Method for detecting magnetic particle relaxation time based on impulse square wave excitation and curve fitting

Abstract

The present application belongs to the field of magnetic particle biomedical detection and imaging, and particularly relates to a magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting, aiming at solving the problem that the prior art cannot effectively distinguish and detect the NMR relaxation time and Brownian relaxation time of magnetic nanoparticles. The method comprises: obtaining a sample signal generated by a magnetic nanoparticle sample under the excitation of a pulsed square wave excitation magnetic field; combining the sample signal, adjusting the relaxation time related parameters in the pre-constructed double exponential decay function and performing the fitting of the magnetization curve, and then obtaining a target magnetization curve matched with the sample signal; and taking the relaxation time related parameters corresponding to the target magnetization curve as the magnetic particle relaxation time detection result. The present application can effectively distinguish and detect the NMR relaxation time and Brownian relaxation time of magnetic nanoparticles, and can expand the application development related to the NMR relaxation time or Brownian relaxation time.

Description

Technical Field

[0001] This invention belongs to the field of magnetic particle biomedical detection and imaging, specifically relating to a method and system for detecting magnetic particle relaxation time based on pulse square wave excitation and curve fitting, as well as a method for predicting functional parameters based on magnetic particle relaxation time detection. Background Technology

[0002] Superparamagnetic iron oxide nanoparticles are widely used in medical imaging. Magnetic resonance imaging (MRI) utilizes magnetic nanoparticles (or simply magnetic particles) as a T2 / T2 ratio... Shortening-effect negative contrast agents are used for cell imaging and drug delivery. Magnetic particle imaging uses magnetic nanoparticles as tracers with nonlinear magnetization properties for vascular imaging and functional neuroimaging. Magnetoacoustic computed tomography (MECT) uses ultrasound generated by magnetic nanoparticles to detect labeled cells and prostate tumors. Currently, advanced applications of magnetic nanoparticles in medical imaging include in vivo inflammation detection, hyperthermia (such as temperature imaging in high-intensity focused ultrasound and magnetothermal therapy), viscosity imaging in cardiovascular diseases, disease management of Alzheimer's disease, and magnetic braking and tracking imaging.

[0003] The relaxation time of the magnetization response under an excitation magnetic field is an important characteristic of magnetic nanoparticles. The basic relaxation mechanisms include internal rotation (Niehn relaxation) and external physical rotation (Brown relaxation). Accurate measurement of the relaxation time can be used to characterize the in vivo state of magnetic nanoparticles and can be applied to various clinical applications.

[0004] Currently, sinusoidal excitation is widely used to generate dynamically changing magnetic fields to excite magnetic nanoparticles. However, both of these relaxation times are functions of the applied magnetic field and decrease as the magnetic field amplitude increases. There is no fixed Nillet or Brownian relaxation time throughout the excitation period. Therefore, it is difficult to decouple the Nillet and Brownian relaxation times under traditional sinusoidal excitation methods.

[0005] Under sinusoidal excitation, one approximation method for detecting relaxation time is to use the total relaxation time of the signal broadening and hysteresis. Specifically, the relaxation process of magnetic nanoparticles can be modeled as a first-order Debye process, where the convolution kernel is a single exponential decay function, and the relaxation time constant in the kernel is the so-called Debye relaxation time. This simple model has been experimentally verified to be useful for matching relaxation effects. Based on this, researchers have proposed a relaxation imaging technique called TAURUS (TAU estimation by recovering the underlying mirror symmetry), which estimates the Debye relaxation time constant by recovering the underlying mirror symmetry signal from sinusoidal excitation. Another approximation method for detecting relaxation time is to consider only Brownian relaxation while ignoring Niehr relaxation. This approach is based on the premise that Brownian relaxation dominates when the sinusoidal excitation frequency is high and the diameter of the magnetic nanoparticles is greater than 25 nm.

[0006] It is evident that there is currently no effective means to distinguish and detect the Nieer relaxation time and Brownian relaxation time of magnetic nanoparticles, which restricts the development of applications related to Nieer relaxation time or Brownian relaxation time. Summary of the Invention

[0007] To address the aforementioned problems in the prior art, namely, the inability of existing technologies to effectively distinguish and detect the Niehr relaxation time and Brownian relaxation time of magnetic nanoparticles, this invention proposes a magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting. This method includes:

[0008] Step S10: Obtain the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field;

[0009] Step S20: Combine the sample signal, adjust the relaxation time related parameters in the pre-constructed double exponential decay function and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; use the relaxation time related parameters corresponding to the target magnetization intensity curve as the magnetic particle relaxation time detection result.

[0010] The relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation.

[0011] In some preferred embodiments, the magnetic nanoparticle sample includes a single magnetic particle sample; the single magnetic particle sample is a sample containing only one type of magnetic nanoparticle.

[0012] The pulsed square wave excitation magnetic field includes a trapezoidal pulsed square wave excitation magnetic field.

[0013] In some preferred embodiments, by combining the sample signal, adjusting the relaxation time-related parameters in the pre-constructed double exponential decay function and fitting the magnetization curve, a target magnetization curve matching the sample signal is obtained. The method is as follows:

[0014] Step S21: Integrate the sample signal to obtain the original data curve of the non-adiabatic magnetization intensity of the magnetic nanoparticle sample, which is used as the first curve;

[0015] Step S22: Based on the sample signal and combined with the relaxation time related parameters of the current group, the magnetization intensity curve is fitted by a pre-constructed double exponential decay function to obtain the magnetization intensity curve corresponding to the relaxation time related parameters of the current group, which is used as the second curve.

[0016] Step S23: Determine whether the point error between the second curve and the first curve meets the preset requirements. If it does, then the second curve is used as the target magnetization intensity curve. If not, adjust the relaxation time related parameters using the preset parameter range corresponding to each relaxation time related parameter. After adjustment, proceed to step S22.

[0017] In some preferred embodiments, the sample signal is integrated to obtain the raw data curve of the non-adiabatic magnetization of the magnetic nanoparticle sample, and the method is as follows:

[0018]

[0019]

[0020] in, This represents the original non-adiabatic magnetization. This indicates the received signal, i.e., the sample signal; Represents a static excitation field Magnetization intensity at that point T represents time, and T represents the waveform period of the pulse square wave excitation magnetic field.

[0021] In some preferred embodiments, the double exponential decay function is:

[0022]

[0023] in, This represents the non-adiabatic magnetization intensity corresponding to the relaxation time-related parameters of the current group; This represents the maximum magnetization intensity under static field amplitude. Indicates the time of magnetic field reversal; Indicates the NieR relaxation time constant; Indicates the Brownian relaxation time constant; ; This represents the percentage of NieR relaxation. This represents the percentage of Brownian relaxation. .

