Method for predicting temperature distribution in magnetic hyperthermia of biological tissue based on MPI image

Abstract

The present application relates to a kind of biological tissue inside magnetic heat therapy temperature distribution prediction method based on MPI image, comprising the following steps: step S1: the geometric model and heat transfer model of biological tissue are constructed;Step S2: obtain MPI image and chromaticity bar, and pre-process;Step S3: based on the chromaticity bar after pre-processing, the MPI image after pre-processing is analyzed twice using the initial concentration distribution of magnetic nano-particle obtained;Step S4: the concentration distribution of magnetic nano-particle in biological tissue during diffusion is simulated using the method of finite element to solve convection-diffusion equation;Step S5: the concentration distribution of magnetic nano-particle in step S4 is used as initial value to solve heat transfer equation, and the temperature distribution in biological tissue is predicted.The present application realizes the concentration extraction of magnetic nano-particle based on MPI image, and can predict the temperature distribution in biological tissue after heating.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of modeling of magnetic nanoparticles, and particularly relates to a method for predicting temperature distribution in magnetic hyperthermia of biological tissues based on MPI images. BACKGROUND

[0002] Magnetic nanoparticle hyperthermia is a new tissue thermal ablation technology. Under the action of an alternating magnetic field, magnetic nanoparticles convert magnetic field energy into heat energy, thereby increasing the temperature of the target region to a specific temperature and achieving the purpose of local tissue ablation. A magnetic particle imaging system can obtain the concentration distribution of magnetic nanoparticles, thereby realizing precise magnetic hyperthermia. However, when using a magnetic nanoparticle imaging system to track the spatial distribution of magnetic nanoparticles, the magnetic nanoparticle concentration distribution data must be obtained on the original MPI imager. The concentration distribution data of the magnetic nanoparticles cannot be obtained by simply viewing the MPI image, and therefore it is necessary to process and analyze the MPI image to extract the concentration distribution information of the magnetic nanoparticles. Therefore, it is particularly important to extract the concentration of each pixel point in the MPI image. In addition, it is also of great practical significance to explore the concentration distribution of magnetic nanoparticles during diffusion and to predict the temperature distribution during magnetic nanoparticle hyperthermia. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a method for predicting temperature distribution in magnetic hyperthermia of biological tissues based on MPI images, which realizes the extraction of the concentration of magnetic nanoparticles based on MPI images and can predict the temperature distribution in biological tissues after heating.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0005] A method for predicting temperature distribution in magnetic hyperthermia of biological tissues based on MPI images comprises the following steps:

[0006] Step S1: constructing a geometric model and a heat transfer model of the biological tissue;

[0007] Step S2: obtaining an MPI image and a color bar and preprocessing;

[0008] Step S3: based on the preprocessed color bar, performing secondary analysis on the preprocessed MPI image to obtain the initial concentration distribution of the magnetic nanoparticles;

[0009] Step S4: using a finite element method to solve a convection-diffusion equation to simulate the concentration distribution of the magnetic nanoparticles in the biological tissue during diffusion;

[0010] Step S5: using the concentration distribution of the magnetic nanoparticles in step S4 as an initial value to solve a heat transfer equation and predict the temperature distribution in the biological tissue.

[0011] Further, the geometric model of the biological tissue comprises an ellipse with a long axis a and a short axis b and an irregular figure, the ellipse is contained in the irregular figure, and the area where the ellipse is located represents a first tissue region, and the remaining part is a second tissue region.

[0012] Further, the heat transfer model of the biological tissue is constructed based on the geometric model, the first tissue region and the second tissue region are both set as biological heat conduction regions, and meanwhile, the heat conduction material attribute parameters, the alternating magnetic field intensity and frequency, and the biological heat attribute parameters of the first tissue region and the second tissue region are set.

[0013] Further, the step S2 specifically comprises:

[0014] Step S21: acquiring an MPI image and a corresponding color bar with a magnetic nanoparticle concentration scale value;

[0015] Step S22: adjusting the size of the MPI image and the color bar;

[0016] Step S23: respectively performing gray scale processing on the MPI image and the color bar, and the gray scale processing adopts an average value method, and the average value method formula is:

[0017] Y=(R+G+B) / 3

[0018] wherein Y is a gray value, and R, G and B are respectively a red channel component, a green channel component and a blue channel component of the image.

