An intracranial microwave imaging method, device and storage medium with adaptive hierarchical confocal

Abstract

The present invention belongs to the technical field of digital image processing, and specifically relates to an intracranial microwave imaging method, device and storage medium with adaptive hierarchical confocal. When using antenna arrays with different numbers of antennas and antenna arrangement structures, the algorithm can adaptively optimize antenna selection, select the best calculated antenna pairs for imaging at different target positions, and achieve hierarchical imaging of the intracranial region; calculate the transmission time of the microwave signal emitted from antenna #imgabs0#, reflected at the target point #imgabs1# and received by antenna #imgabs2#, calculate the propagation speed of the signal in the skull, introduce a gain coefficient, perform time shift summation on the reflected signals received by different antenna pairs, reconstruct the relative gray-scale image of the target area, and detect the intracranial hemorrhage area. The present invention optimizes the imaging process by adaptively selecting antenna combinations, improves the algorithm performance, image quality and reduces the positioning error.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital image processing, and particularly relates to an intracranial microwave imaging method, device and storage medium for adaptive hierarchical confocal imaging. Background Art

[0002] As an important non-destructive testing technology, microwave imaging can penetrate materials to a certain depth to detect internal hidden targets or structures, and has great application potential in fields such as medical diagnosis, geological exploration, and industrial inspection. In the biomedical field, it can penetrate human tissues and provide key reference information for early disease diagnosis by doctors.

[0003] Microwave imaging is mainly divided into microwave tomography, microwave thermoacoustic imaging, and confocal microwave imaging (CMI). Microwave tomography realizes the iteration of the field distribution and dielectric constant distribution by estimating the electric field distribution at specific frequency points and iteratively comparing it with the electric field intensity at the receiving points, and finally converges to a result close to the true distribution. Representative algorithms include iterative algorithms BIM, DBIM, CSI, etc. based on the Born approximation, and non-iterative algorithms based on singular value decomposition. Microwave thermoacoustic imaging combines ultrasound and microwaves to achieve the localization and treatment of lesions. Confocal microwave imaging technology is based on the synthetic aperture radar (SAR) imaging algorithm, and uses the electromagnetic parameter differences between the target and the background medium to qualitatively identify high-scattering regions, thereby locating the target. This technology transmits and receives microwave signals through an antenna array, including processes such as measuring the scattered electric field, extracting the target response, 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 the 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 target contour localization.

[0004] In recent years, confocal microwave imaging technology has developed rapidly in medical imaging. In 1998, the Hagness team at the University of Wisconsin in the United States 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 certain progress in clinical trials; in 2021, a team at the University of Queensland in Australia used a circular antenna array composed of 8 antennas to image the pig knee joint, covering normal knee joints and knee joints simulating different degrees of ligament tears, and successfully constructed a high-resolution (1mm³) equivalent model of the pig knee joint.

[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:

[0010] An adaptive layered confocal intracranial microwave imaging method comprises the following steps:

[0011] Step 1: According to the number of existing antennas and location, determine the distance between all antennas ;

[0012] Step 2: Test the known model and collect all antennas When transmitting, the antenna Received signal ;

[0013] Step 3: Initialize the parameters to be optimized according to the test data of the known model, construct the imaging loss function, and obtain the optimal antenna combination at different target positions during imaging through iteration, and dynamically optimize the antenna selection to calculate the image pixel values;

[0014] The step 3 dynamically optimizes the antenna selection to calculate the image pixel values, which specifically includes the following steps:

[0015] Step 301: Initialize the parameters to be optimized , which is used for the subsequent antenna selection optimization step of imaging;

[0016] Step 302: Calculate the transmission time when the microwave signal is emitted from the antenna , reflected at any target point , and received by the antenna , where the propagation speed formula is used to calculate the propagation speed of the signal in the cranial cavity, where and are the relative permittivity and relative permeability of the biological tissue respectively, is the speed of light in vacuum, is the distance from the antenna to the target ; is the distance from the antenna to the target point ; ;

