31 results about "Coil sensitivity" patented technology

The invention provides an image phase calculation method and an image phase processing system for magnetic resonance phase difference imaging and a high-density phase array coil, and belongs to the field of magnetic resonance phase difference blood flow measurement. The image phase calculation method comprises the following steps: S1, collecting original complex images SFC and SFE of n coil channels; s2, performing complex number division on the original complex number images SFC and SFE of each coil channel to eliminate the influence of the coil sensitivity phase and the coil element, and obtaining a result after complex number division of each coil channel; s3, summing the results obtained by dividing the complex numbers of all coil channels to obtain the phase sum of each channel of the original complex image; and S4, obtaining the phase difference of the combined phase image according to the phase sum of each channel of the original complex image. The method can effectively remove the influence of coil phase sensitivity and magnetic field nonuniformity, and is especially suitable for low-field application with poor magnetic field uniformity.

Image reconstruction systems and methods include providing a sensitivity map of a coil of a magnetic resonance imaging (MRI) system to a neural network. The systems and methods also include providing interleaved k-space data to the neural network, where the interleaved k-space data includes partial k-space data interleaved with zero or synthetic k-space data to provide an extended field of view (FOV) that is different than a FOV utilized during acquisition of the partial k-space data, where the partial k-space data is obtained during a scan of a region of interest of the MRI system. The systems and methods also include outputting a final reconstructed MR image from the neural network based at least on the sensitivity map and the interleaved k-space data, where the final reconstructed MR image includes the FOV utilized during the acquisition of the partial k-space data.

This invention discloses a rapid online dynamic magnetic resonance imaging (MRI) reconstruction method based on subspace tracking, relating to the field of medical image processing technology. The method includes: Step 1, obtaining a coil sensitivity map from pre-scanned full-sample data and constructing an encoding operator using an undersampling template; Step 2, decomposing the image into two components and sequentially solving for the component values ​​corresponding to each frame, thereby rapidly acquiring the image of the current frame. This method can be applied to interventional procedures guided by MRI images. It achieves rapid scanning through undersampling and rapid reconstruction of undersampling data using the proposed online reconstruction algorithm. Physicians can promptly determine the position of catheters, guidewires, or puncture needles during intervention based on the rapidly reconstructed MRI image.

A method according to the invention for determining magnetic resonance coil sensitivity data comprises the steps of: - using a coil that comprises at least two antenna elements and one coil channel for each antenna element, recording reference data of individual slices, wherein the recorded reference data completely samples k-space at least in a central region of k-space according to Nyquist, - loading at least one set of data recorded with the coil that is subsampled according to Nyquist, - determining a supplementary kernel for at least one loaded set of data recorded with the coil based on the recorded reference data, - determining reconstructed reference data that is complete according to Nyquist at least in a central region of k-space from the loaded sets of data recorded with the coil using the determined supplementary kernels, - determining coil sensitivity data of the coil based on the reconstructed reference data.

The invention relates to a magnetic resonance imaging method and device. The method comprises the following steps: performing magnetic resonance scanning on a target object according to a preset sampling track to obtain multi-phase original scanning data of the target object; the original scanning data of different periods comprises scanning data of different slice layers of the target object; determining a coil sensitivity graph according to the original scanning data of each phase, and reconstructing the original scanning data of each phase according to the coil sensitivity graph to obtain a multi-phase scanning image of the target object; and performing magnetic resonance imaging based on the multi-phase scanning image. According to the method, the static high-definition imaging requirement and the dynamic high-definition imaging requirement can be met at the same time through one scanning process, and the multi-phase scanning image obtained through reconstruction can be suitable for various different imaging modes, so that magnetic resonance images in different imaging modes are obtained; the magnetic resonance imaging efficiency can be improved, and the magnetic resonance imaging effect can also be improved.

The present invention provides a fast multi-contrast magnetic resonance diffusion imaging and reconstruction method and system, including: using a keyhole echo plane sequence to undersample k-space data of multiple diffusion-weighted images through multiple receiving coils; constructing an objective function including a multi-coil image fidelity term and an image regularization term based on the coil-space-contrast domain tensor low-rank (Low-RankTensorConstraint); minimizing the objective function through an optimization algorithm, reconstructing the diffusion images of all receiving coils and obtaining relevant quantitative parameter images according to the specific way of contrast change. The present invention implements undersampling of k-space through a keyhole-EPI sequence, improves the resolution of multi-contrast diffusion imaging, shortens the echo time, thereby improving the deformation artifacts of the image and reducing the imaging time; avoids the step of solving the coil sensitivity map, reduces the image artifacts caused by variable-density EPI imaging, and improves the robustness and accuracy of the reconstruction result.

