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

Abstract

The application discloses a method and device for determining the center frequency of magnetic resonance, a storage medium and a terminal. A first frequency spectrum corresponding to a first signal and a second frequency spectrum corresponding to a second signal are determined, 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; a plurality of effective wave peaks in the first frequency spectrum and the second frequency spectrum are identified, and a diffusion coefficient corresponding to each effective wave peak is calculated, the effective wave peak being a wave peak corresponding to each human tissue component; and the center frequency of a magnetic resonance system is determined based on the signal intensity, frequency and diffusion coefficient of each effective wave peak. The magnetic resonance signals under the zero diffusion gradient and the applied diffusion gradient magnetic field are combined, the diffusion coefficient of the effective wave peak is analyzed by spectrum analysis, each component wave peak is identified, and the center frequency is determined accordingly, thereby overcoming the limitations of relying on experience judgment or only based on spectrum feature analysis, and improving the recognition accuracy of the center frequency of magnetic resonance in a complex signal environment.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging technology, and in particular to a method, apparatus, storage medium, and terminal for determining the center frequency of magnetic resonance imaging. Background Technology

[0002] In magnetic resonance imaging (MRI), accurate determination of the center frequency is crucial for image quality and diagnostic results, directly affecting image clarity and the accuracy of lesion detection. Existing methods for determining the center frequency primarily rely on resonance frequency localization based on water signals, and more recently, methods based on material relaxation properties to assist in center frequency correction. These methods can improve the accuracy and stability of center frequency determination to some extent, meeting the basic requirements of conventional imaging scenarios. However, these methods still suffer from problems such as signal shift and insufficient search accuracy when facing complex signal environments. Summary of the Invention

[0003] This application provides a method, apparatus, storage medium, and terminal for determining the center frequency of magnetic resonance imaging, in order to solve the technical problems of signal offset and insufficient search accuracy in existing center frequency determination methods.

[0004] In a first aspect, embodiments of this application provide a method for determining the center frequency of a magnetic resonance imaging (MRI) signal, the method comprising: Determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the 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. Multiple effective peaks in the first and second spectra are identified, and the diffusion coefficient corresponding to each effective peak is calculated. The effective peaks are the 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.

[0005] Secondly, embodiments of this application provide a device for determining the center frequency of magnetic resonance imaging, the device comprising: 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. 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 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. 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.

[0006] Thirdly, embodiments of this application provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps of the method described above.

[0007] Fourthly, embodiments of this application provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is adapted to be loaded by the processor and to execute the steps of the above-described method.

[0008] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following: This application provides a method for determining the center frequency of a magnetic resonance imaging (MRI) system. The method involves determining a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal. The first signal is an MRI signal acquired under a zero-diffusion gradient magnetic field, and the second signal is an MRI signal acquired under an applied diffusion gradient magnetic field. Multiple effective peaks in the first and second spectra are identified, and the diffusion coefficient corresponding to each effective peak is calculated. Each effective peak corresponds to a peak representing a different human tissue component. The center frequency of the MRI system is determined based on the signal intensity, frequency, and diffusion coefficient of each effective peak. First, magnetic resonance signals were acquired under both zero-diffusion gradient magnetic fields and applied diffusion gradient magnetic fields, obtaining spectra under two different conditions. This established a basis for comparing the spectra of the same tissue component under different diffusion conditions, providing a data prerequisite for introducing the diffusion coefficient as a physical criterion. This allows for more accurate identification and differentiation of signals from different tissue components by comparing the spectral characteristics under the two conditions, thereby improving the stability and accuracy of center frequency determination. Next, the effective peaks in the spectrum were identified and their diffusion coefficients were calculated, which can quantitatively describe the diffusion behavior of different tissue components in a magnetic field. This facilitates the use of significant differences in diffusion coefficients among different tissue components to distinguish the physical nature of each component in a multi-material coexistence scenario, improving the objectivity and accuracy of subsequent peak classification. Finally, by combining the signal intensity, frequency, and diffusion coefficient of each effective peak, the center frequency of the magnetic resonance system was automatically determined. This automatically eliminates spurious peaks or noise interference, achieving highly robust center frequency correction without manual intervention, effectively ensuring the quality and diagnostic reliability of subsequent magnetic resonance imaging. In the method of this application, the peaks of each component are identified by combining the magnetic resonance signal with the zero diffusion gradient and the magnetic resonance signal with the applied diffusion gradient magnetic field, and the center frequency is determined accordingly by combining the effective peak diffusion coefficient of the spectrum analysis. This overcomes the limitations of relying on empirical judgment or only based on spectrum feature analysis, and improves the accuracy of magnetic resonance center frequency identification in complex signal environments. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 An exemplary system architecture diagram of a method for determining the center frequency of magnetic resonance provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for determining the center frequency of magnetic resonance imaging (MRI) according to an embodiment of this application; Figure 3 A flowchart illustrating a method for determining the center frequency of magnetic resonance imaging (MRI) according to an embodiment of this application; Figure 4 A flowchart illustrating the specific implementation of a method for determining the center frequency of magnetic resonance provided in this application embodiment; Figure 5 A schematic diagram of signal acquisition in a method for determining the center frequency of magnetic resonance provided in an embodiment of this application; Figure 6 A flowchart illustrating a method for determining the center frequency of magnetic resonance imaging (MRI) according to an embodiment of this application; Figure 7 A schematic diagram of the signal intensity map and the diffusion coefficient spectrum in a method for determining the center frequency of magnetic resonance provided in an embodiment of this application; Figure 8 A schematic diagram illustrating the effect of implementing a method for determining the center frequency of magnetic resonance according to an embodiment of this application; Figure 9 A structural block diagram of a magnetic resonance center frequency determination device provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Detailed Implementation

