Method and device for determining magnetic resonance center frequency, storage medium and terminal

Abstract

The invention discloses a magnetic resonance center frequency determination method and device, a storage medium and a terminal. A first frequency spectrum corresponding to the first signal and a second frequency spectrum corresponding to the second signal are determined, the first signal is a magnetic resonance signal collected under the zero diffusion gradient magnetic field, and the second signal is a magnetic resonance signal collected under the diffusion gradient magnetic field; a plurality of effective wave crests in the first frequency spectrum and the second frequency spectrum are identified, diffusion coefficients corresponding to the effective wave crests are calculated, and the effective wave crests are wave crests corresponding to the human tissue components; and determining the center frequency of the magnetic resonance system based on the signal intensity, the frequency and the diffusion coefficient of each effective wave crest. Through the magnetic resonance signals of the zero diffusion gradient and the applied diffusion gradient magnetic field, the effective peak diffusion coefficient is analyzed in combination with the spectrum, the peak of each component is identified, and the center frequency is determined accordingly, so that the limitation of empirical judgment or analysis based on spectrum characteristics is overcome, and the identification accuracy of the magnetic resonance center frequency in a complex signal environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of magnetic resonance imaging, and in particular to a method and device for determining a magnetic resonance center frequency, a storage medium, and a terminal. BACKGROUND

[0002] In magnetic resonance imaging (MRI) technology, accurate determination of the magnetic resonance center frequency is crucial for imaging quality and diagnostic results, which directly affects the clarity of the image and the accuracy of lesion detection. Existing methods for determining the center frequency mainly include resonance frequency positioning based on water signals, and methods for correcting the center frequency based on the relaxation characteristics of substances proposed in recent years, which can improve the accuracy and stability of center frequency determination to a certain extent and meet the basic needs of conventional imaging scenarios. However, these methods still have problems such as signal deviation and insufficient search accuracy when facing complex signal environments. SUMMARY

[0003] The present application provides a method and device for determining a magnetic resonance center frequency, a storage medium, and a terminal to solve the technical problems of signal deviation and insufficient search accuracy in existing center frequency determination methods.

[0004] In a first aspect, the present application provides a method for determining a magnetic resonance center frequency, which includes: determining a first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal, the first signal being a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal acquired under an applied diffusion gradient magnetic field; identifying a plurality of effective peaks in the first frequency spectrum and the second frequency spectrum, and calculating diffusion coefficients corresponding to each effective peak, the effective peak being a peak corresponding to each human tissue component; determining the center frequency of the magnetic resonance system based on the signal intensity, frequency, and diffusion coefficient of each effective peak.

[0005] In a second aspect, the present application provides a device for determining a magnetic resonance center frequency, which includes: a spectrum determination module configured to determine a first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal, the first signal being a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal acquired under an applied diffusion gradient magnetic field; a parameter determination module configured to identify a plurality of effective peaks in the first frequency spectrum and the second frequency spectrum, and calculate diffusion coefficients corresponding to each effective peak, the effective peak being a peak corresponding to each human tissue component; A result determining module is configured to determine the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak.

[0006] In a third aspect, the embodiments of the present application provide a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the steps of the method.

[0007] In a fourth aspect, the embodiments of the present application provide a terminal, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is suitable for being loaded by the processor and executing the steps of the method.

[0008] The technical solutions provided by some embodiments of the present application have at least the following beneficial effects: The present application provides a method for determining the center frequency of magnetic resonance, determining the first frequency spectrum corresponding to the first signal and the second frequency spectrum corresponding to the second signal, the first signal being a magnetic resonance signal collected under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal collected under an applied diffusion gradient magnetic field; identifying a plurality of effective wave peaks in the first frequency spectrum and the second frequency spectrum, and calculating the diffusion coefficient corresponding to each effective wave peak, the effective wave peak being a wave peak corresponding to each human tissue component; and determining the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak. First, the magnetic resonance signals are collected under the zero diffusion gradient magnetic field and the applied diffusion gradient magnetic field respectively to obtain the frequency spectra under two different conditions, thereby constructing a frequency spectrum comparison basis for the same tissue component under different diffusion conditions, providing a data premise for introducing the diffusion coefficient as a physical criterion, so that the subsequent signals of different tissue components can be more accurately identified and distinguished by comparing the spectral characteristics under the two conditions, thereby improving the stability and precision of the center frequency determination; then, the effective wave peaks in the frequency spectrum are identified and the diffusion coefficients thereof are calculated, which can quantitatively describe the diffusion behavior of different tissue components in the magnetic field, thereby facilitating the use of the significant difference in the diffusion coefficients of different tissue components to realize the physical essence distinction of each component in a multi-substance coexistence scene, and improving the objectivity and accuracy of the subsequent peak classification; finally, the center frequency of the magnetic resonance system is automatically determined by comprehensively considering the three dimensions of the signal intensity, frequency and diffusion coefficient of each effective wave peak, which can automatically exclude false peaks or noise interference, thereby realizing the center frequency correction with high robustness and without manual intervention, and effectively guaranteeing the quality and diagnostic reliability of the subsequent magnetic resonance imaging. In the method of the present application, the magnetic resonance signals under the zero diffusion gradient and the applied diffusion gradient magnetic field are combined with the diffusion coefficient of the effective wave peak identified by the spectral analysis to identify the component wave peaks and determine the center frequency accordingly, which overcomes the limitations of relying on experience judgment or only based on spectral feature analysis, and improves the recognition accuracy of the magnetic resonance center frequency in a complex signal environment. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0010] Figure 1 An exemplary system architecture diagram of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 2 A flowchart of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 3 A flowchart of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 4 A flowchart of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 5 A schematic diagram of signal acquisition in a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 6 A flowchart of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 7 A schematic diagram of signal intensity map and diffusion coefficient spectrum in a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 8 An effect diagram after implementation of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 9 A structure block diagram of a device for determining a magnetic resonance center frequency provided by an embodiment of the present application is shown in the figure. Figure 10 A structure diagram of a terminal provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0011] In order to make the features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0012] The following description relates to the drawings, where like elements are referred to with like numbers. The implementations described in the following examples do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present disclosure as described in the appended claims.

[0013] As a key parameter of signal acquisition and image reconstruction, the accurate determination of the magnetic resonance center frequency is crucial for the imaging quality and diagnostic results in magnetic resonance imaging technology. Because the center frequency directly determines the reference of radio frequency excitation and signal reception, if there is a deviation, it will lead to image artifacts, signal shift and even reconstruction error, especially in complex tissues involving the coexistence of multiple resonance components such as water, fat, silicone and the like. The existing methods for determining the center frequency mainly include resonance frequency positioning based on water signal, including using the known water-fat frequency difference to locate the water signal, or selecting the resonance peak through manual intervention, and the method of assisting center frequency correction based on the relaxation characteristics of substances proposed in recent years, which can help to distinguish the frequency position of different components to some extent, so as to correct the center frequency of the magnetic resonance system.

