Magnetic particle relaxation signal processing and analyzing method based on optical pump magnetometer

Abstract

The invention discloses a magnetic particle relaxation signal processing and analysis method based on an optical pump magnetometer. The method comprises a particle magnetization device, a signal acquisition mode and a data processing method. The particle magnetization device applies a pulse uniform direct-current field near superparamagnetic nanoparticles, so that the superparamagnetic nanoparticles are quickly magnetized and then relaxed, magnetic field signals generated after the particles are relaxed are detected and collected by using OPM, and background collection and filtering cancellation are carried out under the same condition, so that the magnetic field signals generated after the particles are relaxed are obtained. And performing curve fitting estimation according to the particle intrinsic relaxation parameter to obtain a particle relaxation field signal. The invention provides a relaxation detection, data processing and calibration method under an OPM extremely weak magnetic measurement condition, and is expected to supplement the existing spatial quantitative analysis method of superparamagnetic nanoparticles.

Description

Technical Field

[0001] The invention relates to the field of biomedical engineering, and in particular to a magnetic particle relaxation signal processing and analysis method based on an optical pump magnetometer. Background Art

[0002] Superparamagnetic nanoparticles are a commonly used contrast agent in medicine and have important application value in tumor cell detection and treatment. Considering its biological safety and easy preparation, ferroferric oxide or iron oxide nanoparticles are usually used as detection objects. In addition, iron oxide nanoparticles have magnetic properties of superparamagnetism and high magnetic saturation rate, so their relaxation and magnetic field response characteristics can be effectively used for detection and imaging. With the development and maturity of superconducting quantum interference device (SQUID) and optical pumping magnetometer (OPM), biological extremely weak magnetic field detection has received widespread attention and research. Among them, optical pumping magnetometer has the advantages of flexible layout, multi-axis measurement, high sensitivity and low cost, so it has been rapidly developed in the field of biological magnetic measurement.

[0003] Common particle detection imaging methods include enhanced magnetic resonance imaging (MRI), magnetic particle imaging (MPI), and magnetic relaxation imaging (MRXI). The basic method is to magnetize particles through AC or DC magnetic fields. Since particles have superparamagnetism and can quickly generate specific magnetic field response signals, magnetic field response parameters can be extracted based on signal characteristics, and the particles can be imaged in space to obtain the spatial distribution results of the particles.

[0004] At present, there are few studies on magnetic relaxation imaging using the DC magnetization method. In addition, in magnetic relaxation imaging, most of the superconducting quantum interference devices (SQUIDs) are used as detection sensors, while there are very few studies on the use of optical pumping magnetometers (OPMs) for magnetic relaxation detection. There is a lack of magnetic relaxation detection and signal processing analysis methods using optical pumping magnetometers.

[0005] Relaxation detection of superparamagnetic nanoparticles using DC-SQUID usually adopts a gradient meter setting, which is insensitive to changes in the DC field. Therefore, when the particles are magnetized with a DC field, the influence of the background can be effectively suppressed in hardware. However, in the process of using an optically pumped magnetometer vector meter for detection, the background field generated by the magnetization device cannot be ignored, so an effective background removal method is needed to restore the effective relaxation component of the particles in the signal. In addition, in order to more accurately locate the source and quantitatively analyze the spatial particle distribution, it is necessary to more accurately estimate the relaxation parameters of the particles. In addition to the recovery of particle signals, there is still a lack of relaxation signal processing methods targeting the characteristics of the selected particles themselves. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a relaxation signal detection and processing and analysis method of superparamagnetic nanoparticles based on an optically pumped magnetometer, which realizes spatial magnetic particle detection based on an optically pumped magnetometer, restores particle relaxation signals and correctly estimates relevant parameters, laying a foundation for reconstructing the spatial source-based and quantitative distribution of magnetic particles.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is:

[0008] The method for processing and analyzing magnetic particle relaxation signals based on optical pumping magnetometer is characterized in that a process and method for processing and analyzing particle relaxation signals when the characteristics of superparamagnetic nanoparticles are known are proposed. First, superparamagnetic nanoparticles or cells or biological models carrying superparamagnetic nanoparticles that can be measured by optical pumping magnetometer need to be selected as the test object, and the particles are fixed in a fixed state and placed on a magnetization platform by freeze drying, absorption or targeting; then, the magnetization and relaxation signals of particles with different contents under the same magnetization conditions are measured, and the empty background signal without particles or pure water under the same magnetization conditions is collected; finally, the average signal of the empty background and magnetization relaxation collected multiple times is filtered and cancelled, and then curve fitting is performed. After the signal processing operation is completed, the particle content is calibrated.

