86 results about "K-space" patented technology

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 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.

The invention relates to the technical field of magnetic resonance imaging, and discloses a motion estimation guided magnetic resonance four-dimensional dynamic imaging method and device, and the method comprises the steps: obtaining a three-dimensional single-frame static image of magnetic resonance and an under-acquired low-resolution four-dimensional dynamic image sequence, the low-resolution four-dimensional dynamic image sequence is a multi-frame low-resolution four-dimensional dynamic image obtained by carrying out undersampling on K space acquisition data; utilizing a motion encoder in the image sequence prediction model to extract motion estimation information in the low-resolution four-dimensional dynamic image sequence; inputting the three-dimensional single-frame static image into a structure encoder in the image sequence prediction model to obtain static anatomical structure information; and fusing and decoding the motion estimation information and the anatomical structure information through a decoder to obtain a high-resolution four-dimensional dynamic image sequence. The problems that an existing magnetic resonance four-dimensional imaging technology is long in scanning time, complex organ movement is difficult to capture, and time-space consistency is poor are solved.

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.

The invention discloses a non-Cartesian magnetic resonance rapid intelligent imaging method for k-space direct learning, and relates to the technical field of medical image processing and magnetic resonance imaging, and the method comprises the steps: firstly obtaining non-Cartesian full-sampling k-space data from a magnetic resonance instrument, obtaining non-Cartesian under-sampling k-space data through combining an under-sampling operator and a zero filling operator, and carrying out the calculation of the non-Cartesian full-sampling k-space data; forming a training set; then designing a magnetic resonance fast reconstruction network based on k space direct learning non-Cartesian sampling of a graph neural network, network reasoning and loss function constraint; solving an optimal parameter set of the magnetic resonance fast reconstruction network; and finally, inputting non-Cartesian under-sampling k space data to be reconstructed into the trained magnetic resonance rapid reconstruction network to reconstruct a magnetic resonance image. According to the non-Cartesian magnetic resonance imaging method, k-space data similar feature fusion is carried out through the graph neural network, and under-sampling k-space missing point direct filling learning is carried out through the convolutional network, so that rapid and low-error non-Cartesian magnetic resonance intelligent image reconstruction is realized.

The invention relates to a magnetic resonance imaging method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring under-sampling K space data of an imaging object; determining an initial reconstruction image based on the under-sampled K space data; the initial reconstruction image is input into a trained correction model, an intermediate reconstruction image is obtained based on the output of the correction model, and the output of the correction model is the deviation between the initial reconstruction image and a full-sampling reconstruction image; and performing distortion correction on the intermediate reconstructed image based on the under-sampled K space data to obtain a target reconstructed image. According to the invention, the quality of the target reconstructed image is effectively improved while the image reconstruction speed is improved.

The invention provides a magnetic resonance image reconstruction method and device, an imaging system and a storage medium, and the method comprises the steps: inputting to-be-reconstructed undersampling K space data into an image reconstruction model, and obtaining a reconstructed image; and the image reconstruction model is trained by taking model training loss value minimization as a target: performing inverse Fourier transform and down-sampling on the full-sampling K-space data to obtain a full-sampling image and under-sampling K-space data, and performing randomization, interpolation and inverse Fourier transform on the under-sampling K-space data in sequence to obtain a frequency domain training image; and fusing an undersampling training image obtained by inverse Fourier transform of the undersampling K space data and an undersampling image obtained by sequentially carrying out multiple times of second downsampling and interpolation on the undersampling training image to obtain an image domain training image, and calculating a loss value of the training image obtained by fusing the frequency domain and the image domain and the full-sampling image. According to the invention, through adaptive filling and reconstruction of the image domain and the frequency domain, efficient, rapid and high-quality magnetic resonance image recovery is realized.

The invention discloses a multi-contrast magnetic resonance image reconstruction method and device based on detail contour feature perception. The method comprises the following steps: acquiring a target modal initial image and an auxiliary modal initial image; constructing an iterative network formed by alternately cascading image domain reconstruction units and data consistency layers, wherein each image domain reconstruction unit comprises an encoder and a decoder; in the first iteration, target modal initial images and auxiliary modal initial images are spliced and then input, an encoder extracts shared features firstly, then global contour features and high-frequency detail features are separated in parallel, and potential features are obtained through collaborative fusion; the decoder takes the potential features as input and outputs an image domain preliminary reconstruction result; the data consistency layer transforms the preliminary result into a k space, performs consistency correction on the preliminary result and a target modal sampling point, and then inversely transforms the preliminary result back to an image domain to complete one iteration; and splicing the current output and the auxiliary modal initial image again, inputting the spliced image into a next round of iteration, and repeating the process until a preset number of times to obtain a final target modal magnetic resonance image.

