2155results about "Measurements using NMR imaging systems" patented technology

A method for material detection is described. The method may include extracting, from a material database, a resonance frequency for the target material. The method may further include comparing an application type to entries in a scan database. The scan database may store pre-defined scanning patterns and corresponding application types. The method may include extracting, from the scan database, a scan sequence for the application type. Also, the method may include instructing a gimbal to follow positions in the scan sequence. The method may also include transmitting into an environment an RF signal when the gimbal is at the positions in the scan sequence. The method may further include receiving a response signal from the environment. The method may include analyzing the response signal for resonance characteristics that indicate a presence of the target material. Additionally, the method may include generating a haptic feedback when the target material is detected.

A magnetic resonance image reconstruction device according to an embodiment is a magnetic resonance image reconstruction device that reconstructs magnetic resonance image data in which an artifact due to undersampling is removed or reduced based on undersampled k-space data, and includes a reconstruction unit reconstructing the magnetic resonance image data using a reconstruction network having a correction module. The correction module includes a regularization block generating second image data by performing a regularization process on first image data using a first neural network, and a data consistency block generating third image data by performing a data consistency process so that k-space data corresponding to the second image data approaches the undersampled k-space data. The correction module further includes at least one of a data consistency adjustment block adjusting the data consistency process and a regularization adjustment block adjusting the regularization process.

The invention relates to the technical field of magnetic resonance imaging, in particular to an intelligent sparse low-rank non-Cartesian magnetic resonance dynamic imaging method, which comprises the following steps of: acquiring magnetic resonance k-space data which is subjected to non-Cartesian continuous sampling by adopting multiple coils, continuously sampling data, rearranging the continuously sampled data along a time dimension by using a framing operator to obtain under-sampled k-space data, and then inputting the framed non-Cartesian under-sampled k-space data to be reconstructed into a trained network for image reconstruction. And reconstructing a final dynamic image through depth space-time sparsity, time low-rank learning, data consistency verification and loss function constraint. Compared with the prior art, the non-Cartesian magnetic resonance dynamic image reconstruction method has the advantages that the image is reconstructed through multiple times of network feedback iteration, the reconstruction speed of the non-Cartesian magnetic resonance dynamic image is greatly increased, and the temporal-spatial resolution is improved.

The present disclosure provides various systems and methods for magnetic resonance imaging. In one aspect, a method for magnetic resonance imaging can include receiving k-space data sets acquired by radiofrequency (RF) coils. Each of the k-space data sets can correspond to a different one of the RF coils. Each of the k-space data sets can be truncated and / or under sampled. The method can further include generating partial images of a field of view based on the k-space data sets and generating an initial image based on the partial images. The initial image can be full image of the field of view. The method can further include applying an iterative image reconstruction technique to generate an updated image based on the initial image.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

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. In this way, a trade-off between noise removal and preservation of small image details can be controlled. Moreover, the invention relates to an MR system (1) and to a computer program for an MR system (1).

The invention relates to a magnetic resonance imaging method and device and a storage medium, and the method comprises the steps: scanning a target region through employing a first magnetic resonance sequence, setting a plurality of echo times, and collecting a plurality of spin echo images; recording a plurality of echo times and corresponding dispersion sensitivity coefficients according to the first magnetic resonance sequence; scanning the target area by using the second magnetic resonance sequence to obtain an apparent dispersion coefficient; and according to the plurality of spin echo images, the plurality of echo times, the dispersion sensitivity coefficient and the apparent dispersion coefficient, fitting to obtain a transverse relaxation time constant quantitative image for correcting the dispersion influence. According to the method and the device, the problem of dispersion when a magnetic resonance transverse relaxation time constant quantitative image is obtained through a variable echo time spin echo method in the prior art is solved, and the beneficial effect of effectively improving the quantitative accuracy is achieved.

A method for material detection may include extracting, from a material database, a resonance frequency for the target material. The method may further include comparing an application type to entries in a scan database. The scan database may store pre-defined scanning patterns and corresponding application types. The method may include extracting, from the scan database, a scan sequence for the application type. Also, the method may include instructing a gimbal to follow positions in the scan sequence. The method may also include transmitting into an environment an RF signal when the gimbal is at the positions in the scan sequence. The method may further include receiving a response signal from the environment. The method may include generating a haptic feedback to indicate a directionality or a proximity to the target material. The method may include analyzing the response signal for resonance characteristics that indicate a presence of the target material.