[0009] Furthermore, the object to be tested is characterized in that the superparamagnetic nanoparticles are biocompatible nanomaterials of iron oxide or ferroferric oxide with a core diameter of 23-27 nm, and the particle shape is a spherical or nearly spherical polygonal structure. 4 The anisotropy constant is of high magnitude, its VSM hysteresis loop has a high saturation value and steep characteristics, and the remanence tends to zero.

[0010] Furthermore, the superparamagnetic nanoparticles are characterized by having a coating material with a targeted or non-agglomerated functional group. The coating material can make the superparamagnetic nanoparticles present two states, one is a free state that can rotate and move freely, and the other is a bound state that is opposite to it. In the relaxation process after magnetization, the free state particles are mainly dominated by Brownian relaxation, and the bound state particles are mainly dominated by Neel relaxation. The particles can produce Neel relaxation of milliseconds to seconds under the magnetization condition of mT magnetic field, and Brownian relaxation of Neel relaxation time is much smaller than several orders of magnitude, which satisfies the Neel relaxation process. The signal can be measured in the optical pump magnetic intensity timing domain, while the Brownian process is not measurable.

[0011] Furthermore, the superparamagnetic nanoparticle relaxation signal detection method based on optical pumping magnetometer is characterized in that it comprises the following steps:

[0012] Step 1, collecting analog signals of pulse magnetization and attenuation under an empty background condition where no particles are placed or pure water is placed by means of an optical pump magnetometer, and synchronously collecting analog electrical signals representing the magnetization pattern;

[0013] Step 2, placing the particles in a bound state and detecting and collecting analog signals of magnetization and relaxation of the particles under the same pulsed magnetization conditions, and synchronously collecting analog electrical signals representing the magnetization pattern;

[0014] Step 3, changing the content of the particles used, repeating step 2, performing analog-to-digital conversion on the analog signals collected above at the same sampling rate to obtain corresponding digital signals, and saving the obtained data.

[0015] Furthermore, the method for processing the superparamagnetic nanoparticle relaxation signal based on the optically pumped magnetometer is characterized by comprising the following steps:

[0016] Step 1, according to the conversion digital signal characterizing the magnetization mode, the data collected by the optical pump magnetometer in step 1 and step 2 in right 4 are divided, and multiple groups of attenuation signals under empty background conditions and particle relaxation signal data under the binding state are respectively extracted and saved;

[0017] Step 2, aligning the extracted data sets on the time axis, and superimposing and averaging them to obtain the original average background decay signal and the original average particle relaxation signal respectively;

[0018] Step 3, filtering and canceling the original average background decay signal and the original average particle relaxation signal, specifically, taking the original average background decay signal as a reference, removing the background decay component in the original average particle relaxation signal by an adaptive filtering method, and obtaining the particle relaxation signal data after filtering;

[0019] Step 4, calculating the theoretical relaxation signal and theoretical relaxation time of the particles under corresponding magnetization conditions according to the number of selected particles and their magnetic parameters;

[0020] Step 5, according to the particle theoretical relaxation signal calculated in step 4, the filtered particle relaxation signal data is subjected to baseline correction and basis function fitting to obtain the time domain characteristics of the particle relaxation signal after correction and fitting;

[0021] Step 6, based on the particle theoretical relaxation time of steps 4 and 5 and the particle relaxation signal after correction and fitting, reverse estimate the initial time of the particle relaxation signal, intercept or supplement the signal length, and obtain the final processed particle relaxation signal.