[0024] In some preferred embodiments, the preset requirement is whether the point error between the second curve and the first curve is less than a preset error threshold, or whether the average of the two-point errors of all position points of the second curve and the first curve is less than a preset average error threshold.

[0025] In some preferred embodiments, the pulsed square wave excitation magnetic field is generated by a pulsed square wave relaxor; wherein, the pulsed square wave relaxor comprises:

[0026] The digital acquisition card is used to generate analog signals of pulse square waves; it is also used to digitize input sample signals.

[0027] An AC power amplifier is used to amplify the analog signal;

[0028] The transmitting coil is used to transmit amplified analog signals to generate a pulsed square wave excitation magnetic field;

[0029] A current sensor is used to monitor the emission waveform of the AC power amplifier in real time.

[0030] A receiving coil is used to receive the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field;

[0031] A low-noise preamplifier is used to amplify the sample signal and send it to the digital acquisition card.

[0032] In some preferred embodiments, the transmitting coil is a hollow cylinder comprising multiple Litz coils; the multiple Litz coils in the transmitting coil are wound around the outer surface of the hollow cylinder;

[0033] The receiving coil is a two-section gradient meter type, coaxially placed inside the transmitting coil; both the upper and lower sections of the receiving coil include multiple layers of Litz coils; the multiple layers of Litz coils of the upper and lower sections of the receiving coil are respectively wound around the outer surface of their corresponding sections;

[0034] The upper half of the receiving coil is provided with a multi-layer Litz coil for receiving the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; the lower half of the receiving coil is provided with a Litz coil for fine-tuning to suppress direct transmission feedthrough.

[0035] A second aspect of the present invention proposes a magnetic particle relaxation time detection system based on pulsed square wave excitation and curve fitting, the system comprising:

[0036] The sample signal acquisition module is used to acquire the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field;

[0037] The relaxation time detection module is used to combine the sample signal, adjust the relaxation time-related parameters in the pre-constructed double exponential decay function, and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; the relaxation time-related parameters corresponding to the target magnetization intensity curve are used as the magnetic particle relaxation time detection result.

[0038] The relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation.

[0039] A third aspect of the present invention proposes a method for predicting functional parameters based on magnetic particle relaxation time detection, the method comprising:

[0040] Based on the above-mentioned magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting, the magnetic particle relaxation time detection results of the magnetic nanoparticle sample are determined.

[0041] Based on the magnetic particle relaxation time detection results, the functional parameters of the magnetic nanoparticle sample are predicted to obtain the functional parameter prediction results.

[0042] The functional parameters include viscosity, temperature, microenvironment, and the free or bound state of magnetic particles.

[0043] The beneficial effects of this invention are:

[0044] This invention can effectively distinguish and detect the Nieer relaxation time and Brownian relaxation time of magnetic nanoparticles, and can expand the application development related to Nieer relaxation time or Brownian relaxation time.

[0045] 1) Compared to existing technologies that use sinusoidal excitation, this invention generates a pulsed square wave excitation magnetic field and receives the sample signal generated by a single magnetic particle sample under the excitation of the pulsed square wave excitation magnetic field. Based on the sample signal, by adjusting the relaxation time-related parameters in a preset double exponential decay function, a target magnetization curve matching the sample signal is fitted. The adjustment result of the relaxation time-related parameters corresponding to the target magnetization curve is used as the magnetic particle relaxation time detection result, including the Nille relaxation time constant, the Brown relaxation time constant, and the percentage of Nille relaxation and Brown relaxation. Therefore, this invention innovatively proposes an effective means to distinguish and detect the Nille relaxation time and Brown relaxation time of magnetic nanoparticles, which can expand the application development related to Nille relaxation time or Brown relaxation time.

[0046] 2) After determining the magnetic particle relaxation time detection result of a single magnetic particle sample using the provided magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting, the functional parameters of the single magnetic particle sample can be predicted based on the magnetic particle relaxation time detection result. These functional parameters include viscosity, temperature, microenvironment, and the free or bound state of the magnetic particles. Therefore, this invention can predict multiple functional parameters of single magnetic particle samples using magnetic particle relaxation time detection, enabling wide application in fields such as biology and medicine. Attached Figure Description

[0047] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart illustrating a magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting, according to an embodiment of the present invention.

[0049] Figure 2 (a) and (b) in the figure are schematic diagrams of the signal curve and magnetization intensity curve under trapezoidal pulse excitation, respectively;

[0050] Figure 3 In the figure, (a) and (b) are the MH curves of Synomag-D with 1% glycerol and Synomag-D with 30% gelatin under trapezoidal pulse excitation, respectively.

[0051] Figure 4 The graph shows the results of recovering magnetization using the Debye relaxation model during the field rise phase.

[0052] Figure 5 Figures (a) and (b) in the figure show the results of evaluating Synomag-D using transmission electron microscopy, and the results based on... Figure 2 The MH curve in the diagram shows the particle-related size calculation.

[0053] Figure 6 This is a schematic diagram of the voltage and current waveforms applied in the TWR excitation coil according to an embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of a pulse square wave relaxor according to an embodiment of the present invention;

[0055] Figure 8 This is a curve showing the result of analyzing the recovery magnetization during the flat phase of a magnetic field using a single-exponential or double-exponential relaxation model, according to one embodiment of the present invention.

[0056] Figure 9 In the figure, (a) and (b) represent the fitted magnetization intensity curve and the derived signal curve of the magnetic field rising and flat phases in the embodiment of the present invention, respectively;

[0057] Figure 10 This is a schematic diagram of the framework of a magnetic particle relaxation time detection system based on pulse square wave excitation and curve fitting according to an embodiment of the present invention.

[0058] Figure 11 This is a relaxation time curve of Synomag-D under different magnetic field amplitudes as viscosity increases, according to an embodiment of the present invention.

[0059] Figure 12This is a relaxation time characteristic curve of Synomag-D at different temperatures according to an embodiment of the present invention;

[0060] Figure 13 This is a schematic diagram illustrating the sensitivity of relaxation time characteristics in viscosity prediction according to an embodiment of the present invention;

[0061] Figure 14 This is a schematic diagram illustrating the sensitivity of relaxation time characteristics in temperature prediction according to an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0064] To facilitate understanding of this scheme, a brief explanation will first be given of pulse square wave excitation, Debye relaxation time in the prior art, and the inventive concept of the embodiments of this invention.

