Multi-dimensional magnetic resonance imaging method and apparatus

Abstract

This invention provides a multidimensional magnetic resonance imaging (MRI) method and apparatus, comprising: determining the acquisition sequence and acquisition parameters for MRI; performing full sampling of the sample under test based on the acquisition parameters to obtain full-sample one-dimensional signal data, and generating a full-sample one-dimensional distribution of the sample under test using a Laplace transform algorithm; performing sparse sampling based on the acquisition parameters to obtain a multidimensional magnetic resonance signal of the sample under test, and extracting multidimensional magnetic resonance sampling data of each monomer element based on the multidimensional magnetic resonance signal; determining the initial multidimensional magnetic resonance distribution of each monomer element based on the multidimensional magnetic resonance sampling data of each monomer element using a Laplace transform algorithm; iteratively updating the initial multidimensional magnetic resonance distribution to obtain the updated multidimensional magnetic resonance distribution of each monomer element; and generating a multi-parameter spectrum of the sample under test based on the updated multidimensional magnetic resonance distributions. This multidimensional magnetic resonance imaging method and apparatus improves the accuracy of the obtained multidimensional magnetic resonance distribution spectrum.

Description

Technical Field

[0001] This invention relates to the field of magnetic resonance imaging technology, and in particular to a multidimensional magnetic resonance imaging method and apparatus. Background Technology

[0002] Magnetic resonance imaging (MRI) utilizes the basic principles of nuclear magnetic resonance (NMR) to observe the phenomenon of varying free-induction decay signals generated by the different relaxation times of the surrounding tissue environment within an object. By applying an external radio frequency field and using Fourier transform to spatially encode the signal, the specific internal structure of the object can be obtained, and a three-dimensional image of it can be created. MRI is widely popular due to its high tissue sensitivity, accurate spatial localization, and lack of radioactivity. A voxel is the smallest imaging unit in MRI, and MRI itself largely uses the average signal of voxels, lacking specificity for the microscopic structure within voxels.

[0003] Multidimensional magnetic resonance (MMR) technology is one of the effective methods to solve this problem. Originating from two-dimensional nuclear magnetic resonance (NMR) technology in the frequency domain, it can acquire magnetic resonance data in two dimensions, thereby distinguishing overlapping spectral peaks on a two-dimensional spectrum. Therefore, MMR technology can obtain the component distribution within a single voxel and has the potential for sub-voxel imaging. MMR measurements are widely used in material characterization, such as in food, petroleum, catalysts, groundwater flow, and oils in corn. Compared with traditional one-dimensional magnetic resonance methods, MMR requires a higher number of acquisitions and can measure the correlation of multiple parameters, thus significantly improving spectroscopic resolution and the ability to distinguish different components.

[0004] The solution for multidimensional magnetic resonance (MMR) data is a process of obtaining a distributed spectrum, which is an ill-posed problem. Even a small change in the acquisition process can lead to significant changes in the results. To reduce the number of acquisitions required for MMR, current MMR imaging techniques generally employ sparse sampling. However, the multi-parameter spectral structure obtained based on sparse sampling has low resolution, making it difficult to guarantee the accuracy of the obtained MMR distribution map. Therefore, improving the accuracy of the acquired MMR distribution map during MMR imaging is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a multidimensional magnetic resonance imaging method and apparatus to solve one or more problems existing in the prior art.

[0006] According to one aspect of the present invention, a multidimensional magnetic resonance imaging method is disclosed, the method comprising:

[0007] Determine the acquisition sequence and acquisition parameters for magnetic resonance imaging;

[0008] Based on the acquisition parameters, the sample under test is fully sampled to obtain the fully sampled one-dimensional signal data of the sample under test. Based on the fully sampled one-dimensional signal data, the fully sampled one-dimensional distribution of the sample under test is generated by the Laplace transform algorithm.

[0009] Based on the acquisition parameters, sparse sampling is performed on the sample under test to obtain the multidimensional magnetic resonance signal of the sample under test. Multidimensional magnetic resonance sampling data of each monomer is extracted based on the acquired multidimensional magnetic resonance signal. The initial multidimensional magnetic resonance distribution of each monomer is determined by the Laplace transform algorithm based on the multidimensional magnetic resonance sampling data of each monomer.

