A method for evaluating the imaging effect of a magnetic resonance contrast agent and a system therefor

Abstract

The application discloses a method and system for evaluating the imaging effect of a magnetic resonance contrast agent, comprising the following steps: (1) establishing an MRI image data set of a magnetic resonance object; (2) reading the magnetic resonance image data, selecting the first and last layers and the middle key layer for analysis, carrying out contour line segmentation on a target region, and constructing a region of interest through interpolation fitting and morphological operation; (3) calculating the gray scale histogram in the region of interest of the image, superimposing the histogram of each MRI image, and carrying out normalization processing to obtain the histogram distribution information on the three-dimensional space of the tumor; (4) processing the MRI image pixels by the Otsu method to obtain the best threshold value; and (5) fusing and analyzing the obtained threshold value and the histogram to evaluate the MRI result. The application provides a precise quantitative visual evaluation method for improving the accuracy of tumor imaging effect recognition through computer-aided analysis and identification of the MRI image, and has great significance for diagnosis and treatment.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of nanomedical image processing, and particularly relates to a method and system for evaluating the imaging effect of a magnetic resonance contrast agent. BACKGROUND

[0002] Breast cancer (BC) is a phenomenon of uncontrolled proliferation of breast epithelial cells under the action of various carcinogens, and is the most common cancer in women, accounting for one-fourth of the global female cancer diagnoses. Among them, triple-negative breast cancer lacks effective targeted therapy due to the lack of expression of hormone receptors and human epidermal growth factor receptor-2, and is difficult to benefit from endocrine therapy and anti-HER2 targeted therapy. Clinical practice has found that the risk of postoperative recurrence and metastasis of triple-negative breast cancer is greatly increased, and the 5-year survival rate of patients is still very low. Therefore, depicting the tumor location with imaging data and making accurate image diagnosis can help patients understand the degree of surgical risk and decide whether to perform surgical biopsy and resection, providing more information for surgery.

[0003] Existing medical image examination techniques play an important role in tumor diagnosis and prognosis, and help to control and reduce the impact of the disease. Magnetic resonance imaging (MRI) is one of the most commonly used medical imaging techniques. MRI has the characteristic of non-invasiveness (using non-ionizing radiation) and can display various tissues at high resolution and good contrast. At the same time, MRI can produce multiple images with different contrast display capabilities for the same tissue region by applying different image acquisition protocols and parameters. These images provide more useful anatomical information about the same tissue region, and the complementary information from different contrast mechanisms helps researchers to more accurately analyze. In manual diagnosis, the number of MRI images to be analyzed is large, and the sensitivity of the human eye to MRI images may decrease with the increase in the number of images, which will bring difficulties to the differential diagnosis of doctors. In addition, manual diagnosis is time-consuming, depends on subjective judgment, and is difficult to quantify, which may lead to judgment errors.

[0004] The prior art machine learning method has been widely studied in medical image processing. Using computer assistance for imaging analysis has become a mainstream research direction, therefore, it is very important to use computer assistance to judge the pros and cons of magnetic resonance imaging results. At present, the conventional diagnosis is mostly performed by imaging physicians through lesion morphological characteristics or semi-quantitative hemodynamic information for empirical diagnosis. This method has strong subjective dependence and large error, which affects the accuracy of the judgment result. In addition, scholars have used the signal ratio (TNR) value between tumor tissue and adjacent normal muscle tissue to quantify the signal attenuation degree of tumor tissue after contrast on the magnetic resonance image. The greater the signal attenuation amplitude, the smaller the TNR value, which represents the better imaging effect. However, this method can only perform overall evaluation, and has large error, and cannot represent the distribution of contrast agent in the tumor. Therefore, it is very important to establish a comprehensive and accurate MRI contrast effect standardization evaluation system.

