Adaptive layered confocal intracranial microwave imaging method and device and storage medium

Abstract

The invention belongs to the technical field of digital image processing, and particularly relates to a self-adaptive layered confocal intracranial microwave imaging method and device and a storage medium. When antenna arrays with different antenna numbers and antenna arrangement structures are used, the algorithm can perform self-adaptive optimization antenna selection; the optimal calculation antenna pair is selected for imaging at different target positions, and intracranial layered imaging is achieved; the method comprises the following steps: calculating the transmission time when a microwave signal is transmitted from an antenna # imgabs0 # and received by an antenna # imgabs2 # after being reflected at a target point # imgabs1 #, calculating the propagation speed of the signal in the brain, introducing a gain coefficient, carrying out time shifting summation on reflected signals received by different antennas, reconstructing a relative grayscale image of a target area, and detecting an intracranial hemorrhage area. The imaging process is optimized by adaptively selecting the antenna combination, the algorithm performance and the image quality are improved, and the positioning error is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of digital image processing, and in particular relates to an adaptive layered confocal intracranial microwave imaging method, equipment and storage medium. Background Art

[0002] As an important non-destructive testing technology, microwave imaging can penetrate materials to a certain depth and detect hidden targets or structures inside. It has great application potential in medical diagnosis, geological exploration, industrial testing and other fields. In the biomedical field, it can penetrate human tissue and provide key reference information for doctors to diagnose diseases in the early stage.

[0003] Microwave imaging is mainly divided into microwave tomography, microwave thermoacoustic imaging and confocal microwave imaging (CMI). Microwave tomography estimates the electric field distribution at a specific frequency point and iteratively compares it with the electric field intensity at the receiving point to achieve iteration of the field distribution and dielectric constant distribution, and finally converges to a result close to the real distribution. Representative algorithms include iterative algorithms based on Born approximation such as BIM, DBIM, CSI, and non-iterative algorithms based on singular value decomposition. Microwave thermoacoustic imaging combines ultrasound and microwaves to achieve lesion positioning and treatment. Confocal microwave imaging technology is based on the synthetic aperture radar (SAR) imaging algorithm. It uses the difference in electromagnetic parameters between the target and the background medium to qualitatively identify high scattering areas, thereby locating the target. The technology transmits and receives microwave signals through an antenna array, including measuring scattered electric fields, extracting target responses, phase compensation, amplitude compensation, and confocal reconstruction. It has the advantages of simple calculation, fast imaging speed, and high resolution. Compared with the iterative imaging algorithm based on Born approximation, confocal microwave imaging is more sensitive to dielectric boundaries, has a faster imaging speed, and is more suitable as a basic algorithm for rapid positioning of target contours.

[0004] In recent years, confocal microwave imaging technology has developed rapidly in medical imaging. In 1998, the Hagness team at the University of Wisconsin, USA, first applied confocal microwave imaging technology to breast tumor detection; the MARIA system developed by the University of Bristol in the UK used a hemispherical radio wave antenna array to image the breasts of 225 female patients with malignant and benign lesions, and made some progress in clinical trials; in 2021, a team from the University of Queensland in Australia used a circular antenna array consisting of 8 antennas to image pig knee joints, covering normal knee joints and knee joints simulating different degrees of ligament tears, and successfully constructed a high-resolution (1mm³) pig knee joint equivalent model.

[0005] In terms of confocal microwave imaging algorithm optimization, the main research focuses on improving imaging parameters and antenna structures. IbtisamAmdaouch et al. improved imaging capabilities by planning and calculating antenna positions, enabling small tumors to be located using only four antennas; AOAsok et al. designed high-gain dual antennas and used SAR imaging to achieve ultra-wideband (UWB) microwave imaging; in addition, various UWB antennas such as patch antennas and Vivaldi antennas have also been applied to microwave imaging.

[0006] However, confocal microwave imaging systems usually use multi-antenna arrays, which require a large number of antennas to ensure uniform coverage of signals at different locations during the delay and sum process. When the number of antennas is limited, obvious imaging artifacts will appear near the transmitting and receiving antennas, resulting in reduced imaging quality and positioning accuracy. Moreover, due to factors such as cost constraints or antenna interference, the number of antennas is often difficult to increase, further reducing the imaging quality. Therefore, how to dynamically adjust the appropriate antenna combination for imaging inversion to improve the imaging effect has become an important issue that needs to be solved urgently.