[0014] However, the traditional method has limitations such as insufficient precision and dependence on empirical manual intervention when facing complex signal environments such as fat signal dominated, multi-substance mixed signals or signals affected by magnetic field inhomogeneity. Specifically, in clinical applications, for parts such as breasts and abdomens containing a large amount of fat, the signal intensity of fat is close to or even exceeds that of water, and a considerable proportion of the population has non-water and non-fat components such as silicone prosthesis in their breasts. In this case, the mixed signals of multiple substances are easy to form overlapping peaks, and the traditional method relying on the water-fat frequency difference or empirical manual intervention is difficult to accurately locate the resonance frequency of the water signal. Further, the method based on relaxation characteristics is susceptible to radio frequency field inhomogeneity, causing the T1 relaxation characteristics of each component to deviate on the frequency spectrum, affecting the distinction effect; in addition, the tissues in different parts of the human body exhibit inconsistent relaxation behavior, for example, the T1 relaxation values of liver tissue (about 800 milliseconds) and fat tissue (300 milliseconds-500 milliseconds) are relatively close, and the T2 relaxation value of fat (40 milliseconds-100 milliseconds) is relatively close to the T2 relaxation value of brain white matter (about 70 milliseconds), which causes certain difficulty in distinguishing fat and water in these tissues, thereby affecting the accuracy and reliability of diagnosis.

[0015] Therefore, the embodiments of the present application provide a method for determining the center frequency of magnetic resonance to solve the technical problems of signal shift and insufficient search precision existing in the existing center frequency determination method.

[0016] Please refer to Figure 1 , Figure 1An exemplary system architecture diagram of a method for determining a magnetic resonance center frequency is provided in the embodiments of the present application.

[0017] As shown in Figure 1 , the system architecture can include a terminal 101, a network 102 and a server 103. The network 102 is used to provide a communication link medium between the terminal 101 and the server 103. The network 102 can include various types of wired communication links or wireless communication links, for example: the wired communication links include optical fiber, twisted pair or coaxial cable, and the wireless communication links include Bluetooth communication link, Wireless-Fidelity (Wi-Fi) communication link or microwave communication link, etc.

[0018] The terminal 101 can interact with the server 103 through the network 102 to receive messages from the server 103 or send messages to the server 103, or the terminal 101 can interact with the server 103 through the network 102 to receive messages or data sent by other users to the server 103. Exemplarily, the terminal 101 acquires magnetic resonance signals, i.e. first signals and second signals, under zero diffusion gradient magnetic field and under applied diffusion gradient magnetic field, respectively. The acquired signal data is transmitted to the server 103 through the network 102. After receiving the magnetic resonance signal data from the terminal 101, the server 103 stores and pre-processes the magnetic resonance signal data, and performs spectral analysis on the first signals and the second signals to generate corresponding first spectrum and second spectrum.

[0019] The terminal 101 can be hardware or software. When the terminal 101 is hardware, it can be various electronic devices, including but not limited to tablet computers, laptop computers and desktop computers connected with magnetic resonance scanning devices, etc. When the terminal 101 is software, it can be installed in the above-mentioned electronic devices, which can be implemented as multiple software or software modules (for example: to provide distributed services), or as a single software or software module, which is not specifically limited here.

[0020] In the embodiments of the present application, the terminal 101 first determines a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal, the first signal is a magnetic resonance signal acquired under zero diffusion gradient magnetic field, and the second signal is a magnetic resonance signal acquired under applied diffusion gradient magnetic field; then the terminal 101 identifies a plurality of effective wave peaks in the first spectrum and the second spectrum, and calculates diffusion coefficients corresponding to each effective wave peak, the effective wave peak is a wave peak corresponding to each human tissue component; finally, the terminal 101 determines the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak.

[0021] The server 103 can be a service server providing various services. It should be noted that the server 103 can be hardware or software. When the server 103 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the server 103 is software, it can be implemented as multiple software or software modules (for example, to provide distributed services), or as a single software or software module, which is not limited here.

[0022] Alternatively, the system architecture can also not include the server 103, that is, the server 103 can be an optional device in the embodiments of the present specification, that is, the method provided in the embodiments of the present specification can be applied to a system structure including only the terminal 101, and the embodiments of the present application do not limit this.

[0023] It should be understood that Figure 1 The number of terminals, networks and servers in the above system architecture is only illustrative, and can be any number of terminals, networks and servers according to the needs of implementation.

[0024] Please refer to Figure 2 , Figure 2 A flowchart of a method for determining a magnetic resonance center frequency provided by an embodiment of the present application. The execution subject of the embodiment of the present application can be a terminal that executes the determination of the magnetic resonance center frequency, or a processor in the terminal that executes the method for determining the magnetic resonance center frequency, or a service for determining the magnetic resonance center frequency in the terminal that executes the method for determining the magnetic resonance center frequency. For the convenience of description, the specific execution process of the method for determining the magnetic resonance center frequency will be introduced below with the execution subject being the processor in the terminal as an example.

[0025] As shown in Figure 2 The method for determining the magnetic resonance center frequency can at least include: S202, determining a first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal, the first signal being a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal acquired under an applied diffusion gradient magnetic field.

[0026] Optionally, in magnetic resonance imaging, a plurality of different human tissue components are to be distinguished, which have different resonance frequencies, for example water, fat and medical silicone prosthesis have different resonance frequencies, taking water as the center reference, the frequency of fat is usually about 3.5ppm lower than that of water, and the hydrogen proton frequency in medical silicone prosthesis is about 4.7ppm lower than that of water. Such differences in resonance frequency provide a preliminary basis for peak position identification in the spectrum. However, in actual clinical scenarios, due to factors such as main magnetic field inhomogeneity and magnetic susceptibility effect, a single component often appears as multiple split or broadened peaks, and the peaks of different components may overlap, and it is difficult to distinguish reliably in the face of complex signal environment only relying on frequency difference. At the same time, the hydrogen proton diffusion ability of these substances has a difference of orders of magnitude, for example, the water molecules in the human body have high degrees of freedom, and the diffusion coefficient is about 10 -3 mm 2 / s order of magnitude, while the proton diffusion coefficient in fat is only 10 -5 mm 2 / s order of magnitude, with a difference of 1-2 orders of magnitude. Such a huge difference in diffusion coefficient provides another dimension of effective information for distinguishing different tissue components.

