14 results about "Magnetic resonance scanner" patented technology

A computer system that performs pairwise harmonization is described. During operation, the computer system may receive information specifying magnetic-resonance diffusion measurements, where the magnetic-resonance diffusion measurements are associated with at least an individual and a first site. Then, the computer system may perform the pairwise harmonizing of the magnetic-resonance diffusion measurements based at least in part on target magnetic-resonance diffusion measurements, where the target magnetic-resonance diffusion measurements are associated with a population and one or more target sites, and the magnetic-resonance diffusion measurements and the target magnetic-resonance diffusion measurements may be acquired using different magnetic-resonance scanners. Next, the computer system may perform quality control on the pairwise harmonized magnetic-resonance diffusion measurements, where the quality control may include comparing the pairwise harmonized magnetic-resonance diffusion measurements associated with the first site and the one or more target sites.

The embodiment of the present application discloses a method and device for limiting radio frequency alternating magnetic field in magnetic resonance imaging. The method comprises: measuring the vertical distance between a local coil placed on the scanned part of a patient and the center of the detection hole of a magnetic resonance (MR) scanner; determining the deviation between the field strength of the B1 field at the position of the local coil and the field strength of the B1 field at the center of the detection hole according to the vertical distance; calculating the conversion coefficient between the field strength of the B1 field at the position of the local coil and the field strength of the B1 field at the center of the detection hole according to the deviation; and calculating the maximum allowable field strength of the B1 field at the center of the detection hole according to the field strength of the B1 field required when the surface temperature of the local coil is equal to the upper limit of the safe temperature and the conversion coefficient. The embodiment of the present application can limit the B1 field in a smaller but still effective field strength range, thereby reducing the waste of the performance of the B1 field while ensuring the safety of the patient and the quality of the MR imaging.

A method and a system include obtaining calibration scan data or low resolution images of a subject acquired with a magnetic resonance (MR) scanner of an MR imaging system. The method and the system also include inputting the calibration data or the low resolution images into a trained deep learning-based multi-mask segmentation network. The method and the system further include outputting labeled mask images for different anatomical stations, wherein a respective mask of a respective labeled mask image highlights an anatomical landmark of interest in each respective anatomical station of the different anatomical stations. The method and the system even further include determining an extent of a respective anatomical landmark of interest in each respective anatomical station of the different anatomical stations for a respective localizer scan for each respective anatomical station based at least on a respective label mask image for each respective anatomical station.

A method for performing magnetic resonance (MR) scanner simulation in a magnetic resonance imaging (MRI) simulator is described, the method comprising: inputting data parameters into a web interface of the MRI simulator, wherein the inputted data parameters are at least pulse sequences and an anatomical model; connecting the web interface and a cloud-based simulator engine of the MRI simulator to pass the data parameters to the cloud-based simulator engine, the method comprising: importing a pulse sequence calculation model; setting the inputted data; and slice selection on the obtained images in the web interface; the method further comprising: recomputing the data parameters to provide one or more simulated MR signals, the recomputing being performed in the cloud, and wherein the method further comprises: reconstructing an MR image based on the one or more simulated MR signals, the reconstruction of the MR image being performed in the cloud; and sending the MR image to the web interface.

A computer system that performs pairwise harmonization is described. During operation, the computer system may receive information specifying magnetic-resonance diffusion measurements, where the magnetic-resonance diffusion measurements are associated with at least an individual and a first site. Then, the computer system may perform the pairwise harmonizing of the magnetic-resonance diffusion measurements based at least in part on target magnetic-resonance diffusion measurements, where the target magnetic-resonance diffusion measurements are associated with a population and one or more target sites, and the magnetic-resonance diffusion measurements and the target magnetic-resonance diffusion measurements may be acquired using different magnetic-resonance scanners. Next, the computer system may perform quality control on the pairwise harmonized magnetic-resonance diffusion measurements, where the quality control may include comparing the pairwise harmonized magnetic-resonance diffusion measurements associated with the first site and the one or more target sites.

The invention discloses a magnetic resonance imaging sequence trajectory rewinding compensation method and device, a medium and a product, and relates to the technical field of magnetic resonance imaging, and the method comprises the steps: collecting the actual gradient waveforms of three gradient axes in a set time period after an excitation radio frequency pulse is applied to a water model in a magnetic resonance scanner in the current repetition time; setting ideal gradient waveforms of three gradient axes in a set time period; any gradient axis is determined as a current axis; determining the residual gradient time moment of the current axis based on the actual gradient waveform and the ideal gradient waveform of the current axis in the set time period, and determining the waveform parameter of the current axis of the rewinding compensation gradient pulse; and applying a component of the rewinding compensation gradient pulse on the current axis to the current axis based on the residual gradient time moment of the current axis and the waveform parameter of the current axis of the rewinding compensation gradient pulse at the end moment of the phase disturbing gradient so as to realize the rewinding compensation of the magnetic resonance imaging sequence trajectory. According to the invention, the problems of quantitative deviation and image blurring of T2 weighted imaging are solved.