[0065] This invention assumes that the period of the pulsed square wave excitation is T. The following explanation uses a trapezoidal pulse as a specific example of a pulsed square wave. For a trapezoidal wave with a period of T, the rising and flat phases of the magnetic field within half a period can be represented as follows:

[0066] (1)

[0067] in, Indicates time; This represents the magnetic field amplitude during the flat phase. This represents the ratio of the time during which the magnetic field is flat to the total pulse time. The slope representing the upward phase:

[0068] (2)

[0069] In the study of this embodiment of the invention, the half-cycle of the wave is much longer than the relaxation time, which means that within each pulse cycle, the magnetic field is fully magnetized at the end of the flat phase. During the first half-cycle, the non-adiabatic magnetization of the magnetic nanoparticles can be calculated by integrating the received signal:

[0070] (3)

[0071] in, This represents the original non-adiabatic magnetization. This indicates the received signal, that is, the signal generated by the received magnetic particles under the excitation of the trapezoidal pulse excitation magnetic field; Represents a static excitation field The magnetization intensity at that location.

[0072] See Figure 2 , Figure 2 The black solid line in (a) is the signal curve under trapezoidal pulse excitation, that is, the curve corresponding to the received signal; Figure 2 In diagram (b), the solid black line represents the magnetization curve under trapezoidal pulse excitation, referred to as the M-curve; the dashed curves in both diagrams represent the corresponding magnetic field strength curves, referred to as the H-curves. See also... Figure 2 Understanding, static excitation field magnetization at The signal AUC (Area Under Curve, defined as the area under the ROC curve and the coordinate axis) over the entire half-cycle can be used to estimate it:

[0073] (4)

[0074] from Figure 2 It can be seen that the MH curve under trapezoidal pulse excitation can be divided into a magnetic field rising stage and a magnetic field flat stage. The curve of the magnetic field rising stage has the same trend as the rising excitation magnetic field. However, the MH curve of the rising stage depends on the slope of the rising magnetic field and the relaxation time of the magnetic nanoparticles. Since the excitation field amplitude is constant, the MH curve of the field flat stage is basically a vertical line. The embodiments of the present invention assume that the magnetization is fully recovered at the end of the magnetic field flat stage. The fully recovered magnetization under different magnetic field amplitudes can be fitted with the Langevin function to estimate the particle size. The Langevin function fitting is shown in Equation (5):

[0075] (5)

[0076] (6)

[0077] in, The magnetic moment of the magnetic nanoparticle; This represents the saturation magnetization, which is 0.551. ; Particle size is indicated, and the unit is nanometers (N). ); Represents the Boltzmann constant; Indicates the permeability of free space; Indicates Absolute temperature in units of 1.

[0078] For the experimental verification results regarding the estimation of magnetic particle size using the Langevin function fitting, please refer to [link to relevant documentation]. Figure 3 understand, Figure 3 (a) shows the MH curves of Synomag-D magnetic particles and 1% glycerol under pulsed trapezoidal excitation; Figure 3 (b) shows the MH curves of Synomag-D magnetic particles and 30% gelatin under pulsed trapezoidal excitation. The fully recovered magnetization at the end of each half-cycle under different excitation field amplitudes can be fitted using the Langevin function (black curve) to estimate the particle size of Synomag-D. Figure 3 The particle size estimate obtained in (b) is 9.38 nm. Figure 3 The particle size estimate obtained in (a) is 25.26 nm, and these size estimates have been verified to be consistent with existing publicly available data.

[0079] During the rising phase of the magnetic field, the relationship between magnetization under non-adiabatic and adiabatic conditions is expressed as follows:

[0080] (7)

[0081] in, Represents the Heaviside step function; This represents convolution in the time domain; non-adiabatic and adiabatic represent non-adiabatic and adiabatic conditions, respectively. In this model, For the Debye relaxation time constant, please refer to [link / reference]. Figure 4 understand, Figure 4 The black dot curve represents the original data, while the other curve is the magnetization curve recovered using the Debye relaxation model during the field rise phase. Magnetization under adiabatic conditions can be calculated using the Langevin function:

[0082] (8)

[0083] Debye relaxation time can be viewed as a combination of Neill relaxation time and Brown relaxation time:

[0084] (9)

[0085] in, , These represent the Niehr relaxation time constant and the Brown relaxation time constant, respectively, with the values ​​representing the corresponding relaxation times. The Niehr and Brown relaxation times are typically difficult to measure separately, and currently there is no suitable formula for distinguishing between them.

[0086] This invention, through research, has found that during the flat phase of the magnetic field under pulsed square wave excitation, the relaxation time of magnetic nanoparticles can be considered constant. Pulsed excitation provides a steady-state signal, avoiding signal broadening and hysteresis caused by relaxation effects. Pulsed excitation includes two phases: a magnetic field rise phase and a magnetic field flat phase. The former generates a rapidly changing magnetic field; during this phase, the Nieer and Brownian relaxation times are not constant and cannot be decoupled, only the Debye relaxation process can be evaluated. During the magnetic field flat phase, the magnetic field does not change with time; the Nieer and Brownian relaxation times are constant and may differ, thus allowing for decoupling. Based on this, this invention proposes a double-exponential relaxation theory, a theory of magnetization reversal recovery during pulsed excitation, used to describe the magnetization recovery signal in pulsed square wave excitation. Based on this theory, this invention proposes a magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting.

[0087] like Figure 1 As shown, the first embodiment of the present invention provides a method for detecting the relaxation time of magnetic particles based on pulse square wave excitation and curve fitting, which may include the following steps:

[0088] Step S10: Obtain the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field;

[0089] In this embodiment, the magnetic nanoparticle sample includes a single magnetic particle sample; a single magnetic particle sample is a sample containing only one type of magnetic nanoparticle. The single magnetic particle sample can be various commercially available magnetic nanoparticles, such as iron carboxymethylglucamine, Perimag, Synomag-D, and the Dongna series magnetic particles, or it can be various self-developed and synthesized magnetic nanoparticles. The single magnetic particle sample can be known or unknown, and no specific limitation is made here.

[0090] Synomag-D consists of dextran-coated multinuclear magnetic nanoparticles with an initial iron concentration of 6 mg / ml. Please see [link / reference]. Figure 5 ,in Figure 5 (a) shows the results of evaluating Synomag-D by transmission electron microscopy. Synomag-D particles are composed of monodisperse nanoflower-shaped iron oxide particles. Figure 5 (b) is based on Figure 3The particle-related dimensions were calculated from the MH curve, with an average cluster size of 25.26 nm, a small core size of 9.38 nm, and a core size of approximately 9 nm.

[0091] Furthermore, single magnetic nanoparticles such as Synomag-D can be placed in a buffer solution composed of a mixture of water and glycerol. By changing the composition of the glycerol, the viscosity of the single magnetic particle sample can be altered, thereby simulating different biological viscosity environments. For example, Synomag-D with an original iron concentration of 6 mg / ml has the lowest viscosity, which increases after adding a buffer solution composed of a mixture of water and glycerol. In this embodiment of the invention, to obtain single magnetic particle samples with different viscosities, a series of water-glycerol mixtures with glycerol concentrations ranging from 1% to 70% wt can be prepared as buffer solutions for diluting the magnetic nanoparticles.