[0022] Furthermore, the method for processing the superparamagnetic nanoparticle relaxation signal based on the optically pumped magnetometer, step 4 comprises:

[0023] Step 1, obtaining the particle size distribution parameters of the particle system by electron microscopy testing and other methods, fitting the particle distribution curve, and estimating the number of superparamagnetic nanoparticles;

[0024] Step 2: construct a magnetization model based on magnetization parameters and particle VSM characteristics. According to the set magnetization field intensity and magnetization time parameters and the particle saturation magnetization intensity, use the Langevin function to calculate the particle magnetization ratio and estimate the initial relaxation field intensity after the particle is magnetized;

[0025] Step 3, construct a relaxation model based on particle size distribution and particle number. Establish a Neel relaxation function with particle size as the integrand, and the integrand is a linear combination of exponential or logarithmic functions. Substitute the particle number estimated in step 1 and the magnetization ratio calculated in step 2 into the relaxation model to calculate the theoretical relaxation signal of the particle, and calculate the theoretical relaxation time based on 1 / e of the initial field strength of the signal.

[0026] Furthermore, the method for processing and analyzing the relaxation signal of superparamagnetic nanoparticles based on an optically pumped magnetometer is characterized in that the amplitude of the relaxation signal of particles with different contents at the initial moment is obtained according to the analysis, and the calibration coefficient of the particle content and signal intensity is obtained by linear fitting the data, and is used for quantitative analysis of the particles.

[0027] The beneficial effects of the present invention are:

[0028] The present invention provides a relaxation signal detection device and a measurement method for superparamagnetic nanoparticles that can be used under optical pumping magnetometer measurement conditions. The device can effectively suppress the influence of background attenuation generated by the magnetization process on the working state of the optical pumping magnetometer and particle relaxation detection, and can effectively restore the relaxation signal generated by the particles, thereby obtaining a more accurate particle source determination and quantitative estimation in space. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of the particle relaxation signal processing method of the present invention.

[0030] Figure 2 Schematic diagram of the modeling method for obtaining prior information of particle characteristics of the present invention.

[0031] Figure 3 A schematic diagram of the original relaxation and processed results of superparamagnetic nanoparticles according to an embodiment. DETAILED DESCRIPTION

[0032] The present invention is described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] The invention discloses a superparamagnetic nanoparticle relaxation signal detection, processing and analysis method based on an optical pump magnetometer. The method comprises the following steps: selecting iron oxide superparamagnetic nanoparticles with measurable Neel relaxation and unmeasurable Brownian relaxation characteristics, magnetizing the particles in a pulsed DC magnetization field to make them relax, suppressing the interference of the magnetization background field in a filtering cancellation manner, restoring and reconstructing the particle relaxation signal, preprocessing the reconstructed signal based on the magnetic characteristics of the particles, analyzing and obtaining characteristic parameters characterizing the particle relaxation, and finally obtaining a calibration coefficient for particle quantification.

[0034] In the first part, the present invention provides a modeling method for prior information about particle characteristics. A superparamagnetic nanoparticle system initially appears in a macroscopic state of no magnetic moment. The DC uniform magnetic field generated by the magnetizing coil enables most particles to be magnetized within a few hundred milliseconds to a few seconds. For superparamagnetic iron oxide nanoparticles, the magnetic field size to complete the magnetization is in the order of millitesla. After being magnetized, the particles will return to their original state of no magnetic moment, and this process is manifested as particle relaxation. When the particles are in a free motion state, they appear macroscopically as liquid or colloid. At this time, the particles can be restored to the initial state by physical rotation, which appears microscopically as Brownian thermal motion. This relaxation mode is called Brownian relaxation. When the particles are in a fixed state, they appear as agglomerates or combined with cells and macromolecules. At this time, the hydrodynamic diameter of the particles is greater than 100nm. At this time, the particles cannot be restored to the initial state by physical rotation, and can only restore the magnetic moment through the nuclear spin with intrinsic properties. This relaxation mode that requires a longer time to recover is called Neel relaxation.

[0035] Brownian relaxation time τ B and Neel relaxation time τ N It can be expressed by the following formula:

[0036]

[0037] When Brownian relaxation and Neel relaxation exist simultaneously in a system, the relaxation time is expressed as the effective relaxation time τ eff :

[0038]

[0039] Where η is the viscosity of the local environment, V h is the hydrodynamic volume of the particle, V p is the particle core volume, τ0 is the characteristic damping time constant, k B =1.38×10 -23 JK -1 is the Boltzmann constant, K[J / m 3 ] is magnetic anisotropy, and T[K] represents temperature.