[0010] The initial multidimensional magnetic resonance distribution is iteratively updated based on the full-sample one-dimensional distribution and the Laplace transform algorithm to obtain the updated multidimensional magnetic resonance distribution of each monomer element.

[0011] The multi-parameter spectrum of the tested sample is generated based on the updated multidimensional magnetic resonance distribution of each monomer.

[0012] In some embodiments of the present invention, the initial multidimensional magnetic resonance distribution is iteratively updated based on the full-sample one-dimensional distribution and the Laplace transform algorithm to obtain the updated multidimensional magnetic resonance distribution of each of the monomers, including:

[0013] Based on the initial multidimensional magnetic resonance distribution and the acquisition parameters, the synthetic multidimensional magnetic resonance signal of each monomer element is obtained, and the multidimensional magnetic resonance distribution corresponding to the synthetic multidimensional magnetic resonance signal is obtained by the Laplace transform algorithm.

[0014] Determine whether the mean square error between the multidimensional magnetic resonance distribution corresponding to the synthesized multidimensional magnetic resonance signal and the full-sample one-dimensional distribution is less than a preset threshold. If it is less than the preset threshold, the multidimensional magnetic resonance distribution corresponding to the synthesized multidimensional magnetic resonance signal is the reconstructed multidimensional magnetic resonance distribution of the corresponding monomer.

[0015] In some embodiments of the present invention, the synthetic multidimensional magnetic resonance signal of each of the monomers is obtained based on the initial multidimensional magnetic resonance distribution and the acquisition parameters, including:

[0016] The initial kernel function is determined based on the acquired parameters;

[0017] Calculate the product of the initial multidimensional magnetic resonance distribution and the initial kernel function, where the product is the synthesized multidimensional magnetic resonance signal.

[0018] In some embodiments of the present invention, the multi-parameter spectrum of the tested sample is generated based on the updated multidimensional magnetic resonance distribution of each of the monomers, including:

[0019] Determine the region of interest of the sample being tested;

[0020] The updated multidimensional magnetic resonance distribution of each monomer in the region of interest is integrated to obtain the multi-parameter spectrum of the sample under test.

[0021] In some embodiments of the present invention, sparse sampling of the sample under test based on the acquisition parameters includes:

[0022] The acquisition parameters are optimized to obtain the optimized acquisition parameters;

[0023] The sample under test is sparsely sampled based on the optimized acquisition parameters.

[0024] In some embodiments of the present invention, the optimization algorithm used to optimize the acquisition parameters is a genetic algorithm.

[0025] In some embodiments of the present invention, multidimensional magnetic resonance sampling data of each monomer element is extracted based on the acquired multidimensional magnetic resonance signal, and the initial multidimensional magnetic resonance distribution of each monomer element is determined by a Laplace transform algorithm based on the multidimensional magnetic resonance sampling data of each monomer element, including:

[0026] Multidimensional magnetic resonance sampling data of each monomer element are extracted based on the acquired multidimensional magnetic resonance signals;

[0027] The signal-to-noise ratio of each monomer is calculated based on the extracted multidimensional magnetic resonance sampling data of each monomer.

[0028] Determine whether the signal-to-noise ratio of each monomer is greater than a preset value, and classify monomers with a signal-to-noise ratio less than the preset value as noise.

[0029] In some embodiments of the present invention, the method further includes: generating an image acquisition report, the image acquisition report including the acquisition sequence, acquisition parameters, updated multidimensional magnetic resonance distribution of the corresponding monomer, and the multiparameter spectrum.

[0030] According to another aspect of the present invention, a multidimensional magnetic resonance imaging system is also disclosed, the system comprising a processor and a memory, the memory storing computer instructions, the processor executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implementing the steps of the method as described in any of the above embodiments.

