Human body boron 10 analysis system and method based on BNCT

Abstract

The present invention relates to the field of radioisotope analysis technology, specifically to a BNCT-based human body boron 10 analysis system and method. The system includes a boron 10 distribution detection module, a voxel reconstruction imaging module, a concentration region division module, and a concentration correlation analysis module. In the present invention, by capturing the decay frequency, position, and time information of boron 10, the comprehensiveness and spatial accuracy of data acquisition are improved, the spatial positioning processing of frequency accumulation is refined, and the frequency value within the voxel is associated with the spatial coordinate, ensuring that the concentration value mapping accurately reflects the actual distribution status of each voxel position. In concentration division, the frequency characteristics are compared with the threshold, high- and low-frequency areas are marked, and regional frequency characteristics are accurately portrayed. This supports the detailed calculation of concentration differences and frequency characteristics, and the regional concentration and frequency difference data are analyzed item by item. This deepens the understanding of the differences of boron 10 in different regions, significantly improves the accuracy of boron 10 distribution detection and the depth of information interpretation, and provides data support for application optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of radioisotope analysis, and in particular to a system and method for analyzing boron 10 in the human body based on BNCT. Background Art

[0002] The field of radioisotope analysis technology involves the use of radioisotopes for quantitative and qualitative analysis of elements or compounds, primarily for detecting the distribution and concentration of trace elements in materials or organisms. This type of technology utilizes the decay characteristics and radiation signatures of radioactive markers to generate data through specific imaging or detection methods under non-invasive conditions to achieve precise measurement of the target substance. Due to its high sensitivity and high precision, radioisotope analysis is widely used in material composition analysis, environmental monitoring, and biological research. It is particularly suitable for tracking and evaluating the distribution of low-content elements or targets that are difficult to measure directly. This technology can also be combined with high-resolution imaging methods to visualize analytical data, thereby providing detailed multi-dimensional data support for the distribution of chemical components in complex samples.

[0003] The Human Boron-10 Analysis System is a specialized technology system designed to assess the isotopic distribution of boron-10 in the human body. Using non-invasive imaging equipment and radiolabeled boron compounds, the system measures the concentration and distribution of boron-10 in human tissue. Using multidimensional image processing techniques, the system reconstructs the distribution of boron-10 within different tissue regions, creating detailed three-dimensional images. The system's primary purpose is to provide real-time, accurate data support for boron-10 distribution, optimize the application of boron compounds in living organisms, and enable scientists to better assess the dynamic characteristics of boron-10.

[0004] While existing technologies offer high sensitivity and are non-invasive, they are limited in terms of spatial accuracy and feature capture of boron-10 isotope distributions. Decay frequency information is separated from location information, resulting in a lack of correlation between decay frequency and spatial position between voxels. This distorts some data and weakens the ability to interpret the distribution. Current methods lack refinement in concentration threshold comparisons, making it impossible to accurately label high- and low-frequency regions, impacting feature recognition in low-content regions and making the description of the differential distribution of regions with different concentrations unclear, which can easily lead to confusion in concentration feature maps. Existing systems rely on presenting the distribution of the entire image, making it difficult to decompose concentration differences and frequency features item by item. This leads to the easy neglect of subtle differences in regional analysis, affecting the accuracy and interpretability of concentration feature analysis, and thus ineffective in dealing with complex distribution environments. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a human body boron 10 analysis system and method based on BNCT.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: The human body boron 10 analysis system based on BNCT includes:

[0007] The Boron 10 distribution detection module detects the Boron 10 isotope in the human body based on BNCT. By capturing the decay frequency, location parameters and decay event time information of Boron 10 in the voxel, the module obtains the voxel decay frequency set. Based on the voxel decay frequency set, the module integrates the frequency data and location information of the voxel to obtain the initial Boron 10 distribution data.

