Cerebral hemorrhage magnetic induction tomography image reconstruction method and system

Abstract

The invention discloses a cerebral hemorrhage magnetic induction tomography image reconstruction method and a cerebral hemorrhage magnetic induction tomography image reconstruction system, which are applied to the technical field of magnetic induction tomography and comprise the following steps of: constructing a human brain simulation model according to an actual structure of a human brain, and circularly exciting the human brain simulation model to obtain a numerical value required by imaging; on the basis of an improved POCS-TVM algorithm, performing cerebral hemorrhage magnetic induction tomography image reconstruction by adopting a human brain simulation model and the numerical value required by imaging to obtain a reconstructed image; and performing interpolation processing on the reconstructed image by adopting cubic convolution interpolation to generate a new reconstructed image after interpolation processing. In this way, by improving the total variation calculation formula and introducing the cubic convolution interpolation to perform interpolation processing on the reconstructed image, the cerebral hemorrhage area and the hemorrhage amount can be more accurately positioned, meanwhile, imaging artifacts can be effectively reduced, and the edge of the cerebral hemorrhage area in imaging is smoother and closer to the real hemorrhage condition.

Description

Technical Field

[0001] The present disclosure relates to the technical field of magnetic induction tomography, and in particular to a method and system for reconstructing magnetic induction tomography images of cerebral hemorrhage. Background Art

[0002] Cerebral hemorrhage is a serious brain disease, which refers to the rupture of blood vessels in the brain, causing blood to leak into the brain tissue. This situation can occur in the brain parenchyma, or it may occur under the meninges or other parts of the brain. The diagnosis of cerebral hemorrhage mainly relies on imaging examinations. Cerebral hemorrhage imaging technology is an important branch in the field of neurology, which involves the use of different imaging technologies to diagnose and evaluate cerebral hemorrhage. Existing cerebral hemorrhage imaging technologies include computed tomography (CT) and magnetic resonance imaging (MRI). Although the two technical means are mature cerebral hemorrhage imaging technologies, they still have certain limitations. For example, CT has poor detection effect on small-scale bleeding and chronic bleeding and there is radiation; MRI detection takes a long time and requires high cooperation from patients. In addition, the detection costs of both are high, not suitable for large-scale popularization at the grassroots level, and cannot be monitored in real time and dynamically.

[0003] Magnetic Induction Tomography (MIT) is a non-contact rapid imaging technology based on the principle of electromagnetic induction. It uses an alternating magnetic field to stimulate human tissue to generate eddy currents and generate a secondary magnetic field. By analyzing the changes in the secondary magnetic field, the distribution of internal conductivity of the human body is inferred and imaging information is obtained. It has the advantages of no radiation, low equipment cost, and fast imaging speed. The performance of the image reconstruction method of magnetic induction tomography of intracerebral hemorrhage is the key to this imaging technology. The current MIT imaging algorithm for intracerebral hemorrhage has the disadvantages of low imaging resolution and severe artifacts, which will affect the clinical diagnosis results of the location and amount of intracerebral hemorrhage.

[0004] Therefore, there is an urgent need for a method and a corresponding system that can effectively solve the problems of low imaging resolution and severe artifacts in the magnetic induction tomography algorithm for cerebral hemorrhage. Summary of the invention

[0005] The present invention provides a method and system for reconstructing magnetic induction tomography images of cerebral hemorrhage. Through the method for reconstructing magnetic induction tomography images of cerebral hemorrhage based on an improved POCS-TVM algorithm, at least the technical problems of low imaging result resolution and serious artifacts in the existing magnetic induction tomography algorithms for cerebral hemorrhage are solved.

[0006] According to a first aspect of the present disclosure, a method for reconstructing magnetic induction tomography images of cerebral hemorrhage is provided, comprising the following steps:

[0007] Construct a human brain simulation model based on the actual structure of the human brain, perform cyclic excitation on the human brain simulation model, and obtain the numerical values ​​required for imaging;

[0008] Based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for the imaging, the magnetic induction tomography image of cerebral hemorrhage is reconstructed to obtain a reconstructed image;

[0009] The reconstructed image is interpolated using cubic convolution interpolation to generate a new reconstructed image after interpolation.