[0019] Further, the step S3 specifically comprises:

[0020] Step S31: acquiring a pixel matrix of the MPI image and the color bar processed in the step S2;

[0021] Step S32: selecting a column element in the pixel matrix corresponding to the color bar and performing equal-interval sampling;

[0022] Step S33: quantizing the concentration values corresponding to the color bar at equal intervals according to the number of sampled data points;

[0023] Step S34: taking the pixel values obtained by sampling in the step S32 as the horizontal coordinates of data points, and taking the concentration values quantized at equal intervals in the step S33 as the vertical coordinates of the corresponding data points, fitting the data points to obtain a curve of the pixel values and the concentration values;

[0024] Step S35: calculating the concentration values corresponding to each pixel point of the processed MPI image according to the fitted curve;

[0025] Step S36: taking the concentration data of each pixel point as a known data set, and obtaining a concentration distribution map of the magnetic nanoparticles by using a bilinear interpolation method.

[0026] Further, the convection-diffusion equation is:

[0027]

[0028] Wherein, C is the concentration of the magnetic fluid, D is the diffusion coefficient of the magnetic fluid, v is the flow velocity of the magnetic fluid, t is the diffusion time of the magnetic fluid.

[0029] Further, D in the convection-diffusion equation is approximated by using the effective diffusion coefficient D eff

[0030]

[0031] Wherein, D0 is the reference diffusion coefficient, a is the radius of the magnetic nanoparticle, a f is the radius of the tissue fiber, and φ is the volume fraction.

[0032] Further, the step S5 is specifically: taking the concentration distribution in step S4 as the initial value, predicting the temperature distribution in the biological tissue after heating by solving the Pennes bio-heat transfer equation, and the Pennes bio-heat transfer equation is:

[0033]

[0034] Wherein, ρ is the density of the tissue, c is the specific heat capacity of the tissue, T is the absolute temperature of the tissue, t is the heat transfer time, ω b is the blood perfusion rate, ρ b is the blood density, c b is the specific heat capacity of the blood, T b is the blood temperature, k is the thermal conductivity of the tissue, Q m is the metabolic heat of the unit volume in the tissue, α is the power dissipation correction coefficient, P is the power dissipation of the magnetic nanoparticle, and the subscript i is 1 or 2, representing the first tissue and the second tissue respectively.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] The present application realizes the secondary processing analysis and utilization of the MPI image, extracts the concentration distribution of the magnetic nanoparticle, and can simulate the concentration distribution of the magnetic nanoparticle during diffusion and predict the temperature distribution in the biological tissue after heating. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is the flow chart of the method of the present application.

[0038] Figure 2 is the schematic diagram of the geometric model constructed in an embodiment of the present application.

[0039] ​Figure 3 is the initial concentration distribution of magnetic nanoparticles extracted from the MPI image in an embodiment of the present application.

[0040] Figure 4 is the magnetic nanoparticle concentration distribution during diffusion 15h in an embodiment of the present application.

[0041] Figure 5 is the temperature distribution in the biological tissue after heating for 30min in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The present application will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] Please refer to Figure 1 , the present application provides a kind of based on MPI image's biological tissue in magnetic heat treatment temperature distribution prediction method, comprising the following steps:

[0044] Step S1: constructing the geometric model and heat transfer model of biological tissue;

[0045] Step S2: obtaining MPI image and color bar, and preprocessing;

[0046] Step S3: based on the color bar after preprocessing, the secondary analysis of the MPI image after preprocessing is carried out using the initial concentration distribution of magnetic nanoparticles obtained;

[0047] Step S4: the method of finite element is used to solve convection-diffusion equation to simulate the magnetic nanoparticle concentration distribution in biological tissue during diffusion;

[0048] Step S5: the magnetic nanoparticle concentration distribution in step S4 is used as initial value to solve heat transfer equation, and the temperature distribution in biological tissue is predicted.

[0049] Preferably, in the embodiment, as shown in Figure 2 , constructing the geometric model of biological tissue includes constructing an ellipse with a long axis a=5cm and a short axis b=3cm and an irregular figure, the area where the ellipse is located represents the first tissue region, and the rest is the second tissue region.

