CN112545475B - FDTD-based tumor detection method and device based on antenna array confocal imaging algorithm - Google Patents
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Abstract
The invention discloses a brain tumor detection method based on FDTD antenna array confocal imaging algorithm, which comprises the following steps: 1. establishing a human brain model; 2. establishing a radiation simulation antenna array and an excitation source; 3. grouping selection frequency and transceiving antenna groups; 4. performing a confocal imaging algorithm; 5. and (5) processing the similarity. The invention also discloses a brain tumor detection device based on the FDTD antenna array confocal imaging algorithm, which comprises the following components: the model establishing module is used for acquiring a three-dimensional model of a human head structure to obtain a two-dimensional grid model; the antenna and signal source configuration module is used for setting an antenna signal source; a frequency and transmit-receive antenna selector traversing the frequency matrix and the transmit-receive antenna group matrix; a tumor detector for executing confocal imaging algorithm to obtain a similarity matrix; and the similarity processing module is used for superposing and normalizing the similarity matrixes to obtain the tumor lesion points. The invention can detect early brain tumor more quickly and accurately, and can be widely applied to the field of life science.
Description
Technical Field
The invention relates to the fields of life science and artificial intelligence, in particular to a brain tumor detection method and device based on FDTD antenna array confocal imaging algorithm.
Background
In recent years, the incidence and mortality of brain cancer have increased year by year, and the life health of each patient is jeopardized.
If the early detection can be carried out on the tumor, the death rate of the patient can be greatly reduced, and the treatment difficulty and the pain of the tumor patient are reduced. Since in the early stage of carcinogenesis, as the tumor grows, it reaches the surrounding normal tissue to become cancerous, there is a significant difference in electromagnetic parameters between normal tissue and tumor tissue. Based on the characteristics of computational electromagnetism, it is possible to identify the slight difference by performing numerical calculation on a human tissue structure model, so that the microwave imaging method for early detection of tumor is gradually popularized in the industry in recent years.
However, in the existing research, much researchers focus on the ultra-wideband microwave imaging technology and the detection of relatively single dispersive models of tissues (such as breast cancer) by using FDTD.
These techniques have the following problems:
(1) The ultra-wideband microwave imaging technology is used for realizing early detection and positioning of tumors (such as breast tumors) under a dispersive model, and the tumors cannot be imaged well in a complex environment (such as the inside of a human brain).
(2) The traditional methods such as the nuclear magnetic resonance imaging method, the cerebrovascular angiography method, the stereotactic biopsy method and other detection means have the problems of high cost, high expenditure, low sensitivity and accuracy and the like.
Therefore, there is no good method for effectively applying the microwave imaging technology to the early diagnosis field of brain cancer.
Disclosure of Invention
The invention aims to overcome the defects of the background technology and provide a brain tumor detection method and device based on an FDTD antenna array confocal imaging algorithm, so that the early brain tumor can be detected more quickly, more accurately and at lower cost.
The invention provides a brain tumor detection method based on FDTD antenna array confocal imaging algorithm, which comprises the following steps: s1, establishing a three-dimensional model and a two-dimensional slice model of a human brain; s2, establishing a radiation simulation antenna array and an excitation source; s3, grouping and selecting a plurality of frequencies and a transmitting-receiving antenna group for detection; s4, executing a confocal imaging algorithm to execute target detection; and S5, carrying out similarity processing on the target detected in the step S4.
In the above technical solution, the step S1 specifically includes the following steps: s11, establishing a human body structure Zubal three-dimensional model; s12, gridding the three-dimensional model; and S13, intercepting the head model and establishing a two-dimensional section of the head model.
In the above technical solution, the step S2 specifically includes the following steps: s21, uniformly placing a circle of antenna on the surface of the skin to discretize errors; s22, adopting a configuration mode of separating a transmitting antenna from a receiving antenna to enable the transmitting antenna and the receiving antenna to form an antenna array; s23, selecting Gaussian modulation pulse as a signal source, and calculating scattering parameters.