In a method for measuring brain free water content, in response to an RF excitation field generated on the basis of a magnetic resonance fingerprinting sequence and applied to the brain, an equilibrium magnetization mixed term (M0) signal is acquired from radiation emitted by each excited voxel of the brain, to obtain an M0 value of each voxel of the brain; a receive coil sensitivity (RP) value of each voxel of the brain is acquired; the M0 value of each voxel of the brain is divided by the RP value of the corresponding voxel to obtain a proton density (PD) value of each voxel of the brain; a PD value of cerebrospinal fluid is taken to be a reference PD value; and the PD value of each voxel of the brain is divided by the reference PD value to obtain the free water content of each voxel of the brain. The method advantageously increases the speed and accuracy of measurement of brain free water content.

A method for reconstructing a motion-corrected magnetic resonance image of a subject. The method comprises receiving a motion trajectory comprising a series of motion states; determining similarities across the motion states and forming groups of motion states, such that each group contains motion states that are similar to each other, and determining a single group motion state representing each group; for each group of motion states, computing a single transformation operator and building a group-specific NuFT forward model, in which a mismatch between the coil sensitivity maps and the magnetic resonance image caused by a motion of the subject during the acquisition is reduced by operating the transformation operator on the coil sensitivity maps and / or on the magnetic resonance image; and reconstructing the motion-corrected magnetic resonance image by minimizing the data consistency error between the received k-space data and the group-specific NuFT forward models, wherein each NuFT forward model comprises the image, the coil sensitivity maps, a non-uniform Fourier Transformation, and a phase factor corresponding to the motion state held by the subject at the time of acquiring the corresponding subset of k-space data.

The invention discloses a magnetic resonance image reconstruction method, device and equipment, a medium and a product. The method comprises the following steps: determining a coil sensitivity mapping image set according to k space data to be reconstructed corresponding to a magnetic resonance image set to be reconstructed; according to the coil sensitivity mapping image set, performing zero-filling reconstruction on the to-be-reconstructed k space data to obtain an initial magnetic resonance reconstruction image set; determining a target magnetic resonance reconstruction image set corresponding to the magnetic resonance image set to be reconstructed according to the initial magnetic resonance reconstruction image set, the coil sensitivity mapping image set and a pre-trained target image reconstruction model; wherein the target image reconstruction model comprises a space-time convolutional network and a data consistency layer, the space-time convolutional network comprises a space-time deformation convolutional layer, and the embodiment of the invention improves the reconstruction effect of the magnetic resonance image.

A method for generating a motion-corrected magnetic resonance image dataset of a body region of a subject, the method comprising receiving magnetic resonance data acquired of the body region; receiving information on the body region covered by the magnetic resonance image dataset; weighting at least part of the received magnetic resonance data by reducing the signal originating from parts of the body region that are expected to have undergone non-rigid and / or independent motion during the acquisition, thereby producing weighted magnetic resonance data; and estimating the motion-corrected image dataset by minimizing the data consistency error between the magnetic resonance data acquired in the imaging protocol and a forward model described by an encoding matrix, wherein the encoding matrix includes motion parameters, Fourier encoding, and optionally subsampling and / or coil sensitivities of a multi-channel coil array, wherein the estimation includes at least one step of estimating motion parameters from the weighted magnetic resonance data.

Various examples relate to SENSitivity Encoding (SENSE) reconstruction of Magnetic Resonance Imaging (MRI) images. Multiple coil sensitivity maps per coil of a receiver coil array are used, e.g., obtained from an Eigenvalue-based Spatially Constrained Iterative Reconstruction Technique (ESPIRIT) autocalibration protocol.

