174 results about "K-space" patented technology

Magnetic resonance imaging uses a pulse sequence formed to include a pre-pulse, an RF excitation pulse, an encoding gradient pulse, and a reading gradient pulse. The encoding gradient pulse has an encoding amount determined to allow a data acquisition position in a k-space to be directed outward from a center of the k-space. A train of pulses including the RF excitation pulse, the encoding gradient pulse, and the reading gradient pulse is repeated to allow the number of times of data acquisition in the k-space to become larger as approaching to a central region of the k-space. The pre-pulse is formed to be reduced in an application rate to the RF excitation pulse as approaching to an outward position in the k-space. By way of example, this pulse sequence is used for contrast enhanced MRA carried out under a dynamic scan.

A method of chemical species suppression for MRI imaging of a scanned object region including acquiring K space data at a first TE, acquiring K space data at a second TE, reconstructing images having off resonance effects, estimating off resonance effects at locations throughout the reconstructed image, and determining the first and second chemical species signals at image locations of the scanned object from the acquired signals and correcting for blurring resulting from off resonance effects due to B0 inhomogeneity.

The invention provides a magnetic resonance imaging K space movement artifact correction parallel acquisition reconstruction method. The method comprises the following steps: an imaged object is placed in a magnetic resonance imaging system to acquire magnetic resonance signals for filing the K space; acquisition is performed on the K space in the multiple-blade manner, wherein the inter-blade K space geometrical relationship is that rotation is performed for an angle; correction is performed on the acquired blades according to the PROPELLER algorithm to calculate the inter-blade data change due to rigid body motion and calculate the movement calibration coefficient of each blade; for each blade, other appropriate blades are selected separately, and the other blades are transformed into the Cartesian coordinate system of the blade according to the movement calibration coefficient to calculate the coil consolidation coefficient in the blade data missing direction; the data missed by the blade is filled according to the calculated coefficient; after same operations are performed on all of the blades, the data filling of each blade is completed, then the complete K space is filled according to the PROPELLER algorithm and transformed to the image domain through appropriate transformation to obtain images. By adopting the method, parallel acquisition reconstruction can be performed without acquiring calibration data while effectively removing movement artifact so as to improve the acquisition speed.

The embodiment of the invention discloses a magnetic resonance image reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring undersampled magneticresonance data; and inputting the magnetic resonance data into an image reconstruction model based on an alternating direction multiplier algorithm to obtain a reconstructed target magnetic resonanceimage, the image reconstruction model being a pre-trained result of a mathematical model in which a data fidelity term and a regular term are both uncertain terms. According to the embodiment of theinvention, the method solves a problem that a data fidelity term in a model suitable for an ADMM-net method is represented by employing a 2-norm between a reconstructed k space and a sampling point ina magnetic resonance image reconstruction process, is not a most effective data consistency guarantee method, and is not suitable for all image reconstruction conditions. The image quality after image reconstruction based on the ADMM algorithm can be improved.

An MRI apparatus includes a data processor for obtaining first k space data including a first unacquired line by undersampling an MR signal based on a first value of an MR parameter, and for obtaining second k space data including a second unacquired line by undersampling an MR signal based on a second value of the MR parameter; and an image processor for interpolating a portion of first unacquired line data in the first k space data based on the second k space data, and a portion of second unacquired line data in the second k space data based on the first k space data.

The embodiment of the invention discloses a magnetic resonance imaging method, and the method comprises the steps: obtaining a first magnetic resonance image of a target patient, and the first magnetic resonance image being obtained according to the partial K space data of a to-be-detected region of the target patient; inputting the first magnetic resonance image into a pre-trained image processing neural network model, wherein the pre-trained image processing neural network model is obtained by training a training set of low-quality magnetic resonance imaging and corresponding high-quality magnetic resonance imaging; and obtaining a second magnetic resonance image which is output by the image processing neural network model and corresponds to the first magnetic resonance image. Compared with the first magnetic resonance image, the second magnetic resonance image obtained by using the image processing neural network model for processing is closer to an image obtained by using all K space data, so that more organ tissue information of the patient can be obtained by using the second magnetic resonance image, the resolution of the image obtained by magnetic resonance imaging is improved, and the signal-to-noise ratio is improved.