[0011] To make the features and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0013] As a key parameter for signal acquisition and image reconstruction, the accurate determination of the center frequency in magnetic resonance imaging (MRI) is crucial for image quality and diagnostic results. The center frequency directly determines the reference for radio frequency excitation and signal reception; any deviation can lead to image artifacts, signal shifts, and even reconstruction errors, especially in complex tissues involving multiple resonant components such as water, fat, and silica. Existing methods for determining the center frequency primarily rely on resonant frequency localization based on water signals. This includes using known water-fat frequency differences to locate the water signal, or manually selecting resonance peaks. More recently, methods based on material relaxation properties for center frequency correction have been proposed, which can help distinguish the frequency positions of different components to some extent, thereby achieving center frequency correction in the MRI system.

[0014] However, traditional methods have limitations when faced with complex signal environments (such as predominantly fat signals, mixed signals from multiple substances, or signals affected by magnetic field inhomogeneities), including insufficient accuracy and reliance on manual intervention based on experience. Specifically, in clinical applications, for areas containing large amounts of fat, such as the breast and abdomen, the signal intensity of fat is close to or even exceeds that of water. Furthermore, a significant proportion of individuals have non-aqueous, non-fat components in their breasts, such as silicone implants. In these situations, the mixed signals from multiple substances easily form overlapping peaks, making it difficult to accurately locate the resonant frequency of the water signal using traditional methods that rely on the frequency difference between water and fat or manual intervention based on experience. Furthermore, relaxation-based methods are susceptible to radio frequency field inhomogeneities, causing deviations in the T1 relaxation characteristics of each component across the frequency spectrum, thus affecting the differentiation effect. In addition, tissues in different parts of the human body exhibit inconsistent relaxation behaviors. For example, the T1 relaxation values ​​of liver tissue (approximately 800 ms) and adipose tissue (300 ms-500 ms) are relatively similar, while the T2 relaxation values ​​of adipose tissue (40 ms-100 ms) and brain white matter (approximately 70 ms) are relatively similar. This makes it difficult to distinguish between adipose tissue and water in these tissues, thereby affecting the accuracy and reliability of the diagnosis.

[0015] Therefore, this application provides a method for determining the center frequency of magnetic resonance, in order to solve the technical problems of signal offset and insufficient search accuracy in existing center frequency determination methods.

[0016] Please see Figure 1 , Figure 1An exemplary system architecture diagram of a method for determining the center frequency of magnetic resonance provided in an embodiment of this application.

[0017] like Figure 1 As shown, the system architecture may include a terminal 101, a network 102, and a server 103. The network 102 serves as the medium for providing a communication link between the terminal 101 and the server 103. The network 102 may include various types of wired or wireless communication links, such as wired communication links including fiber optic cables, twisted-pair cables, or coaxial cables, and wireless communication links including Bluetooth communication links, Wireless-Fidelity (Wi-Fi) communication links, or microwave communication links, etc.

[0018] Terminal 101 can interact with server 103 via network 102 to receive messages from or send messages to server 103. Alternatively, terminal 101 can interact with server 103 via network 102 to receive messages or data sent to server 103 by other users. For example, terminal 101 acquires magnetic resonance signals, namely a first signal and a second signal, under zero-diffusion gradient magnetic field and under applied diffusion gradient magnetic field conditions, respectively. The acquired signal data is transmitted to server 103 via network 102. After receiving the magnetic resonance signal data from terminal 101, server 103 stores and preprocesses the data, and performs spectral analysis on the first and second signals to generate corresponding first and second spectra.

[0019] Terminal 101 can be hardware or software. When terminal 101 is hardware, it can be various electronic devices, including but not limited to tablet computers, laptops, and desktop computers connected to magnetic resonance scanning equipment. When terminal 101 is software, it can be installed in the electronic devices listed above, and it can be implemented as multiple software programs or software modules (e.g., to provide distributed services), or it can be implemented as a single software program or software module, without specific limitations.