[0007] Therefore, the purpose of the present invention is to provide an adaptive layered confocal microwave imaging algorithm to address the problems of antenna coupling signal interference and imaging quality being limited by the number of antennas in traditional confocal microwave imaging algorithms in intracranial hemorrhage imaging. The algorithm performance and image quality are improved and positioning errors are reduced by adaptively selecting antenna combinations to optimize the imaging process. Summary of the invention

[0008] The purpose of the present invention is to provide an adaptive layered confocal intracranial microwave imaging method, device and storage medium. In view of the problems of antenna coupling signal interference and imaging quality being limited by the number of antennas in the traditional confocal microwave imaging algorithm in intracranial hemorrhage imaging, the present invention optimizes the imaging process by adaptively selecting antenna combinations, improves algorithm performance, image quality and reduces positioning errors.

[0009] The technical solution adopted by the present invention is as follows: An adaptive layered confocal intracranial microwave imaging method comprises the following steps: Step 1: According to the number of existing antennas and location, determine the distance between all antennas ; Step 2: Test the known model and collect all antennas When transmitting, the antenna Received signal ; Step 3: Initialize the optimized parameters based on the test data of the known model, construct the imaging loss function, obtain the optimal antenna combination for different target positions during imaging through iteration, and dynamically optimize the antenna selection to calculate the image pixel value; The step 3 dynamically optimizes antenna selection to calculate image pixel values, specifically comprising the following steps: Step 301: Initialize optimized parameters , which is used in the subsequent antenna selection optimization step for imaging; Step 302: Calculate the microwave signal from the antenna Launch at any target point After reflection, it is Received transmission time , where the propagation speed formula is Calculate the propagation speed of the signal in the brain, where and are the relative permittivity and relative magnetic permeability of biological tissues, is the speed of light in a vacuum, It's an antenna To the target distance; It's an antenna To the destination distance; Step 303: Introducing gain coefficient ,in and Respectively represent antenna and The gain factor, is the distance compensation coefficient, in the two-dimensional cylindrical wave simulation ,This coefficient comprehensively considers the impact of antenna directivity and path attenuation on the signal; Step 304: According to the formula , get all antenna combinations under the current parameters ; Step 305: Utilization Perform computational imaging, where It's an antenna Post-launch antenna The received time domain waveform signal, is the impulse function, It's time. is the internal transmission delay of the system; Step 306: According to the formula Calculate the current imaging loss, where: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels in the image; Substitute the imaging loss into the optimizer for the optimized parameter Optimize, repeat iterative steps 302 to 306 until the imaging loss meets the requirement, record the best antenna combination under the current number and position of antennas, and use the best antenna combination for intracranial layered imaging during detection; Step 4: Detect and image the real target based on the optimized antenna pair combination; In step 4, the real target is detected and imaged. The detection process only needs to collect all antenna combinations obtained in step 3, which reduces the time required for detection. The data obtained by detection is imaged using steps 302 to 305 to obtain an imaging result. .

[0010] A computing device for adaptive layered confocal intracranial microwave imaging, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the intracranial microwave imaging method.

[0011] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the intracranial microwave imaging method is implemented.

[0012] The technical effects achieved by the present invention are: The adaptive layered confocal microwave imaging algorithm proposed in the present invention effectively improves the deep imaging performance by dynamically adjusting antenna compensation. The output image report is clearer and more accurate, and can more accurately detect the intracranial hemorrhage area.

[0013] Compared with the traditional confocal microwave imaging algorithm, the imaging performance of the algorithm of the present invention is better under different antenna number configurations (8, 12, 16 and 24 antennas). The experimental results show that the adaptive layered confocal microwave imaging algorithm is effective in reducing imaging errors and improving image quality. Its average and maximum MRPAD values ​​are lower than those of the traditional algorithm. In most samples, half the number of antennas can achieve the same imaging quality as the traditional algorithm.

[0014] The present invention provides a valuable reference for the application of confocal microwave imaging technology in the biomedical field, and is of great significance for the real-time implementation of future algorithms and hardware miniaturization research. It is expected to provide a non-invasive, real-time and rapid imaging report for the clinical diagnosis and treatment of intracranial hemorrhage. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is an adaptive antenna selection optimization flow chart of an adaptive layered confocal intracranial microwave imaging method of the present invention; Figure 2This is a schematic diagram of the confocal imaging principle of the present invention; Figure 3a , Figure 3b , Figure 3c as well as Figure 3d The comparison charts respectively show the traditional confocal imaging and adaptive layered confocal imaging under four different original modeling conditions; Figure 4a It is the icon corresponding to the original modeling and the standard image; Figure 4b This is a comparison chart of traditional confocal imaging and adaptive confocal layered imaging under different numbers of antennas under the original modeling of 4a; Figure 5 It is a comparison chart of traditional confocal imaging and adaptive confocal layered imaging MRPAD under different numbers of antennas in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific embodiments of the present invention, and does not strictly limit the scope of protection of the specific claims of the present invention.