In the above technical solution, in the step S22, the configuration mode of separating the transmitting and receiving antennas specifically includes: s221, single-transmitting and single-receiving, namely one transmitting antenna and one receiving antenna, combining the transmitting antenna and the receiving antenna into the same antenna, and calculating scattering parameters S11 on the basis; s222, single-transmitting and multi-receiving, that is, one transmitting antenna is provided with a plurality of receiving antennas to form an antenna array, and based on this, the scattering parameters S21, S31 or S41 are calculated.
In the above technical solution, the step S4 specifically includes the following steps: s41, detecting each group of antennas and frequencies in an antenna position matrix and a frequency matrix; s42, supposing that each grid in the grid model is a possible tumor grid, connecting the selected tumor imaginary point with a transmitting antenna and a receiving antenna; s43, traversing a head two-dimensional section grid model matrix, finding tissue small blocks on a connecting line, counting the number of the various tissue small blocks, calculating the total phase shift of scattering parameters of all the small blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by electromagnetic values to obtain the similarity value of the tumor hypothetical position; s44, solving the similarity value of all possible tumor positions to obtain a similarity matrix of the selected receiving and transmitting antenna and the selected frequency.
In the above technical solution, in the step S43, a specific process of "calculating the total phase shift of the scattering parameters of all the small blocks" is as follows: under the two premises of assuming that a certain tissue small block is a tumor imaginary point or not, firstly calculating scattering parameters to make difference, then calculating phase shift, and quantizing the phase shift result in the range of 0-2 pi, and expressing the result in radian system.
In the above technical solution, in the step S43, a specific process of "comparing the phase shift of the scattering parameter calculated by the electromagnetic value to obtain the similarity value of the assumed tumor position" is as follows: comparing the calculated scattering parameter phase shift with the calculated scattering parameter phase shift, and quantifying the result within the range of 0-1 to obtain the similarity value of the tumor hypothesis point, namely the tumor probability, wherein the closer the value is to 0, the more likely the tumor hypothesis point is to be an actual tumor point.
The invention also provides a brain tumor detection device based on the FDTD antenna array confocal imaging algorithm, which comprises the following parts: the model building module is used for obtaining a Zubal three-dimensional model of a human head structure through a scanner or 3D model generating equipment, meshing the three-dimensional model, and cutting head slices in a layering mode to obtain a head two-dimensional grid model; the antenna and signal source configuration module is used for arranging transmitting and receiving antennas around the outside of the human brain model in the model building module and setting an antenna signal source; the frequency and receiving-transmitting antenna selector is used for performing two-dimensional traversal on all possible frequency matrixes and receiving-transmitting antenna group matrixes; the tumor detector is used for carrying out tumor detection by a plurality of detection units in parallel executing a confocal imaging algorithm under the condition of a plurality of frequencies and a plurality of groups of transceiving antenna groups to obtain a similarity matrix under each condition; and the similarity processing module is used for superposing and normalizing the similarity matrixes obtained by the tumor detector, and finding out the grid position with the maximum similarity, namely the most possible tumor lesion point.
In the above technical solution, the tumor detector includes the following parts: a detection unit: detecting for each set of antennas and frequencies in an antenna position matrix and a frequency matrix; a mesh transceiver unit: assuming that each grid in the grid model is a possible tumor grid, connecting the selected tumor with transmitting and receiving antennas; a phase shift calculation unit: traversing the head two-dimensional section grid model matrix, finding out tissue small blocks on a connecting line, counting the number of various tissue small blocks, calculating the total phase shift of scattering parameters of all the small blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by electromagnetic values to obtain the similarity value of the tumor hypothetical position; similarity matrix unit: and solving similarity values of all possible tumor positions to obtain a similarity matrix of the selected receiving and transmitting antenna and the selected frequency.