Embodiments of the present application provide a dynamic magnetic resonance image reconstruction method, device and equipment, the method comprising: acquiring dynamic magnetic resonance raw data of a target object by using a center-periphery alternating acquisition mode; performing channel compression on the raw data to obtain compressed data; extracting a time basis function, a coil sensitivity map and down-sampling data based on the compressed data; constructing an optimization expression of a spatial basis function according to the time basis function, the coil sensitivity map, the down-sampling data and a down-sampling mask, taking a weighted sum of a reconstruction error and a difference between adjacent frames as an optimization target; eliminating the time dimension in the process of solving the optimization expression of the spatial basis function based on a conjugate gradient descent algorithm to obtain an optimal solution of the spatial basis function; and reconstructing a dynamic magnetic resonance image of the target object according to the optimal solution of the spatial basis function and the time basis function. Not only the reconstruction speed is improved, but also the reconstruction quality is improved.

The invention discloses a magnetic resonance training data generation method and system based on GPU acceleration Bloch simulation and combined with modal transformation, and relates to medical imaging. The method comprises the following steps: establishing a multi-parameter virtual object library, and defining voxel-level T1, T2, PD and B0 / B1 distribution and coil sensitivity; according to an input sequence and acquisition parameters, time domain simulation is carried out by adopting GPU parallel Bloch solution, and non-ideal factors are injected as required; reconstructing to obtain k space and image domain data and generating paired labels; target comparison, organs and scenes are expanded through rule-driven or data-driven modal transformation; samples and metadata are packed to support traceability and verification. The system is composed of a parameter configuration module, a virtual object library module, a GPU simulation module, a reconstruction module, a modal transformation module and a data set management module. On the premise that physical consistency is guaranteed, data generation efficiency and diversity are remarkably improved, and the method is suitable for downstream tasks such as reconstruction, artifact suppression and quantitative imaging.

Techniques are provided to determine magnetic resonance coil sensitivity data. This includes acquiring reference data of individual slices via a coil comprising at least two antenna elements and one coil channel per antenna element, the acquired reference data fully sampling the k-space at least in a central region of the k-space. The techniques further include loading at least undersampled data set acquired via the coil, and determining a supplementary kernel for at least one loaded set of data acquired via the coil based upon the acquired reference data. The techniques also include determining, at least in a central region of the k-space, reconstructed reference data that is fully sampled from the loaded sets of data acquired by means of the coil using the determined supplementary kernels, and determining coil sensitivity data of the coil on the basis of the reconstructed reference data.

Techniques are provided for image reconstruction in parallel MR imaging, in which a respective set of regularly undersampled MR measurement data in k-space representing an imaged object is received for each of a plurality of coil channels. For each pair of coil channels of the plurality of coil channels, a respective set of reconstruction weights for reconstructing MR data at k-space points, which are not measured according to the undersampling, from the MR measurement data, is received. For each of the plurality of coil channels, a respective coil sensitivity map is determined depending on the respective sets of reconstruction weights for the respective coil channel. A reconstructed MR image is generated based on the coil sensitivity maps.

The invention discloses a magnetic resonance fast imaging method for multi-coil weighting and sensitivity estimation joint reconstruction based on deep learning, and relates to magnetic resonance image fast reconstruction. Magnetic resonance structure image data is used as model training data to obtain a full-sampling image, a real coil sensitivity image is used as a training label, and a training set is k-space random noise addition and simulation under-sampling to obtain under-sampling noise addition image data and an under-sampling sensitivity image; designing a channel weighted sensitivity estimation joint reconstruction deep learning network; constructing a network loss function; solving optimal parameters of the model network by utilizing the training set; and inputting to-be-reconstructed data into the network for reconstruction, and outputting a reconstructed image. A channel weighting algorithm and a coil sensitivity estimation module are introduced, the robustness of the network under the condition of a low signal-to-noise ratio of a coil is enhanced, a sensitivity image containing high-frequency information is generated, the quality of a reconstructed image is improved, the clinical scanning acceleration multiple is improved, the reconstruction speed is high, the noise robustness is high, the denoising effect is good, and the reconstruction quality is high.

The present application relates to a system and method of magnetic resonance imaging. The method includes acquiring at least two target k-space datasets by populating k-space with target magnetic resonance signals acquired by at least two coils of a magnetic resonance imaging device along a spiral trajectory. The method includes acquiring a coil sensitivity of each of the at least two coils. The method includes acquiring a point spread function corresponding to the spiral trajectory. The method includes generating a target image based on an objective function.

During operation, a computer system may acquire magnetic resonance (MR) signals associated with a sample from a measurement device or memory. Then, the computer system may access a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, where coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using coefficients, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer system may solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients using: a forward model that uses the MR information as inputs and simulates response physics of the sample, the MR signals and the predetermined set of coil magnetic field basis vectors.