[0031] According to another aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0032] The multidimensional magnetic resonance imaging method and apparatus disclosed in this invention first acquires the full-sample one-dimensional distribution of the sample under test, then performs sparse sampling on the sample to obtain the multidimensional magnetic resonance signal, and further acquires the initial multidimensional magnetic resonance distribution of each monomer based on the multidimensional magnetic resonance signal. The initial multidimensional magnetic resonance distribution of the monomer is then iteratively updated to obtain the updated multidimensional magnetic resonance distribution, and finally, a multi-parameter spectrum of the sample is generated based on the updated multidimensional magnetic resonance distribution of each monomer. This method improves the resolution of the multi-parameter spectrum structure obtained based on sparse sampling and also improves the accuracy of the acquired multidimensional magnetic resonance distribution spectrum.

[0033] In addition to the above, when performing sparse sampling on the test sample, this application optimizes the acquisition parameters using a genetic algorithm and performs sparse acquisition based on the optimized acquisition parameters. This method obtains acquisition parameters that make the kernel function more stable before acquisition, reduces the ill-conditioned nature of the kernel function matrix, and reduces the acquisition time, thereby achieving fast and accurate acquisition.

[0034] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the written description, claims, and drawings.

[0035] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0036] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. The components in the drawings are not drawn to scale but are merely illustrative of the principles of the invention. For ease of illustration and description of certain parts of the invention, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to the invention. In the drawings:

[0037] Figure 1 This is a schematic flowchart of a multidimensional magnetic resonance imaging method according to an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the process of reconstructing a multidimensional magnetic resonance distribution using an iterative inverse Laplace transform according to an embodiment of the present invention.

[0039] Figure 3 This is a flowchart illustrating the optimization of acquisition parameters according to an embodiment of the present invention.

[0040] Figure 4 This is a flowchart illustrating a multidimensional magnetic resonance imaging method according to another embodiment of the present invention.

[0041] Figure 5 This is a schematic diagram of an image acquisition report according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0043] It should be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0044] It should be emphasized that the term "including / comprises / has" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0045] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0046] Figure 1 This is a schematic flowchart of a multidimensional magnetic resonance imaging method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the multidimensional magnetic resonance imaging method includes at least steps S10 to S50.

[0047] Step S10: Determine the acquisition sequence and acquisition parameters for magnetic resonance imaging.

[0048] In this step, the acquisition parameters depend on the specific acquisition sequence selected. For example, acquisition parameters determined based on the selected acquisition sequence include inversion time, diffusion-sensitive gradient value (b-value), echo time, and / or pulse repetition time. Understandably, there is no consensus on the setting of conventional multidimensional magnetic resonance acquisition experimental parameters. The inversion time used in experiments with longitudinal relaxation time T1 and transverse relaxation time T2 is generally logarithmically distributed; the diffusion-sensitive gradient value (b-value) used in diffusion experiments is generally linearly distributed, or has a gradient linear distribution, and the corresponding diffusion-sensitive gradient value is a square-like distribution. Since the logarithmic distribution is the slowest way for singular values ​​to decay, the corresponding kernel function matrix properties are also more stable. Furthermore, since the specific acquisition parameters depend on the acquisition sequence, in other embodiments, the acquisition parameters may include other types of parameters besides those mentioned above.

[0049] Step S20: Perform full sampling on the sample under test based on the acquisition parameters to obtain the full sampling one-dimensional signal data of the sample under test, and generate the full sampling one-dimensional distribution of the sample under test based on the full sampling one-dimensional signal data through the Laplace transform algorithm.

[0050] In this step, a kernel function is first generated based on the acquisition parameters determined in step S10, and then the full-sample one-dimensional signal data of the tested sample is obtained based on this kernel function. The kernel function corresponding to the acquisition parameters can be further expressed as:

[0051]

[0052]

[0053] k(b, D) = e -bD ;

[0054] Where t1 is the reversal time, t2 is the echo time, b represents the diffusion-sensitive gradient value, T1 represents the longitudinal relaxation time, T2 represents the transverse relaxation time, and D represents the diffusion signal. The relationship between the one-dimensional signal data s(β) and the one-dimensional distribution f(ω) of the magnetic resonance relaxation or diffusion characteristics can be represented by a first-kind Friedholm integral equation, such as s(β)=∫f(ω)k(β,ω)dω; where k(β,ω) is the kernel function, β is the experimental parameter, and β can specifically take the values ​​t1, t2, and b.