Provided is a method for determining huff and puff fluid volumes in multi-scale spaces of shale oil reservoirs, including the following steps: rock sample pretreatment; porosity stress sensitivity testing; high pressure hg injection testing and nuclear magnetic resonance testing benchmarking; online nuclear magnetic resonance testing; data processing and analysis. Firstly, a changing law of multi-scale spaces of shale oil reservoirs with an effective stress is characterized through a pore stress sensitivity measurement and online nuclear magnetic resonance measurement combined experiment, and then the huff and puff fluid volumes of multi-scale spaces of shale oil reservoirs under a condition of a certain stimulated reservoir volume are calculated. A usage amount of a fracturing fluid for a fracturing horizontal well in the shale oil reservoir is determined from the perspective of giving full play to the imbibition replacement role of micro-nano pores.

Disclosed herein is a medical system (100, 400) comprising: a magnetic resonance imaging system (102) configured for providing a respiratory signal. The respiratory signal is descriptive of a respiratory position as well as of inhalation and exhalation of the subject. The execution of machine executable instructions (140) causes the computational system to repeatedly: perform (200) the individual acquisition of k-space data using the magnetic resonance imaging system; receive (202) the respiratory signal; bin (204) the individually acquired k-space data into one of a predetermined number of k-space bins (148) using the respiratory signal and perform outlier rejection. Execution of the machine executable instructions further causes the computational system to reconstruct (208) a magnetic resonance image for each of the predetermined number of k-space bins to provide a respiratory-state resolved magnetic resonance image (150).

The invention discloses an electromagnetic interference suppression method and system for ultra-low field magnetic resonance imaging, and belongs to the technical field of nuclear magnetic resonance, and the system comprises an electromagnetic interference signal collection module which is composed of a plurality of EMI coils and corresponding signal collectors and actively detects EMI signals from the environment; the electromagnetic interference suppression module is used for performing model training by using the data, performing electromagnetic interference suppression through a model, predicting and eliminating an EMI component based on the electromagnetic interference suppression model, and outputting k space data without EMI; and the image reconstruction module is used for receiving the EMI-free k-space data and carrying out averaging and image reconstruction on the data so as to generate a high-quality MRI image. According to the invention, EMI noise is effectively suppressed, image quality is improved, and mobile and portable development of MRI equipment is promoted.

A method of double-contrast magnetic resonance fingerprinting including: optimizing, using Cramér-Rao lower bound (CRLB) via a computer system, radiofrequency (RF) pulse parameters in an MRF sequence; loading, via the computer system, the RF pulse parameters optimized into an MRI scanner; and capturing raw k-space data by selecting different signal contrast modules for dual-contrast encoding at varying repetition times (TR); reconstructing, via the computer system, at least one image including a plurality of pixels; defining, via the computer system, a dynamic range and step size for tissue parameters; and creating, via the computer system, a dictionary based on Bloch equations, the dynamic range, and the step size; and comparing, via the computer system, signal evolution for each of the plurality of pixels in the at least one image to the dictionary to determine quantitative parameters for each of the plurality of pixels, and generating a quantitative parameter map.

The present disclosure provides magnetic resonance phantom imaging phantoms and method of assembling thereof. In one aspect, a magnetic resonance imaging phantom can include a plurality of modular components. The plurality of modular components can include a first modular component, a second modular component, a shell, and a lid. The second modular component can be different than the first modular component. The shell can be structured to receive at least one modular component. The lid can be attachable to the shell to enclose the at least one modular component received within the shell.

The invention provides a system, a method, a storage medium and a magnetic resonance imaging (MRI) system for deep learning technology for magnetic resonance image reconstruction. The magnetic resonance imaging (MRI) system includes: a magnetic system including: a B0 magnet configured to provide a B0 field for the MRI system; a gradient coil configured to provide a gradient field for the MRI system; and at least one RF coil configured to detect a magnetic resonance (MR) signal; and a controller configured to: control the magnetic system to acquire MR spatial frequency data using non-Cartesian sampling; and generating an MR image from the acquired MR spatial frequency data using a neural network model, the neural network model comprising one or more neural network blocks, the one or more neural network blocks comprising a first neural network block, where the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transform.