The invention discloses a rapid magnetic resonance diffusion kurtosis parameter imaging method based on a model, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: performing signal acquisition on a measured target; establishing a DKI mathematical model; calculating sensitivity information S of the multi-channel coil; performing initial reconstruction on the down-sampling k space data of each b value and each diffusion coding direction to obtain an initial complex image; performing DKI tensor estimation according to the amplitude information of the initial complex image to obtain an initial DKI tensor; extracting phase information P of each b value and each diffusion coding direction; constructing a target function with regularization constraint; solving a DKI tensor enabling the objective function to be minimum; calculating a DKI quantization parameter; and calculating a DKI image according to the DKI tensor obtained by optimization solution and the DKI mathematical model. According to the method, the DKI tensor can be directly reconstructed from the downsampling k space data, two steps of image reconstruction and tensor fitting are not needed, error transmission is avoided, and the DKI parameter imaging quality is further improved.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.

A line-field optical coherence tomography (OCT) system and method provide enhanced imaging accuracy through k-linearization of interference data. The system includes a swept laser source, a line-field sensor, and a single board computer (SBC) with processing capabilities. The system uses a frequency reference to produce a periodic reference pattern, which is detected by a subset of pixels in the line-field sensor. The SBC determines a resampling curve based on the reference pattern to correct non-linearities in the wavelength tuning of the laser. The resampling curve is applied to k-linearize the interference data, enabling the generation of high resolution transform-limited depth profiles through inverse Fourier transform. Methods are disclosed for recalculating the resampling curve periodically, continuously, or adaptively based on a linearity threshold. The system can further update the laser's tuning function dynamically to ensure consistent performance. These advancements enable precise and efficient OCT imaging for applications such as ophthalmology, angiography, and other diagnostic uses.

The invention relates to the technical field of magnetic resonance imaging, and discloses a magnetic resonance four-dimensional dynamic imaging method and device driven by a physical model, and the method comprises the steps: obtaining the magnetic resonance K space data of a to-be-imaged part in a complete motion process; extracting a preset number of three-dimensional static images in the magnetic resonance K space data; analyzing the three-dimensional static image by using the target training model to obtain a dynamic change mode of the three-dimensional static image, and predicting a static image of a future time frame based on the dynamic change mode to obtain a four-dimensional image sequence; and applying physical constraint to the four-dimensional image sequence and correcting data in the K space domain by using a preset physical driving module, and carrying out multiple iterations to obtain a target four-dimensional dynamic imaging sequence. According to the method, high-resolution imaging of the prediction model is utilized, a small number of static images can generate the magnetic resonance four-dimensional dynamic sequence, and the problems that magnetic resonance imaging is limited in providing organ tissue dynamic motion information and complex dynamic motion joint functions and lesion conditions are difficult to comprehensively evaluate are solved.

The invention provides a magnetic resonance free respiration data extraction method based on a respiratory wave extraction algorithm, and relates to the technical field of respiration acquisition, and the method comprises the following steps: obtaining a rapid imaging sequence, and extracting a complex number sampling value of a center k space line of the rapid imaging sequence in a z direction as an initial input signal; performing z-direction IFFT interpolation processing on the initial input signal to output a high-resolution z-t signal matrix; performing time dimension signal smoothing processing on the high-resolution z-t signal matrix; performing normalization processing on the z-axis signal at each time point based on a smoothing processing result; the z-axis signals of all the channels are spliced along the longitudinal axis according to the normalization processing result to form a unified matrix; the unified matrix is subjected to standard PCA decomposition, and principal components of the unified matrix are extracted to obtain respiratory wave data; according to the method, the breathing signal waveform can be stably extracted from the MRI original k space in the free breathing state without depending on the breath holding condition, and the structure consistency and time synchronism of breathing waves are improved.