[0040] After the particles are magnetized, a measurable Neel relaxation signal is generated, which can be modeled as a function that characterizes Neel relaxation. For a particle system, its induced relaxation field is expressed as the integral of the Neel relaxation function with the particle volume as a variable.

[0041] First, the particle magnetization process is modeled. The particle magnetization process can be described by the following Langevin function.

[0042]

[0043] Among them, M H It represents the net magnetization intensity of the particles after magnetization, M s represents the saturation magnetization, H represents the applied magnetization field strength, μ0=4π×10 -7 N / A 2 is the permeability of free space, and L(x) is the Langevin function that characterizes the magnetization process. The ratio of magnetization can be expressed as The saturation magnetization M of the particle system can be obtained by measuring the particle hysteresis loop using a vibrating sample magnetometer (VSM). s .

[0044] Next, estimate the number of particles in the system. Use transmission electron microscopy (TEM), scanning electron microscopy (SEM), or atomic force microscopy (AFM) to test the particle size distribution. For well-prepared and uniformly distributed nanoparticles, the particle distribution can be quickly fitted by measuring the distribution parameters. After measuring the average particle size and the variance or standard deviation of the particle size distribution, one of the following normal distribution functions is selected as the fitting function.

[0045]

[0046] Among them, μ is the mean value of the particle volume, and σ is the standard deviation of the particle volume distribution. According to the fitted distribution function f(μ), the calculation method of the particle number N is obtained:

[0047]

[0048] Where ρ is the density of the particle liquid or colloid system used, ρ nano is the density of the iron oxide component of the particles, and V is the volume of the particle system used.

[0049] Finally, based on the estimated number of particles and magnetization ratio, the relaxation signal generated by the particles is simulated using an exponential or logarithmic function. The final particle signal is modeled as follows:

[0050]

[0051] Among them, g(t,t mag ,K,H,T) is the relaxation function, which is related to time t and magnetization time t mag The exponential or logarithmic decay function can be expressed as or Where τ is the characteristic relaxation time, which is expressed as a function of magnetocrystalline anisotropy K, magnetizing field intensity H, and temperature T. The particle Neel relaxation time expression is: τ can be directly expressed as the particle Neel relaxation time τ N , or expressed as τ according to actual conditions N Finally, the theoretical characteristic relaxation time τ can be calculated based on the obtained particle signal. s , that is, the signal drops to the initial maximum value The time required.

[0052] In the second part, the present invention provides a particle relaxation signal reconstruction and recovery method. Figure 1 As shown, the method can be divided into the following six steps:

[0053] Step 1: Use an optically pumped magnetometer to collect signals of N-segment particle excitation magnetization and relaxation processes and background excitation magnetization and recovery processes.

[0054] Step 2: remove the excitation time part and obtain the segmented background signal b = {b i |i=1,2,…,N} and the original relaxation signal of the particle n={n i |i=1,2,…,N}, where b i and n i The length is t r ×F sample The vector of r is the relaxation time after segmentation, Fsample Indicates the sampling frequency of the signal.

[0055] Step 3: Superimpose the segmented signals b and n in the time domain and take the average to obtain the particle average relaxation signal and background average signal, i.e., b m and n m . b m =sum(b) / N,n m =sum(n) / N, where sum(·) is the summation function.

[0056] Step 4: average background signal b m and the particle average relaxation signal n m Perform adaptive filtering to remove the particle average relaxation signal n m Average background signal b m Component: The average relaxation signal of the particle n m As the filter input signal, b m As the expected signal, the particle relaxation signal is obtained after filtering, which is expressed as n r .

[0057] Step 5: convert the particle relaxation signal n obtained in step 3 r Perform basis function fitting and obtain the intrinsic relaxation time t of the particle system according to the particle size distribution function. R According to n r The time axis vector t (from 0 to t r -1 / Fsample, the interval is 1 / Fsample, the length is t r ×F sample The basis function matrix is ​​constructed by solving the coefficients of the basis function components to obtain the final fitting result, that is, the final processed signal n f .

[0058] Step 6: The final processed signal n f Perform characteristic analysis to obtain the calibration coefficient of its intensity with respect to particle content.