[0055] Furthermore, for ease of solution, the continuous form of the one-dimensional signal data s(β) can be converted into a discrete form, then the discrete form of the one-dimensional signal... S(β i ) represents the signal under different acquisition parameters, ω nFor the relaxation or diffusion parameters, which represent the range of interest, a relatively large range is typically estimated based on actual conditions for solving. ∈(β) represents the experimental noise, which is generally assumed to be Gaussian noise in the simulation. Furthermore, due to the kernel function k(β)... i ω n Since β is in exponential form, from the signal S(β) i ) and kernel function k(β) i ω n Solve for the distribution F(ω) n The process of generating the full-sample one-dimensional distribution of the tested sample is an inverse Laplace transform, also known as the Laplace inversion process. Therefore, it can be seen that the full-sample one-dimensional distribution of the tested sample can be generated from the fully sampled one-dimensional signal data using the Laplace transform algorithm.

[0056] Step S30: Based on the acquisition parameters, sparse sampling is performed on the sample to obtain the multidimensional magnetic resonance signal of the sample. Multidimensional magnetic resonance sampling data of each monomer is extracted based on the acquired multidimensional magnetic resonance signal. Based on the multidimensional magnetic resonance sampling data of each monomer, the initial multidimensional magnetic resonance distribution of each monomer is determined by the Laplace transform algorithm.

[0057] In this step, the sample under test is further sparsely sampled based on the determined acquisition parameters to obtain the multidimensional magnetic resonance signal of the sample. Specifically, the multidimensional magnetic resonance signal can be a two-dimensional magnetic resonance signal, a three-dimensional magnetic resonance signal, etc. For example, the continuous representation of a two-dimensional magnetic resonance signal is as follows:

[0058] s(β1,β2)=∫∫f(ω1,ω2)k(β1,β2,ω1,ω2)dω1dω2;

[0059] The discrete representation is as follows:

[0060]

[0061] Where K(β1,β2,ω1,ω2) is the kernel function, and F(ω n ,ω m ) is a two-dimensional distribution, ∈(β) i ,β j ω represents the noise generated during the experiment. n and ω m These are all relaxation or diffusion parameters used to characterize the distribution (such as T1, T2, etc.), β i and β j The parameters are for acquisition. It should be understood that the examples listed above represent the manifestations of two-dimensional magnetic resonance signals, while the manifestations of more dimensional magnetic resonance signals are similar, and will not be elaborated here.

[0062] After acquiring the multidimensional magnetic resonance signal, the multidimensional magnetic resonance sampling data of each monomer element is further extracted. Similar to the one-dimensional magnetic resonance signal, the discrete representation of the multidimensional magnetic resonance signal based on each monomer element can be used to determine the multidimensional magnetic resonance distribution of each monomer element through the Laplace transform algorithm. The multidimensional magnetic resonance distribution of the monomer element determined in this step is the initial multidimensional magnetic resonance distribution of the monomer element.

[0063] In one embodiment, sparse sampling of the sample under test based on the acquisition parameters includes: optimizing the acquisition parameters to obtain optimized acquisition parameters; and performing sparse sampling of the sample under test based on the optimized acquisition parameters. In this embodiment, the acquisition parameters before optimization are based on the initial acquisition parameters determined in step S10. Since the multidimensional magnetic resonance signal obtained by the sparse sampling method exhibits more severe ill-conditioned properties during the Laplace inversion solution process, this embodiment optimizes the initial acquisition parameters to obtain optimized acquisition parameters. Then, sparse sampling of the sample under test using the optimized acquisition parameters ensures the accuracy of solving the sparsely sampled multidimensional magnetic resonance signal. For example, the optimization algorithm used to optimize the acquisition parameters is a genetic algorithm. Optimized sampling based on a genetic algorithm allows the most effective information to be retained during undersampling, shortening the acquisition time to a certain extent and improving the accuracy of multidimensional magnetic resonance imaging.

