Cerebral hemorrhage MIT area detection method, device, equipment, medium and product

Abstract

The invention discloses a cerebral hemorrhage MIT area detection method, device and equipment, a medium and a product, and relates to the field of brain image processing. The method comprises the following steps: dividing a brain imaging area into a plurality of areas with set angles, adopting different coil arrays in different areas, acquiring magnetic field and conductivity information in different coil arrays, and establishing a plurality of sample sets by taking the magnetic field information and the conductivity information as sample pairs; adopting the plurality of sample sets to train a stacked auto-encoder to obtain a plurality of MIT prediction models; determining the occurrence position of a focus, and selecting an array which enables the focus to be located in a detection side-n-degree to n-degree region in different coil arrays based on the occurrence position of the focus; acquiring magnetic field information by adopting the selected array, and obtaining phase information; selecting an MIT prediction model corresponding to the selected array, inputting the phase information into the MIT prediction model, and predicting to obtain conductivity distribution; and completing image reconstruction based on the conductivity distribution. The image reconstruction effect can be improved.

Description

Technical Field

[0001] The present application relates to the field of brain image processing, and in particular to a method, device, equipment, medium and product for detecting MIT area of ​​cerebral hemorrhage. Background Art

[0002] Magnetic Induction Tomography (MIT), also known as electromagnetic tomography, is a new type of non-contact, radiation-free imaging technology. The MIT system is usually composed of an excitation coil, a detection coil, a signal processing system and a host computer. The MIT system applies an external alternating magnetic field through the coil to excite the eddy current inside the object, and measures the electromagnetic signal induced by the eddy current. Then, the inverse problem algorithm is used in the host computer to reconstruct the conductivity distribution inside the object, realizing non-invasive conductivity imaging and analysis.

[0003] At present, MIT technology has good application prospects in the field of biomedical imaging. Cerebral hemorrhage disease develops rapidly and has a high mortality rate. Rapid and accurate detection and diagnosis are extremely critical for the treatment and recovery of patients. Current methods such as computed tomography (CT) and magnetic resonance imaging (MRI) have a good effect on the examination of cerebral hemorrhage. The advantages of MIT technology such as low cost, no radiation, and real-time monitoring can make it a good auxiliary method for preoperative and postoperative bedside monitoring and supplementary preliminary testing. It has good clinical application prospects.

[0004] Currently, due to space limitations and the desire for greater convenience, the number of coils that can be set up in the construction of the MIT system is limited, that is, the imaging information obtained is limited, and in practical applications, the signal is easily affected by environmental noise, which seriously affects the image reconstruction effect. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for detecting the MIT area of ​​cerebral hemorrhage, which can improve the image reconstruction effect.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for detecting MIT area of ​​cerebral hemorrhage, comprising:

[0008] The brain imaging area is divided into multiple areas with set angles, and different coil arrays are used in different areas to obtain magnetic field information and conductivity information when using different coil arrays;

[0009] The magnetic field information and conductivity information of different coil arrays are used as sample pairs to establish multiple sample sets;

[0010] Using the plurality of sample sets to respectively train the stacked autoencoders until the trained stacked autoencoders meet the set conditions, thereby obtaining a plurality of MIT prediction models;

[0011] Determine the location of the lesion, and select an array from different coil arrays so that the lesion is located in the area from -n° to n° on the detection side based on the location of the lesion;

[0012] Acquiring magnetic field information using an array that places the lesion in the area from -n° to n° on the detection side, and obtaining phase information based on the magnetic field information;

[0013] Selecting an MIT prediction model corresponding to an array that locates the lesion in the area from -n° to n° on the detection side, and inputting the phase information into the MIT prediction model to predict the conductivity distribution;

[0014] Image reconstruction is performed based on the conductivity distribution.

[0015] Optionally, the brain imaging area is divided into four 90° areas.

[0016] Optionally, n° is 45°.

[0017] Optionally, a computer tomography method or a magnetic resonance imaging method is used to determine the location of the lesion.