The brain tumor detection method and device based on the FDTD antenna array confocal imaging algorithm have the following beneficial effects: by establishing a human brain two-dimensional section model, electromagnetic numerical calculation simulation and application of a microwave imaging algorithm, the early brain tumor can be detected more quickly, more accurately and more intuitively with less cost.
Drawings
Fig. 1 is a schematic overall flow chart of a brain tumor detection method based on FDTD antenna array confocal imaging algorithm according to the present invention;
fig. 2 is a schematic flowchart of a specific step S1 in the brain tumor detection method based on FDTD confocal imaging algorithm with antenna array according to the present invention;
fig. 3 is a schematic flowchart of the step S2 in the brain tumor detection method based on FDTD confocal antenna array imaging algorithm according to the present invention;
fig. 4 is a schematic flow diagram of the antenna array confocal imaging algorithm in step S4 of the brain tumor detection method based on FDTD antenna array confocal imaging algorithm of the present invention;
fig. 5 is a schematic structural diagram of a brain tumor detection device based on FDTD antenna array confocal imaging algorithm according to the present invention;
fig. 6 is a schematic structural diagram of a tumor detector in the brain tumor detection device based on the FDTD confocal antenna array imaging algorithm according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and examples, which should not be construed as limiting the invention.
Fig. 1 is a schematic flow chart of a brain tumor detection method based on FDTD antenna array confocal imaging algorithm according to the present invention; as shown in fig. 1, the method comprises the following steps:
s1, establishing a three-dimensional model and a two-dimensional slice model of a human brain;
specifically, as shown in fig. 2: s11, obtaining a human body Zubal three-dimensional structure model according to scanning: each human tissue has its label, and different tissue types are determined by these labels. The reference numbers here have been determined by relatively well-established NMR models, so that the file names correspond directly to the number of slices in the partition.
S12, gridding the three-dimensional model of the human body structure;
s13, obtaining a head two-dimensional model through model slicing;
gridding allows each tissue to be composed of small pieces resembling "cells" for easy viewing. In fact, the gridding process is a process of matrix characteristic arrangement, the ID file is actually a coded large matrix, the numerical value of each element in the matrix is actually the number of the corresponding organization, the size of the whole matrix is input according to needs, the whole matrix and an air layer form a human body model close to reality, and then Mesh (gridding) is carried out on the human body model.
S2, establishing a radiation simulation antenna array and an excitation source;
specifically, as shown in fig. 3: s21, for the requirements of antenna design, the number of the antennas is reduced as much as possible, and the cost is reduced, so that a circle of antennas are uniformly placed on the surface of the skin, the error which possibly occurs can be discretized, and the accuracy of signal receiving is improved.
The FDTD (finite Difference time Domain) algorithm principle is as follows:
the FDTD algorithm core is that electric field intensity and magnetic field intensity components are alternately sampled and dispersed in time and space, a Maxwell rotation equation is changed into a differential equation through the dispersion mode, so that iterative solution in time is facilitated, the purpose can be achieved without matrix inversion operation, and as long as initial values and boundary conditions in the solved problem are given, the distribution of space electromagnetic fields at each later moment can be obtained sequentially and gradually by the FDTD method, and the space electromagnetic fields can be solved sequentially and gradually in time.
S22, the scheme adopts a configuration mode of separating transmitting and receiving antennas:
s221, a transmitting antenna and a receiving antenna, combining the transmitting antenna and the receiving antenna into the same antenna (i.e. implementing single-transmitting and single-receiving), and calculating the scattering parameter S11.
S222, a transmitting antenna is provided with multiple receiving antennas (i.e. single-transmitting and multiple-receiving are implemented), and an antenna array is formed, and the scattering parameters S21, S31, S41, etc. are calculated respectively. This reduces the stringent requirements on the antenna design.
And S23, a confocal imaging algorithm is adopted to avoid complex scattered field calculation, and Gaussian modulation pulses are selected as a signal source.