[0020] In this embodiment, terminal 101 first determines the first spectrum corresponding to the first signal and the second spectrum corresponding to the 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. Then, terminal 101 identifies multiple effective peaks in the first and second spectra and calculates the diffusion coefficient corresponding to each effective peak. The effective peaks are peaks corresponding to each human tissue component. Finally, terminal 101 determines the center frequency of the magnetic resonance system based on the signal strength, frequency, and diffusion coefficient of each effective peak.

[0021] Server 103 can be a business server providing various services. It should be noted that server 103 can be hardware or software. When server 103 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 103 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module; no specific limitations are made here.

[0022] Alternatively, the system architecture may not include server 103. In other words, server 103 may be an optional device in the embodiments of this specification. That is, the method provided in the embodiments of this specification can be applied to a system structure that only includes terminal 101. The embodiments of this application do not limit this.

[0023] It should be understood that Figure 1 The number of terminals, networks, and servers shown is only illustrative; the number can be any number of terminals, networks, and servers depending on the implementation requirements.

[0024] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for determining the center frequency of a magnetic resonance imaging (MRI) sensor, provided in an embodiment of this application. The execution entity in this embodiment can be a terminal executing the determination of the MRI center frequency, a processor within the terminal executing the method for determining the MRI center frequency, or a MRI center frequency determination service within the terminal executing the method for determining the MRI center frequency. For ease of description, the following example uses a processor within a terminal as the execution entity to illustrate the specific execution process of the method for determining the MRI center frequency.

[0025] like Figure 2 As shown, methods for determining the center frequency of magnetic resonance imaging can include at least the following: S202. Determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the 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.

[0026] Optionally, magnetic resonance imaging (MRI) needs to distinguish various human tissue components, which differ in their magnetic resonance frequencies. For example, water, fat, and medical silicone prostheses occupy different positions on the magnetic resonance frequency spectrum. Using water as a central reference, the frequency of fat is typically about 3.5 ppm lower than that of water, while the hydrogen proton frequency in medical silicone prostheses is about 4.7 ppm lower than that of water. This difference in resonance frequency provides a preliminary basis for peak position identification in the spectrum. However, in actual clinical scenarios, due to factors such as inhomogeneity of the main magnetic field and magnetic susceptibility effects, a single component often exhibits multiple split or broadened peaks, and the peaks of different components may overlap. Relying solely on frequency differences is insufficient for reliable differentiation in complex signal environments. Furthermore, these substances exhibit orders-of-magnitude differences in their hydrogen proton diffusion capabilities. For instance, water molecules in the human body have high degrees of freedom, with a diffusion coefficient of approximately 10. -3 mm 2 The diffusion coefficient of protons in fat is on the order of / s, while the diffusion coefficient of protons in fat is only 10. -5 mm 2 The difference is on the order of / s, with a range of 1–2 orders of magnitude. This significant difference in diffusion coefficients provides another dimension of effective information for distinguishing different tissue components.

[0027] Based on this, this application introduces diffusion behavior characteristics as reference information in addition to the traditional frequency difference dimension. By acquiring two sets of signals under zero-diffusion coding and diffusion gradient coding, proton density-weighted and diffusion-weighted spectra are obtained, respectively. Since the uniformity of the gradient field is much higher than that of the radio frequency field, the signal coding obtained by the diffusion gradient coding method is relatively more stable in space. The resulting spectral information can more accurately reflect the diffusion characteristics of each tissue component. Therefore, by combining spectral characteristics and diffusion coefficients, the frequency peak positions of components such as water and fat can be accurately identified, thereby precisely determining 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 from human tissue without applying any diffusion-sensitive gradient magnetic field, thereby obtaining a first signal. This signal reflects the original resonance response of each hydrogen-containing component in the human body under the influence of diffusion weighting, and its intensity is mainly determined by proton density and inherent relaxation characteristics. Subsequently, the first signal is subjected to frequency domain transformation to obtain the corresponding first spectrum, which reflects the fundamental frequency distribution of each tissue component under zero diffusion conditions.

[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 peak.

[0036] Optionally, after quantifying the diffusion characteristics of each effective peak, information from three dimensions—frequency position, signal strength, and diffusion coefficient—is integrated to perform a comprehensive analysis and component attribution judgment on all effective peaks, thereby making the final determination of the center frequency.