[0017] like Figure 1-Figure 5 As shown, an adaptive layered confocal intracranial microwave imaging method comprises the following steps: Step 1: According to the number of existing antennas and location, determine the distance between all antennas ; Step 2: Test the known model and collect all antennas When transmitting, the antenna Received signal ; Step 3: Initialize the optimized parameters based on the test data of the known model, construct the imaging loss function, obtain the optimal antenna combination for different target positions during imaging through iteration, and dynamically optimize the antenna selection to calculate the image pixel value; The step 3 dynamically optimizes antenna selection to calculate image pixel values, specifically comprising the following steps: Step 301: Initialize optimized parameters , which is used in the subsequent antenna selection optimization step for imaging; Step 302: Calculate the microwave signal from the antenna Launch at any target point After reflection, it is Received transmission time , where the propagation speed formula is Calculate the propagation speed of the signal in the brain, where and are the relative permittivity and relative magnetic permeability of biological tissues, is the speed of light in a vacuum, It's an antenna To the target distance; It's an antenna To the destination distance; Step 303: Introducing gain coefficient ,in and Respectively represent antenna and The gain factor, is the distance compensation coefficient, in the two-dimensional cylindrical wave simulation ,This coefficient comprehensively considers the impact of antenna directivity and path attenuation on the signal; Step 304: According to the formula , get all antenna combinations under the current parameters ; Step 305: Utilization Perform computational imaging, where It's an antenna Post-launch antenna The received time domain waveform signal, is the impulse function, It's time. is the internal transmission delay of the system; Step 306: According to the formula Calculate the current imaging loss, where: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels in the image; Substitute the imaging loss into the optimizer for the optimized parameter Optimize, repeat step 302 to step 306 until the imaging loss meets the requirement, record the best antenna combination under the current number and position of antennas, and use the best antenna combination for intracranial layered imaging during detection; Step 4: Detect and image the real target based on the optimized antenna pair combination; In step 4, the real target is detected and imaged. The detection process only needs to collect all antenna combinations obtained in step 3, which reduces the time required for detection. The data obtained by detection is imaged using steps 302 to 305 to obtain the imaging result. .

[0018] In the present invention, the purpose of the present invention is to provide an adaptive layered confocal microwave imaging algorithm to address the problems of antenna coupling signal interference and imaging quality being limited by the number of antennas in traditional confocal microwave imaging algorithms in intracranial hemorrhage imaging. The imaging process is optimized by adaptively selecting antenna combinations, thereby improving algorithm performance, image quality and reducing positioning errors.

[0019] Experimental simulation settings: According to the above invention content, an ellipsoid brain model with a long axis of 17 cm and a short axis of 13 cm was constructed, and a circular hemorrhage area with a diameter of 2-4 cm was embedded inside. An imaging frequency of 0.5-3 GHz was selected, the time domain signal-to-noise ratio (SNR) was set to 5 dB, and circular antenna arrays consisting of 8, 12, 16 and 24 uniformly distributed antennas were arranged respectively.

[0020] Performance evaluation indicators: In order to quantitatively evaluate the performance of the adaptive layered confocal microwave imaging algorithm and the impact of different antenna numbers on imaging quality, the present invention uses the mean relative pixel absolute difference (MRPAD) as an image quality evaluation indicator. MRPAD measures the image difference by calculating the average relative absolute difference of the corresponding pixels of two images. The formula is as follows: in, represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels in the image. In the present invention, the standard image is obtained through a simulation experiment with 180 antennas, aiming to achieve an ideal perfect imaging effect. Although this configuration is difficult to implement in practical applications due to limitations such as antenna size and mutual coupling effects, it can provide an ideal reference benchmark for evaluating the effects of different imaging parameters and antenna combinations on imaging quality. The smaller the MRPAD value, the smaller the difference between the two images, and the higher the image quality.

[0021] Since different antenna combinations may cause changes in the pixel values ​​of the imaging results, the present invention adopts a normalization method to scale the maximum pixel values ​​of all imaging results proportionally to 1, eliminating the problem of inconsistent pixel values ​​caused by differences in antenna combinations, and ensuring that the MRPAD indicator can more fairly and accurately reflect the performance of the imaging algorithm.

[0022] Through the above specific implementation methods, the effectiveness and superiority of the adaptive layered confocal microwave imaging algorithm proposed in the present invention can be effectively verified, and a new and effective method for intracranial hemorrhage detection is provided.

[0023] The adaptive layered confocal microwave imaging algorithm proposed in the present invention effectively improves the deep imaging performance by dynamically adjusting antenna compensation. The output image report is clearer and more accurate, and can more accurately detect the intracranial hemorrhage area.