[0027] Based on this, the present application introduces the diffusion behavior characteristics as reference information on the basis of the traditional frequency difference dimension, and obtains the proton density weighted and diffusion weighted spectra by collecting two groups of signals under zero diffusion encoding and diffusion gradient encoding. Since the uniformity of the gradient field is much higher than that of the radio frequency field, the signal coding brought by the diffusion gradient encoding method is relatively more stable in space, so that the obtained spectrum information can more accurately reflect the diffusion characteristics of each tissue component, so that the frequency peak position of components such as water and fat can be accurately identified by combining the spectrum characteristics and the diffusion coefficient, so as to accurately determine the center frequency of the magnetic resonance system.

[0028] Specifically, in the first excitation stage, the magnetic resonance imaging system is controlled to excite and receive signals of the human tissue without applying any diffusion sensitive gradient magnetic field, thereby obtaining a first signal. The signal reflects the original resonance response of each hydrogen-containing component of the human body under the influence of zero diffusion weight, and its intensity is mainly determined by the proton density and inherent relaxation characteristics. Then, the first signal is processed by frequency domain transformation to obtain a corresponding first spectrum, which reflects the basic frequency distribution of each tissue component under zero diffusion condition.

[0029] Furthermore, in the second excitation stage, while keeping other imaging parameters consistent with the first stage, a diffusion gradient magnetic field of preset intensity is introduced only during signal acquisition. Under these conditions, a second magnetic resonance signal is acquired. Because the diffusion capacity of hydrogen protons varies significantly among different tissue components, the intensity of each component in this signal will decrease to varying degrees due to diffusion attenuation. Similarly, a frequency domain transformation is performed on this second signal to obtain the corresponding second spectrum, whose peak positions are basically consistent with the first spectrum, but the amplitude of each peak changes due to the diffusion effect.

[0030] It should be noted that the breast is one of the most complex scenarios in magnetic resonance imaging (MRI) for measuring center frequency. The components that need to be distinguished are usually water, fat, and medical silicone implants. For ease of illustration, the following embodiments and descriptions will use this scenario as a typical application scenario for detailed explanation, so as to demonstrate the effectiveness and superiority of the technical solution of this application in actual clinical applications.

[0031] S204. Identify multiple effective peaks in the first and second spectra, and calculate the diffusion coefficient corresponding to each effective peak. The effective peaks are the peaks corresponding to each human tissue component.

[0032] Optionally, after obtaining the first and second spectra, the method in this embodiment further performs peak analysis and component discrimination. First, peak detection is performed on the two spectra to identify all local amplitude maxima, forming a preliminary peak set. Considering that actual magnetic resonance signals are susceptible to system noise, magnetic field inhomogeneity, radio frequency interference, and other factors, false peaks or redundant peaks generated by the splitting of a single component may appear in the spectrum. Therefore, effective peak screening is performed based on the morphological characteristics of the peaks (such as width, height, symmetry, etc.) to exclude peaks that obviously do not conform to the characteristics of human tissue components.

[0033] Furthermore, due to the introduction of the diffusion gradient magnetic field, the peaks of genuine human tissue components should exhibit certain regularities in the two spectra (such as amplitude variations and similar frequencies), while interference peaks often lack such regularity. Therefore, for the remaining peaks after initial screening, their performance in the first and second spectra is further analyzed, and the effective peaks representing each human tissue component are accurately identified accordingly. These effective peaks truly correspond to the resonant response of a certain hydrogen-containing human tissue component, rather than being caused by random noise or environmental disturbances.

[0034] Optionally, for each effective peak, the signal attenuation of the component before and after the application of the diffusion gradient is evaluated by combining the changes in its signal intensity amplitude in the first and second spectra, and then its diffusion coefficient is calculated. This diffusion coefficient reflects the diffusion ability of hydrogen protons in each tissue component and is an important parameter for distinguishing different tissue components.

[0035] S206. Determine the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak.

[0036] Optionally, after completing the quantification of the diffusion characteristics of each effective wave peak, the information in the three dimensions of frequency position, signal intensity and diffusion coefficient is fused, and comprehensive analysis and component attribution judgment are performed on all effective wave peaks, so as to perform final determination of the center frequency.

[0037] In the embodiment of the present application, a method for determining the center frequency of magnetic resonance is provided, which comprises determining a first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal, the first signal being a magnetic resonance signal collected under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal collected under an applied diffusion gradient magnetic field; identifying a plurality of effective wave peaks in the first frequency spectrum and the second frequency spectrum, and calculating the diffusion coefficients corresponding to each effective wave peak, the effective wave peak being a wave peak corresponding to each human tissue component; and determining the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak. First, the magnetic resonance signals are collected under the zero diffusion gradient magnetic field and the applied diffusion gradient magnetic field respectively, and the frequency spectra under two different conditions are obtained, which constructs a frequency spectrum comparison basis for the same tissue component under different diffusion conditions, provides a data premise for introducing the diffusion coefficient as a physical criterion, and makes it possible to more accurately identify and distinguish the signals of different tissue components by comparing the spectral characteristics under the two conditions, thereby improving the stability and precision of the center frequency determination. Next, the effective wave peaks in the frequency spectrum are identified and the diffusion coefficients are calculated, which can quantitatively describe the diffusion behavior of different tissue components in the magnetic field, thereby facilitating the use of the significant difference in the diffusion coefficients of different tissue components to realize the physical essence distinction of each component in the multi-substance coexistence scene, and improving the objectivity and accuracy of the subsequent peak classification. Finally, the center frequency of the magnetic resonance system is automatically determined by comprehensively considering the three dimensions of signal intensity, frequency and diffusion coefficient of each effective wave peak, which can automatically exclude false peaks or noise interference, thereby realizing the center frequency correction with high robustness and without manual intervention, and effectively guaranteeing the quality and diagnostic reliability of subsequent magnetic resonance imaging. In the method of the present application, the magnetic resonance signals under the zero diffusion gradient and the applied diffusion gradient magnetic field are combined with the spectral analysis of the diffusion coefficients of the effective wave peaks to identify the component wave peaks and determine the center frequency accordingly, which overcomes the limitations of relying on experience judgment or only based on spectral feature analysis, and improves the recognition accuracy of the center frequency of magnetic resonance in a complex signal environment.

[0038] Referring to Figure 3 , Figure 3 A flowchart of a method for determining the center frequency of magnetic resonance provided in the embodiment of the present application is shown.

[0039] As Figure 3 shown, the method for determining the center frequency of magnetic resonance can at least include: S302, performing a first excitation on the human tissue in the target imaging field of view based on a zero diffusion gradient magnetic field, collecting a first signal obtained by the first excitation, and performing a second excitation on the human tissue based on a diffusion gradient magnetic field after a preset time length, and collecting a second signal obtained by the second excitation.