[0064] Figure 3 This is a flowchart illustrating the optimization of acquisition parameters according to an embodiment of the present invention. In this embodiment, the experimental parameters are reversal time, diffusion-sensitive gradient value (b-value), echo time, and pulse repetition time. Specifically, when optimizing these acquisition parameters, an initial population is first generated based on the original acquisition parameters; then, a multi-objective fitness function is constructed, and the fitness value of each individual in the initial population is calculated based on the constructed multi-objective fitness function; further, selection, crossover, and mutation operations are performed on the initial population to obtain a progeny population, and a Pareto optimal solution is obtained based on the progeny population. The Pareto optimal solution is the optimized acquisition parameters. In this embodiment, the initial acquisition parameters are optimized using an optimization algorithm to obtain optimized acquisition parameters that make the kernel function more stable, thereby reducing the ill-conditioned properties of the kernel function and thus acquiring multidimensional magnetic resonance signals in a better acquisition mode. The use of a genetic algorithm as the optimization algorithm is only one preferred example. In other embodiments, the optimization algorithm can also be a dictionary learning algorithm, deep learning algorithm, etc., in addition to a genetic algorithm.

[0065] In the above embodiments, when constructing the fitness function for multiple objectives, an initial kernel function is first constructed based on the initial acquisition parameters, and this initial kernel function is as described in step S20. Singular value decomposition is then performed on the initial kernel function, and finally, the fitness function for multiple objectives is constructed based on the singular values ​​of the initial kernel function, the experimental time (or waiting time), and the diffusion-sensitive gradient value. The fitness function for multiple objectives is as follows:

[0066]

[0067] Where y1, y2, y3, and y4 are all objective functions, n is the number of singular values ​​of the initial kernel function, and S n Let δ be the singular values ​​of the initial kernel function, TI be the singular value threshold, TI be the experimental time or waiting time, and b be the diffusion-sensitive gradient value. In this embodiment, the fitness function is constructed under the following conditions: minimum experimental time (minimum y4), maximum number of singular values ​​of the initial kernel function (maximum y1), maximum singular values ​​of the minimum kernel function (maximum y2), and minimum diffusion-sensitive gradient value (minimum y3). For this multi-objective fitness function, the optimal solution of the multi-objective fitness function can be obtained by evaluating it using the Pareto optimality method.

[0068] Step S40: Based on the full-sample one-dimensional distribution and the Laplace transform algorithm, the initial multidimensional magnetic resonance distribution is iteratively updated to obtain the updated multidimensional magnetic resonance distribution of each monomer.

[0069] In this step, the initial multidimensional magnetic resonance distribution of the monomer obtained in step S30 is iteratively updated. The reconstructed multidimensional magnetic resonance distribution of the monomer is obtained by iteratively updating the initial multidimensional magnetic resonance distribution of the monomer obtained by the Laplace inversion method, which can improve the accuracy of the obtained multidimensional magnetic resonance distribution image.

[0070] For example, iteratively updating the initial multidimensional magnetic resonance distribution based on the full-sample one-dimensional distribution and the Laplace transform algorithm to obtain the updated multidimensional magnetic resonance distribution of each of the monomers includes: obtaining the synthesized multidimensional magnetic resonance signal of each of the monomers based on the initial multidimensional magnetic resonance distribution and the acquisition parameters; obtaining the multidimensional magnetic resonance distribution corresponding to the synthesized multidimensional magnetic resonance signal through the Laplace transform algorithm; determining whether the mean square error between the multidimensional magnetic resonance distribution corresponding to the synthesized multidimensional magnetic resonance signal and the full-sample one-dimensional distribution is less than a preset threshold; if it is less than the preset threshold, the multidimensional magnetic resonance distribution corresponding to the synthesized multidimensional magnetic resonance signal is the reconstructed multidimensional magnetic resonance distribution of the corresponding monomer.

[0071] The process of obtaining the synthetic multidimensional magnetic resonance signal of each monomer based on the initial multidimensional magnetic resonance distribution and the acquisition parameters includes: determining an initial kernel function based on the acquisition parameters; calculating the product of the initial multidimensional magnetic resonance distribution and the initial kernel function, and the calculated product is the synthetic multidimensional magnetic resonance signal.