[0018] In a second aspect, the present application provides a device for detecting MIT area of ​​cerebral hemorrhage, comprising:

[0019] A signal generator, used for generating an excitation signal;

[0020] An excitation coil, connected to the signal generator, for generating a magnetic field under the action of the excitation signal; the magnetic field acts on the brain to be tested;

[0021] A detection coil array for acquiring magnetic field information;

[0022] A phase-locked amplifier, connected to the detection coil array and the signal generator, for generating phase information based on the magnetic field information;

[0023] A host computer is connected to the phase-locked amplifier and is used to implement the above-mentioned MIT area detection method for cerebral hemorrhage to complete image reconstruction based on the phase information.

[0024] Optionally, the excitation coil and the detection coil array form a coil array.

[0025] Optionally, the excitation coil and the detection coil array are placed relative to each other at 180° with the patient's brain as the center; the excitation coil and the detection coil array are rotated at a set angle to obtain magnetic field information at different set angles.

[0026] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned MIT region detection device for cerebral hemorrhage.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned MIT area detection device for cerebral hemorrhage is implemented.

[0028] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned MIT area detection device for cerebral hemorrhage.

[0029] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0030] The present application provides a method, device, equipment, medium and product for detecting the MIT area of ​​cerebral hemorrhage. After determining the location of the lesion, the lesion is always located in the -n° to n° area on the detection side, and the magnetic field signal obtained by the array detection corresponding to the lesion being located in the -n° to n° area on the detection side is converted into a phase signal, which is input into the MIT prediction model corresponding to the selected array for imaging, thereby obtaining a better image reconstruction effect and improving the reconstruction accuracy when the imaging information is limited. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 This is a flow chart of a method for detecting MIT area of ​​cerebral hemorrhage in one embodiment of the present application;

[0033] Figure 2 A schematic diagram of an implementation flow of a method for detecting MIT regions of cerebral hemorrhage provided in one embodiment of the present application;

[0034] Figure 3 A schematic diagram of the structure of a device for detecting MIT area of ​​cerebral hemorrhage provided in one embodiment of the present application;

[0035] Figure 4 A schematic diagram of a model provided for another embodiment of the present application;

[0036] Figure 5 A phase difference information curve diagram of a lesion located at (-45, 45) provided in an embodiment of the present application;

[0037] Figure 6 A phase difference information curve diagram of a lesion located at (45, 45) provided in an embodiment of the present application;

[0038] Figure 7 A phase difference information curve diagram of a lesion located at (-70, 0) provided in an embodiment of the present application;

[0039] Figure 8 A phase difference information curve diagram of a lesion located at (0,0) provided in an embodiment of the present application;

[0040] Fig. 9 A phase difference information curve diagram of a lesion located at (70,0) provided in an embodiment of the present application;

[0041] Fig.10 A phase difference information curve diagram of a lesion located at (-45, -45) provided in an embodiment of the present application;

[0042] Fig.11 A phase difference information curve diagram of a lesion located at (45, -45) provided in an embodiment of the present application;

[0043] Fig.12 This is a reconstruction result diagram of a lesion located at (-50, 0) under different noise conditions provided by an embodiment of the present application;

[0044] Fig.13 A reconstruction result diagram under different noise conditions with the lesion located at (50,0) provided in another embodiment of the present application;

[0045] Fig.14 A reconstruction result diagram of a phantom located at the excitation side provided in another embodiment of the present application;

[0046] Fig.15 A reconstruction result diagram of a phantom located at the detection side provided in another embodiment of the present application;

[0047] Fig.16 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0048] Description of reference numerals:

[0049] 101 - signal generator, 102 - phase-locked amplifier, 103 - host computer, 104 - excitation coil, 105 - lesion, 106 - detection coil array. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0052] This embodiment provides a method for detecting MIT regions of cerebral hemorrhage. Figure 1 and Figure 2 As shown, the method includes:

[0053] Step 100: Divide the brain imaging area into a plurality of areas at set angles, use different coil arrays in different areas, and obtain magnetic field information and conductivity information when using different coil arrays.

[0054] For example, the brain imaging area is divided into four 90° areas, the excitation coil and the detection coil are placed 180° relative to each other, and all the coils are moved 90° four times at the same time to build the corresponding coil array and model, and obtain the magnetic field information and conductivity information of different coil arrays.

[0055] Step 101: Using magnetic field information and conductivity information of different coil arrays as sample pairs to establish multiple sample sets.