[0092] Furthermore, single magnetic nanoparticles such as Synomag-D can be placed in a mixture of water and gelatin gel, using this mixture as an embedding matrix for the single magnetic nanoparticles to further limit their Brownian mobility. In this invention, the hardness of the single magnetic particle sample can be altered by changing the gelatin composition to simulate different biological stiffness environments. For example, this invention can use a water-gelatin gel with a gelatin concentration of 5% to 30% wt as the embedding matrix for the magnetic nanoparticles. The preparation process can, for example, involve adding gelatin powder to ddH2O (double-distilled water) and heating it to a certain temperature, such as 65°C, until the gelatin powder is completely dissolved. The magnetic nanoparticles are then uniformly dispersed in the resulting mixture, and the sample is solidified at a set temperature, such as 4°C. The iron concentration of the single magnetic particle sample can be set to 0.5 mg / ml, etc.

[0093] Furthermore, in this embodiment of the invention, the obtained single magnetic particle samples can be placed at different temperatures to study the relaxation behavior of magnetic nanoparticles at different temperatures. Specifically, for example, a digital temperature-controlled water bath can be used to heat the single magnetic particle samples to 25°C, 37°C, and 52°C, etc., and relaxation time can be detected and analyzed at each temperature, etc.

[0094] Regarding the generation of the pulsed square wave excitation magnetic field and the acquisition of the sample signal, any method or device capable of generating a pulsed square wave excitation magnetic field can be used to generate the required pulsed square wave excitation magnetic field, and any signal receiving method or signal receiving device can be used to receive the sample signal generated by the single magnetic particle sample under the excitation of the pulsed square wave excitation magnetic field. No specific limitations are made here.

[0095] In one optional embodiment, the step of generating a pulsed square wave excitation magnetic field and receiving the sample signal generated by a single magnetic particle sample under the excitation of the pulsed square wave excitation magnetic field may include:

[0096] A pulsed square wave excitation magnetic field is generated using a pre-designed pulsed square wave relaxor, and the sample signal generated by a single magnetic particle sample under the excitation of the pulsed square wave excitation magnetic field is received.

[0097] The pulsed square wave relaxor (TWR) of this invention integrates pulsed square wave excitation magnetic field generation and sample signal reception. The TWR design eliminates the need for a resonant circuit, allowing for trapezoidal waveform excitation. For details, please refer to [link to relevant documentation]. Figure 7 , Figure 7 (a) shows the structural components and connection diagram of the TWR; Figure 7 (b) in the diagram is a physical schematic of the transmitting coil and the receiving coil; Figure 7 In (c) of the diagram, the black curve represents the voltage waveform in voltage control mode, and the gray curve represents the trapezoidal waveform generated in the transmitting coil when the voltage waveform in voltage control mode is sent to the power amplifier. Regarding the received signal, Figure 7 In the diagram, (d) represents the background and MNPs frequency domain signals, indicating significant feedthrough suppression due to the design of the TWR's receiving coil. Here, MNPs represent magnetic nanoparticles.

[0098] For details, please see Figure 7 In (a), the pulse square wave relaxor may include:

[0099] The digital acquisition card is used to generate analog signals of pulse square waves; it is also used to digitize input sample signals.

[0100] An AC power amplifier is used to amplify the analog signal;

[0101] The transmitting coil is used to transmit amplified analog signals to generate a pulsed square wave excitation magnetic field;

[0102] A current sensor is used to monitor the emission waveform of the AC power amplifier in real time.

[0103] A receiving coil is used to receive the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field;

[0104] A low-noise preamplifier is used to amplify the sample signal and send it to the digital acquisition card.

[0105] Figure 7In (a), the workbench can be a computer, etc. DAQ NI6363 indicates an optional data acquisition card that can perform digitization at a sampling rate of 1 MS / s; the AC power amplifier can be a product model such as AE Techron 7548; the low-noise preamplifier can be a product model such as SR560.

[0106] In one optional embodiment, the transmitting coil is a hollow cylinder comprising multiple layers of Litz coils; the multiple layers of Litz coils in the transmitting coil are wound around the outer surface of the hollow cylinder;

[0107] The receiving coil is a two-section gradient meter type, coaxially placed inside the transmitting coil; both the upper and lower sections of the receiving coil include multiple layers of Litz coils; the multiple layers of Litz coils of the upper and lower sections of the receiving coil are respectively wound around the outer surface of their corresponding sections.

[0108] The upper half of the receiving coil is provided with a multi-layer Litz coil for receiving the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; the lower half of the receiving coil is provided with a Litz coil for fine-tuning to suppress direct transmission feedthrough.

[0109] In one alternative embodiment, physical examples of the transmitting coil and receiving coil can be found in [reference needed]. Figure 7 (b) wherein the transmitting coil has an inner diameter of 26 mm and a length of 100 mm, and is composed of four layers of 124 turns of 300 strands of 0.1 mm Litz wire, providing a sensitivity of 1.42 mT / A and 98% magnetic field homogeneity in the sample area; the upper and lower parts of the receiving coil are each composed of two layers of coil, each including 30 strands of 0.1 mm Litz wire; the upper part of the receiving coil is provided with 100 turns of Litz wire to cover a sample area with a length of 12 mm and a diameter of 10 mm, which is the sampling area; the sample area is provided with a sample holder for placing single magnetic particle samples, and the sample holder is suitable for a conventional 500 μL Ependorf tube.

[0110] Regarding the working process of the pulse square wave relaxor: To generate a pulse square wave excitation field, a data acquisition card is used to generate an analog signal, which is amplified using an AC power amplifier and then sent to the transmitting coil. A current sensor is used to monitor the transmitted waveform in real time. Figure 7 In (c), the red curve represents the voltage waveform in voltage control mode. This voltage waveform is sent to the power amplifier and generates a trapezoidal waveform in the transmitting coil, such as... Figure 7The green curve in (c) is used for illustration. The transmitting coil in this embodiment of the invention is designed to have relatively high inductance and low resistance to allow for trapezoidal waveform excitation. The inductance of the transmitting coil was measured at f = 2kHz. and resistance The inductive reactances are 144.9 μH and 88.5 mΩ, respectively. At 2 kHz ( The calculated resistance is 1.8 Ω, which is significantly higher than the resistance. Therefore, the current-voltage relationship of the transmitting coil can be expressed as:

[0111] (10)

[0112] in, It is the applied voltage. This is the current in the coil. To generate a trapezoidal current waveform, Figure 6 The voltage waveform is sent to the amplifier. In this embodiment of the invention, the period of the trapezoidal wave is set to 500 μs, and the rise time accounts for 5% (i.e., 25 μs). The field amplitude is set to 0.5 to 10 mT, with an interval of 0.5 mT.