[0005] The main role of the contrast agent is to increase the contrast between normal tissue and abnormal tissue, which is manifested in the image as the gray value of the contrast agent infiltration area being less than (negative contrast) or higher than (positive contrast) the normal tissue, so that the location of the contrast agent can be extracted using image segmentation means for subsequent imaging effect evaluation. Image segmentation is an important research field in MRI image processing technology, and is also a difficulty and hotspot in research. Due to the influence of external factors during image acquisition, the image is not particularly accurate during segmentation. For example, the absence of an image can bring technical problems to image segmentation. Therefore, for image segmentation, it is extremely important to do well in segmentation preprocessing and improve the image segmentation algorithm, and it has very significant research value and use significance. Image segmentation can be divided into two categories according to the discontinuity and similarity of image intensity values. The first category is to divide the image according to the sudden change of intensity, such as the edge of the image. The second category is to divide a image into regions that are similar according to a predetermined criterion. At present, there are many mainstream segmentation methods, including histogram thresholding, edge detection, etc., among which the boundary tracking algorithm in edge detection method is commonly used, which needs to be manually set the starting point and needs to be processed repeatedly, which takes a long time and is not efficient, and in serious cases, program dead loop may occur. Due to the limitations of the segmentation algorithm itself, there are still two problems: first, there is no image segmentation method that can be applied to all types of images; second, there is no evaluation criterion for standard image segmentation processing. From the development of image analysis, there are mainly two trends: one is to apply new segmentation methods or comprehensive methods to new research fields; the second is to develop the original image segmentation method appropriately. In the present application, the original threshold segmentation method is developed appropriately to comprehensively and accurately evaluate the effect of magnetic resonance imaging. Threshold segmentation algorithm is a relatively classic algorithm in image segmentation algorithm, which has good segmentation effect for images with large difference in background and target gray scale, and has the advantages of simple calculation, fast speed, etc. Otsu method (OTSU) is an algorithm for determining the threshold value of image binarization. This method is also known as the maximum inter-class variance method, which is considered to be the best algorithm for threshold selection in image segmentation, and is simple to calculate and not affected by image brightness and contrast, so it has been widely used in digital image processing.

[0006] At present, the method for evaluating the effect of magnetic resonance imaging of the contrast agent is not clear, and the previous classification method has the problems of large error and strong subjective dependence. Although the prior art has used some calculation methods to evaluate the magnetic resonance results, the error is still large, and the optimization of the problem is not ideal, there is a lack of systematic method, which leads to low precision, and the evaluation of the advantages and disadvantages of the application effect of the contrast agent lacks persuasiveness. SUMMARY

[0007] The technical problem solved by the present application is to provide a method for evaluating the imaging effect of magnetic resonance contrast agents, which can more accurately evaluate the magnetic resonance imaging effect, judge the application effect of magnetic resonance contrast agents, and reduce the subjective evaluation error.

[0008] Technical scheme: In order to solve the above technical problem, the present application provides a method for evaluating the imaging effect of magnetic resonance contrast agents, comprising the following steps:

[0009] (1) Establishing the MRI image data set of the magnetic resonance object;

[0010] (2) Preprocessing the obtained MRI image data set, selecting the first and last two layers and the middle key layer for analysis, and constructing the region of interest (ROI) by contour line segmentation, interpolation fitting and morphological operation after the target region is segmented;

[0011] (3) Determining the range of the magnetic resonance object and finding the pixel points of the corresponding coordinates, calculating the histogram of each layer of MRI image, quantifying the gray scale of the image to 0-255, and superimposing the histogram of each layer of MRI image to obtain the histogram distribution information in the three-dimensional space of the magnetic resonance object;

[0012] (4) Threshold calculation based on the Otsu method (maximum inter-class threshold method);

[0013] (5) Based on the results of steps (3) and (4), fusion analysis, the closer the proportion of gray values less than the threshold value is to 1, the better the T2 magnetic resonance imaging effect is, and vice versa. The MRI contrast agent effect is evaluated to evaluate the MRI contrast agent effect;

[0014] In step (1), the magnetic resonance object is a solid tumor, including but not limited to breast cancer, lung cancer, gastric cancer, brain tumor, etc.

[0015] The present application also includes constructing a tumor model mouse as a magnetic resonance object for magnetic resonance imaging, and collecting mouse MRI data. Preferably, the mouse model is a tumor-bearing mouse, specifically a 4T1 breast cancer mouse. The imaging magnetic field strength used in magnetic resonance imaging includes but is not limited to 1.5T, 3T and 7T.

[0016] In step (2), the region of interest includes the selection of the tumor region of interest. The region selection software includes but is not limited to imagej, drawing, AnatomySketch, and the most optimal is AnatomySketch, which can draw the ideal tumor region in multiple two-dimensional magnetic resonance images of the MRI subset, and can construct a three-dimensional tumor image to help judge whether the selected region is appropriate.