[0072] refer to Figure 2 The initial multidimensional magnetic resonance (MMR) distribution of the monomers is obtained based on the acquisition parameters optimized using a genetic optimization algorithm. At this point, a multidimensional magnetic resonance (MMR) signal is synthesized using the MMR distribution of the monomers and the full-sample initial kernel function matrix corresponding to the initial acquisition parameters. Then, based on the synthesized MMR signal, the updated MMR distribution of the monomers is obtained using the Laplace inversion method, for use in the next iteration to synthesize the updated MMR signal of the monomers. The reconstruction process of the multidimensional magnetic resonance distribution will iterate n times until the mean square error (MSE) of the updated signal Su(n) and the measured signal S is less than a preset value ∈ , i.e., MSE = ||S||. u (n)-S‖2<∈; where the approximate signal Su(n) is the synthesized multidimensional magnetic resonance signal determined in this iteration, and S is the initial magnetic resonance signal of the corresponding monomorphic element, ∈ is the preset threshold, which can be set according to actual needs. In this embodiment, the multidimensional magnetic resonance distribution obtained by inverting from the accelerated sampling data using the Laplace inversion method is the result after reconstruction or reconstruction using the iterative Laplace inversion proposed in this application; that is, this invention iterates the unmeasured dataset in the optimized accelerated sampling multidimensional MR signal to obtain the best estimate of the true value, while using the measured signal (i.e., the fully sampled 1D and the optimized accelerated sampling 2D) as prior knowledge and keeping it unchanged during the iteration. In addition, it should be understood that when reconstructing the multidimensional magnetic resonance data of each monomorphic element in the optimized acceleration, in addition to using the iterative inverse Laplace transform method, other artificial intelligence algorithms such as deep learning and reinforcement learning can also be used.

[0073] Step S50: Generate a multi-parameter spectrum of the sample under test based on the updated multidimensional magnetic resonance distribution of each monomer.

[0074] After obtaining the reconstructed multidimensional magnetic resonance distributions of each monomer, a multi-parameter spectrum of the sample is further generated. In generating the multi-parameter spectrum of the sample, the region of interest (ROI) is first determined; then, the updated multidimensional magnetic resonance distributions of each monomer within the ROI are integrated to obtain the multi-parameter spectrum of the sample. In this step, after reconstructing the multidimensional magnetic resonance distributions of each monomer, a series of reconstructed multidimensional distribution maps are obtained. Multiple ROIs are then determined based on the range of the multidimensional magnetic resonance distributions of interest. Each ROI corresponds to a unique microstructure. The distribution value of each voxel within this ROI is integrated to obtain the multi-parameter spectrum of the microstructure of the sample.

[0075] Figure 4 This is a flowchart illustrating a multidimensional magnetic resonance imaging method according to another embodiment of the present invention. Figure 4 As can be seen, the corresponding multidimensional magnetic resonance imaging system includes a sampling preparation module, a multidimensional magnetic resonance acquisition parameter optimization module, a multidimensional magnetic resonance data acquisition module, a single voxel data processing module, a multi-voxel data processing module, and a parameter and result export module.

[0076] The sampling preparation module is used to select a suitable radio frequency coil according to the size of the sample under test, place the sample under test at the center of the magnetic resonance imaging, tune the radio frequency coil, shim the sample, and determine the multidimensional magnetic resonance acquisition sequence and initial acquisition parameters. The multidimensional magnetic resonance acquisition parameter optimization module is used to optimize the initial acquisition parameters included in the determined acquisition sequence using an optimization algorithm to obtain optimized acquisition parameters. The multidimensional magnetic resonance data acquisition module is used to optimize the sampling of indirect measurement parameters of the sample under test using a nuclear magnetic resonance instrument to obtain optimized sparse sampling data. The voxel data processing module is used to process the optimized sampling data voxel by voxel to obtain a high-resolution multidimensional magnetic resonance parameter distribution; specifically, the voxel data processing module extracts multidimensional magnetic resonance sampling data of each voxel based on the acquired multidimensional magnetic resonance signal; calculates the signal-to-noise ratio (SNR) of each voxel based on the extracted multidimensional magnetic resonance sampling data; determines whether the SNR of each voxel is greater than a preset value, and treats voxels with an SNR less than the preset value as noise.