[0010] According to the above aspects and any possible implementation, an implementation is further provided, wherein the human brain simulation model includes an air domain, a coil array, a scalp layer, a skull layer, a cerebrospinal fluid layer, a brain parenchyma layer, and a hemorrhage area;

[0011] The values ​​required for imaging include coil observation values ​​and a sensitivity matrix.

[0012] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of reconstructing the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for the imaging to obtain the reconstructed image is:

[0013] Assign an initial value to the one-dimensional reconstructed image, and set the number of iterations of the image reconstruction process to obtain the initialization iteration result;

[0014] Perform ART iteration to calculate and obtain an iteratively reconstructed image;

[0015] Apply non-negative condition constraints to the iteratively reconstructed image and calculate the incremental factor;

[0016] The total variation gradient calculation method is improved based on the gradient relationship between the central pixel and the adjacent pixels, and the improved total variation gradient formula is obtained.

[0017] The total variation gradient and the gradient direction are calculated according to the total variation gradient formula;

[0018] An iteration condition is set, and the image is iteratively updated according to the total variation gradient direction to obtain an updated image.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the calculation process of the increment factor is:

[0020]

[0021] in, is a non-negative condition constraint, is the increment factor, and K2 is the iteration number counter of the process of minimizing the total variation.

[0022] According to the above aspects and any possible implementation, an implementation is further provided, wherein the improved total variation gradient formula is:

[0023]

[0024] Among them, ε is to prevent the positive disturbance added when the denominator is 0, I i,j is the pixel value of the pixel point (i, j) in the reconstructed image, I i+1,j is the pixel value of the pixel point (i+1,j) in the reconstructed image, I i,j+1 is the pixel value of the pixel point (i, j+1) in the reconstructed image, I i-1,j+1 is the pixel value of the pixel point (i-1, j+1) in the reconstructed image, I i+1,j-1 is the pixel value of the pixel point (i+1, j-1) in the reconstructed image, I i,j-1 is the pixel value of the pixel point (i, j-1) in the reconstructed image, I i,j+1 is the pixel value of pixel (i, j+1) in the reconstructed image.

[0025] According to the above aspects and any possible implementation, an implementation is further provided, wherein the total variation gradient and the gradient direction are:

[0026]

[0027] in, is the total variation gradient, is the gradient direction, K1 is the iteration counter of the image reconstruction process, K2 is the iteration counter of the process of minimizing the total variation, ||·|| TV is the total variation regularization.

[0028] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the process of iteratively updating the image according to the total variation gradient direction is:

[0029]

[0030] Among them, α is the adjustment factor, is the increment factor, K2 is the iteration counter of the process of minimizing the total variation, is the total variation gradient.

[0031] According to the above aspects and any possible implementation, an implementation is further provided, wherein the iteration condition is:

[0032] If the number of iterations K2 < the number of iterations for minimizing the total variation, then return to recalculate the incremental factor; if the number of iterations K2 > the number of iterations for minimizing the total variation, then determine whether the overall number of iterations satisfies K1 < the total number of iterations of the algorithm. If so, return to re-iterate ART.

[0033] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further comprises: evaluating the new reconstructed image by using a peak signal-to-noise ratio, a structural similarity index, and an image correlation coefficient, specifically:

[0034]

[0035] Among them, MAX I is the maximum pixel value in the image, I(i,j) is the original image pixel value, K(i,j) is the reconstructed image pixel value, m and n are the height and width of the image, μ1 and μ2 are the pixel means of the original image and the reconstructed image, respectively, σ 12 is the covariance of the two images, c1 and c2 are constants used to stabilize the denominator, is the true conductivity distribution, To reconstruct the conductivity distribution of the image.