[0037] In this embodiment, a method for determining the center frequency of a magnetic resonance system is provided. This method involves determining 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. Multiple effective peaks in the first and second spectra are identified, and the diffusion coefficient corresponding to each effective peak is calculated. Each effective peak corresponds to a peak representing a component of human tissue. The center frequency of the magnetic resonance system is determined based on the signal intensity, frequency, and diffusion coefficient of each effective peak. First, magnetic resonance signals were acquired under both zero-diffusion gradient magnetic fields and applied diffusion gradient magnetic fields, obtaining spectra under two different conditions. This established a basis for comparing the spectra of the same tissue component under different diffusion conditions, providing a data prerequisite for introducing the diffusion coefficient as a physical criterion. This allows for more accurate identification and differentiation of signals from different tissue components by comparing the spectral characteristics under the two conditions, thereby improving the stability and accuracy of center frequency determination. Next, the effective peaks in the spectrum were identified and their diffusion coefficients were calculated, which can quantitatively describe the diffusion behavior of different tissue components in a magnetic field. This facilitates the use of significant differences in diffusion coefficients among different tissue components to distinguish the physical nature of each component in a multi-material coexistence scenario, improving the objectivity and accuracy of subsequent peak classification. Finally, by combining the signal intensity, frequency, and diffusion coefficient of each effective peak, the center frequency of the magnetic resonance system was automatically determined. This automatically eliminates spurious peaks or noise interference, achieving highly robust center frequency correction without manual intervention, effectively ensuring the quality and diagnostic reliability of subsequent magnetic resonance imaging. In the method of this application, the peaks of each component are identified by combining the magnetic resonance signal with the zero diffusion gradient and the magnetic resonance signal with the applied diffusion gradient magnetic field, and the center frequency is determined accordingly by combining the effective peak diffusion coefficient of the spectrum analysis. This overcomes the limitations of relying on empirical judgment or only based on spectrum feature analysis, and improves the accuracy of magnetic resonance center frequency identification in complex signal environments.

[0038] Please see Figure 3 , Figure 3 This is a flowchart illustrating a method for determining the center frequency of magnetic resonance imaging, as provided in an embodiment of this application.

[0039] like Figure 3 As shown, methods for determining the center frequency of magnetic resonance imaging can include at least the following: S302. Perform a first excitation on the human tissue in the target imaging field of view based on a zero-diffusion gradient magnetic field, collect the first signal obtained from the first excitation, and perform a second excitation on the human tissue based on a diffusion gradient magnetic field after a preset time, collect the second signal obtained from the second excitation.

[0040] Optionally, Figure 4 This is a flowchart illustrating the specific implementation of a method for determining the center frequency of magnetic resonance imaging, as provided in this application embodiment. Figure 4 As shown in S402, the method of this embodiment first acquires dual-mode magnetic resonance signals for subsequent analysis through two excitation processes.

[0041] Specifically, Figure 5 This is a schematic diagram of signal acquisition in a method for determining the center frequency of magnetic resonance provided in an embodiment of this application, as shown below. Figure 5 As shown, signal acquisition is achieved based on a spin echo sequence. During the first excitation, a 90-degree radio frequency pulse is first applied to the human tissue in the target imaging field of view (FOV) using a standard spin echo tomography sequence to flip the longitudinal magnetization vector to the lateral plane. Subsequently, after an appropriate time delay, a 180-degree radio frequency pulse is applied to refocus the proton phase to generate a spin echo signal. No diffusion gradient is applied during the first excitation; therefore, the acquired signal mainly reflects the proton density and relaxation characteristics of various components within the tissue, denoted as the first signal, and its signal intensity S1 can be described by the following formula:

[0042] in, The density represents the signal strength; e is the natural base; TE is the echo time, which is the time interval from the excitation pulse to the received echo signal; T2 is the transverse relaxation time, which is the speed at which the proton loses phase coherence in the magnetic field.

[0043] Furthermore, after a preset duration, a second excitation is performed. A 90-degree and a 180-degree radio frequency pulse are applied, but a pair of diffusion gradient pulses (i.e., the two pulses in the "Diffusion Gradient" row of the figure) are symmetrically introduced between the two pulses. Due to the differences in the molecular mobility of hydrogen protons in different tissue components, this diffusion gradient pulse causes a significant attenuation of the signal from free water molecules, while the signal attenuation is smaller for confined components such as fat or silica. The second signal acquired thus contains information about the diffusion effect, and its signal intensity S2 can be described by the following formula:

[0044] Where b is the applied diffusion coefficient sensitivity factor, and D is the diffusion coefficient.

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

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

[0047] It should be noted that the specific implementation of the signal acquisition described above is merely an example. In other feasible alternatives, instead of using spin echo sequences, gradient echo (GRE) or free induction decay (FID) signals can be used as the basic acquisition method. The number of excitations can also be extended to more than two to improve the accuracy of diffusion coefficient estimation. In addition, the flip angle setting can be adjusted according to 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 selectively exciting a single layer (such as the middle layer of the target imaging field of view), the entire slab region can be excited, or only high-resolution sampling can be performed on a local voxel at the center of the target imaging field of view, to adapt to different imaging requirements and application scenarios.

[0048] S304. 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.

[0049] Optionally, Fourier transforms are performed on the acquired first and second signals respectively to convert the time-domain signals into frequency-domain representations, thereby obtaining their respective first and second spectra. These two spectra are roughly aligned on the frequency axis, but the amplitudes of each resonance peak exhibit varying degrees of attenuation due to the diffusion effect, providing basic data for subsequent component identification based on diffusion behavior.

[0050] S306. Identify multiple peaks in the first and second spectra, and determine the effective peaks at the same frequency in the first and second spectra from among the peaks.