[0056] Step 102: using multiple sample sets to train stacked autoencoders (SAE) respectively until the trained stacked autoencoders meet the set conditions, thereby obtaining multiple MIT prediction models.

[0057] Step 103: Determine the location of the lesion, and based on the location of the lesion, select an array from different coil arrays that enables the lesion to be located in the area from -n° to n° on the detection side.

[0058] For example, CT, MRI and other methods are used to determine the location of the lesion, and an array is selected from multiple coil arrays so that the lesion is located in the area of ​​-45° to 45° on the detection side.

[0059] Step 104: Acquire magnetic field information using an array that places the lesion in the area from -n° to n° on the detection side, and obtain phase information based on the magnetic field information.

[0060] Step 105: Select the MIT prediction model corresponding to the array that makes the lesion located in the area from -n° to n° on the detection side, input the phase information into the MIT prediction model, and predict the conductivity distribution.

[0061] Step 106: completing image reconstruction based on the conductivity distribution.

[0062] In summary, the SAE-based MIT area detection method for cerebral hemorrhage provided in this application can obtain better reconstruction effects when the number of coils is limited and there is noise. After the location of the lesion is preliminarily determined by CT, MRI and other methods, the coil array is adjusted according to the location of the lesion, so that the lesion is always located in an area that is more sensitive to detection. The lesion information obtained in this way is information with more obvious characteristics, which can enable the MIT system to obtain better reconstruction effects in actual situations where imaging information is limited, thereby improving reconstruction accuracy while ensuring convenience, and is applied to preoperative and postoperative bedside monitoring, providing doctors with a real-time understanding of the patient's condition. An optimized method for MIT detection.

[0063] In another exemplary embodiment of the present application, a device for detecting MIT area of ​​cerebral hemorrhage is provided. Figure 3 The device shown includes: a signal generator 101, an excitation coil 104, a detection coil array 106, a phase-locked amplifier 102 and a host computer 103. The excitation coil 104 is connected to the signal generator 101. The phase-locked amplifier 102 is connected to the detection coil array 106 and the signal generator 101 respectively. The host computer 103 is connected to the phase-locked amplifier 102. The detection coil array 106 includes a plurality of detection coils. Figure 3 The reference number 105 represents a lesion.

[0064] The signal generator 101 generates an excitation signal, generates a magnetic field in the imaging domain through the excitation coil 104 to act on the object under test, and then obtains magnetic field information through the detection coil array 106, and connects the phase-locked amplifier to output phase information to the host computer 103. The host computer 103 implements the above-mentioned MIT area detection method for cerebral hemorrhage to complete image reconstruction based on phase information.

[0065] In another exemplary embodiment of the present application, the excitation coil 104 and the detection coil array 106 form a coil array.

[0066] In another exemplary embodiment of the present application, the excitation coil 104 and the detection coil array 106 are placed 180 degrees relative to each other with the patient's brain as the center. The excitation coil 104 and the detection coil array 106 are rotated at a set angle to obtain magnetic field information at different set angles.

[0067] In another exemplary embodiment of the present application, a brain model is constructed by processing CT and MRI real images using finite element simulation software. The method of detecting the brain model using the excitation coil and detection coil array provided above is as follows: Figure 4 As shown, the excitation coil and the detection coil array are placed 180° opposite to each other. Among them, the half of the brain close to the excitation coil is the excitation side, and the half of the brain close to the detection coil array is the detection side. At the same time, the brain can be approximately regarded as a circle and divided into four 90° areas. And by moving the coil array 90°, the lesion is always located in the -45° to 45° area on the detection side. During the detection process, the excitation coil is energized to generate a variable main magnetic field B0 in the imaging space. Under the action of the main magnetic field, the human tissue will generate a secondary induced magnetic field ΔB. Different pathological tissue morphologies will affect the value of ΔB. On the other side, the detection coil collects the synthetic magnetic field B0+ΔB around the head by moving the position of the detection coil. According to the MIT theory, the phase delay of B0+ΔB relative to B0 is Δφ, which is linearly related to the conductivity of the conductor and is expressed by the phase difference.