[0017] In step (3), the corresponding pixel points of the coordinates are found, the histogram of each MRI image is calculated, and superposition is performed.

[0018] In step (1), the following steps are included: first, a high-molecular polymer wrapped with magnetic nanoparticles is prepared as a contrast agent, and a magnetic resonance object is introduced; then, MRI is performed to obtain an MRI data set.

[0019] In step (2), the following steps are included: the MRI data set obtained in step (1) is 3D data, each subset is composed of multiple two-dimensional image data, the first and last layers and the key intermediate layers are analyzed using AnatomySketch software, the target region is segmented by contour line, and the region of interest in the image is constructed by interpolation fitting and morphological operation.

[0020] In step (3), the following steps are included: the magnetic resonance image can be divided into multiple two-dimensional images, based on the image pixel points, the maximum and minimum values of the image gray scale are found by traversing search according to the size of the image gray scale value, the image gray scale is quantized to 0-255 by linear transformation, the number of pixels of different gray scale values is counted, a two-dimensional image histogram is obtained, the histograms of multiple two-dimensional images are superimposed, the frequency of each gray scale is calculated according to formula (1), and a three-dimensional data frequency distribution histogram is obtained.

[0021]

[0022]

[0023] In the formula, k is the kth gray scale value of the two-dimensional image, N k is the number of pixels with gray scale value k in the image, N is the total number of pixels in the image, L is the number of gray scales, and H(k) is the frequency of pixels with gray scale k. The histograms of multiple two-dimensional images are superimposed, and a three-dimensional data frequency distribution histogram is obtained after normalization.

[0024] In step (4), the best threshold value is calculated based on the Otsu method.

[0025] The basic idea of threshold segmentation is simple: the gray scale value of each pixel is compared with the threshold value selected at the beginning, and the pixels are classified according to the comparison results, and are divided into two categories: foreground or background.

[0026] The threshold segmentation method includes but is not limited to determining the threshold value according to the histogram valley, iterative threshold selection method, minimum mean square error method, maximum inter-class variance method (Otsu method), etc.

[0027] Preferably, the threshold calculation method is Otsu's binarization. Otsu's method is an algorithm for automatically selecting the threshold value of an image to maximize the class variance between the foreground and background. The maximum class variance means the minimum error rate.

[0028] Preferably, step (4) comprises the following steps: calculating the optimal threshold value by Otsu's method, and separating the contrast agent region by using the threshold value. The specific steps of Otsu's method are as follows: assuming that an image has N pixels and L gray levels, the gray value distribution of the whole image is 0, 1,..., L-1, and the probability of the gray value i is p i Otsu's method assumes that there is a threshold value k to divide all pixels of the image into two classes C1 (gray value range 0-k-1) and C2 (gray value range T-L-1), the mean values of the two classes of pixels are m1 and m2, and the global mean value of the image is m G . At the same time, the probabilities of the pixels being divided into C1 and C2 classes are p1 and p2, respectively. Therefore, we have:

[0029] p1m1+p2m2=m G (1)

[0030] p1+p2=1 (2)

[0031] According to the concept of variance, the inter-class variance expression is:

[0032] σ 2 =p1(m1-m G ) 2 +p2(m2-m G ) 2 (3)

[0033] Substituting equation (1) into equation (3) simplifies to:

[0034] σ 2 =p1p2(m1-m2) 2 (4)

[0035] wherein:

[0036]

[0037]

[0038]

[0039] According to the above formula, k is traversed from the minimum gray value of the image to the maximum gray value, and k when sigma in formula (4) is maximum is the optimal threshold T. According to formula (8), the image is binarized. Wherein g(x, y) is the binarized image, and f(x, y) is the original two-dimensional image. The foreground part we need is g(x, y) = 0, which is expressed as a black area on the image.

[0040]

[0041] Wherein, step (5) is fusion analysis according to the calculated threshold and the histogram, and the closer the proportion of the gray value less than the threshold value to 1, the better the T2 magnetic resonance imaging effect, and vice versa, the worse the T2 imaging effect. The MRI contrast agent effect is evaluated in this way.