[0113] The sample signal, excited by a trapezoidal pulse, is received by a receiving coil and amplified by a low-noise preamplifier. Digitization is performed using a data acquisition card at a sampling rate of 1 MS / s. To suppress direct feedthrough, this embodiment of the invention performs digital background subtraction before each measurement, reducing the direct feedthrough to approximately -100 dB through gradient attenuation and the background subtraction system. See [link to relevant documentation]. Figure 7 As shown in Figure (d), the background and MNPs frequency domain signal indicate significant feedthrough suppression due to the design of the gradient meter receiving coil, where MNP Signal represents the MNPs frequency domain signal.

[0114] Step S20: Based on the sample signal, adjust the relaxation time-related parameters in the pre-constructed double exponential decay function and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; use the relaxation time-related parameters corresponding to the target magnetization intensity curve as the magnetic particle relaxation time detection result; wherein, the relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation;

[0115] In this embodiment, the relaxation process of magnetization during the flat phase of the magnetic field under pulsed excitation is similar to the longitudinal (T1) relaxation process of the reverse recovery pulse in magnetic resonance imaging. Reverse recovery uses an initial pulse of 180° to reverse the longitudinal magnetization intensity, which recovers with a time constant T1. This invention assumes that the magnetic field suddenly switches from a negative peak to a positive peak during pulsed square wave excitation, and vice versa. After each pulsed square wave excitation with the magnetic field reversed by 180 degrees, sample signals are acquired for curve fitting, and the magnetization intensity of the magnetic nanoparticles is recovered through two relaxation effects. The two-step magnetization response in the Brownian state occurs after the Niehr state. Under pulsed excitation with the magnetic field increasing from zero, the magnetization process is fitted by a preset double exponential decay function. The magnetization curve fitting method is performed in an interactive data language for relaxation time feature extraction; the interactive data language can include IDL, MATLAB, Java, C, Python, etc., and is not limited here. The data file about Synomag-D received in TWR is loaded into the IDL program.

[0116] Specifically, by combining the sample signal, adjusting the relaxation time-related parameters in the pre-constructed double exponential decay function and fitting the magnetization intensity curve, a target magnetization intensity curve matching the sample signal is obtained. The specific processing procedure is as follows:

[0117] Step S21: Integrate the sample signal to obtain the original data curve of the non-adiabatic magnetization intensity of the magnetic nanoparticle sample, which is used as the first curve;

[0118] Integrate the sample signal as shown in formula (3).

[0119] Step S22: Based on the sample signal and combined with the relaxation time related parameters of the current group, the magnetization intensity curve is fitted by a pre-constructed double exponential decay function to obtain the magnetization intensity curve corresponding to the relaxation time related parameters of the current group, which is used as the second curve.

[0120] The double exponential decay function is:

[0121] (11)

[0122] in, This represents the non-adiabatic magnetization intensity corresponding to the relaxation time-related parameters of the current group; Indicates time; Indicates the time of magnetic field reversal; Indicates the NieR relaxation time constant; Indicates the Brownian relaxation time constant; ; This represents the percentage of NieR relaxation. This represents the percentage of Brownian relaxation. .

[0123] During the preliminary research process, the embodiments of the present invention discovered that, through assumptions or Setting the value to 0 can transform the double exponential recovery process into a single exponential process, but the curve fitting results will be worse than those of the double exponential process. For details, please refer to [link to relevant documentation]. Figure 8 As shown, Figure 8 The cyan and red curves represent the analysis of magnetization recovery during the flat magnetic field phase using a single-exponential relaxation model and a double-exponential relaxation model, respectively. The black dotted line represents the original data curve. Figure 8 Combination Figure 4 understand. Figure 8 In this study, the double-exponential model exhibits better curve fitting results than the single-exponential model. Therefore, in this embodiment of the invention, a double-exponential decay function is used to fit the recovered magnetization. See [link to relevant documentation]. Figure 9 understand. Figure 9 In (a), the black dotted lines represent the fitted magnetization curves during the rising and flattening phases of the magnetic field. Figure 9 In (b) of the figure, the black dotted lines represent the derived signal curves for the rising and flattening phases of the magnetic field. In both figures, the Debye relaxation model is used to fit the gray magnetization curve (non-dotted) during the rising phase, and the double exponential model (represented by Bi-ex in the figure) is used to fit the black magnetization curve (non-dotted) during the flattening phase. The signal curve is the reciprocal of the magnetization curve with respect to time, and the magnetic field strength curve is a gray dotted curve. In the case of very small values ​​close to zero, there is almost no Brownian relaxation effect, only the Neillian relaxation effect.

[0124] The NieR relaxation time under zero magnetic field condition is defined as:

[0125] (12)

[0126] in, Indicates the core volume; Represents the anisotropy constant; This represents the relaxation time in the zero-field case; Indicates The absolute temperature is in units; in trapezoidal excitation, the NieR and Brownian relaxation times extracted from the inversion recovery process during the flat phase of the magnetic field may differ from the relaxation times in the zero-field case. The Brownian relaxation time is defined as:

[0127] (13)

[0128] in, Indicates fluid viscosity; This represents the hydrodynamic volume of a particle.

[0129] Regarding step S22, embodiments of the present invention can... or As a set of relaxation time-related parameters, and with preset parameter ranges set for each parameter, for example, The corresponding preset parameter range can be [0, 10us]. The corresponding preset parameter range can be [10us, 200us]. or The corresponding preset parameter range can be (0,1), etc. A set of relaxation time-related parameters can be determined as initial values ​​using the lower limit of the preset parameter range for each parameter. Then, by using the preset double exponential decay function to perform curve fitting at different times, a corresponding fitted magnetization curve can be obtained. For an explanation of the concept of curve fitting, please refer to the prior art.

[0130] Step S23: Determine whether the point error between the second curve and the first curve meets the preset requirements. If it does, then the second curve is used as the target magnetization intensity curve. If not, adjust the relaxation time related parameters using the preset parameter range corresponding to each relaxation time related parameter. After adjustment, proceed to step S22.