[0051] Optionally, such as Figure 4 As shown in S404, peak detection is performed on the first spectrum and the second spectrum respectively to obtain two sets of discrete peak information. Specifically, the frequency coordinates of all peaks are extracted from the first spectrum to form a frequency array f1, and the signal strength corresponding to each peak is recorded to form a signal strength array s1; similarly, the frequency array f2 and the corresponding signal strength array s2 are extracted from the second spectrum.

[0052] Optionally, because actual magnetic resonance signals are susceptible to noise, magnetic field inhomogeneity, or sampling errors, each of the two spectra may contain some non-true spurious peaks, or the same component may appear as slightly different peak positions in the two spectra due to minor frequency offsets. Therefore, directly using all peak values ​​for subsequent analysis may lead to misjudgment. Based on this, frequency position alignment screening is further performed. All frequency values ​​in f1 and f2 are traversed to find frequency points that exist in both arrays or overlap within a preset tolerance range, and these common frequency positions are grouped into a new frequency set fp. Each frequency point in this set appears stably in the spectra under two different diffusion conditions, indicating good repeatability and physical authenticity, representing the effective peaks corresponding to each human tissue component.

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

[0054] Optionally, for each frequency in fp, the corresponding signal amplitude is retrieved from s1 and s2 respectively for subsequent diffusion coefficient calculation. Specifically, in the first spectrum, the signal intensity at each frequency is obtained and denoted as array sp1; in the second spectrum, the signal intensity at the same frequency is obtained and denoted as array sp2, where the signal intensity at the same frequency position in the two spectra corresponds to the associated signal intensity of the same human tissue component. Since the two acquisitions differ only in whether a diffusion gradient is introduced, and other imaging parameters remain consistent, the difference between sp1 and sp2 mainly reflects the molecular diffusion capability of the substance corresponding to the frequency component. Based on this, the ratio of the two signal intensities, sp2 / sp1, is calculated, which is the signal attenuation ratio of the corresponding tissue component under the current diffusion coding conditions. Further, in the aforementioned 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 calculated.

[0055] S310. 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, determine the water peak corresponding to the water component; use the frequency of the water peak as the center frequency of the magnetic resonance system.

[0056] Optionally, based on the diffusion coefficients corresponding to each effective peak, they can be initially classified into different categories. For example, peaks with significantly higher diffusion coefficients tend to correspond to free water molecules, while peaks with significantly lower diffusion coefficients are more likely to originate from components such as adipose tissue with restricted movement or silicone implants. It is known that different human tissue components exhibit stable chemical shift patterns in the magnetic resonance spectrum. Based on this, further refined discrimination can be achieved by combining the relative frequency positions and signal intensity differences between these peaks. For example, the resonance frequencies of fat and water show predictable relative shifts. Based on this, the water peak among the effective peaks can be identified, and the frequency corresponding to this peak is determined as the center frequency of the magnetic resonance imaging system.

[0057] This application provides a method for determining the center frequency of magnetic resonance imaging (MRI). By determining the associated signal intensity of each effective peak at the same frequency in the first and second spectra and calculating the signal attenuation ratio to obtain the diffusion coefficient, this method can more accurately reflect the actual diffusion characteristics of each tissue component. This provides an objective and quantifiable basis for distinguishing different human tissue components, improving the accuracy of component identification. By combining the diffusion coefficient, frequency position, and signal intensity of each effective peak with the known distribution patterns of human tissue components in the spectrum, the method intelligently identifies the true water peak and uses its frequency as the center frequency. This avoids relying on the absolute intensity of the water peak or human experience, and can still achieve high accuracy even in complex scenarios where fat or silicone signals dominate. The method achieves high-precision center frequency correction. By retaining only peaks with consistent frequency positions in the first and second spectra as valid peaks, it effectively filters out spurious peaks caused by noise, magnetic field inhomogeneity, or sampling errors. This ensures that subsequent diffusion coefficient calculations and component classification are based solely on real and stable tissue resonance signals, improving the robustness and reliability of the entire method. Two independent excitations are used to acquire zero-diffusion and diffusion-weighted signals respectively, and Fourier transforms are used to construct the corresponding dual spectra, providing a physical basis for subsequent diffusion-based analysis. Furthermore, this process control is easily integrated into clinical scanning protocols, and due to the high stability of the gradient field, the obtained data is less affected by system inhomogeneity, ensuring the accuracy and repeatability of center frequency correction.

[0058] Please see Figure 6 , Figure 6 This is a flowchart illustrating a method for determining the center frequency of magnetic resonance imaging, as provided in an embodiment of this application.

[0059] like Figure 6 As shown, methods for determining the center frequency of magnetic resonance imaging can include at least the following: S602. 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.

[0060] Optionally, for details regarding step S602, please refer to the description in step S202, which will not be repeated here.

[0061] S604. Determine multiple target points in the first spectrum and the second spectrum, compare the signal strength of each target point and its corresponding neighboring points, and determine the neighboring points of each target point as points that meet the preset distance conditions in terms of frequency.