The invention relates to a magnetic resonance image reconstruction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a K space data set of a plurality of phases of at least one layer of a detection object, wherein the K space data set of the at least one layer is under-sampled; performing weighted combination on the K space data sets of the plurality of phase phases to generate a synthesized K space data set, and generating a coil sensitivity distribution diagram according to the synthesized K space data set; performing initialization reconstruction on the K space data sets of the plurality of phases to obtain initial dynamic magnetic resonance images; performing machine learning-based iterative reconstruction on the initial dynamic magnetic resonance image to obtain a dynamic magnetic resonance image of the detected object; an iterative reconstruction of machine learning includes a coincidence term generated based on a K-space dataset of multiple phase phases and a coil sensitivity profile. By adopting the method, the reconstruction quality of the magnetic resonance image can be improved.

The invention discloses an adaptive total variation weight magnetic resonance image reconstruction method based on a local structure and global information measurement, and the method comprises the steps: 1, obtaining under-sampling k space data K (x, y); step 2, establishing a CS-MRI reconstruction model; step 3, initializing adaptive TV regularization weight optimization; 4, calculating local structure information measurement; 5, calculating global information measurement; step 6, calculating a self-adaptive TV regularization weight; 7, optimizing the reconstruction model; 8, reconstructing a final image; and step 9, evaluating and optimizing. Through the above steps, the method significantly improves the quality of the image reconstructed by CS-MRI through the self-adaptive adjustment of the TV regularization weight in combination with the local structure and global information measurement, and provides higher fidelity and robustness for clinical application.

The invention discloses a method for measuring the Berry phase of the physical band of a topological system, and the method comprises the steps: enabling each lattice point in cells of the topological system to correspond to an acoustic cavity, constructing the coupling between the acoustic cavities through a feedback circuit system, and constructing an acoustic physical model of the cells; a transient phase modulator TPM is utilized to introduce time delay of time-dependent variation to a coupling signal, time-dependent evolution of a coupling phase is achieved, then adiabatic evolution of a system wave function in a Brillouin region k space is achieved, and k is a Brillouin region wave vector parameter; by stripping a dynamic phase from a total phase evolved by a system wave function, direct measurement of a physical band Berry phase is realized. An effective experimental measurement scheme is provided for measurement of the Berry phase of the topological system body, and the method is not only suitable for various one-dimensional or high-dimensional topological systems, but also can be popularized to a multi-energy-band non-Arbel topological system, and has good universality.

The invention discloses a parallel MRI (Magnetic Resonance Imaging) reconstruction method based on a scanning specific triple attention improved residual network, and belongs to the field of MRI imaging. And acquiring full-sampling multi-coil k-space data, and performing multiple operations on the full-sampling multi-coil k-space data to obtain under-sampling multi-coil k-space data. Designing a specific triple attention improved residual parallel MRI reconstruction network (SS-TARNet) based on scanning, wherein the SS-TARNet is a reconstruction network model with adaptive and multi-scale feature extraction; constructing a mixed loss function of the reconstructed network model; solving the optimal parameters of the reconstructed network model by using the training set, the verification set and the mixed loss function; and for the test set, performing MRI image reconstruction by using the optimal parameters of the reconstruction network model to obtain a reconstructed image. The final reconstruction result is amplitude fusion of the sum. According to the method, the reconstruction results obtained by the two different networks are uniquely utilized to perform amplitude fusion output, and a better reconstruction effect is obtained, so that the reconstruction performance in the under-sampling MRI scene is greatly improved.

The invention discloses a magnetic resonance fingerprint reconstruction method based on a double-domain geometric structure and spatial prior depth expansion, and relates to the technical field of magnetic resonance fingerprint imaging. The method comprises the following steps: (1) acquiring magnetic resonance fingerprint undersampling k space data; (2) establishing a magnetic resonance fingerprint reconstruction model based on fingerprint domain manifold structure prior and tissue characteristic domain sparse prior; (3) iteratively solving the reconstruction model by using a dominant minimization and near-end gradient algorithm; (4) adopting an effective strategy to obtain a closed-form solution of a near-end mapping sub-problem, and expanding all iteration steps to the deep neural network; (5) training the network by using the existing data to obtain a network model; and (6) reconstructing magnetic resonance fingerprints and various tissue parameter diagrams by using the trained network. According to the method, the structured geometric information of the fingerprint data and the spatial characteristics of the organization parameter diagram are fully utilized by expanding the network, and an excellent reconstruction effect can be presented under the condition of high-power undersampling of the data.