The invention discloses an equal voxel magnetic resonance diffusion imaging method and an equal voxel magnetic resonance diffusion imaging device based on multi-plate simultaneous excitation, whereinthe method comprises the following steps: S1, performing excitation for a tested target for many times through multi-plate simultaneous excitation pulse, and in a process of excitation each time, performing signal acquisition for the tested target through a multi-channel coil, and thereby obtaining k space data acquired through down sampling in excitation each time; S2, by a multi-excitation diffusion imaging reconstruction algorithm uniting the k space and an image domain, recovering k space position data which is not sampled in excitation each time; S3, correcting edge artifacts through an improved NPEN algorithm, thereby obtaining an imaged image. The method can obtain high-resolution images and also can keep relatively high signal-to-noise ratio at the same time, can reduce interference of three-dimensional navigation echo errors and improve quality and stability of reconstructed images, and can improve signal-to-noise ratio efficiency and scanning efficiency on the basis of guaranteeing the quality of the images.

The invention relates to a magnetic resonance imaging method and a magnetic resonance imaging device. The magnetic resonance imaging method comprises the following steps: generating a plurality of K space diagrams on the basis of magnetic resonance sensation signals which are arranged in a parallel manner; according to the plurality of K space diagrams, reconstructing a first sub-image corresponding to a reading gradient pulse in a first direction, and a second sub-image corresponding to a reading gradient pulse in a second direction; calculating the phase difference between the first sub-image and the second sub-image; updating a sensitivity distribution diagram according to the phase difference; according to a sensitivity distribution diagram before updating and a sensitivity distribution diagram after updating, performing sensitivity encoding reconstruction on the plurality of K space diagrams, thereby obtaining magnetic resonance images.

The embodiment of the invention discloses a magnetic resonance image processing method and device, a storage medium and a magnetic resonance imaging system. The method comprises the steps of obtainingto-be-corrected data, inputting the to-be-corrected data into an artifact correction model, and generating initial correction data, wherein the artifact correction model is obtained through pre-training based on a neural network model, and a k space corresponding to the initial correction data comprises more high-frequency components than the k space corresponding to the to-be-corrected data; performing weighting processing on the to-be-corrected data and the initial correction data to generate weighting results, and performing fusion processing on the two weighting results to generate targetcorrection k space data; and reconstructing the target correction k space data to generate an artifact-corrected target correction magnetic resonance image. Through the technical scheme, the magneticresonance image with a better artifact correction effect is obtained under the condition that the resolution and the signal-to-noise ratio of the artifact-corrected magnetic resonance image are basically unchanged and the scanning time is not increased.

The invention discloses a heart magnetic resonance imaging method based on a multi-scale low-rank model, and provides a new method for heart magnetic resonance imaging researching. A radial sampling track is adopted to realize undersampling of heart K space data, and full heart data radial undersampling is realized, and thereafter magnetic resonance data acquisition speed is accelerated, and then scanning time of a magnetic resonance device is reduced. The minimization expression of the heart magnetic resonance data based on multi-scale low-rank decomposition is used to improve the precision of the magnetic resonance imaging. A convex optimization problem of a reconstructed image is solved by an alternating direction multiplier method, and a speed of magnetic resonance imaging reconstruction is improved, and therefore the texture of the result of the reconstructed image is clear, and the edge is smooth.

The invention relates to a neural network magnetic resonance image reconstruction method based on double-domain alternating convolution. The neural network magnetic resonance image reconstruction method is characterized by comprising the following steps: the step 1, obtaining a K space data set; the step 2, generating under-sampling K space data; the step 3, establishing a coding and decoding neural network structure of double-domain alternating convolution; the step 4, training a coding and decoding neural network model of double-domain alternating convolution by using the under-sampled K space data generated in the step 2 and image domain data obtained by performing inverse Fourier transform on the K domain information in the step 1; and the step 5, reconstructing the undersampled magnetic resonance data by using the trained double-domain alternating coding and decoding neural network to obtain a magnetic resonance reconstructed image with relatively high definition. According to the method, accelerated reconstruction of magnetic resonance imaging is realized by using the small kernel convolutional neural network on the K domain, artifacts caused by breaking through the Nyquist sampling limit are eliminated, meanwhile, clear magnetic resonance imaging can be reconstructed, and the reconstruction precision is improved.

The invention discloses an inverse dispersion method for eliminating a Gibbs annular artifact of a magnetic resonance image. The method comprises the following steps of: reading an MR artifact image acquired through magnetic resonance imaging; calculating the gradient of each pixel from the image data and inversely diffusing the grey values of all pixels along the gradient direction; forming limiting conditions of grey value diffusion so as to make the whole diffusion process capable of adaptively adjusting the diffusion speed and to guarantee the stability of the whole diffusion process; obtaining the directional diffuseness of a diffusion equation which progresses the grey value; and updating the grey values of the pixels to acquire the corrected MR image. A high-quality image can be acquired by performing data correction on the artifact generated by cutting of the k space data.