[0050] Preferably, in the embodiment, the corresponding biological heat transfer model is constructed according to the biological tissue geometric model constructed in step S1, and the first tissue and the second tissue region are both set as biological heat conduction region. The constant pressure heat capacity, thermal conductivity, density and metabolic heat source of the first tissue and the second tissue region are respectively set as: 3750J·Kg -1 ·K -1 , 0.59W·m -1 ·K -1 , 1050Kg·m -3 , 684.2W / m3 , 3540 J·Kg -1 ·K -1 , 0.52 W·m -1 ·K -1 , 1079 Kg·m -3 , 5790 W / m 3 . The alternating magnetic field strength and frequency are H0 = 15 kA·m -1 , f = 300 kHz.

[0051] In the embodiment, the step S2 is specifically:

[0052] Step S21: acquiring an MPI image and a corresponding color bar with magnetic nanoparticle concentration scale values;

[0053] Step S22: adjusting the size of the MPI image and the color bar;

[0054] Step S23: respectively performing grayscale processing on the MPI image and the color bar, and the grayscale processing adopts an average value method, and the average value method formula is:

[0055] Y = (R + G + B) / 3

[0056] wherein Y is a grayscale value, and R, G and B are respectively a red channel component, a green channel component and a blue channel component of the image.

[0057] In the embodiment, the step S3 is specifically:

[0058] Step S31: acquiring a pixel matrix of the MPI image and the color bar after the processing in the step S2;

[0059] Step S32: selecting a column element in the pixel matrix corresponding to the color bar and performing equal-interval sampling;

[0060] Step S33: quantizing the concentration values corresponding to the color bar at equal intervals according to the number of the sampled data points;

[0061] Step S34: taking the pixel values obtained by the sampling in the step S32 as horizontal coordinates of data points, and taking the concentration values quantized at equal intervals in the step S33 as vertical coordinates of the corresponding data points, and fitting the data points to obtain a curve of the pixel values and the concentration values;

[0062] Preferably, the fitted curve is:

[0063] Y(x) = 1.01 * 10 -7 *x 3 - 3.848 * 10 -5 *x 2 + 0.007153 * x - 0.01096

[0064] Wherein, x is the pixel value of the pixel point, Y(x) is the concentration value corresponding to the pixel point.

[0065] Step S35: Calculate the concentration value corresponding to each pixel point of the processed MPI image according to the fitting curve;

[0066] Step S36: Take the concentration data of each pixel point as a known data set, and obtain the concentration distribution map of the magnetic nano-particles by using the bilinear interpolation method.

[0067] In this embodiment, the convection-diffusion equation is:

[0068]

[0069] Wherein, C is the concentration of the magnetic fluid, D is the diffusion coefficient of the magnetic fluid, v is the flow velocity of the magnetic fluid, and t is the diffusion time of the magnetic fluid.

[0070] Preferably, in this embodiment, D in the convection-diffusion equation uses the effective diffusion coefficient D eff to approximate:

[0071]

[0072] Wherein, D0 is the reference diffusion coefficient, a is the radius of the magnetic nano-particle, a f is the radius of the tissue fiber, and φ is the volume fraction. Figure 4 is the concentration distribution map of the magnetic nano-particles simulated for 15h of diffusion.

[0073] In this embodiment, step S5 is specifically: taking the concentration distribution in step S4 as the initial value, predicting the temperature distribution in the biological tissue after heating by solving the Pennes bio-heat transfer equation, and the Pennes bio-heat transfer equation is:

[0074]

[0075] Wherein, ρ is the density of the tissue, c is the specific heat capacity of the tissue, T is the absolute temperature of the tissue, t is the heat transfer time, ω b is the blood perfusion rate, ρ b is the blood density, c b is the specific heat capacity of the blood, T b is the blood temperature, k is the thermal conductivity of the tissue, Q m is the metabolic heat per unit volume in the tissue, α is the power dissipation correction coefficient, P is the power dissipation of the magnetic nano-particles, and the subscript i is 1 or 2, representing the first tissue and the second tissue respectively.

[0076] Figure 5 is the temperature distribution map in the biological tissue predicted after heating for 30min.

[0077] The above description is only the preferred embodiment of the present application, and any equivalent change and modification made according to the scope of the present application should be included in the scope of the present application.