[0062] Optionally, to accurately extract all potential resonance peaks from the first and second spectra, the method in this embodiment employs an automated peak-finding method based on local neighborhood comparison. Specifically, each spectrum is first discretized into a series of sampling points on the frequency axis; these sampling points are the target points to be detected. For each target point, its corresponding neighboring points are determined according to a preset frequency neighborhood range (e.g., extending two frequency sampling intervals to the left and right of the point), and the signal strength of the current target point is compared one by one with the signal strengths of all its corresponding neighboring points. This preset distance condition can be flexibly set according to the spectral resolution, noise level, etc., 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, determine the current target point as a peak in the spectrum; determine the effective peaks at the same frequency in the first and second spectra from each peak.

[0064] Optionally, if the signal strength of the current target point is greater than the amplitude of all its neighboring points, then the point is determined to be a local maximum, i.e., a peak. By traversing all target points in the spectrum and performing the above judgment, the frequency positions of all peaks in the spectrum and their corresponding signal strengths can be obtained. Based on this, the effective peaks at the same frequency in the first and second spectra are determined from each peak. For details, 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, where the effective peak is the peak corresponding to each human tissue component.

[0066] Optionally, for details regarding step S608, please refer to step S204, which will not be repeated here.

[0067] S610. Each human tissue component includes at least water and fat components. 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.

[0068] Optionally, Figure 4This is a flowchart illustrating the specific implementation of a method for determining the center frequency of magnetic resonance imaging, as provided in this application embodiment. Figure 4 As shown in S406-S410, each human tissue component includes at least water and fat components. After calculating the diffusion coefficient of each effective peak, the fat peak corresponding to the fat component is first identified, and then the water peak is determined with reference to it.

[0069] Optionally, center frequency measurement in the breast region is still chosen as an example scenario. In the breast region, because the signal intensity of fat is close to or even higher than that of water, and silicone implants may be present, accurate differentiation between fat peaks, silicone peaks, and water peaks is necessary. Specifically, based on known physical properties of human tissue, the diffusion coefficient range applicable to fatty components (e.g., 0.01-0.1 μm) is first determined. 2 / ms), within this range, select all valid peaks that meet the criteria.

[0070] Furthermore, since both fat and silicone implants exhibit confined diffusion characteristics, their diffusion coefficients typically fall within this range. Therefore, the two peaks with the highest signal intensity from these effective peaks with low diffusion coefficients can be selected as the primary analytical targets for fat and silicone. Considering that the resonance frequency of fat is slightly higher than that of silicone under typical magnetic field strengths, the frequency positions of these two peaks are compared. The peak with the relatively higher frequency is identified as the fat peak, while the peak with the lower frequency is assigned to the silicone peak.

[0071] S612. 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; the frequency of the water peak is used as the center frequency of the magnetic resonance system.

[0072] Optionally, after the fatty peak is accurately identified, the method of this embodiment searches for candidate peaks of water components within a reasonable frequency band on its high-frequency side (e.g., shifted upwards by 0.5ppm to 5.5ppm). Within this region, the high diffusion coefficient characteristic unique to water molecules (e.g., [0.4, 3.0]µm) is further considered. 2 Based on the signal strength within the range of / ms and reasonable signal intensity, the water peak representing the water component was finally determined, and the frequency of the water peak was used as the center frequency of the magnetic resonance system.

[0073] Optionally, Figure 7 This is a schematic diagram of the signal intensity map and the diffusion coefficient spectrum in a method for determining the center frequency of magnetic resonance provided in an embodiment of this application. Figure 7As shown, the left subplot displays a comparison of the spectra obtained after Fourier transform of two magnetic resonance signals acquired under zero diffusion gradient (PD) and applied diffusion gradient (DW) conditions. The blue curve represents the first spectrum under zero diffusion conditions, which has a higher amplitude and clearer peaks, reflecting the original resonance response of various human tissue components without diffusion attenuation. The red curve represents the second spectrum under diffusion-weighted conditions. Due to the significant signal attenuation of free water molecules under the diffusion gradient, its main peak 3 (corresponding to the water signal) is significantly reduced; while the signals of motion-restricted components such as peak 1 (corresponding to silica gel) and peak 2 (corresponding to fat) are relatively well preserved.

[0074] Optionally, such as Figure 7 As shown, the right-hand subplot is a distribution of diffusion coefficients corresponding to each characteristic peak. The horizontal axis represents frequency position, and the vertical axis represents diffusion coefficient. Black asterisks mark the diffusion coefficients calculated from the multiple effective peaks identified in the left-hand plot. This subplot reveals significant differences in diffusion behavior among different components: peaks 1 and 2, located in the low-frequency region, have lower diffusion coefficients, corresponding to silica and fat, respectively; while peak 3, located near the center frequency, has a higher diffusion coefficient, consistent with the diffusion characteristics of water. The method in this embodiment combines frequency position, signal strength, and diffusion coefficient to accurately determine that peak 3 is a water peak and uses its frequency as the center frequency of the magnetic resonance system, achieving high-precision, automated correction.