[0040] Optionally, Figure 4 A flowchart of the method for determining the center frequency of magnetic resonance provided by the embodiment of the present application is shown in the following. Figure 4 As shown in S402, the method of the embodiment of the present application first collects bimodal magnetic resonance signals for subsequent analysis through two excitation processes.

[0041] Specifically, Figure 5 A schematic diagram of signal collection in the method for determining the center frequency of magnetic resonance provided by the embodiment of the present application is shown in the following. Figure 5 As shown, the signal collection is realized based on a spin echo sequence. In the first excitation process, a standard spin echo selection sequence is first used to apply a 90-degree radio frequency pulse to the human tissue in the target imaging field of view (FOV) to flip the longitudinal magnetization vector to the transverse plane; then, a 180-degree radio frequency pulse is applied after a proper time delay to rephase the phase of the protons to generate a spin echo signal. During the first excitation, no diffusion gradient is applied, so the collected signal mainly reflects the proton density and relaxation characteristics of each component in the tissue, denoted as a first signal, and the signal intensity S1 can be described by the following formula:

[0042] Among them, represents the signal intensity represented by the density; e is the natural base; TE is the echo time, which represents the time interval from the excitation pulse to the reception of the echo signal; T2 is the transverse relaxation time, which represents the speed of the loss of phase consistency of the protons in the magnetic field.

[0043] Further, the second excitation is performed after a preset time length. A 90-degree radio frequency pulse and a 180-degree radio frequency pulse are also applied, but a pair of diffusion gradient pulses (i.e. the two pulses in the “diffusion gradient” row in the figure) are symmetrically introduced between the two pulses. Due to the difference in the molecular motion ability of hydrogen protons in different tissue components, the diffusion gradient pulse will cause the signal of free water molecules to be significantly attenuated, while the signal of limited components such as fat or silica gel is less attenuated. The second signal collected thereby contains the information of the diffusion effect, and the signal intensity S2 can be described by the following formula:

[0044] Among them, b is the diffusion coefficient sensitive factor applied, and D is the diffusion coefficient.

[0045] Optionally, since the TE value between the two acquisitions is the same, and the T2 value of each tissue component itself is unchanged, the signal attenuation ratio of the first signal and the second signal is mainly affected by the diffusion coefficient of the substance, which can be described by the following formula:

[0046] Based on this physical model, if the signal attenuation ratio can be obtained from the front and back two signals, and the attenuation relationship is inverted using the known b value, the diffusion coefficient of each effective wave peak corresponding to the tissue component can be calculated.

[0047] It should be noted that the specific implementation of the above signal acquisition is only an example. In other feasible alternatives, instead of using a spin echo sequence, a gradient echo (GRE) or free induction decay (FID) signal can be used as the basic acquisition method. The number of excitations can also be extended to more than two to improve the estimation accuracy of the diffusion coefficient. In addition, the setting of the flip angle can also be adjusted according to the imaging requirements, for example, by adjusting the size of the flip angle to control the weight of the diffusion effect in the signal. In terms of spatial excitation strategy, instead of selecting a single layer (for example, the middle layer of the target imaging field of view) for excitation, the entire slab region can be excited, or only the local voxels in the center of the target imaging field of view can be sampled at high resolution to adapt to different imaging requirements and application scenarios.

[0048] S304, Fourier transform the first signal and the second signal to obtain a first frequency spectrum corresponding to the first signal and a second frequency spectrum corresponding to the second signal.

[0049] Optionally, the first signal and the second signal collected are respectively subjected to Fourier transform to convert the time domain signals into frequency domain representations, thereby obtaining the first frequency spectrum and the second frequency spectrum. The two frequency spectrums are roughly aligned on the frequency axis, but the amplitudes of the respective resonance peaks are attenuated to different degrees due to the diffusion effect, providing basic data for subsequent component recognition based on diffusion behavior.

[0050] S306, identifying a plurality of wave peaks in the first frequency spectrum and the second frequency spectrum, and determining effective wave peaks at the same frequency in the first frequency spectrum and the second frequency spectrum from the wave peaks.

[0051] Optionally, as shown in S404 of Figure 4 The frequency array f1 and the signal intensity array s1 are obtained by extracting the frequency coordinates of all the peaks from the first frequency spectrum, and recording the signal intensity corresponding to each peak. Similarly, the frequency array f2 and the signal intensity array s2 are obtained by extracting the frequency coordinates of all the peaks from the second frequency spectrum.

[0052] Optionally, due to the actual magnetic resonance signal is susceptible to noise, magnetic field inhomogeneity or sampling error, two frequency spectrum may each contain some non-real false peaks, or the same component due to a small frequency offset and in two spectrum as slightly different peak position. Therefore, the direct use of all peak values for subsequent analysis may lead to misjudgment. Based on this, further frequency position alignment screening is performed. All frequency values in f1 and f2 are traversed to find the frequency points that exist in both arrays or coincide within a predetermined tolerance range. These common frequency positions form a new frequency set fp. Each frequency point in the set is stably present in the spectrum under two different diffusion conditions, indicating that it has good repeatability and physical reality, representing the effective peak corresponding to each human tissue component.

[0053] S308, and determine the associated signal intensity of each effective peak at the same frequency in the first spectrum and the second spectrum, obtain the signal attenuation ratio of each effective peak according to the associated signal intensity; calculate the diffusion coefficient of the corresponding effective peak based on the signal attenuation ratio.

[0054] Optionally, for each frequency in fp, its corresponding signal amplitude is retrieved from s1 and s2 respectively for subsequent calculation of diffusion coefficient. Specifically, in the first spectrum, the signal intensity at each frequency is obtained, denoted as array sp1; in the second spectrum, the signal intensity at the same frequency is obtained, denoted as array sp2, wherein the same frequency position in the two spectra is the associated signal intensity corresponding to the same human tissue component. Since the two acquisitions only differ in whether to introduce diffusion gradient, and other imaging parameters remain the same, the difference between sp1 and sp2 mainly reflects the molecular diffusion ability of the frequency component corresponding material. Accordingly, the ratio of sp2 / sp1 in signal intensity is calculated, which is the signal attenuation ratio of the corresponding tissue component under the current diffusion encoding condition. Further, in the foregoing formula, using the known b value and the measured sp2 / sp1 ratio, the diffusion coefficient of the tissue component corresponding to the effective peak can be solved.

[0055] S310, according to the signal characteristics of each human tissue component in the spectrum, combining the diffusion coefficient, signal intensity and frequency of each effective peak, determine the water peak corresponding to the water component; the frequency of the water peak is used as the center frequency of the magnetic resonance system.