[0068] The phase difference data comparison results when the lesion is located at seven different positions are as follows Figures 5 to 11 As shown in Figure 5 to Fig.11 It can be seen that compared with the lesion on the side of the excitation coil, the valley characteristics of the phase difference information curve of the lesion on the side of the detection coil array are more obvious, and the valley appears at the detection coil near the lesion. When the lesion is located at (-45, 45) and (45, 45), the phase difference valley is roughly at the 85th and 65th detection points. When the lesion moves to three positions on the X-axis, the phase difference valley moves to the 50th detection point, and when the lesion is located at (-45, -45) and (45, -45), the phase difference valley moves to the 15th and 35th detection points. The change in the position of the lesion will cause the corresponding phase difference curve to change, indicating that it contains the position information of the lesion, and the phase difference information characteristics of the lesion on the side of the detection coil array are more obvious. Since the information characteristics contained in lesions in different positions and regions are different, the effect of image reconstruction and the degree of influence of noise will also be different.

[0069] Based on the above description, the SAE neural network algorithm is used in the image reconstruction process. In this embodiment, an excitation coil and seven detection coils are placed 180° relative to each other, the lesion position is continuously changed, a sample set for network training is constructed for SAE training, and an MIT prediction model is established. The MIT prediction model uses phase difference information as input and conductivity distribution as output, which is ultimately used to reconstruct the imaging.

[0070] For example, the brain can be approximated as a circle and the location of the lesion can be divided into different areas, such as Figure 4 As shown in the figure, the brain model is divided into the excitation side and the detection side according to the position of the excitation coil and the detection coil. Compared with the error results of all lesion reconstructions under superimposed noise conditions, the average relative error of lesion reconstruction in the -45° to 45° area on the excitation side is the largest, and the average relative error of lesion reconstruction in the -45° to 45° area on the detection side is the smallest. The reconstruction results under different noise conditions when the lesion is located in the above two areas are shown in the figure. Fig.12 and Fig.13 As shown in the figure, the lesion at (-50,0) on the excitation side has been offset and blurred under 40dB noise conditions. When the noise increases to 35dB, the lesion at (-50,0) can no longer be reconstructed well. When the noise increases to 25dB, the lesion at (50,0) on the detection side can still be reconstructed well. Under 20dB noise conditions, the lesion at (50,0) also has a certain offset. Fig.12 Part (a) is the actual schematic diagram. Fig.12 Part (b) is a schematic diagram of reconstruction with the lesion located at (-50,0) and 40dB noise conditions. Fig.12 Part (c) is a schematic diagram of reconstruction with the lesion located at (-50,0) and 35dB noise conditions. Fig.12 Part (d) is a schematic diagram of reconstruction with the lesion located at (-50,0) and 30dB noise conditions. Fig.12 Part (e) is a schematic diagram of reconstruction with the lesion located at (-50,0) and 25dB noise conditions. Fig.12 Part (f) is a schematic diagram of reconstruction with the lesion located at (-50,0) and 20dB noise conditions. Fig.13 Part (a) is the actual schematic diagram. Fig.13 Part (b) is a schematic diagram of reconstruction with the lesion located at (50,0) and 40dB noise. Fig.13 Part (c) is a schematic diagram of reconstruction with the lesion located at (50,0) and 35dB noise conditions. Fig.13 Part (d) is a schematic diagram of reconstruction with the lesion located at (50,0) and 30dB noise. Fig.13 Part (e) is a schematic diagram of reconstruction with the lesion located at (50,0) and 25dB noise conditions. Fig.13Part (f) is a schematic diagram of reconstruction when the lesion is located at (50,0) and under 20dB noise conditions.

[0071] In order to further verify the simulation results, a phantom experiment was conducted using the MIT area detection device for cerebral hemorrhage provided above. The phase information of the aluminum rod when it was located in the -45° to 45° area on the excitation side and the -45° to 45° area on the detection side was obtained by the MIT area detection device for cerebral hemorrhage. The results are shown in Figure 2. Fig.14 and Fig.15 As shown in the figure, the reconstruction result of the aluminum rod on the excitation coil side has artifacts, while the aluminum rod on the detection coil side is better reconstructed and has higher reconstruction accuracy. Fig.14 Part (a) is a schematic diagram of the actual situation when the phantom is located on the excitation side. Fig.14 Part (b) is a schematic diagram of the reconstruction of the phantom on the excitation side. Fig.15 Part (a) is a schematic diagram of the actual situation when the phantom is located on the detection side. Fig.15 Part (b) is a schematic diagram of the reconstruction of the phantom located on the detection side.