[0042] Specifically, after the tumor region of interest is selected by using the AnatomySketch software, the histogram is calculated, and then the Otsu method is used for calculation, and the pixel gray value proportion of the contrast agent region of the MRI image is calculated according to the threshold value calculated by the Otsu method and the histogram result, so that the MRI image is evaluated.

[0043] The content of the application also includes a system constructed by the evaluation method. Mainly includes selection of a tumor region of interest, histogram calculation based on a pixel point, threshold value calculation based on the Otsu method, and fusion analysis and evaluation of magnetic resonance imaging effect.

[0044] Wherein, the application provides a kind of high molecular magnetic nano-particle as contrast agent when constructing tumor magnetic resonance imaging data set.

[0045] Wherein, the high molecular polymer includes but is not limited to one or more of polylactic acid, polylactic acid-glycolic acid copolymer, polylactic acid-polyethyleneimine block polymer or polylactic acid-glycolic acid-polyethyleneimine block copolymer;The magnetic nano-particle includes but is not limited to magnetite nano-particle (Fe3O4). High molecular polymer has good amphiphilic property, is beneficial to long cycle, is easy to surface modification, and can achieve specific imaging effect.

[0046] Wherein, as preferably, the high molecular copolymer is polylactic acid-glycolic acid copolymer-polyethylene glycol block copolymer.

[0047] Wherein, as preferably, the polylactic acid-glycolic acid copolymer (PLGA-PEG) has an average molecular weight of 5000-10000 Da, and the optimal value is 10000 Da.

[0048] Wherein, as preferably, the polyethylene glycol has an average molecular weight of 600-5000 Da, and the optimal value is 2000 Da.

[0049] The application further provides a preparation method of the high-molecular magnetic nanoparticle, comprising the following steps: preparing the high-molecular polymer nanoparticle wrapping the magnetic nanoparticle: dissolving the high-molecular polymer and the magnetic nanoparticle in an organic reagent, and adding into an aqueous solution containing an emulsifier, obtaining a primary emulsion by ultrasonic crushing under ice bath condition and transferring into a three-necked bottle, volatilizing the organic reagent by mechanical stirring and ultrasonic oscillation, removing the excess emulsifier and other impurities by ultrafiltration and washing, and collecting the high-molecular polymer nanoparticle (Fe3O4@PLGA-PEG) wrapping the magnetic nanoparticle.

[0050] In the formula, the organic reagent is trichloromethane or dichloromethane.

[0051] In the formula, the emulsifier includes but is not limited to one or a combination of two of polyvinyl alcohol, polyvinylpyrrolidone and polyethylene glycol.

[0052] In the formula, preferably, the emulsifier is polyvinyl alcohol.

[0053] In the formula, preferably, the molecular weight of the polyvinyl alcohol is 20000-40000 Da, and most preferably 31000 Da.

[0054] In the formula, in the step, the ultrasonic crushing time is 5-8 min, the ultrasonic interval time is 2-3 s, and the ultrasonic power is 180-300 W; the mechanical stirring time is 3-5 h, and the stirring speed is 400-600 rpm / min.

[0055] In the formula, preferably, the purification method of the high-molecular magnetic nanoparticle is ultrafiltration, the ultrafiltration tube used for centrifugal ultrafiltration has a molecular weight cut-off of 30-100 kDa, the centrifugal speed is 4000-5000 rpm, the centrifugal time is 3-5 min, and the centrifugal frequency is 3-5 times.

[0056] In the formula, the hydrodynamic size of the high-molecular magnetic nanoparticle is 50-200 nm, and the surface potential is -11.4±4.49 mV.

[0057] Beneficial effects: the method for evaluating the imaging effect of the magnetic resonance contrast agent in the tumor of the application is composed of collecting MRI data set, sketching the region of interest, superimposing the histogram of the calculated MRI data, calculating the best threshold value by Otsu method, and fusing the threshold value and histogram analysis. Compared with the prior art, the tumor region of interest is sketched more accurately and representatively by using the AnatomySketch software, and the data is more comprehensive by using the superimposed histogram calculation, and the image is divided into background and foreground two parts according to the gray scale characteristics by introducing the Otsu method, wherein the region of interest is generally the foreground. Compared with the traditional method, the method proposed in the application avoids the error caused by subjective judgment, constructs a complete evaluation system, more comprehensively processes the MRI image, and obtains more accurate results, and the evaluation of the imaging effect of the magnetic resonance contrast agent is more convincing. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 Preparation flowchart of Fe3O4@PLGA-PEG.