[0036] According to a second aspect of the present disclosure, there is provided a magnetic induction tomography image reconstruction system for cerebral hemorrhage, which is used to implement a magnetic induction tomography image reconstruction method for cerebral hemorrhage as described in the first aspect, comprising: an image reconstruction value acquisition module, an image reconstruction module, and a reconstructed image optimization module;

[0037] The image reconstruction value acquisition module is used to construct a human brain simulation model according to the actual structure of the human brain, perform cyclic excitation on the human brain simulation model, and obtain the values ​​required for imaging;

[0038] The image reconstruction module is used to reconstruct the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for imaging to obtain a reconstructed image;

[0039] The reconstructed image optimization module is used to perform interpolation processing on the reconstructed image by using cubic convolution interpolation to generate a new reconstructed image.

[0040] Compared with the prior art, the present invention has the following technical effects:

[0041] The present invention improves the total variation calculation formula, takes into account the gradient relationship between the central pixel and the eight adjacent pixels, and introduces cubic convolution interpolation to perform interpolation processing on the reconstructed image, which helps to more accurately locate the cerebral hemorrhage area and the size of the bleeding amount. At the same time, it can effectively reduce imaging artifacts, make the edge of the cerebral hemorrhage area in the imaging smoother and closer to the actual bleeding situation, and provide a new and effective algorithm for cerebral hemorrhage magnetic induction tomography detection technology.

[0042] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0044] Figure 1 A schematic diagram of a process for reconstructing a magnetic induction tomography image of cerebral hemorrhage according to an embodiment of the present disclosure is shown;

[0045] Figure 2 A schematic diagram of the structure of a magnetic induction tomography image reconstruction system for cerebral hemorrhage according to an embodiment of the present disclosure is shown;

[0046] Figure 3 A schematic diagram of the structure of a human brain simulation model of a magnetic induction tomography image reconstruction method for cerebral hemorrhage according to an embodiment of the present disclosure is shown;

[0047] Figure 4 A schematic diagram of setting conductivity parameters of various tissues in the human brain according to an embodiment of a method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to an embodiment of the present disclosure is shown;

[0048] Figure 5 A schematic diagram of a cerebral hemorrhage with a bleeding volume of 4.19 mL in the frontal lobe region is shown according to Example 1 of a method for reconstructing a magnetic induction tomography image of cerebral hemorrhage in accordance with an embodiment of the present disclosure;

[0049] Figure 6 A schematic diagram showing the comparison of the reconstruction results of 4.19 mL of bleeding volume in the frontal lobe region according to Example 1 of a magnetic induction tomography image reconstruction method for cerebral hemorrhage according to an embodiment of the present disclosure is shown;

[0050] Figure 7 A schematic diagram of a cerebral hemorrhage with a bleeding volume of 1.4 mL in the left temporal lobe region is shown according to Example 2 of a method for reconstructing a magnetic induction tomography image of cerebral hemorrhage in accordance with an embodiment of the present disclosure;

[0051] Figure 8 A schematic diagram showing a comparison of reconstruction results of 1.4 mL of bleeding volume in the left temporal lobe region according to Example 2 of a magnetic induction tomography image reconstruction method for cerebral hemorrhage according to an embodiment of the present disclosure;

[0052] Fig. 9 A schematic diagram of a cerebral hemorrhage with a bleeding volume of 1.4 mL in the right occipital lobe region is shown according to Example 3 of a method for reconstructing a magnetic induction tomography image of cerebral hemorrhage in accordance with an embodiment of the present disclosure;

[0053] Fig.10 A schematic diagram showing comparison of reconstruction results of 1.4 mL of bleeding volume in the right occipital lobe region according to Example 3 of a magnetic induction tomography image reconstruction method for cerebral hemorrhage according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Reference Figure 1 As shown, this embodiment provides a method for reconstructing magnetic induction tomography images of cerebral hemorrhage, comprising the following steps:

[0057] S101, constructing a human brain simulation model according to the actual structure of the human brain, and cyclically stimulating the human brain simulation model to obtain numerical values ​​required for imaging.

