The present disclosure relates to a coil assembly of an MRI device. The MRI device may be configured to perform an MR scan on a subject. The coil assembly may include one or more coil units, a substrate, and a sensor mounted within or on the substrate. The one or more coil units may be configured to receive an MR signal from the subject during the MR scan. The substrate may be configured to position the one or more coil units during the MR scan. The one or more coil units may be mounted within or on the substrate. The sensor may be configured to detect a motion signal relating to a physiological motion of the subject before or during the MR scan.

The present invention relates to an apparatus (10) for monitoring of a patient undergoing a Magnetic Resonance Image (MRI) scan. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with at least one sensor data of a patient undergoing an MRI scan by an MRI scanner. The input unit is configured to provide the processing unit with at least one scan parameter of the MRI scanner for the MRI scan. The input unit is configured to provide the processing unit with at least one characteristic of the patient. The processing unit is configured to predict a stress level of the patient and/or a predicted motion state of the patient, the prediction or predictions comprising utilization of the at least one sensor data of the patient, the at least one scan parameter of the MRI scanner, and the at least one characteristic of the patient. The output unit is configured to output information relating to the predicted stress level of the patient and/or the predicted motion state of the patient.

A computer-implemented method for performing spatiotemporal background phase correction for phase contrast velocity encoded magnetic resonance imaging includes performing a phase contrast magnetic resonance imaging scan of a region of interest within a patient to yield a complex image and calculating a plurality of filter cut-off frequencies based on physiological limits associated with the patient. A spatiotemporal filter is created based on the plurality of filter cut-off frequencies. This spatiotemporal filter is applied to the complex image to yield a low-pass filtered complex image. Then, complex division is performed using the complex image and the low-pass filtered complex image to yield a corrected image.

The present disclosure relates to a method comprising: receiving (201) acquired k-space data of an object, reconstructing (203) an image from the acquired k-space data, generating (205) reconstructed k-space data from the reconstructed image, determining (207) delta k-space data as a difference between the acquired k-space data and the reconstructed k-space data, splitting (209) the k-space data into one or more data chunks, wherein each data chunk of the data chunks comprises a set of one or more samples having a set of k-space coordinates, for each set of k-space coordinates of the one or more sets of coordinates, selecting (211), from the delta k-space data, a residual data set having the set of k-space coordinates, inputting (213) at least part of the data chunks and corresponding residual data sets to a trained machine learning model, thereby obtaining from the trained machine learning model probabilities of motion corruption for each of the data chunks of the acquired k-space.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system. Execution of the machine executable instructions causes a computational system (106) to: receive (200) measured k-space data (124) acquired according to the first configuration of the magnetic resonance imaging system; receive (202) the reference magnetic resonance image data; receive (204) the synthetic magnetic resonance image data by inputting the reference magnetic resonance image data into the image generating neural network; and reconstruct (206) corrected magnetic resonance image data (132) from the measured k-space data and the synthetic magnetic resonance image data.

An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Provided herein are systems and methods for generating fetal cardiac magnetic resonance (MR) images of a living fetus, within a uterus of a parent of the fetus, by imaging the fetus within the uterus using a magnetic resonance imaging (MRI) system. Also provided herein are methods for deriving information indicative of fetal cardiac cycles from MR data obtained by an MRI system while imaging the fetus, the MR data including MR data for the center of k-space. The derived information may be used to differentiate the fetal cardiac cycles from other sources of noise in the MR data such as the parental cardiac cycles.

A magnetic resonance imaging (MRI) system and methods are provided for producing images of a subject. In some aspects, a method includes identifying a point in the cardiac cycle, performing an inversion recovery (IR) pulse at a selected time point from the pre-determined point, and sampling a k-space segment at an inversion time from the IR pulse that is substantially coincident with the pre-determined point. The method also includes repeating the IR pulse and k-space sampling for multiple inversion times, and multiple segments of k-space, in an interleaved manner, to generate datasets having T1-weighted contrasts determined by their respective inversion times. The method further includes reconstructing three-dimensional (3D) spatially-aligned images using the datasets, and generating a T1 recovery map by combining the 3D images. In some aspects, a prospective/retrospective scheme may be used to obtain data fully sampled in the center of k-space and randomly undersampled in the outer regions.

Methods and systems for diagnosing a condition of a central nervous system are provided. A method includes providing a DBSI-MRI data set obtained from the central nervous system of the subject, and transforming the DBSI-MRI data set to obtain at least one DBSI biomarker value. The method further includes comparing each DBSI biomarker value to at least one corresponding threshold value from a diagnostic database to obtain a relation between each DBSI biomarker value and the at least one corresponding threshold value, and diagnosing the condition according to at least one diagnostic rule, wherein each diagnostic rule defines a candidate condition in terms of the relations between the at least one DBSI biomarker value and the at least one corresponding threshold value.

In a method for supporting a monitoring of a magnetic resonance examination on a patient using a magnetic resonance apparatus, the magnetic resonance examination of the patient is started and a monitoring processor determines current examination information of the ongoing magnetic resonance examination. The current examination information are compared with predefined values in the monitoring processor, and a warning is generated if there is a variation between the current examination information and the predefined values. The warning is presented at a display in communication with the monitoring processor.