[0131] For the magnetization curve obtained in step S22, determine whether the point error between it and the curve obtained after integration meets a preset requirement. It is understood that at each time point, there are actual curve points and fitted curve points. The general principle of curve fitting is that, for the same time, the error (i.e., the absolute value of the difference) between the points on the actual curve and the points on the fitted curve is minimized. In this embodiment of the invention, it can be determined whether the error between the two points at corresponding positions of the target magnetization curve and the integrated curve at the same time is less than a preset threshold, or whether the average of the two-point errors at all positions is less than a preset average error value. If the above preset requirements are met, the magnetization curve obtained in step S22 is taken as the target magnetization curve, and the curve fitting ends. At this time, the adjustment result of the relaxation time-related parameters used when obtaining the target magnetization curve will be obtained, i.e. As the result of magnetic particle relaxation time detection; if the above preset requirements are not met, the parameters are adjusted to obtain a new set of relaxation time related parameters. For example, at least one of the three parameters can be changed to obtain a new set of relaxation time related parameters. The obtained new set of relaxation time related parameters is used to return to the execution steps S22 to S23 and is used as the current set of relaxation time related parameters in steps S22 to S23 for execution.

[0132] Compared to existing technologies that use sinusoidal excitation, this invention generates a pulsed square wave excitation magnetic field and receives the sample signal generated by a single magnetic particle sample under the excitation of the pulsed square wave excitation magnetic field. Based on the sample signal, by adjusting the relaxation time-related parameters in a preset double exponential decay function, a target magnetization curve matching the sample signal is fitted. The adjustment result of the relaxation time-related parameters corresponding to the target magnetization curve is used as the magnetic particle relaxation time detection result, including the Nille relaxation time constant, the Brownian relaxation time constant, and the percentages of Nille relaxation and Brownian relaxation. Therefore, this invention innovatively proposes an effective means to distinguish and detect the Nille relaxation time and Brownian relaxation time of magnetic nanoparticles, which can expand the application development related to Nille relaxation time or Brownian relaxation time.

[0133] A second embodiment of the present invention provides a method for predicting functional parameters based on magnetic particle relaxation time detection, the method comprising:

[0134] Based on the above-mentioned magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting, the magnetic particle relaxation time detection results of the magnetic nanoparticle sample are determined.

[0135] Based on the magnetic particle relaxation time detection results, the functional parameters of the magnetic nanoparticle sample are predicted to obtain the functional parameter prediction results; wherein, the functional parameters include viscosity, temperature, microenvironment, and the free or bound state of the magnetic particles.

[0136] Research has found that the Nillet relaxation time is a function of temperature and the volume of the magnetic nucleus. The Brownian relaxation time, on the other hand, depends on the fluid viscosity, temperature, and the hydrodynamic volume of the magnetic nanoparticles. Both Nillet and Brownian relaxation times are temperature-dependent, and temperature can be considered a functional parameter of the magnetic particles. Accurate measurement of both relaxation times can provide precise temperature measurement methods. Therefore, the achieved temperature measurement can be further applied in fields such as medicine. For example, real-time temperature imaging is crucial in thermal ablation treatments such as magnetothermal therapy and high-intensity focused ultrasound; temperature is associated with poor prognosis in breast cancer patients; heatstroke, characterized by extreme hyperthermia, also requires accurate temperature measurement; and the use of magnetothermal therapy to induce heat shock, combined with temperature monitoring, can be used to assess the anticancer effects of HSP90 inhibitors, etc.

[0137] Besides temperature, the functional parameters of magnetic particles can also include viscosity, microenvironment, and the free or bound state of the magnetic particles.

[0138] One important application of bioviscous environments is in the medical field. For example, studying cellular responses to the viscoelasticity of the matrix surface is crucial for controlling cellular behavior, such as controlling stem cell proliferation. Alternatively, in the tumor microenvironment, the overexpression of indicators such as lymphocytes, lactate, proteins, enzymes, lipids, extracellular secretions, and extracellular matrix components can lead to increased viscosity. Increased whole blood viscosity can raise cardiovascular mortality and the risk of Alzheimer's disease, while blood viscosity decreases in mice with inflammation and peritonitis. Biostiffness environments can also be applied in the medical field, such as the changes in stiffness caused by the intervention of different materials like stents and catheters in biological systems. Therefore, predicting viscosity and stiffness holds promise for widespread application in medical and other fields.

[0139] Among them, the free state of magnetic particles refers to each magnetic particle being independent and unrelated to each other, in a state of free movement; the combined state of magnetic particles refers to multiple magnetic particles being tightly packed together, forming a cluster, unable to move freely, and constrained and restricted by the surrounding magnetic particles.

[0140] The microenvironment refers to the pH value and granzyme environment of the magnetic particles, such as the pH value and granzyme environment of a tumor environment. Granzymes are serine proteases that play an important role in killing virus-infected cells and tumor cells by NK cells and CTLs. Granzymes are granule-related enzymes in cytotoxic T lymphocytes. The pH value and granzymes in the tumor environment can break down bound magnetic particles, causing them to change from a bound state to a free state. This embodiment of the invention can detect the microenvironment by detecting this change.

[0141] In this embodiment, the relationship between the two relaxation times of the magnetic nanoparticles and various functional parameters was studied in advance using several known sample data. The following example uses a single magnetic particle sample of Synomag-D. The viscosity change is achieved by altering the glycerol component in a buffer solution composed of a water-glycerol mixture. For example, a series of water-glycerol mixtures with glycerol concentrations ranging from 1% to 70% wt can be prepared as buffer solutions for diluting Synomag-D. The hardness change is achieved by altering the gelatin concentration in a mixture composed of water and gelatin gel; for example, water-gelatin gels ranging from 5% to 30% wt can be prepared, and so on. Temperature changes can be achieved by heating Synomag-D to multiple temperatures, such as 25°C, 37°C, and 52°C, using a digitally temperature-controlled water bath.

[0142] The following explains the research on some functional parameters:

[0143] 1) Relaxation time characteristics at different viscosities; please refer to [link / reference needed] for details. Figure 11As shown, the Debye time decreases with decreasing viscosity at low field amplitudes but remains constant at high field amplitudes; the Brownian time increases with increasing viscosity. The Neel time decreases slightly with increasing viscosity. The Brownian percentage first increases and then decreases with increasing viscosity. Therefore, it can be concluded that the Brownian relaxation time increases with increasing viscosity.

[0144] 2) Relaxation time characteristics under different stiffnesses; Analysis showed that as gelatin concentration increased, stiffness increased, the proportion of Brownian relaxation time decreased, and the proportion of Neillian relaxation time widened. Clinically, stiffness can correspond to liver cirrhosis, tumor masses, etc. In specific situations, stiffness can also be used as a functional parameter, and its relationship with relaxation time can be used for detection.

[0145] 3) Relaxation time characteristics at different temperatures; based on the magnetization curve fitting method, the Debye relaxation time decreases with increasing temperature. The Brownian relaxation time also decreases with increasing temperature, but this decreasing trend weakens at high viscosity. See details... Figure 12 As shown. Therefore, it can be concluded that the Brownian relaxation time decreases with increasing temperature.