S3, grouping and selecting different frequencies and different transmitting and receiving antenna groups for detection;
s4, executing a confocal imaging algorithm to execute target detection;
as shown in fig. 4, the step S4 specifically includes:
s41, detecting each group of antennas and frequencies in an antenna position matrix and a frequency matrix;
s42, supposing that each grid in the grid model is a possible tumor grid, connecting the selected tumor imaginary points with transmitting and receiving antennas to form a path;
s43, traversing the head two-dimensional section grid model matrix, finding out tissue small blocks on a connecting line, counting the number of various tissue small blocks, calculating the total phase shift of scattering parameters of all the small blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by electromagnetic values to obtain the similarity value of the tumor hypothetical position;
it is worth mentioning that: because each tissue material has its inherent dielectric constant and conductivity, the gaussian modulated pulse will produce a certain amount of phase shift when passing through different materials, and this amount of phase shift not only represents the phase shift inside the material but also represents the phase shift of the boundary when passing through the boundary of different materials. And this phase shift is not only related to the conductivity and dielectric constant of the material, but also to the frequency of the external antenna source at which it is located.
Because the grid matrix is large, not only various tissue structures of the human brain but also an air layer are included, and the phase change of the Gaussian modulation pulse in the air is large and cannot be ignored. Therefore, during calculation, a method of calculating scattering parameters to make difference and then calculating phase shift under the two conditions of assuming whether the point is a tumor imaginary point is adopted, and the phase shift result is quantized to be within the range of 0-2 pi and unified into an arc system. The method effectively reduces echo interference of other tissues and scattering interference of an air layer.
Comparing the scattering parameter phase shift calculated by the method with the calculated scattering parameter phase shift (calculating a difference value), and quantifying the result within the range of 0-1 to obtain the tumor probability (similarity) of the tumor hypothesis point, wherein the closer the value is to 0, the more likely the tumor hypothesis point is to be an actual tumor point;
and S44, returning to the step S42 to select different grid points as tumor hypothesis points again until all possible tissue points on the grid are traversed, and obtaining a similarity matrix of the whole grid.
The working principle of the step S4 is as follows:
the principle of the method is relatively simple, the calculated amount is relatively small, the method is relatively safe, no ray radiation exists, the method does not enter the human body, no oppression is caused, normal tissues are not damaged, the method has considerable detection effect on small-diameter tumors, the resource consumption and the medical cost are relatively low, more people can bear the method, and therefore the microwave imaging method has certain development potential compared with the traditional tumor detection method.
The detection method of the antenna array confocal imaging uses the antenna array radiation detection area which is set up in advance, and a series of antennas are arranged as receiving antennas to be used for receiving the returned electromagnetic signals, and the distance between the reflection point and the signal source is reversely deduced by using the special scattering parameters. Researches show that microwaves with high frequency and high bandwidth can better distinguish tumors, but high-frequency signals are scattered and attenuated very quickly in space propagation, and GHz-bandwidth signals are preferred as tumor detection signals for the sake of image clarity and detection accuracy. Microwave tomography and microwave confocal imaging are two common ways of active microwave imaging.
The microwave tomography utilizes an electromagnetic field outside a scatterer to invert the electromagnetic characteristic parameter distribution of an imaging area so as to obtain the position and the size of a tumor, and the method is combined with a time domain finite difference method at present. The method is characterized in that an integral space model is discretized, the integral space model is divided into a plurality of small space lattices, then the signals received by a receiving antenna are utilized to carry out space-time difference on an electromagnetic field equation, the propagation path of the ultra-bandwidth electromagnetic signals in brain tissues is simulated, and reverse simulation is carried out, so that tumors are positioned. The method is novel, safe and reliable, has a strong electromagnetism theory as a support, and can theoretically accurately position the position of the target to be measured as long as the operation calculation is correct.
