3852results 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.

Denoised magnetic resonance images are generated using a two-step process. An initial set of images is first denoised on a per channel basis using a locally low-rank-based denoising technique. The denoised coil channel images are transformed back into k-space and the denoised k-space data are then applied to a nonlinear image reconstruction. In some instances, the nonlinear image reconstruction can be implemented using a trained neural network. The neural network may be trained using a self-supervised learning technique.

A system and method are provided for creating magnetic resonance (MR) images with reduced motion artifacts from the MR data from which the images are produced. The method includes selecting a candidate image from a plurality of candidate images reconstructed from the MR data. The method also includes registering the candidate image to a reference image, comparing the candidate image to a consistency map, and, based on comparing the candidate image using the consistency map, selecting a blending algorithm. The method also includes generating a blended image using the blending algorithm and the candidate image and repeating these steps for each candidate image. The method also includes performing a Fourier aggregation to generate a combined image and displaying the combined image with reduced motion artifacts compared to the plurality of candidate images.

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 embodiment of the invention provides a magnetic resonance eddy current prediction method, system and device. The method comprises the following steps: acquiring a gradient parameter of a gradient signal of a magnetic resonance system; the gradient parameters are processed through an eddy current prediction model, eddy current field components corresponding to gradient signals are determined, and the gradient signals comprise single gradient signals or gradient signal combinations.

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.

A system and method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. Each blade of the plurality of blades of k-space data is undersampled. The system and method include utilizing a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades. The system and method include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

The invention relates to the technical field of magnetic resonance imaging. The invention discloses a magnetic resonance image rearrangement method and system and a storage medium, and can improve the magnetic resonance imaging quality. The method comprises the following steps: acquiring a PPG signal of a face; while the PPG signal is obtained, an SCG signal of the heart part is obtained; taking a time point corresponding to a positive peak value of each period in the SCG signal as a first trigger point; generating a corresponding first trigger signal based on each first trigger point, and sending all the first trigger signals to the magnetic resonance equipment to trigger the magnetic resonance equipment to execute image acquisition operation to obtain a plurality of initial images; performing compensation calculation on an abnormal positive peak value in the SCG signal based on the PPG signal and an individual model to obtain at least one second trigger point; and generating corresponding second trigger signals based on all the first trigger points and all the second trigger points, and sending all the second trigger signals to the magnetic resonance equipment to trigger the magnetic resonance equipment to perform rearrangement operation on the plurality of initial images to obtain a target image set.

The invention provides a fast magnetic resonance reconstruction method and system based on a multistage wavelet convolution network, and particularly relates to the technical field of magnetic resonance imaging and image reconstruction, and the scheme comprises the steps: collecting multi-channel under-sampling K space data; inverse Fourier transform is carried out on the multi-channel under-sampling K space data to obtain an under-sampling magnetic resonance image; and inputting the undersampled magnetic resonance image into a trained multi-stage wavelet convolutional network for reconstruction to obtain a reconstructed magnetic resonance image, the multi-stage wavelet convolutional network being constructed based on multi-stage wavelet transform and a convolutional neural network. According to the scheme, the magnetic resonance K space data is collected in an undersampling mode, the scanning time can be shortened, the multi-stage wavelet convolution network can effectively reserve high-frequency details of the image in the reconstruction process, and therefore the reconstruction quality and efficiency of the multi-channel magnetic resonance image can be remarkably improved on the whole.

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).

Systems and methods for suppressing electromagnetic interference (EMI) in magnetic resonance (MR) data are provided. The systems and methods include identifying a first subset of the MR data that is affected by EMI and suppressing EMI in the first subset to obtain a second subset of the MR data. Suppressing EMI in the first subset is performed by: applying a filter to the first subset of the MR data in order to suppress contribution of MR spin echo signals in the first subset of the MR data thereby obtaining signal-suppressed MR data; suppressing EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying an inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data. The systems and methods include generating an MR image using the second subset of the MR data and outputting the generated MR image.

A system for quantitative parameter mapping using magnetic resonance (MR) image includes an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images, and a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter. The quantitative parameter mapping neural network can be trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter. The system can further include a display coupled to the quantitative parameter mapping neural network to display the at least one quantitative map.