[0077] For example, when the single-voxel data processing module processes single-voxel data, it first extracts the optimized sampling data for each single voxel, and then calculates the signal-to-noise ratio (SNR) of each single voxel based on the extracted data, SNR = max(S) / std(N); where max(S) is the maximum value of the signal, and std(Noise) is the root mean square magnitude of the noise; further, a regularization factor is determined based on the SNR: when the SNR is less than an empirical value (25 is used as an example in this invention), the single voxel is considered noise and no further processing is performed; when the SNR is greater than or equal to the empirical value, this invention can obtain the non-negative constraint solution f under a specific regularization factor p through a non-negative constraint step, and further obtain the residual distribution between the solution result and the measurement result through the following formula: χ(p) = ||S-Kf(p)||2. The optimal smoothing factor P is further determined. opt , Finally, artificial intelligence methods were used to invert and reconstruct the joint spectrum of the optimized sampled data. Here, K is the kernel function matrix, S is the measured magnetic resonance signal, and p... heel p is the maximum value obtained by taking the second derivative of χ(p). opt This is the optimal smoothing factor obtained after data processing.

[0078] The multi-voxel data processing module is used for multi-voxel distribution map integration to obtain multi-parameter maps of the microstructure of the tested sample. It first identifies the region of interest (ROI) of the tissue using structural images of the sample, such as specific tissues like white matter, gray matter, and cerebrospinal fluid, or the entire brain can be considered as a ROI. Then, it integrates the single-voxel distribution maps within the ROI to obtain the multi-parameter map of the sample. The parameter and result export module automatically generates a document-formatted report titled "Multi-parameter Maps Based on Intelligent Fast Multidimensional Magnetic Resonance Imaging," for reference. Figure 5 The report includes the following information: multidimensional magnetic resonance acquisition sequence, multidimensional magnetic resonance acquisition parameters, total acquisition time, optimized accelerated original and iterative distributions (monomers), optimized accelerated sampling inversion distribution, and multi-parameter spectra.

[0079] Correspondingly, the present invention also provides a multidimensional magnetic resonance imaging system, which includes a processor and a memory. The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any of the above embodiments.

[0080] As can be seen from the above embodiments, the multidimensional magnetic resonance imaging method and apparatus disclosed in this invention first acquires the full-sample one-dimensional distribution of the sample under test, then performs sparse sampling on the sample to obtain the multidimensional magnetic resonance signal, and further acquires the initial multidimensional magnetic resonance distribution of each monomer based on the multidimensional magnetic resonance signal. The initial multidimensional magnetic resonance distribution of the monomer is then iteratively updated to obtain the updated multidimensional magnetic resonance distribution, and finally, a multi-parameter spectrum of the sample is generated based on the updated multidimensional magnetic resonance distribution of each monomer. This multidimensional magnetic resonance imaging method and apparatus, through iterative inverse Laplace transform, can accurately recover unsampled data, improve inversion accuracy, and thus obtain a distribution map with high structural resolution, thereby improving the accuracy of the acquired multidimensional magnetic resonance distribution spectrum.

[0081] In addition to the above, this application optimizes the acquisition parameters using a genetic algorithm when sparsely sampling the test sample, and performs sparse acquisition based on the optimized acquisition parameters. This method obtains acquisition parameters that make the kernel function more stable before acquisition, which can significantly reduce acquisition time and reduce the ill-conditioned nature of the kernel function matrix while maintaining high resolution, thereby achieving fast and accurate acquisition.