S5, processing the similarity matrix obtained by the detection of the S4 under the conditions of different frequencies and different transceiving antenna groups;
the method specifically comprises the following steps: and (5) overlapping and normalizing the similarity matrixes of different frequencies and different receiving and transmitting antenna groups obtained by the detection of the step (S4), and finding out the grid position with the maximum similarity, namely the most possible tumor lesion point.
According to the brain tumor detection method based on the FDTD antenna array confocal imaging algorithm, the algorithm in the step can be used for drawing a human brain tissue including a tumor position map easily, and the tumor position can be detected more quickly and intuitively.
Based on the above embodiments, fig. 5 is a schematic overall frame diagram of a brain tumor detection apparatus based on FDTD antenna array confocal imaging algorithm of the present invention; the tumor detector comprises a model building module 1, an antenna and signal source configuration module 2, a frequency and transceiving antenna selector 3, a tumor detector 4 and a similarity processing module 5;
the model establishing module 1 is used for acquiring a Zubal three-dimensional model of a human head structure through a scanner or 3D model generating equipment, gridding the three-dimensional model, and cutting head slices in a layering mode to obtain a head two-dimensional grid model;
the antenna and signal source configuration module 2 arranges a receiving and transmitting antenna around the outside of the human brain model in the model building module 1 and sets an antenna signal source;
the frequency and transmit-receive antenna selector 3 performs two-dimensional traversal on all possible frequency matrixes and transmit-receive antenna group matrixes;
the tumor detector 4 is characterized in that under the conditions of different frequencies and a plurality of groups of transceiving antenna groups, a plurality of detection units execute a confocal imaging algorithm in parallel to perform tumor detection and obtain a similarity matrix under each condition;
further, as shown in fig. 6, the tumor detector 4 specifically includes:
a detection unit: detecting each group of antennas and frequencies in an antenna position matrix and a frequency matrix;
a mesh transceiving unit: assuming that each grid in the grid model is a possible tumor grid, connecting the selected tumor with transmitting and receiving antennas;
a phase shift calculation unit: traversing the two-dimensional grid model matrix, finding out tissue small blocks on a connecting line, counting the number of various tissue small blocks, calculating the total phase shift of scattering parameters of all the small blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by the electromagnetic numerical value to obtain the similarity value of the tumor hypothetical position;
similarity matrix unit: solving similarity values of all possible tumor positions to obtain a similarity matrix of the selected receiving and transmitting antenna and the selected frequency;
the similarity processing module 5 is used for superposing and normalizing the similarity matrixes obtained by the tumor detector 4, and finding out the grid position with the maximum similarity (namely the minimum phase shift difference value) as the most possible tumor lesion point;
through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Those not described in detail in this specification are within the skill of the art.
Claims (4)
1. A brain tumor detection method based on FDTD antenna array confocal imaging algorithm is characterized in that: the method comprises the following steps:
s1, establishing a three-dimensional model and a two-dimensional slice model of a human brain;
s2, establishing a radiation simulation antenna array and an excitation source;
s3, grouping and selecting various frequencies and a transmitting-receiving antenna group for detection;
s4, executing a confocal imaging algorithm to execute target detection;
s5, carrying out similarity processing on the target detected in the step S4;
the step S1 specifically includes the steps of:
s11, establishing a human body structure Zubal three-dimensional model;
s12, gridding the three-dimensional model;
s13, intercepting a head model and establishing a two-dimensional section of the head model;
the step S2 specifically includes the steps of:
s21, uniformly placing a circle of antenna on the surface of the skin to discretize errors;
s22, adopting a configuration mode of separating a transmitting antenna from a receiving antenna to enable the transmitting antenna and the receiving antenna to form an antenna array;
s23, selecting Gaussian modulation pulses as a signal source, and calculating scattering parameters;
in step S22, the configuration mode of the transmit-receive antenna separation specifically includes:
s221, single-transmitting and single-receiving, namely one transmitting antenna and one receiving antenna, combining the transmitting antenna and the receiving antenna into the same antenna, and calculating scattering parameters S11 on the basis;
s222, single-transmitting and multi-receiving, namely one transmitting antenna is provided with a plurality of receiving antennas to form an antenna array, and scattering parameters S21, S31 or S41 are calculated on the basis;
the step S4 specifically includes the following steps:
s41, detecting each group of antennas and frequencies in an antenna position matrix and a frequency matrix;
s42, supposing that each grid in the grid model is a possible tumor grid, connecting the selected tumor imaginary point with a transmitting antenna and a receiving antenna;
s43, traversing the head two-dimensional section grid model matrix, finding out tissue blocks on a connecting line, counting the number of various tissue blocks, calculating the total phase shift of scattering parameters of all the blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by electromagnetic values to obtain the similarity value of the supposed position of the tumor;
and S44, solving similarity values of all possible tumor positions to obtain a similarity matrix of the selected receiving and transmitting antenna and the selected frequency.