[0056] Optionally, according to the diffusion coefficient size corresponding to each effective wave peak, they can be preliminarily classified into different categories, for example, the wave peak with significantly higher diffusion coefficient tends to correspond to free water molecules, and the wave peak with significantly lower diffusion coefficient is more likely to come from fat tissue or silica gel prosthesis and other components with restricted motion. It is known that different human tissue components have stable chemical shift rules in the magnetic resonance spectrum, on this basis, further combined with the relative frequency position and signal intensity difference between these wave peaks, for example, the resonance frequency of fat and water has an expected relative shift. Based on this, the water peak in each effective wave peak can be determined, and the frequency corresponding to the wave peak is determined as the center frequency of the magnetic resonance imaging system.

[0057] In the embodiment of the present application, a method for determining the center frequency of magnetic resonance is provided. By determining the associated signal intensity of each effective wave peak at the same frequency in the first spectrum and the second spectrum, and calculating the signal attenuation ratio to obtain the diffusion coefficient, the actual diffusion characteristics of each tissue component can be more accurately reflected, thereby providing an objective and quantifiable basis for distinguishing different human tissue components, and improving the accuracy of component identification. By comprehensively considering the diffusion coefficient, frequency position and signal intensity of each effective wave peak, combining the known distribution rules of human tissue components in the spectrum, the real water peak is intelligently identified and its frequency is used as the center frequency, which avoids relying on the absolute intensity of the water peak or artificial experience, and can still achieve high-precision center frequency correction in complex scenarios dominated by fat or silica gel signals. By retaining only the wave peaks with the same frequency position in the first spectrum and the second spectrum as effective wave peaks, the pseudo-peak caused by noise, magnetic field inhomogeneity or sampling error is effectively filtered out, ensuring that the subsequent diffusion coefficient calculation and component classification are based only on real and stable tissue resonance signals, thereby improving the robustness and reliability of the entire method. The zero diffusion and diffusion weighted signals are collected by two independent excitations, and the corresponding double spectrum is constructed by Fourier transform, which provides a physical basis for subsequent analysis based on diffusion behavior, and the process control is easy to integrate into the clinical scanning protocol, and because the gradient field has high stability, the obtained data is less affected by system inhomogeneity, thereby ensuring the accuracy and repeatability of the center frequency correction.

[0058] Referring to Figure 6 , Figure 6 A flowchart of a method for determining the center frequency of magnetic resonance provided in an embodiment of the present application is shown.

[0059] As Figure 6 shown, the method for determining the center frequency of magnetic resonance can at least include: S602, determining a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal, the first signal being a magnetic resonance signal collected under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal collected under an applied diffusion gradient magnetic field.

[0060] Optionally, please refer to the detailed description in step S202, which will not be repeated here.

[0061] S604, determine a plurality of target points in the first spectrum and the second spectrum, compare the signal strength of each target point and the corresponding adjacent point of each target point, and the corresponding adjacent point of each target point is the point meeting the preset distance condition in frequency with each target point.

[0062] Optionally, in order to accurately extract all potential resonance peaks from the first spectrum and the second spectrum, the method of the embodiment of the application adopts an automatic peak searching method based on local neighborhood comparison. Specifically, first, each spectrum is discretized into a series of sampling points on the frequency axis, and these sampling points are the target points to be detected. For each target point, according to a preset frequency neighborhood range (for example, extending 2 frequency sampling intervals to the left and right of the point as the center), the corresponding adjacent points are determined, and the signal strength of the current target point is compared with the signal strength of all corresponding adjacent points one by one. The preset distance condition can be flexibly set according to the spectral resolution, noise level and the like of the magnetic resonance system.

[0063] S606, if the signal strength of the current target point is greater than the signal strength of the corresponding adjacent point of the current target point, it is determined that the current target point is a peak in the spectrum; and effective peaks at the same frequency in the first spectrum and the second spectrum are determined from the peaks.

[0064] Optionally, if the signal strength of the current target point is greater than the amplitude of all adjacent points, it is determined that the point is a local maximum value, that is, a peak. By traversing all target points in the spectrum and performing the above judgment, the frequency position and the corresponding signal strength of all peaks in the spectrum can be obtained. On this basis, effective peaks at the same frequency in the first spectrum and the second spectrum are determined from the peaks. Specifically, please refer to the detailed description in step S306, which will not be repeated here.

[0065] S608, and calculate the diffusion coefficient corresponding to each effective peak, and the effective peak is the peak corresponding to each human tissue component.

[0066] Optionally, please refer to the detailed description in step S204, which will not be repeated here.

[0067] S610, each human tissue component at least includes water component and fat component, the diffusion coefficient, signal strength and frequency of each effective peak are screened according to the diffusion coefficient range, signal strength characteristics and frequency characteristics of each human tissue component in the spectrum, and the fat peak corresponding to the fat component is determined.

[0068] Optionally, Figure 4A flowchart of the implementation of the method for determining the magnetic resonance center frequency provided in the embodiments of the present application is shown in the figure Figure 4 As shown in S406-S410, the human tissue components at least include water component and fat component, after the diffusion coefficients of the effective wave peaks are calculated, the fat peak corresponding to the fat component is first identified, and then the water peak is determined with the fat peak as the reference.

[0069] Optionally, the center frequency measurement of the breast part is still selected as an example scenario, in the breast part, due to the fat signal intensity close to or even higher than the water signal, and the possible existence of silicone prosthesis, accurate distinction of the fat peak, silicone peak and water peak is required in this scenario. Specifically, first, according to the known physical characteristics of human tissues, the diffusion coefficient interval suitable for fat components is determined (for example, 0.01-0.1 μm 2 / ms), and all the effective wave peaks meeting the conditions in this interval are screened out.

[0070] Further, since both fat and silicone prosthesis have limited diffusion characteristics, their diffusion coefficients usually fall within this range, so the first two peaks with the highest signal intensity can be selected from these effective wave peaks with low diffusion coefficients as the main analysis objects of fat and silicone. Considering that the resonance frequency of fat is slightly higher than that of silicone under typical magnetic field strength, the frequency positions of the two peaks are compared, the peak with relatively higher frequency is determined as the fat peak, and the peak with lower frequency is classified as the silicone peak.

[0071] S612, in the preset frequency range of the fat peak, the water peak corresponding to the water component is determined according to the signal intensity characteristics of the water component in the spectrum and the diffusion coefficient range of the water component; and the frequency of the water peak is taken as the center frequency of the magnetic resonance system.

[0072] Optionally, after the fat peak is accurately identified, the method of the embodiments of the present application searches for the candidate peak of the water component in a reasonable frequency band range on the high frequency side of the fat peak (for example, within the range of 0.5 ppm-5.5 ppm upward offset). 2 In this region, further combined with the high diffusion coefficient characteristics (such as [0.4, 3.0] μm / ms) and reasonable signal intensity performance of water molecules, the water peak representing the water component is finally determined, and the frequency of the water peak is taken as the center frequency of the magnetic resonance system.