[0146] The embodiments of the present invention can linearly fit the relaxation time or its characteristics under different magnetic field amplitudes, and use it as the basis for each functional parameter (i.e., viscosity). Elasticity (hardness) and temperature The function is denoted by . The absolute value of the slope of the linear fit is used to calculate the sensitivity.

[0147] (14)

[0148] in, It can represent the sensitivity of any relaxation time to a certain functional parameter; Indicates the initial value of the function parameter; Equation (14) represents the absolute value of the slope of the linear fit; it describes the change in functional parameters per unit (e.g., viscosity change of 1 mPa). The percentage change in relaxation time or characteristic caused by a 1% change in gelatin concentration or a 1°C change in temperature. In embodiments of the present invention, ; ; ℃.

[0149] Please see Figure 13This demonstrates the sensitivity of predicting relaxation time characteristics in viscosity using the magnetization curve fitting method. The sensitivity using the Debye relaxation time model decreases with increasing magnetic field amplitude, reaching a minimum at 6.5 mT, and then increases again with increasing field amplitude. Brownian relaxation time shows higher sensitivity than Debye relaxation time in viscosity prediction. The sensitivity of Brownian relaxation time increases with increasing field amplitude and tends to stabilize at 6.5 mT. Other parameters, such as Niehr relaxation time, Brownian percentage, and Niehr percentage, have much lower sensitivity in viscosity prediction. Figure 14 The sensitivity of relaxation time features in temperature prediction is shown. Among the magnetization curve fitting methods, Brownian relaxation time exhibits the highest sensitivity in temperature prediction. Niehr relaxation time features show the lowest sensitivity in temperature prediction.

[0150] 4) Relaxation time characteristics under different microenvironments; Experiments according to the embodiments of the present invention can conclude that as the pH value of the microenvironment increases or decreases, and as the amount of granzyme increases, the magnetic particles change from the bound state to the free state, the Brownian relaxation time becomes shorter, the corresponding peak amplitude increases, and the proportion increases.

[0151] Therefore, Brownian relaxation time can be used to detect changes in the microenvironment and enable various applications in fields such as biology and medicine. For example, during apoptosis, cytoplasmic viscosity increases. In this invention, magnetic particles can be incorporated into cells, and relaxation analysis can be used to study cytoplasmic / organelle viscosity. For instance, using the aforementioned conclusions, Brownian relaxation time can be used to detect changes in the intracellular microenvironment, thereby determining the number of apoptotic cells. Studies have shown that the method of this invention can detect changes in intracellular viscosity of 1-10 mPa, thus effectively achieving apoptosis monitoring / detection.

[0152] For example, drugs may be released as the microenvironment changes. Based on the relaxation time of magnetic particles and the changing patterns of the microenvironment, combined with pre-determined patterns of drug release with changes in the microenvironment, drug release efficiency can be detected. Of course, the applications of microenvironment detection are not limited to those described above.

[0153] Based on the above conclusions and sensitivity calculation results, embodiments of the present invention can predict functional parameters, such as viscosity or temperature. For example, a certain correspondence can be pre-constructed based on a known single magnetic particle sample, viscosity value, Brownian relaxation time value at the corresponding viscosity value, and corresponding sensitivity calculation results; or based on a known single magnetic particle sample, temperature value, Brownian relaxation time value at the corresponding temperature value, and corresponding sensitivity calculation results, such as obtaining a corresponding prediction model through numerical fitting or machine learning.

[0154] Taking viscosity as an example, a viscosity prediction model can be trained using a pre-set neural network based on a known single magnetic particle sample, its viscosity value, the corresponding Brownian relaxation time at that viscosity value, and the corresponding sensitivity calculation results. For the viscosity prediction task of a single magnetic particle sample, it is only necessary to first use the magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting provided in the first aspect to detect the Brownian relaxation time of the single magnetic particle sample, and then input this Brownian relaxation time into the already trained viscosity prediction model to obtain the predicted viscosity value. It is understandable that the prediction of other functional parameters such as temperature can be achieved in a similar way to viscosity prediction.

[0155] As can be seen, based on the magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting provided in the embodiments of the present invention, by utilizing the pre-studied and determined relationship between relaxation time and specific functional parameters, it is possible to predict functional parameters such as viscosity or temperature, thus expanding the application and development of relaxation time.

[0156] A magnetic particle relaxation time detection system based on pulsed square wave excitation and curve fitting according to a third embodiment of the present invention, such as... Figure 10 As shown, the system includes:

[0157] The sample signal acquisition module 1001 is used to acquire the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field.

[0158] The relaxation time detection module 1002 is used to combine the sample signal, adjust the relaxation time-related parameters in the pre-constructed double exponential decay function, and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; the relaxation time-related parameters corresponding to the target magnetization intensity curve are used as the magnetic particle relaxation time detection result; wherein, the relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation.

[0159] A functional parameter prediction system based on magnetic particle relaxation time detection according to a fourth embodiment of the present invention, the system comprising:

[0160] The detection result acquisition module 2001 is used to determine the magnetic particle relaxation time detection result of the magnetic nanoparticle sample based on the above-mentioned functional parameter prediction system based on magnetic particle relaxation time detection.

[0161] The prediction result acquisition module 2002 is used to predict the functional parameters of the magnetic nanoparticle sample based on the magnetic particle relaxation time detection result, and obtain the functional parameter prediction result; wherein, the functional parameters include viscosity, temperature, microenvironment, and the free state or bound state of magnetic particles.

[0162] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the magnetic particle relaxation time detection system based on pulse square wave excitation and curve fitting described above can be found in the corresponding processes in the aforementioned embodiments of the magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting. Similarly, the specific working process and related descriptions of the functional parameter prediction system based on magnetic particle relaxation time detection described above can be found in the corresponding processes in the aforementioned embodiments of the functional parameter prediction method based on magnetic particle relaxation time detection, and will not be repeated here.

[0163] It should be noted that the magnetic particle relaxation time detection system / functional parameter prediction system based on magnetic particle relaxation time detection provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0164] A fifth embodiment of the present invention provides an electronic device comprising at least one processor and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the above-described method for detecting magnetic particle relaxation time based on pulse square wave excitation and curve fitting / a method for predicting functional parameters based on magnetic particle relaxation time detection.

[0165] A computer-readable storage medium according to a sixth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described magnetic particle relaxation time detection method based on pulse square wave excitation and curve fitting / functional parameter prediction method based on magnetic particle relaxation time detection.

[0166] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic devices and computer-readable storage media described above can be referred to the corresponding processes in the foregoing method examples, and will not be repeated here.