Claims

1. A method for predicting temperature distribution in magnetic hyperthermia of biological tissue based on MPI images, characterized in that, It comprises the following steps: Step S1: constructing a geometric model and a heat transfer model of the biological tissue; Step S2: obtaining an MPI image and a color bar and preprocessing; Step S3: based on the preprocessed color bar, performing secondary analysis on the preprocessed MPI image to obtain an initial concentration distribution of the magnetic nanoparticles; Step S4: solving a convection-diffusion equation to simulate the concentration distribution of the magnetic nanoparticles in the biological tissue during diffusion by using a finite element method; Step S5: taking the concentration distribution of the magnetic nanoparticles in step S4 as an initial value to solve a heat transfer equation and predict the temperature distribution in the biological tissue; The step S3 specifically comprises: Step S31: obtaining the pixel matrix of the MPI image and the color bar processed in step S2; Step S32: selecting a column element in the pixel matrix corresponding to the color bar and performing equal-interval sampling; Step S33: quantizing the concentration value corresponding to the color bar at equal intervals according to the number of the sampled data points; Step S34: taking the pixel value obtained by sampling in step S32 as the abscissa of the data point and taking the concentration value quantized at equal intervals in step S33 as the ordinate of the corresponding data point, fitting the data points to obtain a curve of the pixel value and the concentration value; Step S35: calculating the concentration value corresponding to each pixel point of the processed MPI image according to the fitted curve; Step S36: taking the concentration data of each pixel point as a known data set and obtaining the concentration distribution of the magnetic nanoparticles by using a bilinear interpolation method; The convection-diffusion equation is: Wherein, C is the concentration of the magnetic fluid, D is the diffusion coefficient of the magnetic fluid, v is the flow velocity of the magnetic fluid, and t is the diffusion time of the magnetic fluid.

2. The MPI image-based biological tissue internal magnetic hyperthermia temperature distribution prediction method of claim 1, wherein, The geometric model of the biological tissue comprises an ellipse with a long axis a and a short axis b and an irregular graph, the ellipse is contained in the irregular graph, the area where the ellipse is located represents a first tissue region, and the remaining part is a second tissue region.

3. The MPI image-based biological tissue internal magnetic hyperthermia temperature distribution prediction method of claim 2, wherein, The heat transfer model of the biological tissue is constructed based on the geometric model, the first tissue region and the second tissue region are both set as biological heat conduction regions, and meanwhile, the thermal conduction material attribute parameters, the alternating magnetic field strength and frequency, and the biological heat attribute parameters of the first tissue region and the second tissue region are set.

4. The MPI image-based biological tissue internal magnetic hyperthermia temperature distribution prediction method of claim 1, wherein, The step S2 specifically comprises: Step S21: obtaining an MPI image and a color bar corresponding to a magnetic nanoparticle concentration scale value; Step S22: adjusting the size of the MPI image and the color bar; Step S23: performing grayscale processing on the MPI image and the color bar respectively, and the grayscale processing adopts an average value method, and the formula of the average value method is: Y=(R+G+B) / 3 Wherein, Y is a gray value, and R, G and B are respectively a red channel component, a green channel component and a blue channel component of the image.

5. The MPI image-based biological tissue internal magnetic hyperthermia temperature distribution prediction method of claim 1, wherein, D in the convection-diffusion equation is approximated using an effective diffusion coefficient D eff D = D + Dc wherein D0is a reference diffusion coefficient, a is the radius of the magnetic nanoparticle, a f is the radius of the tissue fiber, and φ is the volume fraction.

6. The MPI image-based biological tissue internal magnetic hyperthermia temperature distribution prediction method of claim 1, wherein, The step S5 specifically comprises: taking the concentration distribution in step S4 as an initial value, predicting the temperature distribution in the biological tissue after heating by solving a Pennes biological heat transfer equation, and the Pennes biological heat transfer equation is: where p is the density of the tissue, c is the specific heat capacity of the tissue, T is the absolute temperature of the tissue, t' is the heat transfer time, ω b is the blood perfusion rate, p b is the blood density, c b is the specific heat capacity of the blood, T b is the blood temperature, k is the thermal conductivity of the tissue, Q m is the metabolic heat per unit volume in the tissue, a is the power dissipation correction factor, P is the power dissipation of the magnetic nanoparticles, and the subscript i is either 1 or 2, representing the first tissue and the second tissue, respectively.

Images