[0017] Step 303: Introduce the gain coefficient , where and represent the gain coefficients of the antennas and respectively, is the distance compensation coefficient, and in the two-dimensional cylindrical wave simulation , this coefficient comprehensively considers the influence of antenna directivity and path attenuation on the signal;

[0018] Step 304: According to the formula , obtain all the antenna combinations under the current parameters ;

[0019] Step 305: Use to perform computational imaging, where is the time-domain waveform signal received by the antenna after the antenna emits, is the pulse function, is the time, is the internal transmission delay of the system;

[0020] Step 306: Calculate the current imaging loss according to the formula In the formula: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels of the image; Substitute the imaging loss into the optimizer to optimize the parameter to be optimized Repeat the iterative steps from step 302 to step 306 until the imaging loss meets the requirements, record the best antenna combination at the current antenna number and position, and use the best antenna combination for layered imaging of the intracranial region during detection;

[0021] Step 4: Perform detection imaging on the real target according to the optimized antenna pair combination;

[0022] During the detection imaging of the real target in step 4, only the acquisition of all antenna combinations obtained in step 3 is required during the detection process, reducing the detection time; The data obtained by detection is imaged using steps 302 to 305 to obtain an imaging result .

[0023] A computing device for intracranial microwave imaging with adaptive layered confocal includes: 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 execute the intracranial microwave imaging method.

[0024] 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.

[0025] The technical effects achieved by the present invention are:

[0026] The adaptive layered confocal microwave imaging algorithm proposed by the present invention effectively improves the deep imaging performance by dynamically adjusting antenna compensation, and the output image report is clearer and more accurate, and can more accurately detect the intracranial hemorrhage area.

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

[0028] The present invention provides valuable reference for the application of confocal microwave imaging technology in the field of biomedicine, is of great significance for the real-time implementation of future algorithms and the research on miniaturization of hardware, and 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

[0029] Figure 1 is the flowchart of adaptive antenna selection optimization for an intracranial microwave imaging method with adaptive hierarchical confocal of the present invention;

[0030] Figure 2 is the schematic diagram of confocal imaging in the present invention;

[0031] Figure 3a , Figure 3b , Figure 3c and Figure 3d respectively represent the comparison charts of traditional confocal imaging and adaptive hierarchical confocal imaging under four groups of different original modellings;

[0032] Figure 4a is the corresponding icon of the original modelling and the standard image; Figure 4b is the comparison chart of traditional confocal imaging and adaptive confocal hierarchical imaging under different numbers of antennas with the original modelling of 4a;

[0033] Figure 5 is the MRPAD comparison chart of traditional confocal imaging and adaptive confocal hierarchical imaging under different numbers of antennas in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives and advantages of the present invention clearer, the present invention will be specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific implementation manners of the present invention, and does not strictly limit the scope of protection specifically claimed by the present invention.

[0035] As Figures 1-5 shown, an intracranial microwave imaging method with adaptive hierarchical confocal includes the following steps:

[0036] Step 1: Determine the distances between all antennas according to the existing number and positions of the antennas ;

[0037] Step 2: Test the known model and collect the signals received by the antennas when all antennas transmit;

[0038] Step 3: Initialize the parameters to be optimized according to the test data of the known model, construct an imaging loss function, and obtain the optimal antenna combination at different target positions during imaging through iteration, and dynamically optimize the antenna selection to calculate the image pixel values;

[0039] In step 3, the antenna selection is dynamically optimized to calculate the image pixel values, which specifically includes the following steps:

[0040] Step 301: Initialize the parameters to be optimized , which is used for the subsequent antenna selection optimization steps during imaging;

[0041] Step 302: Calculate the transmission time of the microwave signal emitted from the antenna , reflected at any target point , and received by the antenna , where the propagation speed formula is used to calculate the propagation speed of the signal in the cranial cavity, where and are the relative permittivity and relative permeability of the biological tissue respectively, is the speed of light in vacuum, is the distance from the antenna to the target ; is the distance from the antenna to the target point ; ;

[0042] Step 303: Introduce the gain coefficient , where and represent the gain coefficients of the antennas and respectively, is the distance compensation coefficient, and in the two-dimensional cylindrical wave simulation , this coefficient comprehensively considers the influence of antenna directivity and path attenuation on the signal;