[0075] Optionally, Figure 8 This diagram illustrates the effect of a method for determining the center frequency of magnetic resonance imaging (MRI) provided in this embodiment. From left to right, it shows a comparison of the imaging results obtained after activating the fat suppression module when peaks 1, 2, and 3 are used as the center frequencies of the MRI system, respectively. It is clearly visible that in the left and middle images, the incorrect setting of the silicone and fat peak frequencies as the center frequency resulted in the fat suppression pulse failing to accurately cover the fat resonance frequency, leading to ineffective suppression of fat tissue and affecting image quality. In contrast, in the right image, using the water peak frequency identified by the method of this embodiment as the center frequency, the fat suppression pulse precisely targets the fat component, achieving efficient suppression of the fat signal while preserving clear display of the water and silicone signals. The "oil" area in the image is virtually free of residue, and the boundary between water and silicone tissue is distinct, resulting in significantly better imaging quality than the former two. This result verifies the effectiveness of the method in accurately identifying the water peak through the joint discrimination of diffusion coefficient and spectral characteristics, ensuring reliable execution of the fat suppression function and fully demonstrating the practical value of this method in achieving high-precision center frequency correction in complex multi-component scenarios.

[0076] In this application embodiment, a method for determining the center frequency of magnetic resonance imaging is provided. First, the fat peak is identified based on the diffusion coefficient, signal strength, and frequency characteristics. Then, using the fat peak as a reference, peaks that conform to the characteristics of water components are searched within a preset high-frequency range. This method fully utilizes the stable physical differences between water and fat in chemical shift and diffusion behavior. Even in complex scenarios where the water signal is weak or there are interfering components such as silica gel, the water peak can be accurately and reliably located, improving the accuracy and robustness of center frequency correction. The method automatically identifies peaks in the spectrum by comparing local neighborhood amplitudes, without the need for complex modeling or manual intervention. The algorithm is simple, efficient, and highly noise-resistant, ensuring that all potential human tissue component peaks are effectively detected, providing a reliable data foundation for subsequent component classification and center frequency determination.

[0077] Please see Figure 9 , Figure 9 This is a structural block diagram of a magnetic resonance center frequency determination device provided in an embodiment of this application. Figure 9 As shown, the magnetic resonance center frequency determination device 900 includes: The spectrum determination module 910 is used to determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the 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. The parameter determination module 920 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. The effective peaks are the peaks corresponding to each human tissue component. The result determination module 930 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.

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

[0079] In some possible embodiments, the result determination module 930 is also used to determine the water peak corresponding to the water component 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; and to use the frequency of the water peak as the center frequency of the magnetic resonance system.

[0080] In some possible embodiments, each human tissue component includes at least water and fat components. The result determination module 930 is further configured to screen the diffusion coefficient, signal intensity, and frequency of each effective peak based on the diffusion coefficient range, signal intensity characteristics, and frequency characteristics of each human tissue component in the spectrum, and determine the fat peak corresponding to the fat component; within the preset frequency range of the fat peak, determine the water peak corresponding to the water component based on the signal intensity characteristics and diffusion coefficient range of the water component in the spectrum.

[0081] In some possible embodiments, the parameter determination module 920 is further configured to identify multiple peaks in the first spectrum and the second spectrum, and determine from each peak a valid 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 multiple target points in the first spectrum and the second spectrum, compare the signal strength of each target point and its corresponding adjacent points, wherein each target point's corresponding adjacent points 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 its corresponding adjacent points, the current target point is determined to be a peak in the spectrum.