The invention relates to the technical field of signal intelligent processing, in particular to a subject neural feedback effectiveness prediction method and system based on a graph neural network, and the method comprises the steps: carrying out the resting state functional magnetic resonance imaging scanning of a subject through a magnetic resonance scanner, and extracting a resting state brain signal time sequence of the subject; obtaining a Pearson's correlation coefficient between interested brain regions of the subject and a corresponding brain region position index based on the resting state brain signal time sequence, and extracting time sequence statistics by performing time domain transformation on the brain signal time sequence; taking the time sequence statistic as a node feature, obtaining an edge feature according to a Pearson's correlation coefficient and a brain region position index, and constructing a brain map based on the resting state fMRI of the subject; and identifying the neural feedback effectiveness of the subject by using the neural feedback effectiveness prediction model. According to the method, the brain image feature extraction process can be simplified, the degree of dependence of previous machine learning prediction model construction on features is reduced, and the universality of a neural feedback effectiveness prediction model is improved.

The invention relates to a method of MR imaging which addresses the interplay of patient-induced artifacts, operational complexity, and the need for precise imaging of clinically relevant areas. The method comprises: acquiring MR imaging data from a patient's anatomy by executing at least one (pre-) scan to generate an image of the anatomy and to extract information including at least one of coil sensitivity distribution, B0 field distribution, B1 field distribution, spatial distribution of flow characteristics, spatial distribution of motion characteristics; identifying an anatomic structure or region of interest within the imaged anatomy; automatically adapting settings of an MR scanner used and / or parameters of an MR imaging sequence or protocol taking the extracted information into account for enhancing image quality or diagnostic utility for the identified anatomic structure or region of interest; and acquiring further MR imaging data from the patient's anatomy using the MR imaging sequence or protocol with the adapted settings and / or parameters to generate one or more optimized images of the identified anatomic structure or region of interest. Moreover, the invention relates to an MR imaging system and to a computer program.

A method for generating a magnetic resonance image for diagnostic purposes takes segmented k-space data corresponding to a magnetic resonance scanner signal and determines, for each k-space segment, an image sharpness corresponding to a subspace of the k-space data generated by removing that k-space segment from the k-space data. The subspace with the highest sharpness is then used to generate the magnetic resonance image.

The present disclosure relates to a method and device for distinguishing a high-fat voxel and generating a fat fraction map by means of magnitude-based multi-echo magnetic resonance imaging, the method including obtaining, with a magnetic resonance scanner, three or more opposed-phase OP image data corresponding to an opposed-phase and three or more in-phase IP image data corresponding to an in-phase in accordance with a plurality of echo times of a subject, confirming, with the magnetic resonance scanner, a voxel value for voxels constituting each of the image data, determining, with the magnetic resonance scanner, a high-fat voxel in which high-fat is located among the image data based on the voxel value, and generating, with the magnetic resonance scanner, a fat fraction map by selectively applying different fat fraction equations in accordance with a result of the determination of the high-fat voxel, and may be applied to other embodiments.

A magnetic resonance ‘MR’ scanner utilizing a passive magnetic shielding technique. The scanner includes a gradient coil inductively coupled to a managed eddy current structure which establishes passive magnetic shielding for the gradient coil due to the inductive coupling.

A method and apparatus for processing MRI data on changes in local brain function and blood perfusion, the method comprising: (1) collecting T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, high-resolution T1-weighted imaging, resting-state fMRI, and 3D-pCASL sequence imaging for each subject on an MRI scanner; (2) processing the resting-state fMRI to obtain the following indicators: local consistency ReHo, low-frequency amplitude ALFF, and low-frequency amplitude fraction fALFF; (3) processing the 3D-pCASL sequence imaging to obtain perfusion indicators in each brain region, including mean cerebral blood flow mCBF, arterial transit time ATT, and arterial cerebral blood volume aCBV; (4) performing statistical analysis between patients and healthy controls on the data obtained in steps (2) and (3), and using correlation analysis to determine the correlation between statistically significant indicators and scale scores.

This invention provides a three-dimensional fast magnetic resonance elastography acquisition and reconstruction method and system, comprising: Step S1: generating vibration using an external vibrator and transmitting it into human tissue to generate fluctuations within the tissue; Step S2: having the magnetic resonance scanner receive a synchronization signal and applying an image acquisition sequence to record the fluctuation information of the human tissue; Step S3: acquiring the raw data and using a fast reconstruction algorithm to obtain an accurate fluctuation image of the human tissue. This invention uses a multi-block alternating acquisition method, which can maintain the signal-to-noise ratio of the image even with short transmission cycles (TRs), thereby increasing the number of samples within the same vibration cycle and improving acquisition efficiency. This invention uses three-dimensional excitation and acquisition, increasing the intensity of the acquired signal itself, thus overcoming the shortcomings of previous methods.

The invention discloses a magnetic resonance intervention image reconstruction method. The method comprises the following steps: S1, collecting non-uniform frequency domain data in real time by using a magnetic resonance scanner; s2, performing data consistency updating on the non-uniform frequency domain data; and S3, inputting the updated data into the neural network to obtain a reconstruction result. According to the invention, a multi-scale online re-parameterized convolutional recurrent neural network MSOR-CRNN is provided, and two core innovations are introduced on the basis of a traditional CRNN architecture: an online re-parameterized module OR; and a multi-scale feature propagation mechanism MSFP is adopted, so that the whole system optimizes the reconstruction effect and realizes performance improvement on the premise of not increasing the calculation burden.