[0043] Step 304: According to the formula , obtain all the antenna combinations under the current parameters;

[0044] Step 305: Use for computational imaging, where is the time-domain waveform signal received by the antenna after being emitted by the antenna , is the pulse function, is the time, is the internal transmission delay of the system;

[0045] Step 306: According to the formula calculate the current imaging loss. In the formula: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels of the image; substitute the imaging loss into the optimizer to optimize the parameter to be optimized Repeat steps 302 to 306 until the imaging loss meets the requirements, record the best antenna combination under the current antenna number and position, and use the best antenna combination for layer imaging of the intracranial region during detection;

[0046] Step 4: Perform detection imaging on the real target according to the optimized antenna pair combination;

[0047] During the detection imaging of the real target in step 4, only the acquisition of all antenna combinations obtained in step 3 is required during the detection process, reducing the detection time; the data obtained by detection is imaged using steps 302 to 305 to obtain the imaging result 。

[0048] In the present invention, the purpose of the present invention is to provide an adaptive layer confocal microwave imaging algorithm for the problems existing in the traditional confocal microwave imaging algorithm in intracranial hemorrhage imaging, such as antenna coupling signal interference and imaging quality being limited by the number of antennas. By adaptively selecting the antenna combination to optimize the imaging process, the algorithm performance, image quality are improved and the positioning error is reduced.

[0049] Experimental simulation settings:

[0050] According to the description in the above invention content, an ellipsoidal brain model with a major axis of 17 cm and a minor axis of 13 cm is constructed, and a circular hemorrhage area with a diameter of 2 - 4 cm is embedded inside. Select an imaging frequency of 0.5 - 3 GHz, set the time-domain signal-to-noise ratio (SNR) to 5 dB, and arrange circular antenna arrays composed of 8, 12, 16, and 24 uniformly distributed antennas respectively.

[0051] Performance evaluation index:

[0052] To quantitatively evaluate the performance of the adaptive layer confocal microwave imaging algorithm and the influence of different numbers of antennas on the imaging quality, the present invention uses the mean relative pixel absolute difference (MRPAD) as the image quality evaluation index. MRPAD measures the image difference by calculating the average of the relative absolute differences of the corresponding pixels of two images. The formula is as follows:

[0053] Among them, represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels of the image. In the present invention, the standard image is obtained through simulation experiments of 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 effect, it can provide an ideal reference benchmark for evaluating the impact 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.

[0054] Since different antenna combinations may cause changes in the pixel values of the imaging results, the present invention adopts a normalization method to proportionally scale the maximum pixel value of all imaging results to 1, eliminating the problem of inconsistent pixel value sizes caused by antenna combination differences, and ensuring that the MRPAD index can more fairly and accurately reflect the performance of the imaging algorithm.

[0055] Through the above specific implementation manners, the effectiveness and superiority of the adaptive hierarchical confocal microwave imaging algorithm proposed by the present invention can be effectively verified, providing a new and effective method for intracranial hemorrhage detection.

[0056] The adaptive hierarchical confocal microwave imaging algorithm proposed by 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.

[0057] Appendix Figure 2 is a simple schematic diagram of this embodiment. The antennas are arranged at equal intervals in a circle outside the skull, and the antennas take turns to transmit and receive signals to detect the skull.

[0058] Appendix Figures 3a-3d and Figures 4a-4b is an intuitive comparison of the imaging results of the algorithm proposed by the present invention and the traditional confocal imaging algorithm under 8 antennas and different numbers of antennas. Under noisy conditions, the imaging results of the present invention are significantly better than those of the traditional confocal imaging.

[0059] Appendix Figure 5 is a comparison of the MRPAD values of the algorithm proposed by the present invention and the traditional confocal imaging under different numbers of antennas. When the number of antennas is small, the algorithm proposed by the present invention can achieve a 50% performance improvement.