The invention discloses a method for generating real space and K space Airy beam arrays based on a metasurface, and belongs to the field of micro-nano optics. The method comprises the following steps: performing analog calculation on the nano antenna by adopting a simulation method, accurately and effectively regulating and controlling the propagation distance and the propagation track of an emergent Airy beam by adjusting the size and azimuth angle distribution of a nano column array, and selecting to obtain the nano antenna meeting the metasurface composition requirement; after the geometric dimensions of the nano-column units are determined, obtaining the dimensions and azimuth angle distribution of the nano-column arrays covering 0-2pi phases according to a phase distribution algorithm for generating real space and K space Airy beam arrays, i.e., determining the dimensions and rotation angles of each nano-column unit, and coding and generating a processing file of a corresponding medium metasurface structure; processing the broadband medium metasurface by utilizing a processing file and adopting a micro-nano processing technology; when different circularly polarized light beams are incident, obtaining Airy beam arrays in a real space and a K space, thereby improving the parallel working efficiency of the device remarkably.

The invention provides a magnetic resonance interventional imaging method and system based on low rank and sparse decomposition, and a medium, and the method comprises the steps of: 1, continuously collecting k space data in a golden angle radial sampling mode in an interventional process; 2, grouping the collected k space data; and 3, reconstructing a magnetic resonance intervention image by adopting a method based on low-rank and sparse decomposition and framelet transformation. According to the method, based on grouped k space collection and reconstruction, reconstruction can be carried out under the condition that little data is acquired, the time resolution is high, and the real-time performance is good.

The invention provides a magnetic resonance imaging method and device, a storage medium and medical equipment, and is used for improving the imaging quality. The magnetic resonance imaging method comprises the steps of obtaining a sampling point set, wherein the sampling point set comprises a preset number of sampling points arranged according to a random time sequence under a Cartesian coordinatesystem in a phase encoding plane; generating a spatial coding gradient according to the sampling point set and a preset sampling track used for acquiring magnetic resonance signals; collecting original navigation data in a non-Cartesian coordinate system and located in a K space center area and imaging data in a Cartesian coordinate system and located on the periphery of the K space together in each repetition time based on the space coding gradient; converting the original navigation data into converted navigation data under the Cartesian coordinate system; and performing compressed sensingreconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.

The invention discloses an accelerated magnetic resonance super-resolution imaging method, device and equipment and a medium, and the method comprises the steps: obtaining partial K space frequency domain data of a to-be-detected region of a target patient, and obtaining a target accelerated magnetic resonance image; inputting the target accelerated magnetic resonance image into a target neural network model which is obtained by training an enhanced super-resolution generative adversarial network constructed based on a residual dense network by using a simulation accelerated magnetic resonance image set synthesized based on a standard sequence magnetic resonance image; and performing super-resolution processing on the target accelerated magnetic resonance image through the target neural network model to obtain a super-resolution accelerated magnetic resonance image. According to the method, the pre-created generative adversarial network is trained through the simulation acceleration magnetic resonance image set synthesized based on the standard sequence magnetic resonance image, the real acceleration magnetic resonance image can be simulated, the imaging quality of the acceleration magnetic resonance image is improved, the image noise is reduced, and the image resolution is improved.

The present invention relates to a magnetic resonance imaging method and device, a storage medium and an electronic device, and aims to reduce data acquisition time in a magnetic resonance imaging process, realize faster magnetic resonance imaging and improve magnetic resonance imaging quality. The magnetic resonance imaging method comprises the following steps: based on a three-dimensional spoiled gradient echo sequence, acquiring downsampling K space feature data with different contrasts through a K space variable density interleaved sampling mode; inputting the downsampling K space featuredata into a multi-parameter quantitative network model which bases on the downsampling K space feature data to reconstruct to obtain images corresponding to complete K space feature data with different contrasts, and obtaining a plurality of parameter quantitative images according to the images corresponding to the complete K space feature data; and determining a plurality of the parameter quantitative images output by the multi-parameter quantitative network model as magnetic resonance imaging results.

A fast imaging method based on a synthetic analysis deconvolution network includes the steps of performing filter learning using an image library on the synthetic analysis deconvolution network integrated with DN and MRF prior information; in the process of filter training, updating the filter and sparse coefficients by the rotation technique, the semi-secondary separation technique and the iterative reweighted least squares method; on the basis of the trained filter, obtaining an initial image from an undersampled K space, and establishing an image reconstruction model; updating the sparse coefficient and a target image on the reconstruction model by using the rotation technique, the semi-quadratic-element separation technique and the iterative reweighted least squares method; and satisfying a convergence condition to obtain a reconstructed image. The invention improves the accuracy of image reconstruction to a certain extent, and also provides a fast imaging system based on a synthetic analysis deconvolution network using the fast imaging method of the synthetic analysis deconvolution network, which can obtain the reconstructed image with a high accuracy.