Methods and systems for magnetic resonance imaging are presented. According to an aspect of the invention a method is disclosed, comprising: obtaining magnetic resonance imaging data by subjecting the patient to a steady-state imaging sequence that samples multiple coherence pathways, wherein two or more echo signals are generated in each interval between successive radiofrequency pulses; deriving susceptibility information from the magnetic resonance imaging data; outputting to a display an image based on the susceptibility information. The invention further provides a time-efficient single scan method from which various image contrasts can be derived, such as for example susceptibility with a combination of any of the T2 weighted image, diffusion weighted image and conductivity, all representing various biomarkers of which combination can lead to distinguishing different types of diseases and / or to distinguishing stages in evolution of a specific disease type (e.g., cancer).

A system for automating a magnetic resonance imaging (MRI) pipeline for a patient is provided. The system includes one or more processors that are configured to receive information that includes patient data and access one or more trained LLMs. The one or more processors are further configured to apply the patient data to the one or more trained LLMs to generate an MRI protocol that includes one or more pulse sequences and pulse sequence parameters. The one or more processors are further configured to store the MRI protocol.

By producing the proper wave interference using superimposed angularly separate waves that overlap with the proper time-phase relationship (called “Time-Correlated Standing-wave Interference”), wave energy is amplified (by “Coherent Intensity Amplification”) and teleported to precise locations. For instance, in one application, energy is teleported to one or more areas within a living body for such therapeutic applications as destroying cancer cells or plaques within arteries. A system implementing this technique creates amplified constructive interference at one or more selected disease locations, while producing destructive interference at surrounding locations. In this application example, the technique allows energy to be “teleported” to tumor cells, plaques, or other diseased cells, for instance, to destroy them, while surrounding healthy cells receive virtually no energy, obviating collateral damage from the treatment. The same method can be used to diagnose disease by detecting energy teleported to different locations.

The embodiment of the invention discloses a scanning NV probe microscope which comprises an atomic force microscopy system used for detecting surface topography information of a sample and an optical detection magnetic resonance system used for detecting surface magnetic field distribution information of the sample, and the atomic force microscopy system comprises a vibration arm and a diamond probe. The optical detection magnetic resonance system comprises an NV color center, a transition excitation light path and a fluorescence detection light path; a sample to be scanned and the transition excitation light path are respectively positioned on two opposite sides of the diamond probe, and the transition excitation light path and the fluorescence detection light path are positioned on the same side of the diamond probe; a first metal coating film is arranged on one side, close to the sample, of the diamond probe and is used for blocking surface fluorescence of the sample. According to the scanning NV probe microscope provided by the embodiment of the invention, the metal coating is arranged at the bottom of the diamond probe to block stray fluorescence from the surface of the sample, so that the problems of low magnetic field imaging signal-to-noise ratio and limited measurement precision of the existing scanning NV probe microscope are solved.

A method for performing magnetic resonance imaging on a subject comprises obtaining undersampled imaging data, extracting one or more temporal basis functions from the imaging data, extracting one or more preliminary spatial weighting functions from the imaging data, inputting the one or more preliminary spatial weighting functions into a neural network to produce one or more final spatial weighting functions, and multiplying the one or more final spatial weighting functions by the one or more temporal basis functions to generate an image sequence. Each of the temporal basis functions corresponds to at least one time-varying dimension of the subject. Each of the preliminary spatial weighting functions corresponds to a spatially-varying dimension of the subject. Each of the final spatial weighting functions is an artifact-free estimation of the one of the one or more preliminary spatial weighting functions.