[0024] Attached Figure 2 This is a simplified schematic diagram of this embodiment. Antennas are evenly spaced in a circle outside the brain, and the antennas take turns sending and receiving signals to detect the brain.

[0025] Attached Figure 3a-3d as well as Figure 4a-4b The imaging results of the algorithm proposed in the present invention and the traditional confocal imaging algorithm under 8 antennas and with different numbers of antennas are intuitively compared. Under noisy conditions, the imaging results of the present invention are significantly better than those of traditional confocal imaging.

[0026] Attached Figure 5 The figure shows the comparison of the MRPAD values ​​of the algorithm of the present invention and traditional confocal imaging under different numbers of antennas. When the number of antennas is small, the algorithm proposed in the present invention can achieve a 50% performance improvement.

[0027] Compared with the traditional confocal microwave imaging algorithm, the imaging performance of the algorithm of the present invention is better under different antenna number configurations (8, 12, 16 and 24 antennas). The experimental results show that the adaptive layered confocal microwave imaging algorithm is effective in reducing imaging errors and improving image quality. Its average and maximum MRPAD values ​​are lower than those of the traditional algorithm. In most samples, half the number of antennas can achieve the same imaging quality as the traditional algorithm.

[0028] The present invention provides a valuable reference for the application of confocal microwave imaging technology in the biomedical field, and is of great significance for the real-time implementation of future algorithms and hardware miniaturization research. It is expected to provide a non-invasive, real-time and rapid imaging report for the clinical diagnosis and treatment of intracranial hemorrhage.

[0029] A computing device for adaptive layered confocal intracranial microwave imaging, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the intracranial microwave imaging method.

[0030] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the intracranial microwave imaging method is implemented.

[0031] The embodiment of the present application also provides an adaptive layered confocal intracranial microwave imaging device: at least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the intracranial microwave imaging method as described above.

[0032] At the hardware level, the computing device includes a processor and optionally an internal bus, a network interface, and a memory.

[0033] The storage may include internal memory, such as high-speed random access memory (RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0034] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0035] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0036] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a computing device for the intracranial microwave imaging method at the logical level. The processor executes the program stored in the memory and is specifically used to execute the aforementioned method.

[0037] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems or computer program products.

[0038] Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0039] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An adaptive layered confocal intracranial microwave imaging method, characterized in that: The following steps are involved: Step 1: According to the number of existing antennas and location, determine the distance between all antennas ; Step 2: Test the known model and collect all antennas When transmitting, the antenna Received signal ; Step 3: Initialize the optimized parameters based on the test data of the known model, construct the imaging loss function, obtain the optimal antenna combination for different target positions during imaging through iteration, and dynamically optimize the antenna selection to calculate the image pixel value; Step 4: Detect and image the real target based on the optimized antenna pair combination.

2. The method for adaptive layered confocal intracranial microwave imaging according to claim 1, characterized in that: The step 3 of dynamically optimizing antenna selection to calculate image pixel values ​​includes the following steps: Step 301: Initialize optimized parameters , which is used in the subsequent antenna selection optimization step for imaging; Step 302: Calculate the microwave signal from the antenna Launch at any target point After reflection, it is Received transmission time , where the propagation speed formula is Calculate the propagation speed of the signal in the brain, where and are the relative permittivity and relative magnetic permeability of biological tissues, is the speed of light in a vacuum, It's an antenna To the target distance; It's an antenna To the destination distance; Step 303: Introducing gain coefficient ,in and Respectively represent antenna and The gain factor, is the distance compensation coefficient, in the two-dimensional cylindrical wave simulation ,This coefficient comprehensively considers the impact of antenna directivity and path attenuation on the signal; Step 304: According to the formula , get all antenna combinations under the current parameters ; Step 305: Utilization Perform computational imaging, where It's an antenna Post-launch antenna The received time domain waveform signal, is the impulse function, It's time. is the internal transmission delay of the system; Step 306: According to the formula Calculate the current imaging loss, where: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels in the image; Substitute the imaging loss into the optimizer for the optimized parameter Optimization is performed, and steps 302 to 306 are repeated until the imaging loss meets the requirements, and the best antenna combination under the current number and position of antennas is recorded, and the best antenna combination is used for intracranial layered imaging during detection.

3. The method for adaptive layered confocal intracranial microwave imaging according to claim 2, characterized in that: In step 4, the real target is detected and imaged. The detection process only needs to collect all antenna combinations obtained in step 3, which reduces the time required for detection. The data obtained by detection is imaged from step 302 to step 305 to obtain an imaging result. .

4. A computing device for adaptive layered confocal intracranial microwave imaging, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intracranial microwave imaging method as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the intracranial microwave imaging method as described in any one of claims 1 to 3 is implemented.

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