Methods and devices for changing a brain state of a person from an initial brain state to a target brain state are described. The method comprises the following steps: receiving information representing the initial brain state, wherein the information comprises the structural composition and the functional architecture of the brain; estimating a likelihood of the brain changing from the initial brain state to the target brain state based at least in part on the received information; determining a non-invasive brain stimulation scheme based at least in part on the received information and the estimated likelihood of the brain changing from the initial brain state to the target brain state; and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation scheme. The method also includes using the brain information to provide information for a design to compute a generic artificial intelligence agent.

Techniques, systems and apparatus are described for magnetic resonance imaging. A method includes generating an MRI image by processing a first image obtained by applying a first order of modules including at least a first control module at a first time point and a first labeling module at a second time point to a target object, wherein the first control module inverts a magnetization of the target object and a second image obtained by applying a second order of modules including at least a second labeling module at a third time and a second control module at a fourth time to the target object, wherein the second labeling module inverts the magnetization of the target object.

A computer-implemented method for transforming magnetic resonance (MR) imaging across multiple vendors is provided. The method comprises: obtaining a training dataset, wherein the training dataset comprises a paired dataset and an un-paired dataset, and wherein the training dataset comprises image data acquired using two or more MR imaging devices; training a deep network model using the training dataset; obtaining an input MR image; and transforming the input MR image to a target image style using the deep network model.

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.

Systems and methods for automatically selecting an optimal inversion time for Flow-Independent Dark-blood Delayed Enhancement (FIDDLE). Deep learning is used to train a neural network to perform myocardium segmentation on REF images associated with FIDDLE images. A separate neural network is trained to find the intersection of the recovery curves of normal myocardium and blood pool. The intersection is used to determine the optimal inversion time.

The present disclosure provides various devices, systems, and methods for iterative shimming in magnetic resonance imaging (MRI). In one aspect, a method of iterative shimming can include generating a field map of a magnetic field B0 from an MRI system. The MRI system can include a shim tray and an array of permanent magnets to generate the magnetic field B0. The shim tray can include slots to receive shim magnets. The method can further include shimming the MRI system by iteratively: applying the field map to a genetic algorithm to determine a set of slots to receive a set of shim magnets, installing the set of shim magnets in the set of slots, generating a next field map of the magnetic field B0, and either performing a next iteration based on the next field map or determining to not perform the next iteration based on the next field map.

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.

A computer-implemented method includes, based on an input dataset defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data-consistency operation for enforcing consistency between the input image and the reconstructed image. The data-consistency operation determines, for multiple K-space positions at which the input dataset comprises respective source data, a contribution of respective K-space values associated with the input dataset to a K-space representation of the reconstructed image.

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).

Systems and methods for simulating structures and images are disclosed. The techniques described herein can include obtaining a first image of a subject. The techniques can include determining a location for simulating a structure within the first image. The techniques can include simulating, according to the location, a shape for the structure. The techniques can include generating a mask according to the location and the shape for the structure. The techniques can include applying the mask to the first image to generate a second image simulating the structure.

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.

Techniques, systems and apparatus are described for magnetic resonance imaging. A method includes generating an MRI image by processing a first image obtained by applying a first order of modules including at least a first control module at a first time point and a first labeling module at a second time point to a target object, wherein the first control module inverts a magnetization of the target object and a second image obtained by applying a second order of modules including at least a second labeling module at a third time and a second control module at a fourth time to the target object, wherein the second labeling module inverts the magnetization of the target object.

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 belongs to the technical field of magnetic resonance imaging, and aims to provide an ultra-low field magnetic resonance imaging method and system, electronic equipment and a product. According to the method, the area of the read-out pre-dispersed phase gradient Grp and the gradient delay compensation of the read-out gradient Gr are performed on each gradient axis of different spokes in an iteration mode, so that the gradient delay difference of the two gradient axes read out by adopting a Radio acquisition method is as small as possible, the k space center is aligned as far as possible to reduce offset, the influence caused by gradient performance difference is reduced, and the accuracy of the measurement result is improved. The image quality is improved; meanwhile, an imaging sequence is improved, and a better imaging effect can be obtained in an ultra-low field by correcting a fast spin echo sequence, so that the average number of times can be set to be 1 at least, denser spokes can be collected, the resolution is improved, meanwhile, two times of circulation are not needed, and the imaging efficiency is improved. Therefore, the scanning time can be shortened on the premise of ensuring that the image resolution and quality meet the requirements.