[0008] The voxel reconstruction imaging module accumulates the decay frequencies within each voxel item by item based on the initial B10 distribution data, associates and locates the voxel frequency data through spatial coordinates, processes the spatial position and frequency values of the voxels, generates a B10 spatial distribution model, and maps the concentration distribution values of the voxel positions according to the B10 spatial distribution model to obtain a voxel B10 concentration structure diagram;

[0009] The concentration region division module compares the concentration threshold with the voxel based on the voxel boron 10 concentration structure map, extracts the frequency characteristics of the region, marks the high-frequency and low-frequency regions, and combines the voxel frequency characteristics and concentration information to generate a characteristic concentration map of the region;

[0010] The concentration correlation analysis module calculates the difference value and frequency characteristics based on the characteristic concentration map of the region and the concentration difference value of boron 10 in the region. It calculates the concentration mean and frequency difference data in the region item by item, compares the concentration and distribution characteristics of the region in turn, and generates the boron concentration characteristic analysis results.

[0011] As a further solution of the present invention, the steps of acquiring the voxel decay frequency set are specifically as follows:

[0012] Based on BNCT, the boron-10 isotope in the human body is detected, and the frequency parameters, location parameters, and time parameters of the boron-10 isotope decay events are recorded. The frequency information of the decay events is used to sort and screen voxels with potential analytical value to establish an initial voxel frequency distribution set.

[0013] The initial voxel frequency distribution set is calibrated to remove abnormal data that deviate from the distribution, and weighted correction is performed using position, time, and frequency weight parameters using the formula:

[0014] ,

[0015] Generate voxel decay frequency calibration results, where represents the voxel decay frequency calibration result, represents the initial voxel decay frequency, and are the spatial and temporal parameters of the voxel, Weight parameter for controlling decay frequency correction;

[0016] Combined with the distribution of the voxel decay frequency calibration results, the matching degree of the decay frequency distribution parameters is analyzed, and based on the peak position of the voxel decay, the decay frequency data that meets the standard distribution is determined to obtain the voxel decay frequency set.

[0017] As a further solution of the present invention, the steps for obtaining the initial distribution data of boron 10 are specifically as follows:

[0018] Based on the voxel decay frequency set, calculating the decay frequency and position parameter combination of each voxel, calling the frequency data of each voxel, and generating a frequency and position correlation matrix;

[0019] The frequency and position correlation matrix is combined with the voxel position parameter, the attenuation frequency is selected and multiplied with the position parameter, and the frequency weight set is called using the formula:

[0020] ,

[0021] Get the frequency and position weight results of the voxels, where represents the frequency and position weight results of the voxels, represents the frequency weight parameter, represents the voxel frequency, represents the voxel position parameter;

[0022] Based on the frequency and position weight results of the voxels, the high-frequency weight position data of the voxels are called, and the distribution of boron 10 is identified through density weighting to obtain the initial distribution data of boron 10.

[0023] As a further solution of the present invention, the steps for obtaining the boron 10 spatial distribution model are specifically as follows:

[0024] Based on the initial boron 10 distribution data, the decay frequency of each voxel is accumulated item by item, and the average frequency of the voxel frequency array is calculated according to the spatial position of the voxel to obtain the initial voxel frequency distribution result;

[0025] Based on the initial voxel frequency distribution result, the voxel frequency array and the average frequency are used to adopt the formula:

[0026] ,

[0027] The frequency deviation of the voxel is calculated, where represents the frequency deviation of the voxel, is the total number of voxels, is the decay frequency of each voxel, is the average frequency, is the frequency weight parameter, is the frequency adjustment coefficient;

[0028] Combined with the frequency deviation of the voxel, the spatial coordinate set and the frequency deviation value are analyzed item by item, the frequency deviation value of each voxel is calibrated, the frequency deviation is associated with the voxel spatial position, and the spatial coordinates and the frequency deviation are operated in correspondence to generate a boron 10 spatial distribution model.