The invention discloses a full-space switchable nanoimprint and holographic display method based on a metasurface, and belongs to the field of micro-nano optics and holographic display. According to the metasurface-based full-space switchable nanoimprint and holographic display method, the metasurface used for achieving full-space switchable nanoimprint and holographic display is composed of nano antennas of different geometric dimensions, and the geometric dimensions and the arrangement mode of the metasurface are determined through an amplitude constrained phase holographic optimization algorithm; the amplitude distribution of the light field passing through the metasurface is restrained into binary image distribution in a real space, and holographic reconstruction is carried out in a wave vector k space. According to the sensitivity of the metasurface to the wavelength of incident light, three different optical modulation modes are realized under three wavelengths; the wavelength sensitivity of the metasurface is utilized to modulate a reflected light beam and a transmitted light beam under different incident light wavelengths, so that full-space switchable nanoimprint and holographic display are realized, and the metasurface can be applied to the fields of optical nanoimprint, counterfeiting prevention, optical switches, optical communication, display and the like.

The application discloses a kind of low field intensity magnetic susceptibility weighted imaging optimization method and system, it is related to magnetic susceptibility weighted imaging field, comprising: obtaining the K space original data obtained by scanning target object;According to K space original data reconstruction image field data, extract the amplitude data and phase data of image field data;Phase data is preliminary unwrapping processing, and unwrapping phase data is obtained;Unwrapping phase data is separated from background field, and local phase diagram is obtained;Local phase diagram and amplitude data are operated based on complex domain phase separation high-pass filter, and high-frequency phase data is obtained;According to high-frequency phase data and amplitude data, the reconstruction of magnetic susceptibility weighted image is carried out, and the magnetic susceptibility weighted image of target object is obtained.The application can significantly reduce the phase artifact of SWI image, and improve the definition of image detail.

The invention provides a respiratory-gating-free free-breathing liver glycogen quantitative magnetic resonance imaging method and system, and the method comprises the steps: applying a chemical exchange saturation transfer imaging sequence to a liver region of a subject, and the imaging sequence comprises saturation pulses of a plurality of saturation frequency deviation points; a radial blade type k-space acquisition strategy is adopted to acquire magnetic resonance signal data, the radial blade type k-space acquisition strategy comprises a plurality of blades, each blade is composed of a group of parallel k-space lines, and different blades are rotationally distributed by taking the k-space center as a rotating shaft; performing image reconstruction on the acquired k space data to obtain magnetic resonance images under different saturation frequency deviations; and performing fitting analysis on the magnetic resonance image by adopting a multi-pool Lorentz model to obtain quantitative information of the liver glycogen. According to the method, the interference of respiratory movement on the CESTMRI data acquisition process can be eliminated, so that the accurate quantification of the liver glycogen in the free respiration state is realized.

The invention relates to a magnetic resonance image processing method and device, and an EPI-based image processing method. The method comprises the steps of obtaining a first sampling image of a target object according to sensitivity information of a receiving coil corresponding to the target object and K space data; correcting artifacts in the first sampling image to obtain a second sampling image of the target object; and iterating data of a preset first magnetic resonance image by taking the second sampling image as a constraint condition and taking the K space data as a fidelity term in an iterative reconstruction process to obtain a magnetic resonance image of the target object. By adopting the method, the problem of aliasing artifacts existing in magnetic resonance imaging images can be solved.

The present invention belongs to the field of magnetic resonance imaging technology, and is specifically a five-dimensional blood flow magnetic resonance imaging method without external gating. The method of the present invention includes: data acquisition; continuous alternating sampling of the center and periphery of the K space to obtain training data and imaging data; image reconstruction; image restoration using the established model according to the training data and imaging data, generating three-dimensional blood flow images corresponding to each time frame during the scan, and calculating the physiological movement information of breathing and heartbeat, so as to realize real-time presentation of five dimensions; image post-processing; according to the extracted breathing and heartbeat signals, the blood flow image obtained by image reconstruction is further divided to form a five-dimensional matrix of heartbeat and breathing, which is used for comprehensive analysis of the influence of breathing on blood flow. The present invention can also flexibly set the required number of heartbeat phases and respiratory phases, and then generate a corresponding five-dimensional data matrix; and can capture the blood flow changes related to physiological pathology between heartbeat cycles.