[0083] In some possible embodiments, the spectrum determination module 910 is further configured to perform a first excitation on human tissue in the target imaging field based on a zero-diffusion gradient magnetic field, acquire a first signal obtained from the first excitation, and perform a second excitation on human tissue based on a diffusion gradient magnetic field after a preset time, acquire a second signal obtained from the second excitation; 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 this embodiment of the application, a device for determining the center frequency of a magnetic resonance imaging (MRI) system is provided. The device includes a spectrum determination module for determining a first spectrum corresponding to a first signal and a second spectrum corresponding to a second signal, wherein the first signal is an MRI signal acquired under a zero-diffusion gradient magnetic field and the second signal is an MRI signal acquired under an applied diffusion gradient magnetic field; a parameter determination module for identifying multiple effective peaks in the first and second spectra and calculating the diffusion coefficient corresponding to each effective peak, wherein the effective peaks are peaks corresponding to various human tissue components; and a result determination module for determining the center frequency of the MRI system based on the signal strength, frequency, and diffusion coefficient of each effective peak. First, the spectrum determination module acquires magnetic resonance signals under both zero-diffusion gradient magnetic fields and applied diffusion gradient magnetic fields, obtaining spectra under two different conditions. This establishes a basis for comparing the spectra of the same tissue component under different diffusion conditions, providing a data prerequisite for introducing the diffusion coefficient as a physical criterion. This allows for more accurate identification and differentiation of signals from different tissue components by comparing the spectral characteristics under the two conditions, thereby improving the stability and accuracy of center frequency determination. Next, the parameter determination module identifies effective peaks in the spectrum and calculates their diffusion coefficients. This quantitatively describes the diffusion behavior of different tissue components in a magnetic field, facilitating the differentiation of the physical essence of each component in a multi-material coexistence scenario by utilizing the significant differences in diffusion coefficients, thus improving the objectivity and accuracy of subsequent peak classification. Finally, the result determination module integrates the signal intensity, frequency, and diffusion coefficient of each effective peak to automatically determine the center frequency of the magnetic resonance system. It can automatically eliminate spurious peaks or noise interference, achieving highly robust center frequency correction without manual intervention, effectively ensuring the quality and diagnostic reliability of subsequent magnetic resonance imaging. In the method of this application, the peaks of each component are identified by combining the magnetic resonance signal with the zero diffusion gradient and the magnetic resonance signal with the applied diffusion gradient magnetic field, and the center frequency is determined accordingly by combining the effective peak diffusion coefficient of the spectrum analysis. This overcomes the limitations of relying on empirical judgment or only based on spectrum feature analysis, and improves the accuracy of magnetic resonance center frequency identification in complex signal environments.

[0085] This application also provides a computer storage medium that can store multiple instructions adapted for loading by a processor and executing the steps of any of the methods described in the above embodiments.

[0086] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Figure 10 As shown, terminal 1000 may include: at least one terminal processor 1001, at least one network interface 1004, user interface 1003, memory 1005, and at least one communication bus 1002.

[0087] The communication bus 1002 is used to realize the connection and communication between these components.

[0088] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0089] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0090] The terminal processor 1001 may include one or more processing cores. The terminal processor 1001 connects to various parts within the terminal 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the terminal processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The terminal processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the terminal processor 1001.

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

[0092] exist Figure 10 In the terminal 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the terminal processor 1001 can be used to call the magnetic resonance center frequency determination program stored in the memory 1005, and specifically perform the following operations: Determine the first spectrum corresponding to the first signal and the second spectrum corresponding to the 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. Multiple effective peaks in the first and second spectra are identified, and the diffusion coefficient corresponding to each effective peak is calculated. The effective peaks are the 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.

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

[0094] In some possible embodiments, when the terminal processor 1001 determines the center frequency of the magnetic resonance system based on the signal strength, frequency and diffusion coefficient of each effective peak, it specifically performs the following steps: based on the signal characteristics of each human tissue component in the spectrum, combined with the diffusion coefficient, signal strength 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.

[0095] In some possible embodiments, each human tissue component includes at least water and fat components. When the terminal processor 1001 determines the water peak corresponding to the water component based on the signal characteristics of each human tissue component in the spectrum, combined with the diffusion coefficient, signal strength, and frequency of each effective peak, it specifically performs the following steps: based on the diffusion coefficient range, signal strength characteristics, and frequency characteristics of each human tissue component in the spectrum, it filters the diffusion coefficient, signal strength, and frequency of each effective peak to determine the fat peak corresponding to the fat component; within the preset frequency range of the fat peak, based on the signal strength characteristics of the water component in the spectrum and the diffusion coefficient range of the water component, it determines the water peak corresponding to the water component.

[0096] In some possible embodiments, when the terminal processor 1001 performs the following steps when identifying multiple valid peaks in the first spectrum and the second spectrum: identifying multiple peaks in the first spectrum and the second spectrum, and determining from each peak the valid peaks that are at the same frequency in the first spectrum and the second spectrum.

[0097] In some possible embodiments, when the terminal processor 1001 performs the task of identifying multiple peaks in the first spectrum and the second spectrum, it specifically performs the following steps: determining multiple target points in the first spectrum and the second spectrum, comparing the signal strength of each target point and its corresponding neighboring points, wherein each neighboring point is a point that meets a preset distance condition in terms of frequency with each target point; if the signal strength of the current target point is greater than the signal strength of its corresponding neighboring point, the current target point is determined to be a peak in the spectrum.

[0098] In some possible embodiments, when the terminal processor 1001 executes the steps of determining the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal, the following steps are specifically performed: the human tissue in the target imaging field is subjected to a first excitation based on a zero-diffusion gradient magnetic field, and the first signal obtained by the first excitation is acquired; after a preset time, the human tissue is subjected to a second excitation based on a diffusion gradient magnetic field, and the second signal obtained by the second excitation is acquired; the first signal and the second signal are subjected to Fourier transform to obtain the first spectrum corresponding to the first signal and the second spectrum corresponding to the second signal.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0100] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0101] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The aforementioned available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0102] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] The above is a description of a method, apparatus, storage medium, and terminal for determining the center frequency of magnetic resonance provided in this application. For those skilled in the art, based on the ideas of the embodiments of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this 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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