The invention discloses a method for removing magnetic resonance head image motion artifacts.. Non-reference image qualitative assessment indexes QITV in the method are used for reflecting image quality. The method comprises the steps of constructing k space data after sampling each time through contraction perception, calculating QITV value of reconstructed images, by observing the change trend of the image QITV value, finding the time when patients make motion in the magnetic resonance screening process, and using k space data without motion to reconstruct contraction perception so as to optimize fully-collected motion artifact images. The image quality of the magnetic resonance motion artifacts can be effectively improved, so that clinical application of magnetic resonance images can bepromoted.

The invention relates to an image domain data generation method, computer equipment and a readable storage medium. The method comprises the following steps that: acquired K space data is acquired, and the K space data is converted into first image domain data; the first image domain data is input into a preset neural network model to generate second image domain data, wherein the neural network model is obtained by training according to K space training data under one or more quality indexes; and the quantized value of second image domain data under the quality index is higher than the quantized value of the first image domain data under the corresponding quality index, and the quality index is used for representing the quality of a magnetic resonance image generated by using the image domain data. According to the method, the quality of the obtained image domain data can be improved.

The invention relates to the field of magnetic resonance, in particular to a reconstruction method and system for magnetic resonance parameter quantitative imaging, and the method comprises the following steps: obtaining full-sampling K space data; obtaining a corresponding reference full-sampling weighted image; obtaining training input data; inputting the training input data into a reconstruction module to obtain a first weighted image; a reconstruction module is optimized based on the calculation operation of the first weighted image and the reference full-sampling weighted image; acquiring a second weighted image based on the first weighted data input generation module; a calculation operation optimization generation module based on the second weighted image and the reference full-sampling weighted image; obtaining a target neural network based on the operation of simultaneously optimizing the reconstruction module and the generation module; obtaining target under-sampling K space data and defining the target under-sampling K space data as target input data; obtaining a target weighted image based on the target input data; and obtaining a target quantitative parameter image corresponding to the target weighted image. The method has the effect of realizing rapid and accurate magnetic resonance parameter imaging.

The invention discloses a magnetic resonance rapid imaging method and system. The method comprises steps of carrying out reference scanning in a target visual field, collecting data of all observation numbers in original data of K space, and defining a start point of a slide complete data set of the K space hereby; carrying out updated scanning of the target visual field, combining a preset parameter pair, and collecting partial observation number data taking the upper half space of the K space as the principal or partial observation number data taking the lower half space of the K space as the principal; after each time of updated scanning, replacing and updating phase code lines in the slide complete data set of the K space corresponding to observation number data collected by each time of updated scanning; and carrying out two dimensional inverse Fourier transform of the updated slide complete data set of the K space, and calculating the absolute value of a value of a transform result and carrying out normalization processing to obtain an image matrix and present a corresponding image. With the adoption of scheme disclosed by the invention, the spatial resolution is maintained high and the time resolution is raised, and the scanning speed is accelerated.

An embodiment of the invention discloses a magnetic resonance imaging method, device and system and a storage medium. The magnetic resonance imaging method comprises the steps of: establishing an initial network model according to an original model of magnetic resonance imaging and an iterative algorithm used for solving the original model, wherein the iterative algorithm comprises an undeterminedsolving operator and an undetermined parameter structure relation; inputting undersampled K space data of a sample into the initial network model to obtain an output magnetic resonance image of the network model, and determining a loss function according to the output magnetic resonance image and a standard magnetic resonance image of the sample; and adjusting network parameters in the initial network model according to the loss function, and generating a network model for magnetic resonance imaging, wherein the network parameters in the initial network model are used for replacing the undetermined solving operator and the undetermined parameter structure relation in the iterative algorithm. According to the magnetic resonance imaging method, the to-be-processed undersampled K space datais acquired, and the undersampled K space data is input to the network model for magnetic resonance imaging to generate the magnetic resonance image, so that the quality of the magnetic resonance image is improved.

The invention provides a multi-contrast MRI image reconstruction method based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a real full-sampling MRI image and a reconstructed MRI image randomly sampled in a K space to form a training set sample, and training a deep convolutional neural network; then taking the under-sampled MRI images with different contrast ratios as input, outputting preliminary full-sampled MRI images with different contrast ratios, extracting structural features and contrast ratio features of the preliminary full-sampled MRI images with different contrast ratios by an encoder, then performing similarity constraint, and finally generating final complete MRI images with different contrast ratios by a generator. According to the method, the defects that at present, most models are reconstructed only through a single-contrast MRI image, and reconstruction lacks of utilization of multi-contrast MRI image associated information are overcome, the reconstruction quality of the MRI image is improved, and the reliability of a diagnosis result of a medical system is guaranteed.