[0058] In this embodiment, in order to achieve the technical effect of the present invention, the present invention constructs a human brain simulation model, which specifically includes an air domain, a coil array, a scalp layer, a skull layer, a cerebrospinal fluid layer, a brain parenchyma layer and a bleeding area.

[0059] During the cyclic excitation process, the human brain simulation model is excited by setting the simulation excitation frequency, excitation current, conductivity parameters of various brain tissues, and cerebral hemorrhage volume, so as to obtain the coil observation value and sensitivity matrix.

[0060] S102. Reconstruct the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for imaging to obtain a reconstructed image.

[0061] This embodiment uses the POCS-TVM algorithm to reconstruct CT images. The POCS-TVM (Projection on Convex Sets-Total Variation Minimization) algorithm is a method based on finite difference transformation. Compared with other methods based on CS theory, this method has the best comprehensive performance in terms of reconstruction speed, reconstruction quality and robustness. Therefore, it is applied to artifact correction to reconstruct higher quality images from incomplete data to solve related problems. POCS refers to the process of projecting data onto a convex set. This embodiment uses the algebraic reconstruction algorithm (Algebraic Reconstruction Technique, ART) because the ART algorithm converges faster. The TVM process is the process of minimizing the total variation, usually using the gradient descent method to search in the opposite direction of the total variation gradient.

[0062] The task of CT image reconstruction is to calculate the image distribution based on the known sensitivity matrix and coil observation values, so as to reconstruct the image. This embodiment improves the traditional POCS-TVM algorithm. The specific process is as follows:

[0063] (1) Initialize the iterative process and perform the first iteration: Assign an initial value to the one-dimensional reconstructed image I. Usually, I k (k=0)=0, k is the number of iterations of the image reconstruction process; wherein the one-dimensional reconstructed image is obtained by arranging the two-dimensional CT image acquired by the human brain simulation model into a one-dimensional vector form.

[0064] (2) Obtain the first iteration result and save it 0 ;

[0065] (3) Perform ART once iteration to obtain an iteratively reconstructed image, specifically:

[0066]

[0067] Where i is the observation number, i = 1, 2, ..., N, φ i is the ith observation, λ is the relaxation factor, S i is the i-th row vector in the sensitivity matrix, is its transpose.

[0068] (4) Perform non-negative constraints on the initial value of the one-dimensional reconstructed image and calculate the incremental factor.

[0069] Specifically:

[0070]

[0071] in, is the increment factor, K1 is the iteration counter of the image reconstruction process, K2 is the iteration counter of the process of minimizing the total variation, is a non-negative constraint.

[0072] (5) Calculate the improved total variation gradient and gradient direction:

[0073]

[0074]

[0075] in, is the total variation gradient, is the gradient direction, K1 is the iteration counter of the image reconstruction process, K2 is the iteration counter of the process of minimizing the total variation, ||·|| TV is the total variation regularization.

[0076] In this embodiment, the total variation calculation formula is improved by integrating the gradient relationship between the central pixel and the eight neighboring pixels, which is specifically:

[0077]

[0078] Among them, ε is to prevent the positive disturbance added when the denominator is 0, I i,j is the pixel value of the pixel point (i, j) in the reconstructed image, I i+1,j is the pixel value of the pixel point (i+1,j) in the reconstructed image, I i,j+1 is the pixel value of the pixel point (i, j+1) in the reconstructed image, I i-1,j+1 is the pixel value of the pixel point (i-1, j+1) in the reconstructed image, I i+1,j-1 is the pixel value of the pixel point (i+1, j-1) in the reconstructed image, I i,j-1 is the pixel value of the pixel point (i, j-1) in the reconstructed image, I i,j+1 is the pixel value of pixel (i, j+1) in the reconstructed image.

[0079] (6) Iteratively updating the image according to the total variation gradient descent direction to obtain an updated image.