[0059] Among them, the adaptive filtering method in step 4 is RLS (Recursive Least Square) recursive least square adaptive filtering, and its processing process is as follows:

[0060] Step 1: Set n m As the input signal, b m is the expected signal, and the signal length is K = t r ×F sample , the selected filter order is M, calculate n m The variance of the vector, denoted by σ;

[0061] Step 2, initialize W0 = 0, P o =σ -1 I, e = 0; where W0 is the initial vector of length M, P o is an M×M matrix, e is a zero vector of length K, and λ is a value between 0.9 and 1;

[0062] Step 3: Perform KM iterations, and perform the following calculations for each i iterations:

[0063] Take n m M vectors, namely x i =n m (iM:i-1), calculate P i =λ -1 (P i-1 -k i x i T P i-1 ), calculate the error vector e(i) = b m (i)-x i T *W i-1 ;

[0064] Update W i =W i-1 +k i e(i);

[0065] After completing i iterations, we get the final W end =W K-M , and iterate again K-M+1 times, taking n m M vectors, namely x i =n m (i-M+1:i), f out (i) = x i *W end The final f out is the output filtering result.

[0066] A modeling method for prior information on particle properties provided in the first part is applied to step 5, such as Figure 2 As shown, the normal distribution function is used to fit the particle distribution to estimate the number of the particle system, and then the Langevin function is combined with the exponential or logarithmic decay function to establish a particle relaxation simulation signal model, and finally the theoretical characteristic relaxation time is obtained according to the simulation signal.

[0067] According to the adaptive filtering, f outThe prior information of particle characteristics is used for curve fitting, and the basis function fitting method is adopted. The processing method is as follows: one of the following two relaxation exponential decay models is determined according to the measured signal and the relaxation function used in the simulation:

[0068]

[0069] Among them, γ is an empirical parameter with a default value of 1, τ s is the theoretical characteristic relaxation time obtained in the first part. Then, the fitting coefficient is obtained according to the least squares fitting method. Finally, the initial value is estimated, and the initial relaxation field strength that meets the relaxation time is inverted and calculated based on the characteristic relaxation time, so that the initial value of the data is reduced to its The time required is equal to the characteristic relaxation time τ s .

[0070] In the curve fitting process, appropriate processing results can also be obtained by upsampling or downsampling.

[0071] like Figure 3 As shown, the original relaxation average signal of the superparamagnetic nanoparticles is compared with the processed signal. On this basis, specific particles of different contents can be detected, and a linear fit is performed based on the initial relaxation field intensity obtained by the process, that is, the initial value of the signal and the corresponding particle content to obtain a linear calibration coefficient of the relaxation field intensity of the selected particle system with respect to the particle content. Finally, the spatial magnetic nanoparticles can be quantitatively analyzed based on the relaxation field intensity and the calibration coefficient after the measured signal processing.

[0072] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing and analyzing magnetic particle relaxation signals based on an optically pumped magnetometer, characterized in that: A process and method for processing and analyzing particle relaxation signals when the characteristics of superparamagnetic nanoparticles are known is proposed. First, superparamagnetic nanoparticles or cells or biological models loaded with superparamagnetic nanoparticles that can be measured by optical pump magnetometers need to be selected as the test objects, and the particles are fixed in a fixed state and placed on a magnetization platform by freeze-drying, absorption or targeting. Then, the magnetization and relaxation signals of particles with different contents under the same magnetization conditions are measured, and the empty background signal without particles or pure water under the same magnetization conditions is collected. Finally, the average signal of the empty background and magnetization relaxation collected multiple times is filtered and canceled, and then curve fitting is performed. After completing the signal processing operation, the particle content is calibrated.

2. The object to be tested according to claim 1, characterized in that: The superparamagnetic nanoparticles are biocompatible nanomaterials made of iron oxide or ferroferric oxide with a core diameter of 23-27 nm, and the particle shape is a spherical or nearly spherical polygonal structure. 4 The anisotropy constant is of high magnitude, its VSM hysteresis loop has a high saturation value and steep characteristics, and the remanence tends to zero.