[0060] Compared with the traditional confocal microwave imaging algorithm, under different antenna number configurations (8, 12, 16, and 24 antennas), the imaging performance of the algorithm of the present invention is better. The experimental results show that the adaptive hierarchical confocal microwave imaging algorithm is remarkable 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, using half the number of antennas can achieve the same imaging quality as the traditional algorithm.

[0061] The present invention provides valuable reference for the application of confocal microwave imaging technology in the field of biomedicine, which is of great significance for the real-time implementation of future algorithms and the research on hardware miniaturization, and is expected to provide a non-invasive, real-time and rapid imaging report for the clinical diagnosis and treatment of intracranial hemorrhage.

[0062] A computing device for intracranial microwave imaging with adaptive hierarchical confocal includes: 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 execute the intracranial microwave imaging method.

[0063] 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.

[0064] An embodiment of the present application also provides an intracranial microwave imaging device with adaptive hierarchical confocal: at least one processor; and,

[0065] 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 execute the intracranial microwave imaging method as described above.

[0066] At the hardware level, the computing device includes a processor, and optionally also includes an internal bus, a network interface, and a memory.

[0067] Among them, the memory may include internal memory, such as high-speed random access memory (Random-Access Memory, RAM), and may also include non-volatile memory, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0068] The processor, network interface, and memory can be interconnected through an internal bus, and the internal bus 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.

[0069] A memory for storing programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0070] 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 foregoing method.

[0071] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products.

[0072] Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0073] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention are implemented according to the conventional means in the art without special explanation and limitation.

Claims

1. An intracranial microwave imaging method based on adaptive hierarchical confocal, characterized in that: Including the following steps: Step 1: Determine the distances between all antennas according to the existing number of antennas and their locations ; Step 2: Test the known model and collect all antennas During transmission, the antenna Received signal ; Step 3: According to the test data of the known model, initialize the parameters to be optimized, construct an imaging loss function, and through iteration, obtain the optimal antenna combination at different target positions during imaging, dynamically optimize antenna selection to calculate image pixel values; Step 4: According to the optimized antenna pair combination, detect and image the real target; In step 3, dynamically optimizing antenna selection to calculate image pixel values includes the following steps: Step 301: Initialize the parameter to be optimized , which is used for the subsequent antenna selection optimization step of imaging; Step 302: Calculate the transmission time of the microwave signal from the antenna emitted, reflected at any target point and received by the antenna . Among them, the propagation speed formula is used to calculate the propagation speed of the signal in the cranial brain, where and are the relative permittivity and relative permeability of the biological tissue respectively, is the speed of light in a vacuum, is the distance from the antenna to the target ; is the distance from the antenna to the target point ; . Step 303: Introduce the gain coefficient , where and represent the gain coefficients of antennas and respectively, is the distance compensation coefficient. In the two-dimensional cylindrical wave simulation , this coefficient comprehensively considers the effects of antenna directivity and path attenuation on the signal; Step 304: According to the formula , all antenna combinations under the current parameters are obtained ; Step 305: Use to perform computational imaging, where is the antenna time-domain waveform signal received by the antenna after transmission, is the pulse function, is the time, is the internal transmission delay of the system; Step 306: According to the formula calculate the current imaging loss. In the formula: represents the pixel value of the standard image, represents the corresponding pixel value of the reconstructed image, is the total number of pixels of the image; substitute the imaging loss into the optimizer to optimize the parameter to be optimized Repeat steps 302 to 306 until the imaging loss meets the requirements, record the best antenna combination at the current antenna number and position, and use the best antenna combination for intracranial hierarchical imaging during detection.

2. An intracranial microwave imaging method based on adaptive hierarchical confocal according to claim 1, characterized in that: In step 4, real targets are detected and imaged. During the detection process, only the acquisition of all antenna combinations obtained in step 3 is required, reducing the time required for detection. The data obtained from the detection is imaged according to steps 302 to 305 to obtain an imaging result. .

3. A computing device for intracranial microwave imaging with adaptive hierarchical confocal, characterized in that: Including: 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 when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the intracranial microwave imaging method according to any one of claims 1-2.

4. 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, it implements the intracranial microwave imaging method according to any one of claims 1-2.

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