[0059] Figure 2 Electron microscope images of Fe3O4 and Fe3O4@PLGA-PEG nanoparticles.

[0060] Figure 3 Particle size and Zeta potential distribution diagram of Fe3O4@PLGA-PEG nanospheres.

[0061] Figure 4 In vivo magnetic resonance imaging data diagram of Fe3O4@PLGA-PEG.

[0062] Figure 5 Tumor region histogram of in vivo magnetic resonance image of Fe3O4@PLGA-PEG.

[0063] Figure 6 MRI image after Otsu threshold value calculation.

[0064] Figure 7 Overall principle diagram of embodiments 2-5 of the application. DETAILED DESCRIPTION

[0065] In order to better understand the application, the following examples further illustrate the application, but the content of the application is not limited to the following examples.

[0066] Example 1 Preparation of Fe3O4@PLGA-PEG nanoparticles

[0067] 5mg Fe3O4 nanoparticles were added to 4mL chloroform containing 100mg PLGA-PEG. After 2 minutes of sonication, the mixture was dispersed in 12mL of 2% fw / v) polyvinyl alcohol (PVA) solution and sonicated under ice bath conditions (time 5min, sonication interval 3s, power 180w). The primary emulsion was transferred to a three-necked flask and sonicated for 3h to evaporate the chloroform. Excess PLGA-PEG, PVA and other impurities were removed by ultrafiltration at 4500rpm / min for 3 times. Finally, Fe3O4@PLGA-PEG nanoparticles were obtained.

[0068] PLGA-PEG used in the above method was purchased from Xi'an Ruishi Company, and polyvinyl alcohol was purchased from Aldrin.

[0069] A small amount of prepared Fe3O4@PLGA-PEG nanoparticles were taken and spread on a copper mesh. The morphology and particle size of the nanoparticles were observed under a transmission electron microscope. As shown in Figure 2 , it can be observed that the Fe3O4@PLGA-PEG nanoparticles are spherical, with a particle size distribution of 50-200nm, and good stability.

[0070] As shown in Figure 3 , the hydrodynamic size and Zeta potential of Fe3O4@PLGA-PEG nanoparticles were measured by Malvern particle size analyzer. The Zeta potential of Fe3O4@PLGA-PEG nanoparticles was about 11.4±4.49mV.

[0071] Example 2 In vivo magnetic resonance imaging of nanoparticles and establishment of tumor MRI data set

[0072] MRI was performed by using MultiSlice Multi Echo method on a 7.0T Micro-MR scanner (PharmaScan, Brukers, Germany). Fe3O4@PLGA-PEG nanoparticles were injected at a dose of 5mg Fe / kg. In vivo MRI was performed before and 3 hours after injection of nanoparticles in the tail vein of 32 4T1 breast cancer mice. The parameters for T2 weighted imaging were as follows: flip angle = 180, matrix = 256x256, TE = 33.0ms, TR = 2500.0ms, FOV = 5x5, SI = 1.0mm / 1.0mm, slices = 11, average = 3, NEX = 1.

[0073] As shown in Figure 4 , the T2 results show that there is a significant accumulation of black nanoparticles at the tumor site at 3 hours, and the T2 effect changes, indicating that the contrast agent performs T2 weighted imaging. MRI data was collected to construct the MRI data set.

[0074] 4T1 breast cancer mouse construction method: after the BALB / c mice were depilated, the tumor site was sterilized with 75% alcohol (usually near the back leg). After the 4T1 breast cancer cells in the logarithmic growth phase were digested and centrifuged, they were resuspended in PBS buffer, counted with a cell counting plate, and the concentration of the 4T1 breast cancer cells was adjusted to about 5x10 7 cells / mL. 0.1 mL of the cell suspension was injected subcutaneously into the mouse, and then the mouse was labeled and returned to the cage to obtain a 4T1 breast cancer mouse. Among them, the clean level BALB / c mouse was female, 6 weeks old, and weighed 18-20 g, purchased from Nanjing Qinglongshan Animal Feeding Center. The 4T1 breast cancer cells were obtained from the Shanghai Institute of Biochemistry and Cell Biology, which belongs to the triple-negative breast cancer cell line.