The application discloses a reconstruction method of planar echo imaging, comprising the following steps: S1, collecting planar echo data and multi-echo gradient echo data of a region to be imaged respectively; S2, performing inverse geometric correction on the multi-echo gradient echo data to generate corrected data; and S3, performing image reconstruction according to the corrected data and the planar echo data to generate an imaging image. The application has the beneficial effect that the correction of the planar echo image is realized by collecting multi-echo gradient echo data and calculating a B0 field, the problem of possible artifacts in the calibration process of the planar echo imaging in the prior art is avoided, and the image of the planar echo imaging is clearer. Meanwhile, the multi-echo gradient echo data is used for coil sensitivity correction, the scanning speed is higher, and the calibration time can be effectively shortened.

Systems and methods for image reconstruction for parallel MR imaging are disclosed that receive a k-space single-coil measurement dataset that includes at least two k-space single-coil measurement sets, transforming the k-space single-coil measurement dataset to an estimated CSM using a coil sensitivity estimation module, and transforming the k-space single-coil measurement dataset and the estimated CSM into a final MR image using an MRI reconstruction module. In some aspects, the coil sensitivity estimation module and MRI reconstruction module include deep learning neural networks trained without the use of ground truth data.

The invention discloses a multi-excitation diffusion kurtosis imaging method based on convex set projection and joint reconstruction, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: carrying out data acquisition; merging the multi-excitation original k space data of which the b value is 0; performing inverse Fourier transform to obtain a corresponding image, and calculating to obtain corresponding coil sensitivity information; performing parallel reconstruction; extracting DKI image phase information; calculating an initial DKI image, and multiplying the initial DKI image by corresponding phase information and coil sensitivity information; performing Fourier transform on the DKI image to a k space; performing data projection operation on the new k space data; performing inverse Fourier transform on the projected k space data to an image domain; the projected DKI images are combined; performing data fitting to obtain a DKI tensor; and performing total variation de-noising on the DKI tensor obtained by fitting to obtain a de-noised DKI tensor. According to the method, joint reconstruction is carried out by using the correlation between DKI images, and meanwhile, the influence of noise on reconstruction is reduced by adopting total variation denoising.

The embodiment of the invention is suitable for the technical field of medical images, and provides a magnetic resonance image processing method and reconstruction method, and the method comprises the steps: obtaining a plurality of layers of first aliasing images of a target object, and obtaining a plurality of first single-layer images of the target object; performing translation and aliasing processing on the first single-layer image in sequence to obtain a second aliasing image; the aliasing modes of the first aliasing image and the second aliasing image are the same; extracting a channel compression matrix from the second aliasing image; and compressing the first aliasing image by using a channel compression matrix to obtain a first compressed aliasing image, and compressing the second aliasing image to obtain a second compressed aliasing image. By adopting the method, the consistency of the coil sensitivity of the first compression aliasing image and the coil sensitivity of the second compression aliasing image can be ensured.

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.

The application discloses an imaging method and device based on parallel imaging, a storage medium and a terminal, relates to the technical field of image data processing, and mainly aims to solve the problem of poor image data accuracy of existing magnetic resonance imaging. The method comprises the following steps: acquiring a first phase encoding interval of parallel imaging pre-scanning, and performing parallel imaging pre-scanning based on a target magnetic resonance sequence and the first phase encoding interval to obtain first imaging data; determining a second phase encoding interval of parallel imaging formal scanning corresponding to the first phase encoding interval, and performing parallel imaging formal scanning based on the target magnetic resonance sequence and the second phase encoding interval to obtain second imaging data; acquiring coil sensitivity information based on the first imaging data, and acquiring a magnetic resonance image based on at least the second imaging data and the coil sensitivity information.

Disclosed herein is a method of operating a medical system (100), comprising: determining (200) initial position data (146) using a motion detection component, wherein the initial position data (146, 152) is descriptive of a subject within an imaging zone; acquiring (202) an initial coil sensitivity map (148) by controlling a magnetic resonance imaging system with coil sensitivity map pulse sequence commands; acquiring (204) imaging k-space data (150) according to a parallel imaging magnetic resonance imaging protocol by controlling the magnetic resonance imaging system with imaging pulse sequence commands; determining (206) subsequent position data (152) using the motion detection component; acquiring (208) at least one subsequent coil sensitivity map (154) by controlling the magnetic resonance imaging system with the coil sensitive map pulse sequence commands if the subsequent position data varies by more than a predetermined change since acquisition of a most recent coil sensitivity map; reconstructing (212) a magnetic resonance image (160, 402) based on the imaging k-space data and the at least one subsequent coil sensitivity map. A medical system is further presented, comprising a computational system configured to perform the method.