A magnetic resonance imaging system detects a magnetic resonance signal generated from a sample, repeatedly acquires data on the detected magnetic resonance signal in an imaging period and arranges the acquired data in k space. The data is acquired in one acquisition pattern of a plurality of different acquisition patterns, which are determined so that an acquisition frequency of data in some areas of k space is different from that in the other areas of k space. An image of the sample is repeatedly reconstructed based on the acquired data and arranged in the k space. The acquisition is controlled to change the acquisition pattern during the imaging period.

The invention relates to a magnetic resonance imaging method and device, a storage medium and computer equipment. On one hand, after K space data is obtained through undersampling, image reconstruction is not directly carried out; instead, data recovery is firstly carried out on undersampling data to obtain corresponding full sampling data, and then a corresponding magnetic resonance image is obtained based on the full sampling data; on the other hand, when data recovery is carried out, the operation is based on reconstruction data of a bare data domain; compared with data of an image domain,loss of amplitude and phase information can be avoided, and compared with data of a K space, more dominant features can be extracted, so that feature information contained in a magnetic resonance image is richer, and the image quality of the magnetic resonance image is improved.

The invention discloses an unsupervised training method and an unsupervised training device for a magnetic resonance parameter imaging model. The unsupervised training method comprises the following steps: inputting acquired under-acquisition k space data into a to-be-trained magnetic resonance parameter imaging model to obtain parameter weighted image data; updating a loss function according to the acquired under-acquisition k space data and the parameter weighted image data; and updating the network parameters of the to-be-trained magnetic resonance parameter imaging model according to the updated loss function. According to the method and device, the loss function based on the parameter weighted image is introduced, calculation of the loss function can be completed by utilizing the under-collected k space data and the parameter weighted image data obtained according to the under-collected k space data, full-collected k space data does not need to be adopted, and unsupervised training of the model is achieved.

The invention discloses a dynamic magnetic resonance image reconstruction method based on improved robust tensor principal component analysis. Precision and speed of an existing dynamic magnetic resonance image reconstruction method need to be improved. The method comprises: using tensors for representing K space data obtained through radial sampling, using a tensor robust principal component analysis tool for constructing an image reconstruction model, and guaranteeing the space integrity of high-dimensional data; providing a new tensor nuclear norm to constrain a low-rank part, so that the overall low-rank constraint is improved while the low-rank processing efficiency is ensured; carrying out time-frequency transformation on the sparse part and then carrying out threshold processing toimprove the reconstruction precision; finally, using an iterative soft threshold shrinkage algorithm to solve the optimization problem of the algorithm, and the image reconstruction quality and the accelerated reconstruction efficiency can be effectively improved. According to the invention, a high-quality diagnosis part image can be reconstructed in a short time, and a clear image can still be obtained at an extremely low sampling rate.

The invention relates to an image reconstruction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the K space data of a plurality of phases ofa target perfusion imaging region, obtaining an initial magnetic resonance image corresponding to each K space data, and building a low-rank joint matrix based on each K space data; and taking the K space data, the initial magnetic resonance images and the low-rank joint matrix as inputs of an objective function, solving a value of the objective function, and then determining an output magnetic resonance image corresponding to the value of the objective function when the value of the objective function meets a preset condition as a reconstructed magnetic resonance image corresponding to each of a plurality of phase phases of the target perfusion imaging area. The method can improve the image reconstruction effect and improve the image reconstruction efficiency.

The invention discloses a magnetic resonance fast imaging method and device based on a convolutional neural network, and the method comprises the steps: collecting a magnetic resonance image, and carrying out the Fourier transformation of the magnetic resonance image, and obtaining k-space data; wherein the zero initialization length is the floating point type vector of the line number of the k space data to construct an image reconstruction network, sampling the k space data, performing inverse Fourier transform on the sampled k space data to obtain an image, and inputting the image into the image reconstruction network to obtain an output; calculating the L1 distance between the output of the image reconstruction network and the target image as a loss function; and obtaining a binary sampling vector according to the floating point type vector obtained by training, compiling a sampling sequence for the magnetic resonance instrument, and inputting the acquired magnetic resonance image into an image reconstruction network to obtain an output high-quality magnetic resonance image. In actual use, magnetic resonance images are collected according to a magnetic resonance sampling sequence, the images are input into a magnetic resonance image reconstruction network, and clear magnetic resonance images are obtained.