2. The brain tumor detection method based on FDTD antenna array confocal imaging algorithm according to claim 1, wherein: in step S43, the specific process of "calculating the total phase shift of the scattering parameters of all the patches" is as follows: under the two premise of assuming that a certain tissue block is a tumor imaginary point or not, firstly calculating scattering parameters to make difference, then calculating phase shift, quantifying the phase shift result in the range of 0-2 pi, and expressing the result in a radian system.
3. The brain tumor detection method based on FDTD antenna array confocal imaging algorithm according to claim 2, wherein: in step S43, the specific process of "comparing the phase shift of the scattering parameter calculated by the electromagnetic value to obtain the similarity value of the assumed tumor position" is as follows: comparing the calculated scattering parameter phase shift with the calculated scattering parameter phase shift, and quantifying the result within the range of 0-1 to obtain the similarity value of the tumor hypothesis point, namely the tumor probability, wherein the closer the value is to 0, the more likely the tumor hypothesis point is to be an actual tumor point.
4. A brain tumor detection device based on FDTD antenna array confocal imaging algorithm is characterized in that: the method comprises the following steps:
the model building module (1) is used for obtaining a Zubal three-dimensional model of a human head structure through a scanner or 3D model generating equipment, gridding the three-dimensional model, and hierarchically cutting head slices to obtain a head two-dimensional grid model;
the antenna and signal source configuration module (2) is used for arranging transmitting and receiving antennas around the outside of the human brain model in the model building module (1) and setting an antenna signal source;
the frequency and transmitting-receiving antenna selector (3) is used for performing two-dimensional traversal on all possible frequency matrixes and transmitting-receiving antenna group matrixes;
the tumor detector (4) is used for carrying out tumor detection by a plurality of detection units in parallel executing a confocal imaging algorithm under the conditions of a plurality of frequencies and a plurality of groups of transceiving antenna groups, and a similarity matrix under each condition is obtained;
the similarity processing module (5) is used for superposing and normalizing the similarity matrix obtained by the tumor detector (4) and finding out the grid position with the maximum similarity, namely the most possible tumor lesion point;
the tumor detector (4) comprises the following parts:
a detection unit: detecting for each set of antennas and frequencies in an antenna position matrix and a frequency matrix;
a mesh transceiver unit: assuming that each grid in the grid model is a possible tumor grid, connecting the selected tumor with transmitting and receiving antennas;
a phase shift calculation unit: traversing the head two-dimensional section grid model matrix, finding out tissue small blocks on a connecting line, counting the number of various tissue small blocks, calculating the total phase shift of scattering parameters of all the small blocks, and comparing the total phase shift with the phase shift of the scattering parameters calculated by electromagnetic values to obtain the similarity value of the supposed position of the tumor;
similarity matrix unit: and solving similarity values of all possible tumor positions to obtain a similarity matrix of the selected transmitting and receiving antenna and the selected frequency.
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