[0075] Example 3: Calculate the histogram in the region of interest of the magnetic resonance image and obtain the gray level histogram of the three-dimensional image by superimposing the slice data

[0076] Read the magnetic resonance two-dimensional image layer by layer in order, quantize the image gray level to 0-255 by linear transformation, traverse the image, if the pixel point is in the region of interest (tumor contrast area), the gray value of the point corresponds to the histogram plus one, if the point is not in the region of interest, do not perform calculation. Finally, the gray level histogram in the region of interest is obtained. Superimpose and normalize the histogram data of different layers, and the obtained is the frequency distribution histogram in the three-dimensional space of the tumor. The abscissa of the normalized histogram represents the number of image gray levels, which is 256 in this example. The ordinate is the probability statistics of each gray value in the three-dimensional image, and the probability sum of all gray values in the image is 1.

[0077] As shown in Figure 5 , the gray level histogram percentile represents the number of pixels with a gray value lower than the percentile, and the difference in percentile is statistically significant, which reflects the distribution of tumor cell gray values to some extent. Figure 5 The abscissa represents the pixel gray value, and the ordinate represents the proportion of the corresponding gray value. As Figure 5 is obvious, there are obvious differences in the distribution of gray values between different samples.

[0078] Example 4: Optimal threshold calculation based on Otsu method

[0079] Suppose an image has N pixels, the gray level is L, the gray value distribution of the whole image is 0, 1..., L-1, and the number of pixels with a gray value of i is N i . The Otsu method assumes that there is a threshold k that divides all pixels into two classes C1 (gray value range 0-k-1) and C2 (gray value range T-L-1), and the mean values of the two classes are m1 and m2, respectively, and the global mean value of the image is m GMeanwhile, the probabilities of a pixel being classified into class C1 and class C2 are p1 and p2, respectively. Therefore:

[0080] p1m1+p2m2=m G (1)

[0081] p1+p2=1 (2)

[0082] Based on the concept of variance, the expression for between-class variance is:

[0083] σ 2 =p1(m1-m G ) 2 +p2(m2-m G ) 2 (3)

[0084] Substituting equation (1) into equation (3) and simplifying, we get:

[0085] σ 2 =p1p2(m1-m2) 2 (4)

[0086] in:

[0087]

[0088]

[0089]

[0090] According to the above formula, k is iterated from the minimum gray value to the maximum gray value of the image, and the optimal threshold T is the k that maximizes equation (4). The image is binarized according to equation (8). Where g(x,y) is the binarized image and f(x,y) is the original two-dimensional image. The foreground part we need is at g(x,y) = 0, which is represented as a black area on the image.

[0091]

[0092] like Figure 6 As shown, after image binarization and segmentation using the threshold obtained by the Otsu method, the black area represents the region where the contrast agent is located. Furthermore, using the Otsu method, the thresholds obtained by formulas (1) to (7) are shown in Table 1. In Table 1, 1 to 32 represent 32 4T1 breast cancer mouse samples.

[0093] Table 1

[0094]

[0095]

[0096] Example 5 Evaluation of MRI contrast agent effect based on histogram and Otsu method fusion analysis

[0097] The threshold value calculated according to the Otsu method is combined with the proportion of gray values less than the threshold value calculated by the histogram. The greater the proportion, the better the T2 magnetic resonance imaging effect. As shown in Table 2, the proportion of gray values less than the threshold value of the 4T1 breast cancer mouse sample 2 is the largest, indicating that the T2 magnetic resonance effect is the best, and conversely, the 4T1 breast cancer mouse sample 3 is relatively poor.

