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. Execution of the machine executable instructions further causes the processor to receive (204) a set of modified signals (128) in response to inputting the set of initial signals into the configured artificial neural network. The configured artificial neural network is configured for removing physiological artifacts from the set of initial signals.
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:
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 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. Execution of the machine executable instructions (161, 164) causes the processor (130) to control the magnetic resonance imaging data processing system (126) to: receive a magnetic resonance imaging data set (144, 500, 800), apply the received magnetic resonance imaging data set (144, 500, 800) as an input to the trained deep learning network (146, 502, 702), process one or more motion artifacts present in the received magnetic resonance imaging data set (144, 500, 800) using the trained deep learning network (146, 502, 702).
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 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 an apparatus (1) for magnetic resonance (MR) examination of a subject (S), comprising: an examination region (3) for accommodating the subject (S) during the MR examination; a radio-frequency system (5) for transmission of a radio-frequency (RF) signal or field into the examination region (3) during the MR examination; and a temperature control system (6) for controlling the temperature of the subject (S) in the examination region (3) during the examination. The temperature control system (6) is configured to actively control or regulate an environment of the subject (S), and thereby the temperature or thermal comformt of the subject (S) based upon a detected and/or an expected temperature of the subject (S) during the MR examination. The invention also provides a method of controlling thermal comfort of the subject (S) during an examination of the subject (S) in a MR apparatus (1), comprising the steps of: estimating and/or detecting a temperature of the subject (S) during the MR examination, and actively controlling or regulating the environment of the subject (S) based upon the estimated and/or detected temperature of the subject (S) during the MR examination.
Abstract:
The invention provides means for determining 3D position data in an MRI system. A method for motion correction of MR data, comprising: generating, by a calculation unit (51), a three-dimensional model, 3D model, of a region of interest (24) of a subject (23) comprising at least one landmark (27) inherent to the subject (23) (S10); obtaining, by a first measuring device (20, 25, 52), a two-dimensional image, 2D image, of at least a part of the subject (23) inside a MRI system (22), wherein the measuring device is arranged inside a bore of the MRI system (22) (S20); determining, by the calculation unit (53), at least one landmark (27) in the 2D image, wherein the at least one landmark (27) in the 2D image corresponds to the at least one landmark (27) of the 3D model (S30); determining, by the calculation unit (54), a 3D position of the region of interest (24) of the subject (23) in the MRI system (22) based on the determined at least one landmark (27) in the 2D image (S40); providing, by the calculation unit (55), the 3D position of the region of interest (24) of the subject (23) for motion correction of MR data (S50).
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:
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 (α) radiated toward the portion of the body during a preparation period (21), and ii) reading RF pulses (β) 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.