting at least one imaging protocol for the received information, wherein the imaging protocol comprises at least part of the set of imaging parameters and associated values; providing the imaging protocol.

Abstract: An imaging system (10) includes: an image acquisition device (12), a device controller (16) comprising an electronic processor (20) programmed to operate the image acquisition device to acquire medical images of a patient and to maintain a machine log (26) storing an operating history of the image acquisition device; a server computer (34) programmed to retrieve patient information from at least one health information system (HIS) (18); and at least one feedback device (50, 52). The device controller, the server computer, or a combination of the device controller and server computer is programmed to implement at least one state machine (30, 44) having a plurality of states defined by values of state variables wherein the states represent respective attainable states of an image acquisition procedure (100) and the image acquisition device.

Abstract: The invention provides for a medical imaging system (100) for acquiring medical image data (144) from an imaging zone (108). The medical imaging system comprises a memory (134) for storing machine executable instructions (140) and medical imaging system commands (142). The medical imaging system commands are configured for controlling the medical imaging system to acquire the medical image data according to a medical imaging protocol. The medical imaging system further comprises a user interface (132). The medical imaging system further comprises a processor (130) for controlling the medical imaging system.

Abstract: The invention provides for a medical instrument (100, 400, 500, 600) comprising an activity measurement system (106) configured for measuring brain activity data (138) from a subject (102). The medical instrument further comprises a stimulus presentation system (108) configured for providing sensory stimulus to the subject. The medical instrument further comprises a memory (130) for storing machine executable instructions (132) and for storing a stimulus reinforcer database (134). The stimulus reinforcer database comprises entries. Each entry comprises commands configured for controlling the stimulus presentation system to provide the sensory stimulus to the subject. The medical instrument further comprises a processor (120) for controlling the medical instrument.

Abstract: When predicting required component service in an imaging device such as a magnetic resonance (MR) imaging device (12), component parameters such as coil voltage, phase lock lost (PLL) events, etc. are sampled to monitor system components. Voltage samples are filtered according to their temporal proximity to coil plug-in and unplug events to generate a filtered data set that is analyzed by a processor (46) to determine whether to transmit a fault report. A service recommendation is received based on the transmitted report and includes a root cause diagnosis and service recommendation that is output to a user interface (50).

Abstract: In an MR imaging method and apparatus, a portion of a body placed in an examination volume of an MR device is subjected to an imaging sequence of RF pulses and switched magnetic field gradients. The imaging sequence is a stimulated echo sequence including i) two preparation RF pulses (?) radiated toward the portion of the body during a preparation period (21), and ii) reading RF pulses (?) radiated toward the portion of the body during an acquisition period (22) temporally subsequent to the preparation period (21). FID signals (I1) and stimulated echo signals (I2) are acquired during the acquisition period (22) with equal T2*-weighting. A B1 map indicating a spatial distribution of the RF field of the preparation RF pulses within the portion of the body is derived from the acquired FID (I1) and stimulated echo (I2) signals.