The invention provides a gradient coil model correction method for ultra-low field magnetic resonance, and relates to the technical field of gradient coil control in medical imaging equipment, and the method comprises the steps: collecting a real voltage signal d (n) and a real current signal x (n) of a gradient coil in real time, and n represents a sampling moment; a gradient coil model is constructed, the gradient coil model comprises a linear model constructed based on an adaptive filtering algorithm and a nonlinear model constructed based on a neural network algorithm, and a first prediction voltage signal # imgabs0 # output by the linear model and a second prediction voltage signal # imgabs1 # output by the nonlinear model are determined according to the real current signal x (n); determining a mixed prediction voltage signal # imgabs4 # of the gradient coil model according to the first prediction voltage signal # imgabs2 # and the second prediction voltage signal # imgabs3 #; and updating the weight of the linear model and the weight of the nonlinear model according to the real voltage signal d (n) and the mixed predicted voltage signal # imgabs5 # so as to adapt to gradient coil model correction under complex working conditions.

The invention discloses a scanning control method and system for a magnetic resonance spectrometer, and the method comprises the following steps: an upper computer analyzes and loads sequence file parameters meeting the Pulseq standard, and integrates the sequence file parameters and pre-configured system parameters into a scanning parameter set; the upper computer generates an executable spectrometer hardware control instruction based on the scanning parameter set, and writes the spectrometer hardware control instruction into a register of a PCIE board card through a PCIE driving module; the PCIE board card generates a hardware control signal according to the content written in the register, controls the spectrometer hardware module to execute corresponding operation, and generates magnetic resonance original data; and the upper computer reads the magnetic resonance original data and reconstructs the magnetic resonance original data into magnetic resonance image data based on analysis of the magnetic resonance original data. According to the invention, the compatibility and flexibility of the system are significantly improved, different upper computers can communicate and cooperate with the spectrometer hardware module, sequence sharing and use among different devices are facilitated, and the integration and maintenance costs of the system are reduced.

The invention belongs to the technical field related to nanometer material testing, and discloses a magnetic nanoparticle relaxation time detection method and an environmental parameter detection method, and the method comprises the following steps: applying an excitation magnetic field around a to-be-detected target mixed with magnetic nanoparticles; obtaining magnetization response generated by the magnetic nanoparticles in any direction of the magnetic field plane, and obtaining phase lag corresponding to any order harmonic term of the magnetization response; according to the function relationship between the phase lag corresponding to any harmonic term and the Brown relaxation time of the magnetic nanoparticles, solving and obtaining the Brown relaxation time of the magnetic nanoparticles; wherein the function relationship comprises a correction term introduced by magnetic dipole interaction of the magnetic nanoparticles. The magnetic dipole interaction among the magnetic nanoparticles is introduced, relaxation time detection of particle space distribution under an external magnetic field is corrected, the interference of the magnetic dipole interaction on the magnetic nanoparticle Brown relaxation time detection process can be effectively reduced, and accurate estimation of the magnetic nanoparticle Brown relaxation time is realized.

The invention discloses a nuclear magnetism safety interval confirmation device, and the device comprises a quantum noise suppression module which is used for eliminating radar signal noise caused by the quantum tunneling effect on the surface of a metal object in an ultrahigh field intensity environment; the multi-sensor fusion center is used for integrating the radar cleaning signal, the real-time magnetic field intensity data and the terahertz penetrability detection result to generate fused object position information; the dynamic magnetic field reconstruction engine is used for calculating a magnetic field distortion gradient by analyzing Hall sensor array data and a magnetohydrodynamic model in real time; the nonlinear danger scoring module is used for receiving object distance, magnetic field gradient, existence time and object classification data; the hierarchical response system is used for triggering a corresponding response according to the danger score; and the cesium atomic clock synchronization module is used for eliminating a timing error caused by a time crystal effect through a block chain timestamp verification mechanism. According to the invention, risk closed-loop control in a high-field-intensity MRI environment is truly realized.

The invention relates to a method for determining the position of a body part in a magnetic resonance imaging (MRI) system, the method comprising the following steps: positioning the body part on a body part cushion on a patient table (4) of the MRI system; determining a magnetic stray field with a magnetic field sensor (2) arranged in or on the body part cushion to thus determine position data; transmitting the position data to an evaluation unit by means of a transmission means; determining the position of the body part in the MRI system based on the position data and the fact that the body part is positioned on the body part cushion, by means of the evaluation unit. The invention further relates to a corresponding method for determining a specific absorption rate of a body part in a MRI system, a body part cushion, and a MRI system.