[0073] Optionally, Figure 7 A schematic diagram of the signal intensity diagram and the diffusion coefficient spectrum in the method for determining the magnetic resonance center frequency provided in the embodiments of the present application is shown in the figure Figure 7As shown, the left subgraph shows the spectral contrast results of two magnetic resonance signals collected under zero diffusion gradient (PD) and diffusion gradient (DW) conditions after Fourier transform. Among them, the blue curve represents the first spectrum under zero diffusion condition, which has high amplitude and clear peak shape, reflecting the original resonance response of each human tissue component without diffusion attenuation influence; the red curve represents the second spectrum under diffusion weighting condition, since the free water molecules are significantly attenuated under the action of diffusion gradient, the main peak 3 (corresponding to water signal) is obviously reduced; while the signals of peak 1 (corresponding to silica gel) and peak 2 (corresponding to fat) and other motion-restricted components are relatively well preserved.

[0074] Optionally, as shown in the left subgraph of FIG. 6, the method provided in this embodiment of the present application can be used to identify the three characteristic peaks in the spectrum of the magnetic resonance signal. As shown, the left subgraph shows the spectral contrast results of two magnetic resonance signals collected under zero diffusion gradient (PD) and diffusion gradient (DW) conditions after Fourier transform. Among them, the blue curve represents the first spectrum under zero diffusion condition, which has high amplitude and clear peak shape, reflecting the original resonance response of each human tissue component without diffusion attenuation influence; the red curve represents the second spectrum under diffusion weighting condition, since the free water molecules are significantly attenuated under the action of diffusion gradient, the main peak 3 (corresponding to water signal) is obviously reduced; while the signals of peak 1 (corresponding to silica gel) and peak 2 (corresponding to fat) and other motion-restricted components are relatively well preserved. Figure 7 As shown, the right subgraph is a diffusion coefficient distribution diagram corresponding to each characteristic peak, the horizontal axis is the frequency position, and the vertical axis is the diffusion coefficient. The black asterisk marks the diffusion coefficient calculated from the multiple effective wave peaks identified in the left graph. As can be seen from the subgraph, different components exhibit obvious differences in diffusion behavior: peaks 1 and 2 located in the low frequency region have lower diffusion coefficients, corresponding to silica gel and fat respectively; while peak 3 located near the center frequency has a higher diffusion coefficient, which is consistent with the diffusion characteristics of water. The method of this embodiment of the present application combines the three dimensions of frequency position, signal intensity and diffusion coefficient, can accurately determine that peak 3 is the water peak, and take its frequency as the center frequency of the magnetic resonance system, and realize high-precision and automatic correction.

[0075] Optionally, Figure 8 The effect schematic diagram of the method for determining the center frequency of magnetic resonance provided in this embodiment of the present application after implementation, wherein the imaging results obtained after turning on the fat suppression module are compared from left to right when taking peaks 1, 2 and 3 as the center frequency of the magnetic resonance system. It can be seen that in the left image and the middle image, since the frequencies of the silica gel peak and the fat peak are mistakenly taken as the center frequency, the fat resonance frequency is not accurately covered by the fat suppression pulse, causing the fat tissue to be not effectively suppressed, affecting the image quality. In the right image, the water peak frequency identified by the method of this embodiment of the present application is taken as the center frequency, the fat suppression pulse accurately acts on the fat component, realizing efficient suppression of the fat signal, while the water signal and silica gel signal are clearly displayed, the "oil" area in the image is basically free of residue, and the water and silica gel tissue boundaries are distinct, the imaging quality is significantly better than the former two. The results verify the effectiveness of the method of this embodiment of the present application in accurately identifying the water peak through the joint identification of the diffusion coefficient and the spectral characteristics, ensuring the reliable execution of the fat suppression function, and fully embodying the practical value of the method in realizing high-precision center frequency correction in complex multi-component scenarios.

[0076] In the embodiment of the present application, a method for determining the center frequency of magnetic resonance is provided. First, the fat peak is identified based on the diffusion coefficient, signal intensity and frequency characteristics, and then the wave peak conforming to the characteristics of water composition is searched in the preset high frequency range with the fat peak as the reference. The stable physical differences of water and fat in chemical shift and diffusion behavior are fully utilized. Even in the complex scene where the water signal is weak or there are interference components such as silica gel, the water peak can be accurately and reliably located, and the accuracy and robustness of the center frequency correction are improved. The wave peak in the spectrum is automatically identified by using the local neighborhood amplitude comparison method, without complex modeling or manual intervention. The algorithm is simple, efficient and strong in noise resistance, and can ensure that all potential human tissue composition peaks are effectively detected, providing a reliable data basis for subsequent composition classification and center frequency determination.

[0077] Please refer to Figure 9 , Figure 9 The structure block diagram of a device for determining the center frequency of magnetic resonance provided by the embodiment of the present application is shown in FIG. 9. As shown in FIG. 9, the device for determining the center frequency of magnetic resonance 900 includes: Figure 9 A spectrum determination module 910 is configured to determine a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal. The first signal is a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal is a magnetic resonance signal acquired under an applied diffusion gradient magnetic field. A parameter determination module 920 is configured to identify a plurality of effective wave peaks in the first spectrum and the second spectrum, and calculate diffusion coefficients corresponding to the effective wave peaks. The effective wave peaks are wave peaks corresponding to each human tissue composition. A result determination module 930 is configured to determine the center frequency of the magnetic resonance system based on the signal intensity, frequency and diffusion coefficient of each effective wave peak.

[0078] In some possible embodiments, the parameter determination module 920 is further configured to determine the associated signal intensity of each effective wave peak at the same frequency in the first spectrum and the second spectrum, obtain the signal attenuation ratio of each effective wave peak according to the associated signal intensity, and calculate the diffusion coefficient of the corresponding effective wave peak based on the signal attenuation ratio.

[0079] In some possible embodiments, the result determination module 930 is further configured to determine the water peak corresponding to the water composition according to the signal characteristics of each human tissue composition in the spectrum, in combination with the diffusion coefficient, signal intensity and frequency of each effective wave peak, and take the frequency of the water peak as the center frequency of the magnetic resonance system.

[0080] ​In some possible embodiments, the human tissue components include at least a water component and a fat component, and the result determination module 930 is further configured to filter the diffusion coefficient, the signal intensity, and the frequency of each effective wave peak according to the diffusion coefficient range, the signal intensity feature, and the frequency feature of each human tissue component in the spectrum, to determine a fat peak corresponding to the fat component; and determine a water peak corresponding to the water component according to the signal intensity feature of the water component in the spectrum and the diffusion coefficient range of the water component in a preset frequency range of the fat peak.