[0080] Specifically:

[0081]

[0082] Among them, α is the adjustment factor, is the increment factor, K2 is the iteration counter of the process of minimizing the total variation, is the total variation gradient.

[0083] If the number of iterations K2 < the number of iterations for minimizing the total variation, then return to recalculate the incremental factor; if the number of iterations K2 > the number of iterations for minimizing the total variation, then determine whether the overall number of iterations satisfies K1 < the total number of iterations of the algorithm. If so, return to re-iterate ART.

[0084] S103 , performing interpolation processing on the reconstructed image by using cubic convolution interpolation to generate a new reconstructed image.

[0085] This embodiment uses a cubic convolution interpolation algorithm to perform interpolation processing on the obtained reconstructed image, approximates the pixel values ​​near the sampling points in the image, thereby generating new interpolation pixel points, thereby enhancing the continuity and edge clarity of the image.

[0086] Cubic Convolution Interpolation is a high-order interpolation method commonly used in image processing, mainly used for image scaling and distortion correction. It estimates the pixel value at the new position by using the information of surrounding pixels, thereby providing a smoother and more accurate image interpolation effect.

[0087] like Figure 2 As shown, this embodiment also provides an image reconstruction system for magnetic induction tomography of cerebral hemorrhage, comprising: an image reconstruction value acquisition module 1, an image reconstruction module 2 and a reconstruction image optimization module 3;

[0088] The image reconstruction value acquisition module 1 is used to construct a human brain simulation model according to the actual structure of the human brain, perform cyclic excitation on the human brain simulation model, and obtain the values ​​required for imaging;

[0089] The image reconstruction module 2 is used to reconstruct the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for imaging to obtain a reconstructed image;

[0090] The reconstructed image optimization module 3 is used to perform interpolation processing on the reconstructed image by using cubic convolution interpolation to generate a new reconstructed image.

[0091] Example 1

[0092] In order to realize the rapid magnetic induction tomography detection of the frontal lobe of the brain in the case of moderate hemorrhage, this embodiment establishes the following Figure 3 The human brain simulation model shown in the figure includes the air domain, coil array, scalp layer, skull layer, cerebrospinal fluid layer, brain parenchyma layer and hemorrhage area. The simulation excitation frequency is 1MHz and the excitation current is 1A. The conductivity parameters of each tissue in the human brain are set as follows: Figure 4 As shown in Figure 2, a brain hemorrhage with a bleeding volume of 4.19 mL in the frontal lobe area was set in the model. Figure 5 shown.

[0093] In order to quantitatively evaluate the performance of the improved algorithm in cerebral hemorrhage detection, the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and image correlation coefficient (CC) were introduced to evaluate the image reconstruction results.

[0094]

[0095] Among them, MAX I is the maximum pixel value in the image, I(i,j) is the original image pixel value, K(i,j) is the reconstructed image pixel value, m and n are the height and width of the image, μ1 and μ2 are the pixel means of the original image and the reconstructed image, respectively, σ 12 is the covariance of the two images, c1 and c2 are constants used to stabilize the denominator, is the true conductivity distribution, To reconstruct the conductivity distribution of the image.

[0096] according to Figure 1 The specific process shown in the figure is to reconstruct the cerebral hemorrhage image according to the present invention after setting up the simulation model, and to verify the performance of the present invention by using four different commonly used cerebral hemorrhage magnetic induction tomography algorithms, namely Direct BackProjection algorithm, Algebra Reconstruction Technique algorithm, Projections onto ConvexSets-Total variation algorithm, and Newton-Raphson algorithm. Figure 6 The reconstruction results of the present invention are compared with those of the above algorithms, and the evaluation indicators are shown in Table 1.