The present invention provides a method, system, medium, and electronic device for undersampling reconstruction of magnetic resonance images, comprising the following steps: modeling an MRI image and a coil sensitivity image as continuous functions; predefining the spatial coordinates of pixel points of the MRI image and the coil sensitivity image; training the continuous function based on the spatial coordinates; inputting the spatial coordinates of the pixel points of the MRI image and the coil sensitivity image into the trained continuous function to obtain an MRI image matrix and a coil sensitivity matrix; obtaining predicted k-space data of the MRI image based on the MRI image matrix and the coil sensitivity matrix according to an MRI forward physical model; and obtaining an MRI reconstructed image based on the actual k-space data and the predicted k-space data. The method, system, medium, and electronic device for undersampling reconstruction of magnetic resonance images of the present invention can perform MRI image reconstruction based on undersampled k-space data expressed by implicit neural networks, thereby further accelerating MRI image acquisition while ensuring the quality of the reconstructed image.

The invention relates to a fat pressing method and device for magnetic resonance diffusion imaging based on water-fat separation, and the method comprises the steps: adjusting the bandwidth corresponding to a target excitation pulse and a target refocusing pulse, so as to change the position of a main peak fat layer excited and refocused in initial magnetic resonance imaging, and inhibit the main peak fat; moving a preset signal reading window to collect diffusion and non-diffusion imaging signals of single excitation or multiple excitation corresponding to the signal reading window at different moving time; constructing a corresponding diffusion imaging signal model, simplifying the diffusion imaging signal model, solving the non-diffusion imaging signal model through a water-fat separation algorithm to obtain a water graph, a fat graph, a magnetic field non-uniformity graph and a relaxation time graph corresponding to the non-diffusion imaging signal model, and calculating a coil sensitivity graph corresponding to the non-diffusion imaging signal model; and solving a diffusion imaging signal model to obtain a diffusion-weighted water map, a fat map and a phase difference between excitation so as to finish inhibition of olefin peak fat.

A method for determining a sensitivity distribution of magnetic resonance (MR) receiving coils may include obtaining a reference image of a region of interest (ROI) of a subject. Contrast information between at least two types of tissues of the ROI may be weakened in the reference image. The method may also include determining, based on the reference image, a preliminary radio frequency (RF) field map corresponding to the ROI. The method may also include obtaining a transmitting field map corresponding to the ROI. The method may also include determining, based on the preliminary RF field map and the transmitting map, a sensitivity distribution of MR receiving coils corresponding to the ROI.

Systems, methods, and apparatuses associated with processing complex-valued MRI data using machine learning (ML) models are described herein. The ML models can be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data can be generated by adding phase information to real-valued MRI data and / or by converting single-coil MRI data to multi-coil MRI data based on coil sensitivity maps. The meta-learning process can include using a portion of the training data to conduct a first round of learning to determine updated model parameters and using a remaining portion of the training data to test the updated model parameters. A loss associated with the testing can then be determined and used to refine the model parameters. The ML models learned using these techniques can be employed for various tasks, including, for example, MRI image reconstruction and / or denoising.

A method for magnetic resonance imaging (MRI) includes acquiring under-sampled k-space measurements from an MRI apparatus using multiple receiver coils; reconstructing an MRI image from the under-sampled k-space measurements and coil sensitivity maps using an unrolled neural network; generating reconstructed multi-coil k-space data from the MRI image by multiplying the MRI image by the coil sensitivity maps followed by performing a Fourier transform; estimating a k-space null-space convolutional kernel from fully-sampled k-space measurements in autocalibration signal lines of the under-sampled k-space measurements; solving a convex optimization problem to produce refined k-space data from the k-space null-space kernel, the under-sampled k-space measurements, and the reconstructed multi-coil k-space data; and producing a refined MRI image from the refined k-space data by performing an inverse Fourier transform followed by a coil combination using the coil sensitivity maps.