[0082] In addition, the invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0083] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0084] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0085] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of multi-dimensional magnetic resonance imaging, characterized by, The method comprises: determining a magnetic resonance imaging acquisition sequence and acquisition parameters; full sampling of a sample under test based on the acquisition parameters to obtain full sampling one-dimensional signal data of the sample under test, and generating a full sampling one-dimensional distribution of the sample under test based on the full sampling one-dimensional signal data through a Laplace transform algorithm; sparse sampling of the sample under test based on the acquisition parameters to obtain multi-dimensional magnetic resonance signal of the sample under test, and extracting multi-dimensional magnetic resonance sampling data of each single voxel from the acquired multi-dimensional magnetic resonance signal, and determining an initial multi-dimensional magnetic resonance distribution of each single voxel based on the multi-dimensional magnetic resonance sampling data of each single voxel through the Laplace transform algorithm; iteratively updating the initial multi-dimensional magnetic resonance distribution based on the full sampling one-dimensional distribution and the Laplace transform algorithm to obtain an updated multi-dimensional magnetic resonance distribution of each single voxel; generating a multi-parameter map of the sample under test based on the updated multi-dimensional magnetic resonance distribution of each single voxel; wherein iteratively updating the initial multi-dimensional magnetic resonance distribution based on the full sampling one-dimensional distribution and the Laplace transform algorithm to obtain the updated multi-dimensional magnetic resonance distribution of each single voxel comprises: obtaining a synthetic multi-dimensional magnetic resonance signal of each single voxel based on the initial multi-dimensional magnetic resonance distribution and the acquisition parameters, and obtaining a multi-dimensional magnetic resonance distribution corresponding to the synthetic multi-dimensional magnetic resonance signal through the Laplace transform algorithm; determining whether a mean square error of the multi-dimensional magnetic resonance distribution corresponding to the synthetic multi-dimensional magnetic resonance signal and the full sampling one-dimensional distribution is less than a preset threshold, and in the case of being less than the preset threshold, the multi-dimensional magnetic resonance distribution corresponding to the synthetic multi-dimensional magnetic resonance signal is a reconstructed multi-dimensional magnetic resonance distribution of the corresponding single voxel; obtaining a synthetic multi-dimensional magnetic resonance signal of each single voxel based on the initial multi-dimensional magnetic resonance distribution and the acquisition parameters comprises: determining an initial kernel function based on the acquisition parameters; calculating a product of the initial multi-dimensional magnetic resonance distribution and the initial kernel function, and the product is the synthetic multi-dimensional magnetic resonance signal.

2. The method of multi-dimensional magnetic resonance imaging of claim 1, wherein, generating a multi-parameter map of the sample under test based on the updated multi-dimensional magnetic resonance distribution of each single voxel comprises: determining a region of interest of the sample under test; performing integral operation on the updated multi-dimensional magnetic resonance distribution of each single voxel in the region of interest to obtain a multi-parameter map of the sample under test.

3. The method of multi-dimensional magnetic resonance imaging of claim 1, wherein, Sparse sampling of the sample under test based on the acquisition parameters comprises: optimizing the acquisition parameters to obtain optimized acquisition parameters; sparse sampling of the sample under test based on the optimized acquisition parameters.

4. The method of multi-dimensional magnetic resonance imaging of claim 3, wherein, The optimization algorithm used for optimizing the acquisition parameters is a genetic algorithm.

5. The method of multi-dimensional magnetic resonance imaging of claim 1, wherein, According to the acquired multi-dimensional magnetic resonance signal, multi-dimensional magnetic resonance sampling data of each single voxel is extracted, and the initial multi-dimensional magnetic resonance distribution of each single voxel is determined based on the multi-dimensional magnetic resonance sampling data of each single voxel through the Laplace transform algorithm, which comprises: extracting multi-dimensional magnetic resonance sampling data of each single voxel from the acquired multi-dimensional magnetic resonance signal; calculating a signal-to-noise ratio of each single voxel based on the extracted multi-dimensional magnetic resonance sampling data of each single voxel; determining whether a signal-to-noise ratio of each of the single voxels is greater than a preset value, and regarding a single voxel with a signal-to-noise ratio less than the preset value as noise.

6. The method of multi-dimensional magnetic resonance imaging of claim 5, wherein, The method further comprises generating an image acquisition report, the image acquisition report comprising the acquisition sequence, acquisition parameters, the updated multi-dimensional magnetic resonance distribution of the corresponding single voxels and the multi-parameter atlas.

7. A multi-dimensional magnetic resonance imaging system, the system comprising a processor and a memory, characterized in that, The memory stores computer instructions, and the processor is configured to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method according to any one of claims 1 to 6.

Images