[0097] Table 1 Frontal lobe area 4.19mL

[0098]

[0099] Example 2

[0100] In order to achieve rapid magnetic induction tomography detection of the frontal lobe region in the case of moderate hemorrhage, a method such as Figure 3 The human brain simulation model shown in the figure includes the air domain, coil array, scalp layer, skull layer, cerebrospinal fluid layer, brain parenchyma layer and hemorrhage area. The simulation excitation frequency is 1MHz and the excitation current is 1A. The conductivity parameters of each tissue in the human brain are set as follows: Figure 4 As shown in Figure 2, a brain hemorrhage with a bleeding volume of 1.4 mL in the left temporal lobe was set in the model. Figure 7 shown.

[0101] according to Figure 1 The specific process shown in the figure is to reconstruct the cerebral hemorrhage image according to the present invention after setting up the simulation model, and to verify the performance of the present invention by using four different commonly used cerebral hemorrhage magnetic induction tomography algorithms, namely Direct BackProjection algorithm, Algebra Reconstruction Technique algorithm, Projections onto ConvexSets-Total variation algorithm, and Newton-Raphson algorithm. Figure 8 The reconstruction results of the present invention are compared with those of the above algorithms, and the evaluation indicators are shown in Table 2.

[0102] Table 2 Left temporal lobe area 1.4mL

[0103]

[0104]

[0105] Example 3

[0106] In order to achieve rapid magnetic induction tomography detection of the frontal lobe region in the case of moderate hemorrhage, a method such as Figure 3 The human brain simulation model shown in the figure includes the air domain, coil array, scalp layer, skull layer, cerebrospinal fluid layer, brain parenchyma layer and hemorrhage area. The simulation excitation frequency is 1MHz and the excitation current is 1A. The conductivity parameters of each tissue in the human brain are set as follows: Figure 4 As shown in Figure 1, a brain hemorrhage with a bleeding volume of 1.4 mL in the right occipital lobe area was set in the model. Fig. 9 shown.

[0107] according to Figure 1 The specific process shown in the figure is to reconstruct the cerebral hemorrhage image according to the present invention after setting up the simulation model, and to verify the performance of the present invention by using four different commonly used cerebral hemorrhage magnetic induction tomography algorithms, namely Direct BackProjection algorithm, Algebra Reconstruction Technique algorithm, Projections onto ConvexSets-Total variation algorithm, and Newton-Raphson algorithm. Fig.10 The reconstruction results of the present invention are compared with those of the above algorithms, and the evaluation indicators are shown in Table 3.

[0108] Table 3 Right occipital lobe area 1.4mL

[0109]

[0110] It can be seen that, compared with the prior art, the present invention improves the total variation calculation formula, takes into account the gradient relationship between the central pixel and the eight adjacent pixels, and introduces cubic convolution interpolation to perform interpolation processing on the reconstructed image; through the present invention, it is helpful to more accurately locate the cerebral hemorrhage area and the size of the bleeding amount, and at the same time can effectively reduce imaging artifacts, make the edge of the cerebral hemorrhage area in the imaging smoother, and closer to the actual bleeding situation, providing a new and effective algorithm for cerebral hemorrhage magnetic induction tomography detection technology.

[0111] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0113] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for reconstructing magnetic induction tomography images of cerebral hemorrhage, characterized in that: The following steps are involved: Construct a human brain simulation model based on the actual structure of the human brain, perform cyclic excitation on the human brain simulation model, and obtain the numerical values ​​required for imaging; Based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for the imaging, the magnetic induction tomography image of cerebral hemorrhage is reconstructed to obtain a reconstructed image; The reconstructed image is interpolated using cubic convolution interpolation to generate a new reconstructed image after interpolation.

2. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 1, characterized in that: The human brain simulation model includes an air domain, a coil array, a scalp layer, a skull layer, a cerebrospinal fluid layer, a brain parenchyma layer and a hemorrhage area; The values ​​required for imaging include coil observation values ​​and a sensitivity matrix.

3. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 1, characterized in that: The process of reconstructing the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for the imaging to obtain the reconstructed image is as follows: Assign an initial value to the one-dimensional reconstructed image, and set the number of iterations of the image reconstruction process to obtain the initialization iteration result; Perform ART iteration to calculate and obtain an iteratively reconstructed image; Apply non-negative condition constraints to the iteratively reconstructed image and calculate the incremental factor; The total variation gradient calculation method is improved based on the gradient relationship between the central pixel and the adjacent pixels, and the improved total variation gradient formula is obtained. The total variation gradient and the gradient direction are calculated according to the total variation gradient formula; An iteration condition is set, and the image is iteratively updated according to the total variation gradient direction to obtain an updated image.

4. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 3, characterized in that: The calculation process of the increment factor is: in, is a non-negative condition constraint, is the increment factor, K2 is the iteration counter of the process of minimizing the total variation, I 0 is the initial value of the reconstructed image, and K1 is the counter of the number of iterations of the image reconstruction process.

5. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 3, characterized in that: The improved total variation gradient formula is: Among them, ε is to prevent the positive disturbance added when the denominator is 0, I i,j is the pixel value of the pixel point (i, j) in the reconstructed image, I i+1,j is the pixel value of the pixel point (i+1,j) in the reconstructed image, I i,j+1 is the pixel value of the pixel point (i, j+1) in the reconstructed image, I i-1,j+1 is the pixel value of the pixel point (i-1, j+1) in the reconstructed image, I i+1,j-1 is the pixel value of the pixel point (i+1, j-1) in the reconstructed image, I i,j-1 is the pixel value of the pixel point (i, j-1) in the reconstructed image, I i,j+1 is the pixel value of pixel (i, j+1) in the reconstructed image.

6. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 3, characterized in that: The total variation gradient and gradient direction are: in, is the total variation gradient, is the gradient direction, K1 is the iteration counter of the image reconstruction process, K2 is the iteration counter of the process of minimizing the total variation, ||·|| TV is the total variation regularization.

7. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 3, characterized in that: The process of iteratively updating the image according to the total variation gradient direction is: Among them, α is the adjustment factor, is the increment factor, and K2 is the iteration counter of the process of minimizing the total variation.

8. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 3, characterized in that: The iteration conditions are: If the number of iterations K2 < the number of iterations for minimizing the total variation, then return to recalculate the incremental factor; if the number of iterations K2 > the number of iterations for minimizing the total variation, then determine whether the overall number of iterations satisfies K1 < the total number of iterations of the algorithm. If so, return to re-iterate ART.

9. The method for reconstructing magnetic induction tomography images of cerebral hemorrhage according to claim 1, characterized in that: The method further includes: evaluating the new reconstructed image using a peak signal-to-noise ratio, a structural similarity index, and an image correlation coefficient, specifically: Among them, MAX I is the maximum pixel value in the image, I(i,j) is the original image pixel value, K(i,j) is the reconstructed image pixel value, m and n are the height and width of the image, μ1 and μ2 are the pixel means of the original image and the reconstructed image, respectively, σ 12 is the covariance of the two images, c1 and c2 are constants used to stabilize the denominator, is the true conductivity distribution, To reconstruct the conductivity distribution of the image.

10. A magnetic induction tomography image reconstruction system for cerebral hemorrhage, used to implement the magnetic induction tomography image reconstruction method for cerebral hemorrhage according to any one of claims 1 to 8, characterized in that: include: An image reconstruction value acquisition module (1), an image reconstruction module (2) and a reconstructed image optimization module (3); The image reconstruction value acquisition module (1) is used to construct a human brain simulation model according to the actual structure of the human brain, perform cyclic excitation on the human brain simulation model, and obtain the values ​​required for imaging; The image reconstruction module (2) is used to reconstruct the magnetic induction tomography image of cerebral hemorrhage based on the improved POCS-TVM algorithm and using the human brain simulation model and the numerical values ​​required for imaging, so as to obtain a reconstructed image; The reconstructed image optimization module (3) is used to perform interpolation processing on the reconstructed image using cubic convolution interpolation to generate a new reconstructed image.

Images