[0167] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0168] The terms “first” and “second” are used to distinguish similar objects, rather than to describe or indicate a specific order or sequence.

[0169] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0170] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for detecting the relaxation time of magnetic particles based on pulsed square wave excitation and curve fitting, characterized in that, The method includes: Step S10: Obtain the sample signal generated by the magnetic nanoparticle sample under the excitation of a pulsed square wave excitation magnetic field; the pulsed square wave excitation magnetic field is generated by a pulsed square wave relaxor. Step S20: Combine the sample signal, adjust the relaxation time related parameters in the pre-constructed double exponential decay function and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; use the relaxation time related parameters corresponding to the target magnetization intensity curve as the magnetic particle relaxation time detection result. The relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation. The pulse square wave relaxor includes: The digital acquisition card is used to generate analog signals of pulse square waves; it is also used to digitize input sample signals. An AC power amplifier is used to amplify the analog signal; The transmitting coil is used to transmit amplified analog signals to generate a pulsed square wave excitation magnetic field; A current sensor is used to monitor the emission waveform of the AC power amplifier in real time. A receiving coil is used to receive the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; A low-noise preamplifier is used to amplify the sample signal and send it to the digital acquisition card; The transmitting coil is a hollow cylinder, comprising multiple layers of Litz coils; the multiple layers of Litz coils in the transmitting coil are wound around the outer surface of the hollow cylinder; The receiving coil is a two-section gradient meter type, coaxially placed inside the transmitting coil; both the upper and lower sections of the receiving coil include multiple layers of Litz coils; the multiple layers of Litz coils of the upper and lower sections of the receiving coil are respectively wound around the outer surface of their corresponding sections. The upper half of the receiving coil is provided with a multi-layer Litz coil for receiving the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; the lower half of the receiving coil is provided with a Litz coil for fine-tuning to suppress direct transmission feedthrough.

2. The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to claim 1, characterized in that, The magnetic nanoparticle sample includes a single magnetic particle sample; the single magnetic particle sample is a sample containing only one type of magnetic nanoparticle. The pulsed square wave excitation magnetic field includes a trapezoidal pulsed square wave excitation magnetic field.

3. The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to claim 2, characterized in that, By combining the sample signal, adjusting the relaxation time-related parameters in the pre-constructed double exponential decay function and fitting the magnetization curve, a target magnetization curve matching the sample signal is obtained. The method is as follows: Step S21: Integrate the sample signal to obtain the original data curve of the non-adiabatic magnetization intensity of the magnetic nanoparticle sample, which is used as the first curve; Step S22: Based on the sample signal and combined with the relaxation time related parameters of the current group, the magnetization intensity curve is fitted by a pre-constructed double exponential decay function to obtain the magnetization intensity curve corresponding to the relaxation time related parameters of the current group, which is used as the second curve. Step S23: Determine whether the point error between the second curve and the first curve meets the preset requirements. If it does, then the second curve is used as the target magnetization intensity curve. If not, adjust the relaxation time related parameters using the preset parameter range corresponding to each relaxation time related parameter. After adjustment, proceed to step S22.

4. The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to claim 3, characterized in that, Integrating the sample signal yields the raw data curve of the non-adiabatic magnetization of the magnetic nanoparticle sample. The method is as follows: ； ； in, This represents the original non-adiabatic magnetization. This indicates the received signal, i.e., the sample signal; Represents a static excitation field Magnetization intensity at the location; T represents time; T represents the waveform period of the pulse square wave excitation magnetic field.

5. The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to claim 4, characterized in that, The double exponential decay function is: ； in, This represents the non-adiabatic magnetization intensity corresponding to the relaxation time-related parameters of the current group; Indicates the time of magnetic field reversal; Indicates the NieR relaxation time constant; Indicates the Brownian relaxation time constant; ; Indicates the percentage of NieR relaxation; This represents the percentage of Brownian relaxation. .

6. The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to claim 3, characterized in that, The preset requirement is whether the point error between the second curve and the first curve is less than a preset error threshold, or whether the average of the two-point errors between all positions of the second curve and the first curve is less than a preset average error threshold.

7. A magnetic particle relaxation time detection system based on pulsed square wave excitation and curve fitting, characterized in that, The system includes: The sample signal acquisition module is used to acquire the sample signal generated by the magnetic nanoparticle sample under the excitation of a pulsed square wave excitation magnetic field; the pulsed square wave excitation magnetic field is generated by a pulsed square wave relaxor. The relaxation time detection module is used to combine the sample signal, adjust the relaxation time-related parameters in the pre-constructed double exponential decay function, and fit the magnetization intensity curve to obtain a target magnetization intensity curve that matches the sample signal; the relaxation time-related parameters corresponding to the target magnetization intensity curve are used as the magnetic particle relaxation time detection result. The relaxation time-related parameters include the Niehr relaxation time constant, the Brown relaxation time constant, the percentage of Niehr relaxation, and the percentage of Brown relaxation. The pulse square wave relaxor includes: The digital acquisition card is used to generate analog signals of pulse square waves; it is also used to digitize input sample signals. An AC power amplifier is used to amplify the analog signal; The transmitting coil is used to transmit amplified analog signals to generate a pulsed square wave excitation magnetic field; A current sensor is used to monitor the emission waveform of the AC power amplifier in real time. A receiving coil is used to receive the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; A low-noise preamplifier is used to amplify the sample signal and send it to the digital acquisition card; The transmitting coil is a hollow cylinder, comprising multiple layers of Litz coils; the multiple layers of Litz coils in the transmitting coil are wound around the outer surface of the hollow cylinder; The receiving coil is a two-section gradient meter type, coaxially placed inside the transmitting coil; both the upper and lower sections of the receiving coil include multiple layers of Litz coils; the multiple layers of Litz coils of the upper and lower sections of the receiving coil are respectively wound around the outer surface of their corresponding sections. The upper half of the receiving coil is provided with a multi-layer Litz coil for receiving the sample signal generated by the magnetic nanoparticle sample under the excitation of the pulsed square wave excitation magnetic field; the lower half of the receiving coil is provided with a Litz coil for fine-tuning to suppress direct transmission feedthrough.

8. A method for predicting functional parameters based on magnetic particle relaxation time detection, characterized in that, The method includes: The magnetic particle relaxation time detection method based on pulsed square wave excitation and curve fitting according to any one of claims 1 to 6 is used to determine the magnetic particle relaxation time detection result of the magnetic nanoparticle sample. Based on the magnetic particle relaxation time detection results, the functional parameters of the magnetic nanoparticle sample are predicted to obtain the functional parameter prediction results. The functional parameters include viscosity, temperature, microenvironment, and the free or bound state of magnetic particles.

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