[0072] In summary, the present application uses the SAE neural network algorithm for image reconstruction in inverse problem reconstruction. The coil array is placed according to the lesion location, so that the imaging area can be divided into four 90° areas. The coil array is adjusted by rotating the excitation coil and the detection coil array 90° at the same time, and is also divided into four corresponding array configurations. Four corresponding MIT prediction models are constructed. After the location of the lesion is determined, the coil array is adjusted and the corresponding MIT prediction model is applied for imaging, so that the lesion is always located in the -45° to 45° area on the detection side. In this way, the MIT area detection device for cerebral hemorrhage can obtain better reconstructed images and reconstruction noise resistance with fewer coil configurations, and can obtain better reconstruction results when the imaging information is limited, thereby improving the reconstruction accuracy.

[0073] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.16As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store imaging results of MIT area detection of cerebral hemorrhage. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting MIT area of ​​cerebral hemorrhage is implemented.

[0074] Those skilled in the art will understand that Fig.16 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0075] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0079] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0080] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for detecting MIT area of ​​cerebral hemorrhage, characterized in that: The MIT region detection method for cerebral hemorrhage comprises: The brain imaging area is divided into multiple areas with set angles, and different coil arrays are used in different areas to obtain magnetic field information and conductivity information when using different coil arrays; The magnetic field information and conductivity information of different coil arrays are used as sample pairs to establish multiple sample sets; Using the plurality of sample sets to respectively train the stacked autoencoders until the trained stacked autoencoders meet the set conditions, thereby obtaining a plurality of MIT prediction models; Determine the location of the lesion, and select an array from different coil arrays so that the lesion is located in the area from -n° to n° on the detection side based on the location of the lesion; Acquiring magnetic field information using an array that places the lesion in the area from -n° to n° on the detection side, and obtaining phase information based on the magnetic field information; Selecting an MIT prediction model corresponding to an array that locates the lesion in the area from -n° to n° on the detection side, and inputting the phase information into the MIT prediction model to predict the conductivity distribution; Image reconstruction is performed based on the conductivity distribution.

2. The method for detecting MIT area of ​​cerebral hemorrhage according to claim 1, characterized in that: The brain imaging area was divided into four 90° areas.

3. The method for detecting MIT area of ​​cerebral hemorrhage according to claim 1, characterized in that: n° is 45°.

4. The method for detecting MIT area of ​​cerebral hemorrhage according to claim 1, characterized in that: The location of the lesion is determined by using a computer tomography method or a magnetic resonance imaging method.

5. A device for detecting MIT area of ​​cerebral hemorrhage, characterized in that: The MIT area detection device for cerebral hemorrhage comprises: A signal generator, used for generating an excitation signal; An excitation coil, connected to the signal generator, for generating a magnetic field under the action of the excitation signal; the magnetic field acts on the brain to be tested; A detection coil array for acquiring magnetic field information; A phase-locked amplifier, connected to the detection coil array and the signal generator, for generating phase information based on the magnetic field information; A host computer is connected to the phase-locked amplifier and is used to implement the MIT area detection method for cerebral hemorrhage according to any one of claims 1 to 4, so as to complete image reconstruction based on the phase information.

6. The MIT area detection device for cerebral hemorrhage according to claim 5, characterized in that: The excitation coil and the detection coil array form a coil array.

7. The MIT region detection device for cerebral hemorrhage according to claim 5, characterized in that: The excitation coil and the detection coil array are placed relative to each other at 180 degrees with the patient's brain as the center; the excitation coil and the detection coil array are rotated at a set angle to obtain magnetic field information at different set angles.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the MIT region detection method for cerebral hemorrhage according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting the MIT region of cerebral hemorrhage according to any one of claims 1 to 4 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for detecting the MIT region of cerebral hemorrhage according to any one of claims 1 to 4 is implemented.

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