[0098] Table 2

[0099]

[0100]

Claims

1. A method for evaluating the imaging effect of magnetic resonance contrast agents, characterized in that, Includes the following steps: (1) Establish an MRI image dataset of the magnetic resonance imaging object; (2) The obtained MRI image dataset is preprocessed, and the first and last layers and the key middle layer are selected for analysis. The target area is segmented by contour lines and then the region of interest is constructed by interpolation fitting and morphological operations. (3) Determine the range of the magnetic resonance object and find the corresponding pixel points, calculate the histogram of each layer of MRI image, quantize the gray value of the image to 0-255, and superimpose the histograms of each layer of MRI image to obtain the histogram distribution information of the magnetic resonance object in three-dimensional space. (4) Threshold calculation based on Otsu's method; (5) Based on the results of steps (3) and (4), perform fusion analysis. When the proportion of gray values ​​less than the threshold is closer to 1, it indicates that the T2 magnetic resonance imaging effect is better; otherwise, the T2 imaging effect is worse. Step (3) includes the following steps: Based on the image pixels, according to the size of the image gray value, traverse and search to find the maximum and minimum values ​​of the image gray value, use linear transformation to quantize the image gray value into 0-255, the number of pixels with different gray values, obtain a histogram of a two-dimensional image, superimpose the histograms of multiple two-dimensional images, calculate the frequency of each gray value, and obtain the frequency distribution histogram of the three-dimensional data. ； In the formula, Let k be the frequency of occurrence of a pixel with gray level k, where k is the k-th gray level value of the two-dimensional image. Then, k is the number of pixels with a gray value of k in the image, N is the total number of pixels in the image, and L is the number of gray levels.

2. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 1, characterized in that: The magnetic resonance object mentioned in step (1) includes solid tumors, including breast cancer, lung cancer, gastric cancer, or brain tumors.

3. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 1, characterized in that: Step (1) includes the following steps: First, a polymer encapsulating magnetic nanoparticles is prepared as a contrast agent and introduced into the magnetic resonance object; then, MRI is performed to obtain an MRI dataset.

4. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 1, characterized in that, Step (2) includes the following steps: the MRI dataset obtained in step (1) is 3D data, each subset consists of multi-layer two-dimensional image data, the first and last layers and the middle key layer are analyzed using AnatomySketch software, the target area is segmented by contour lines and then the region of interest in the image is constructed by interpolation fitting and morphological operations.

5. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 1, characterized in that, Step (4) includes the following steps: Calculate the optimal threshold using the Otsu method, and segment the contrast agent region using the optimal threshold. Assume an image has N pixels, L gray levels, and the gray value distribution of the entire image is 0, 1, ..., L-1. The probability of gray value i is... Otsu's method assumes the existence of a threshold k that divides all pixels in an image into two classes, C1 and C2. The grayscale value range of C1 is 0 to k-1, and the grayscale value range of C2 is T to L-1. The mean values ​​of these two classes are m1 and m2, respectively, and the global mean value of the image is m. G Meanwhile, the probabilities of a pixel being classified into classes C1 and C2 are p1 and p2, respectively. Therefore: p1m1+p2m2=m G (1), p1+p2=1 (2), Based on the concept of variance, the expression for between-class variance is: σ 2 = p1(m1-m G ) 2 + p2(m2-m G ) 2 (3), Substituting equation (1) into equation (3) and simplifying, we get: σ 2 = p1p2(m1-m2) 2 (4), in: ， Based on the above formula, k is iterated from the minimum gray value to the maximum gray value of the image, so that the k corresponding to the maximum σ in equation (4) is the optimal threshold T. The image is then binarized according to equation (8), where... This is the binarized image. Given the original 2D image, the foreground portion we need is... In this case, it appears as a black area on the image.

6. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 1, characterized in that, Step (5) includes the following steps: Based on the threshold calculated by Otsu's method in step (4) and the histogram calculated in step (3), calculate the proportion of the foreground part of the gray value of the region of interest in each subset of the MRI dataset. When the proportion of gray values ​​less than the threshold is closer to 1, it indicates that the T2 magnetic resonance imaging effect is better, and vice versa.

7. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 3, characterized in that, The polymer is one or more of polylactic acid, polylactic acid-glycolic acid copolymer, polylactic acid-polyethyleneimine block polymer, or polylactic acid-glycolic acid-polyethyleneimine block copolymer; the magnetic nanoparticles are iron oxide nanoparticles.

8. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 2, characterized in that, The solid tumor is a tumor-bearing mouse model.

9. The method for evaluating the imaging effect of magnetic resonance contrast agents according to claim 2, characterized in that, The solid tumor was a 4T1 breast cancer model.

10. A system for evaluating the imaging effect of magnetic resonance contrast agents, characterized in that, The system is constructed using the method described in any one of claims 1-9.

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