The present disclosure provides a computing apparatus comprising a processor and computer-readable storage media storing instructions that, when executed, direct the apparatus to: receive input data for a data reconstruction task; process the input data using a neural network model comprising a first set of neural network layers configured to perform data reconstruction and a second set of neural network layers configured to enforce data consistency, wherein both sets of layers are utilized during training and inference; generate intermediate reconstructed data using the first set of layers; generate data-consistent reconstructed data by modifying the intermediate reconstructed data using the second set of layers to enforce consistency with the input data; and output the data-consistent reconstructed data as a final output of the neural network model. The system and techniques provided herein enable improved data reconstruction while maintaining consistency with input data.

The invention discloses a method for navigating a Na-23MRI (Magnetic Resonance Imaging) pulse sequence by an H-1 sampling track, which comprises the following steps of: designing a pulse sequence sequentially comprising an H-1 navigation echo module and a Na-23 ultra-short echo time pulse sequence module, and reconstructing an actually-measured H-1 sampling track by utilizing an H-1 sampling signal corresponding to the H-1 navigation echo module, so as to obtain an actually-measured Na-23 sampling track; and finally, performing image reconstruction on the Na-23 sampling signal based on the actually measured Na-23 sampling track to obtain a Na-23 reconstructed image, thereby remarkably reducing the reconstruction artifacts of the Na-23 image and realizing the optimization of the image quality.

A method for determining a pore throat size distribution in a rock sample includes saturating the rock sample with a fluid to a maximum saturation, acquiring a first set of nuclear magnetic resonance (NMR) measurements of the rock sample, generating based on a first T2 distribution of the first set of NMR measurements a first saturation profile and a first set of slice measurements, obtaining a peak T2 measurement and an average pore throat size measurement by conducting a subsequent NMR measurement procedure, repeating obtaining a peak T2 measurement until the rock sample has a minimum saturation, obtaining a fit function by fitting the average pore throat size measurements to the peak T2 measurements, and generating a pore throat size distribution from the first T2 distribution. A computer system for determining a pore size distribution in a rock sample includes a processor and a memory coupled to the processor.

Disclosed is a system comprising a database storing a preoperative high-resolution image of an object of interest and a control circuit. The control circuit comprises a processor and a memory. The memory stores instructions executable by the processor to obtain a low-field strength magnetic resonance image of the object of interest. The memory stores further instructions executable by the processor to input the low-field strength MRI of the object of interest into a generator model of a pre-trained generative adversarial network. The generator model is pre-trained with low-field strength MRIs and paired high-resolution images to correct image distortions. The memory stores further instructions executable by the processor to output a distortion-corrected image of the object of interest from the generator model based on the low-field strength MRI and transmit the distortion-corrected image of the object of interest to a user interface.

Techniques are disclosed for preparing an MR elastography of a liver, which includes creating a photographic recording of an examination object, determining a position of the liver on the basis of the photographic recording, providing an information item regarding a target position of an oscillation generator on the basis of the position of the liver, arranging the oscillation generator on the examination object dependent upon the information regarding the target position, introducing the examination object with the oscillation generator into a patient receiving region of a magnetic resonance device, capturing overview MR image data from the examination object, and selecting an examination region for transverse slices covering at least one subregion of the liver for a recording of MRE image data on the basis of the overview MR image data.

Disclosed herein is a method for determining one or more control parameters (416) for controlling one or more light sources (142) to adjust lighting conditions of a medical imaging system (100) during an acquisition of optical sensor data (414) using an optical sensor (144). The method comprises starting a feedback loop, in response to receiving an adjustment trigger. The feedback loop comprises controlling an adjusting of the lighting conditions using the one or more light sources (142), receiving adjusted optical sensor data (414) for the adjusted lighting conditions from the optical sensor (144), detecting the target elements (122) within the adjusted optical sensor data (414) using a target detection module (412), and determining confidence values for the detections of the target elements (122). The feedback loop is repeated until the confidence values determined for the detections of the target elements (122) reach a predefined confidence threshold level.