The invention provides an ultra-low field multi-core synchronous magnetic resonance imaging method. The method comprises the following steps: improving a longitudinal magnetization vector of each pre-imaging nuclide in an ultra-low field; performing space coding and signal acquisition on each pre-imaging nuclide to obtain a K space signal set corresponding to an imaging nuclide plane; obtaining total K space data corresponding to the K space signal set, and performing zero filling on the total K space data; based on the total K space data after zero filling, obtaining total frequency distribution corresponding to an imaging nuclide plane; splitting the total frequency distribution to obtain sub-frequency distribution corresponding to each type of pre-imaging nuclide; obtaining sub-K space data corresponding to various pre-imaging nuclides based on the sub-frequency distribution; and based on the sub-K space data, image reconstruction is carried out on the various pre-imaging nuclides, and reconstructed images corresponding to the various pre-imaging nuclides are obtained. According to the method provided by the invention, multi-nuclide synchronous magnetic resonance imaging in an extremely low field can be realized, and the image quality of multi-nuclear magnetic resonance imaging is improved.

The invention discloses a method, a device, equipment and a product for reconstructing a spatially selective core measurement image, which comprises the following steps of: acquiring k-space data of a core to be measured by using a k-space radial sampling mode, and then, acquiring the k-space data of the core to be measured by using the k-space data; constructing an image model based on the image of the rock core to be measured, the k-space data and the contour data of each spoke in the k-space data; then, image reconstruction can be carried out based on the image model, so that in the image reconstruction process, the adverse effect of the contour data of each spoke in the k space data on rock core imaging is eliminated, and the optimal image of the rock core to be measured is obtained; and finally, based on this, obtaining a corresponding spatial selective core measurement image. Therefore, the image reconstruction is realized by utilizing an algorithm, and compared with the traditional technology, the method does not need to change hardware, is high in feasibility, and can greatly reduce the influence of profi on the image quality, so that the image reconstruction quality can be remarkably improved.

The invention is suitable for the technical field of magnetic resonance chemical exchange saturation transfer imaging, and provides a magnetic resonance chemical exchange saturation transfer imaging method, device, equipment, product and storage medium. A gradient echo sequence is adopted to collect saturated water molecule signals, and the collected water molecule signals are stored in a K space; and wave control coding is introduced in the acquisition process, and a CEST image is reconstructed based on a water molecule signal of a K space. The introduction of wave control coding can reduce the scanning time. Due to the fact that the wave control coding sampling efficiency is high, a larger matrix size can be supported within the same time, high-resolution imaging is achieved, the partial volume effect can be relieved, and CEST quantitative accuracy is improved.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining a first set of imaging data, the first set of imaging data being sampled in multiple shots, each shot of the multiple shots corresponding to a plurality of echo times, the first set of imaging data including partially sampled data in a first k space; obtaining a second set of imaging data, the second set of imaging data including fully sampled data in a central region of a second k space; determining fitting data in the first k space based on the first set of imaging data and the second set of imaging data; and / or generating a target image based on the fitting data in the first k space and the first set of imaging data in the first k space.

The invention discloses an MRI (Magnetic Resonance Imaging) motion artifact correction method based on a generative adversarial network. The method comprises the steps that firstly, a data set is collected, and motion artifact simulation is carried out; then, K space data is preliminarily corrected through a frequency domain generator, data in a space domain is finely corrected through a space domain generator, and data consistency between the K space and the space domain is kept by adopting inter-domain data consistency loss in an optimization stage; and finally, evaluating the application effect of the model by using a loss function. According to the method, artifacts or noise in the image can be effectively removed, a clearer corrected image can be obtained, and then a doctor can more accurately recognize illness state details in the sliced image of the patient.

The invention relates to a system and a method for K-space neutron space parallel imaging. A computer-implemented method includes obtaining, via a processing system including one or more processors, multichannel k-space data of a subject acquired with a magnetic resonance imaging scanner. The computer-implemented method further includes combining, via the processing system, information from locally adjacent k-space locations and across a plurality of channels using a subspace convolution kernel on the multi-channel k-space data to generate subspace compressed k-space data, the subspace compressed k-space data has fewer channels than the number of channels used to collect the multi-channel k-space data.

The invention provides a K space data reconstruction method, system and device and a medium, and the method comprises the steps: setting the receiving gain of a K space phase coding line according to a control instruction received by a receiver of a magnetic resonance spectrometer after formal scanning is started; based on the conventional receiving gain and the high receiving gain, scanning the target object to obtain first K space data corresponding to the first part and second K space data corresponding to the second part; normalizing the amplitude of the first K-space data to a high receiving gain level to obtain the normalized first K-space data, and splicing the normalized first K-space data and the second K-space data to obtain complete K-space data; and performing inverse Fourier transform processing on the complete K space data to obtain image data of the target object. According to the invention, the receiving gain parameter can be dynamically adjusted during a single scanning period, and the scanning success rate and the image quality are improved.