[0081] In some possible embodiments, the parameter determination module 920 is further configured to identify a plurality of wave peaks in the first spectrum and the second spectrum, and determine, from each wave peak, an effective wave peak at the same frequency in the first spectrum and the second spectrum.

[0082] In some possible embodiments, the parameter determination module 920 is further configured to determine a plurality of target points in the first spectrum and the second spectrum, compare the signal intensity of each target point with the signal intensity of a neighboring point corresponding to each target point, and determine, if the signal intensity of the current target point is greater than the signal intensity of the neighboring point corresponding to the current target point, the current target point as a wave peak in the spectrum.

[0083] In some possible embodiments, the spectrum determination module 910 is further configured to perform, on human tissue in a target imaging field of view, a first excitation based on a zero diffusion gradient magnetic field, collect a first signal obtained through the first excitation, perform, on the human tissue after a preset time length, a second excitation based on a diffusion gradient magnetic field, collect a second signal obtained through the second excitation, and perform Fourier transform on the first signal and the second signal to obtain a first spectrum corresponding to the first signal and a second spectrum corresponding to the second signal.

[0084] In the embodiment of the present application, a magnetic resonance center frequency determination device is provided, wherein a spectrum determination module is configured to determine a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal, the first signal being a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal acquired under an applied diffusion gradient magnetic field; a parameter determination module is configured to identify a plurality of effective peaks in the first spectrum and the second spectrum, and calculate diffusion coefficients corresponding to the effective peaks, the effective peaks being peaks corresponding to each human tissue component; and a result determination module is configured to determine a center frequency of a magnetic resonance system based on signal intensity, frequency and diffusion coefficient of each effective peak. First, the spectrum determination module acquires magnetic resonance signals under the zero diffusion gradient magnetic field and the applied diffusion gradient magnetic field to obtain the spectra under two different conditions, constructs a spectrum comparison basis for the same tissue component under different diffusion conditions, provides a data premise for introducing the diffusion coefficient as a physical criterion, and enables more accurate identification and differentiation of signals of different tissue components by comparing the spectrum characteristics under the two conditions, thereby improving the stability and accuracy of the center frequency determination. Next, the parameter determination module identifies the effective peaks in the spectrum and calculates the diffusion coefficients, which can quantitatively describe the diffusion behavior of different tissue components in the magnetic field, thereby facilitating the use of the significant difference in diffusion coefficients of different tissue components to realize the physical essence differentiation of each component in a multi-substance coexistence scene, and improving the objectivity and accuracy of subsequent peak classification. Finally, the result determination module automatically determines the center frequency of the magnetic resonance system by comprehensively considering the three dimensions of signal intensity, frequency and diffusion coefficient of each effective peak, can automatically exclude false peaks or noise interference, thereby realizing high-robustness, human-free center frequency correction, and effectively guaranteeing the quality and diagnostic reliability of subsequent magnetic resonance imaging. In the method of the present application, the magnetic resonance signals under the zero diffusion gradient and the applied diffusion gradient magnetic field are combined with the spectrum analysis of the effective peak diffusion coefficients to identify the component peaks and determine the center frequency accordingly, which overcomes the limitations of relying on experience or only based on spectrum feature analysis, and improves the recognition accuracy of the magnetic resonance center frequency in a complex signal environment.

[0085] The embodiment of the present application further provides a computer storage medium, which can store a plurality of instructions, and the instructions are suitable for being loaded and executed by a processor to perform the steps of the method in any one of the above embodiments.

[0086] Please refer to Figure 10 , Figure 10 A structure schematic diagram of a terminal is provided in the embodiment of the present application. As shown in Figure 10 , the terminal 1000 can include at least one terminal processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0087] The communication bus 1002 is configured to realize the connection communication between the components.

[0088] The user interface 1003 can include a display and a camera. Optionally, the user interface 1003 can further include a standard wired interface and a wireless interface.

[0089] The network interface 1004 can include a standard wired interface and a wireless interface (e.g., a WI-FI interface).

[0090] The terminal processor 1001 can include one or more processing cores. The terminal processor 1001 is connected to various parts of the terminal 1000 through various interfaces and lines, and performs various functions of the terminal 1000 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the terminal processor 1001 can be implemented in at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The terminal processor 1001 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly used to process an operating system, a user interface, and an application program. The GPU is used to render and draw the content to be displayed on the display. The modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the terminal processor 1001, but can be implemented by a separate chip.

[0091] The memory 1005 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 1005 can also be at least one storage device located away from the terminal processor 1001. As shown in Figure 10 The memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a determination program of a magnetic resonance center frequency.

[0092] In the terminal 1000 as shown in Figure 10 In the terminal 1000 as shown in, the user interface 1003 is mainly used to provide an interface for user input and obtain user input data; and the terminal processor 1001 can be used to call the determination program of the magnetic resonance center frequency stored in the memory 1005 and specifically perform the following operations: determining a first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal, the first signal being a magnetic resonance signal acquired under a zero diffusion gradient magnetic field, and the second signal being a magnetic resonance signal acquired under an applied diffusion gradient magnetic field; identifying a plurality of effective peaks in the first frequency spectrum and the second frequency spectrum, and calculating a diffusion coefficient corresponding to each effective peak, the effective peak being a peak corresponding to each human tissue component; determining a center frequency of the magnetic resonance system based on the signal intensity, the frequency, and the diffusion coefficient of each effective peak.

[0093] In some possible embodiments, when the terminal processor 1001 performs the calculation of the diffusion coefficient corresponding to each effective peak, it specifically performs the following steps: determining the associated signal intensity of each effective peak at the same frequency in the first frequency spectrum and the second frequency spectrum, obtaining the signal attenuation ratio of each effective peak according to each associated signal intensity; and calculating the diffusion coefficient of the corresponding effective peak based on each signal attenuation ratio.

[0094] In some possible embodiments, the terminal processor 1001, when determining the center frequency of the magnetic resonance system based on the signal strength, frequency and diffusion coefficient of each effective peak, specifically performs the following steps: determining a water peak corresponding to a water component according to the signal characteristics of each human tissue component in the frequency spectrum, in combination with the diffusion coefficient, signal strength and frequency of each effective peak; and taking the frequency of the water peak as the center frequency of the magnetic resonance system.