3. The superparamagnetic nanoparticle according to claim 2, characterized in that: A coating material with a targeted or non-agglomerated functional group. The coating material can make the superparamagnetic nanoparticles present two states, one is a free state that can rotate and move freely, and the other is a bound state that is opposite to it. In the relaxation process after magnetization, the free state particles are mainly dominated by Brownian relaxation, and the bound state particles are mainly dominated by Neel relaxation. The particles can produce Neel relaxation of milliseconds to seconds under the magnetization condition of mT magnetic field, and Brownian relaxation of much smaller than several orders of magnitude Neel relaxation time, satisfying the Neel relaxation process in the optical pump magnetic intensity timing domain. The signal is measurable, while the Brownian process is not measurable.

4. The superparamagnetic nanoparticle relaxation signal detection method based on optical pump magnetometer according to claim 1, characterized in that: The following steps are involved: Step 1, collecting analog signals of pulse magnetization and attenuation under an empty background condition without particles or with pure water by means of an optical pump magnetometer, and synchronously collecting analog electrical signals representing the magnetization pattern; Step 2, placing the particles in a bound state and detecting and collecting analog signals of magnetization and relaxation of the particles under the same pulsed magnetization conditions, and synchronously collecting analog electrical signals representing the magnetization pattern; Step 3, changing the content of the particles used, repeating step 2, performing analog-to-digital conversion on the analog signals collected above at the same sampling rate to obtain corresponding digital signals, and saving the obtained data.

5. The method for processing superparamagnetic nanoparticle relaxation signals based on optically pumped magnetometer according to claim 1 and claim 4, characterized in that: The following steps are involved: Step 1, according to the conversion digital signal characterizing the magnetization mode, the data collected by the optical pump magnetometer in step 1 and step 2 in right 4 are divided, and multiple groups of attenuation signals under empty background conditions and particle relaxation signal data under the binding state are respectively extracted and saved; Step 2, aligning the extracted data sets on the time axis, and superimposing and averaging them to obtain the original average background decay signal and the original average particle relaxation signal respectively; Step 3, filtering and canceling the original average background decay signal and the original average particle relaxation signal, specifically, taking the original average background decay signal as a reference, removing the background decay component in the original average particle relaxation signal by an adaptive filtering method, and obtaining the particle relaxation signal data after filtering; Step 4, calculating the theoretical relaxation signal and theoretical relaxation time of the particles under corresponding magnetization conditions according to the number of selected particles and their magnetic parameters; Step 5, according to the particle theoretical relaxation signal calculated in step 4, the filtered particle relaxation signal data is subjected to baseline correction and basis function fitting to obtain the time domain characteristics of the particle relaxation signal after correction and fitting; Step 6, based on the particle theoretical relaxation time of steps 4 and 5 and the particle relaxation signal after correction and fitting, reverse estimate the initial time of the particle relaxation signal, intercept or supplement the signal length, and obtain the final processed particle relaxation signal.

6. The method for processing superparamagnetic nanoparticle relaxation signals based on an optically pumped magnetometer according to claim 5, wherein step 4 comprises: Step 1, obtaining the particle size distribution parameters of the particle system by electron microscopy testing and other methods, fitting the particle distribution curve, and estimating the number of superparamagnetic nanoparticles; Step 2: construct a magnetization model based on magnetization parameters and particle VSM characteristics. According to the set magnetization field intensity and magnetization time parameters and the particle saturation magnetization intensity, use the Langevin function to calculate the particle magnetization ratio and estimate the initial relaxation field intensity after the particle is magnetized; Step 3, construct a relaxation model based on particle size distribution and particle number. Establish a Neel relaxation function with particle size as the integrand variable, and the integrand is a linear combination of exponential or logarithmic functions. According to the particle number estimated in step 1 and the magnetization ratio calculated in step 2, the theoretical relaxation signal of the particle is calculated by substitution into the relaxation model, and the theoretical relaxation time is calculated according to 1 / e of the initial field strength of the signal.

7. The method for processing and analyzing superparamagnetic nanoparticle relaxation signals based on optically pumped magnetometer according to claim 4 and claim 5, characterized in that: The amplitudes of relaxation signals of particles with different contents at the initial moment were obtained by analysis, and the calibration coefficients of particle content and signal intensity were obtained by linear fitting data, which were used for quantitative analysis of particles.

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