[0095] In some possible embodiments, the human tissue components at least include a water component and a fat component, and the terminal processor 1001, when determining the water peak corresponding to the water component according to the signal characteristics of each human tissue component in the frequency spectrum, in combination with the diffusion coefficient, signal strength and frequency of each effective peak, specifically performs the following steps: determining a fat peak corresponding to a fat component according to the diffusion coefficient range, signal strength characteristics and frequency characteristics of each human tissue component in the frequency spectrum, in combination with the diffusion coefficient, signal strength and frequency of each effective peak; and determining the water peak corresponding to the water component according to the signal strength characteristics of the water component in the frequency spectrum and the diffusion coefficient range of the water component in a preset frequency range of the fat peak.

[0096] In some possible embodiments, the terminal processor 1001, when identifying the plurality of effective peaks in the first frequency spectrum and the second frequency spectrum, specifically performs the following steps: identifying a plurality of peaks in the first frequency spectrum and the second frequency spectrum, and determining the effective peaks at the same frequency in the first frequency spectrum and the second frequency spectrum from the peaks.

[0097] In some possible embodiments, the terminal processor 1001, when identifying the plurality of peaks in the first frequency spectrum and the second frequency spectrum, specifically performs the following steps: determining a plurality of target points in the first frequency spectrum and the second frequency spectrum, comparing the signal strengths of each target point and the adjacent points corresponding to each target point, and the adjacent points corresponding to each target point are the points that meet a preset distance condition in frequency with each target point; and determining the current target point as a peak in the frequency spectrum if the signal strength of the current target point is greater than the signal strength of the adjacent point corresponding to the current target point.

[0098] In some possible embodiments, the terminal processor 1001, when determining the first frequency spectrum corresponding to the first signal and the second frequency spectrum corresponding to the second signal, specifically performs the following steps: performing a first excitation on the human tissue in the target imaging field of view based on a zero diffusion gradient magnetic field, collecting the first signal obtained by the first excitation, and performing a second excitation on the human tissue based on a diffusion gradient magnetic field after a preset time length, and collecting the second signal obtained by the second excitation; and performing Fourier transform on the first signal and the second signal to obtain the first frequency spectrum corresponding to the first signal and the second frequency spectrum corresponding to the second signal.

[0099] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. For example, the division of the modules is merely logical function division. There can be another division manner for the actual implementation. For example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or modules, and can be in electrical, mechanical or other forms.

[0100] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed to a plurality of network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0101] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The above computer program product includes one or more computer instructions. When the above computer program instructions are loaded and executed on a computer, all or part of the above processes or functions according to the embodiments of the present application are generated. The above computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The above computer instructions can be stored in a computer readable storage medium or transmitted by the above computer readable storage medium. The above computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The above computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The above available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital versatile disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)) and the like.

[0102] It should be noted that, for the foregoing method embodiments, for the convenience of description, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0103] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0104] The above is the description of the magnetic resonance center frequency determination method, device, storage medium and terminal provided by the present application. For those skilled in the art, according to the idea of the embodiments of the present application, there will be changes in specific implementation and application range. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for determining the center frequency of magnetic resonance, characterized in that, The method includes: Determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal, wherein the first signal is a magnetic resonance signal acquired under a zero diffusion gradient magnetic field and the second signal is a magnetic resonance signal acquired under an applied diffusion gradient magnetic field; Identify multiple effective peaks in the first spectrum and the second spectrum, and calculate the diffusion coefficient corresponding to each effective peak, wherein the effective peaks are peaks corresponding to each human tissue component; The center frequency of the magnetic resonance system is determined based on the signal intensity, frequency, and diffusion coefficient of each effective peak.

2. The method according to claim 1, characterized in that, The calculation of the diffusion coefficient corresponding to each effective wave peak includes: Determine the associated signal strength of each effective peak at the same frequency in the first spectrum and the second spectrum, and obtain the signal attenuation ratio of each effective peak based on the associated signal strength; The diffusion coefficient of the corresponding effective peak is calculated based on the attenuation ratio of each signal.

3. The method according to claim 1, characterized in that, The determination of the center frequency of the magnetic resonance system based on the signal intensity, frequency, and diffusion coefficient of each effective peak includes: Based on the signal characteristics of each human tissue component in the spectrum, combined with the diffusion coefficient, signal intensity and frequency of each effective peak, the water peak corresponding to the water component is determined. The frequency of the water peak is used as the center frequency of the magnetic resonance system.

4. The method according to claim 3, characterized in that, Each human tissue component includes at least water and fat components. The determination of the water peak corresponding to the water component is based on the signal characteristics of each human tissue component in the spectrum, combined with the diffusion coefficient, signal intensity, and frequency of each effective peak, including: Based on the diffusion coefficient range, signal intensity characteristics, and frequency characteristics of each human tissue component in the spectrum, the diffusion coefficient, signal intensity, and frequency of each effective peak are screened to determine the fat peak corresponding to the fat component. Within the preset frequency range of the fat peak, the water peak corresponding to the water component is determined based on the signal intensity characteristics of the water component in the spectrum and the diffusion coefficient range of the water component.

5. The method according to claim 1, characterized in that, The identification of multiple valid peaks in the first spectrum and the second spectrum includes: Identify multiple peaks in the first spectrum and the second spectrum, and determine the effective peaks that are at the same frequency in the first spectrum and the second spectrum from each peak.

6. The method according to claim 5, characterized in that, The identification of multiple peaks in the first spectrum and the second spectrum includes: Multiple target points are determined in the first spectrum and the second spectrum. The signal strength of each target point and its corresponding neighboring points are compared. The neighboring points of each target point are points that meet the preset distance conditions in terms of frequency with each target point. If the signal strength of the current target point is greater than the signal strength of the corresponding adjacent point, the current target point is determined to be a peak in the spectrum.

7. The method according to claim 1, characterized in that, Determining the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal includes: The human tissue in the target imaging field of view is first excited by a zero-diffusion gradient magnetic field, and the first signal obtained by the first excitation is collected. After a preset time, the human tissue is second excited by a diffusion gradient magnetic field, and the second signal obtained by the second excitation is collected. Perform Fourier transform on the first signal and the second signal to obtain the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal.

8. A device for determining the center frequency of magnetic resonance, characterized in that, The device includes: The spectrum determination module is used to determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal, wherein the first signal is a magnetic resonance signal acquired under a zero diffusion gradient magnetic field and the second signal is a magnetic resonance signal acquired under an applied diffusion gradient magnetic field. The parameter determination module is used to identify multiple effective peaks in the first spectrum and the second spectrum, and to calculate the diffusion coefficient corresponding to each effective peak, wherein the effective peaks are peaks corresponding to each human tissue component; The result determination module is used to determine the center frequency of the magnetic resonance system based on the signal strength, frequency, and diffusion coefficient of each effective peak.

9. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the steps of the method as described in any one of claims 1 to 7.

10. A terminal, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described in any one of claims 1 to 7.

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