[0029] As a further solution of the present invention, the steps for obtaining the voxel boron 10 concentration structure map are specifically as follows:

[0030] Mapping the initial concentration value of the voxel position according to the boron 10 spatial distribution model, calculating and mapping the concentration distribution parameter of each voxel position to the concentration distribution value, performing the mapping calculation on the voxel position, and generating an initial voxel position concentration map;

[0031] Based on the initial voxel position concentration map, the concentration difference is refined and analyzed, and the concentration difference of adjacent voxels is calculated by difference weights, and the concentration difference formula is applied:

[0032] ,

[0033] Calculate the difference between adjacent voxels and generate a concentration difference parameter matrix, where represents the difference between adjacent voxels, represents the concentration value at the voxel location, is the assigned concentration difference weight, is the mapping value of the voxel position, is the total number of pixels, is the average concentration distribution value;

[0034] The concentration difference parameter matrix is used as a judgment standard to eliminate and screen voxel positions that do not meet the local concentration standard, and the voxel distribution is gradually screened and optimized to obtain a voxel boron 10 concentration structure map.

[0035] As a further solution of the present invention, the steps of obtaining the characteristic concentration map of the region are specifically as follows:

[0036] Based on the voxel boron 10 concentration structure diagram, the concentration of each voxel is compared with a preset concentration threshold, the high-frequency and low-frequency voxel distribution areas are extracted, the voxel frequency characteristics of each area are calculated, and the area is marked to obtain the high-frequency and low-frequency area extraction results;

[0037] Based on the high-frequency and low-frequency region extraction results, the concentration characteristic values of the voxels in the high-frequency and low-frequency regions are calculated using the formula:

[0038] ,

[0039] The concentration feature extraction results are obtained, where represents the concentration characteristic value, represents the voxel frequency in the high-frequency region, represents the voxel concentration threshold, is the low frequency region frequency, represents the difference in voxel distribution, is the adjustment factor;

[0040] Combined with the concentration feature extraction results, the frequency features and concentration feature data of the region are used for comparison and classification to generate a characteristic concentration map of the region.

[0041] As a further solution of the present invention, the steps for obtaining the boron concentration characteristic analysis results are specifically as follows:

[0042] Based on the characteristic concentration map of the region, the Boron 10 concentration values in each region are compared item by item, the concentration data of the difference values are extracted, the concentration differences within the regions are recorded, the mean of the difference items is calculated, and preliminary concentration difference data are obtained by comparing the frequency characteristics of each region;

[0043] Based on the preliminary concentration difference data, the concentration mean and frequency difference of each area were calculated area by area using the formula:

[0044] ,

[0045] The concentration mean and frequency difference characteristic data are calculated, where represents the concentration mean and frequency difference eigenvalues, is the concentration value of a voxel in the region, Indicates the average concentration value in the area. is the frequency value of the current area, is the adjustment coefficient of the mean concentration;

[0046] Combined with the concentration mean and frequency difference characteristic data, the frequency difference distribution of voxels in the region is compared, the concentration mean and frequency difference characteristic value of the voxels in each region are calculated, and the concentration and frequency characteristic data of the differentiated regions are compared item by item to obtain the boron concentration characteristic analysis results.

[0047] The BNCT-based human body boron 10 analysis method is performed based on the above-mentioned BNCT-based human body boron 10 analysis system, and includes the following steps:

[0048] S1: Based on BNCT, the boron-10 isotope in the human body is detected, capturing the decay frequency and location parameters of boron-10 within the voxel, recording the time information of the decay event, matching the voxel decay frequency data with the location parameters, integrating the voxel frequency data with the spatial coordinates, classifying and sorting each data item, and generating the initial boron-10 distribution data;

[0049] S2: Based on the initial distribution data of boron 10, the decay frequencies of the voxels are accumulated item by item, the frequency information and the position are associated through the spatial coordinates, the frequency values are aggregated and compared by position, the frequency data of each voxel is bound to the spatial coordinates, and a boron 10 spatial distribution model is generated;

[0050] S3: Based on the B10 spatial distribution model, mapping the B10 concentration parameters of the voxel position, matching the spatial coordinates of the voxel with the concentration value, summarizing the concentration distribution data of the voxels, and correlating the frequency value and coordinate of each position one by one to generate a voxel B10 concentration structure map;

[0051] S4: Based on the voxel boron 10 concentration structure map, the voxel concentration value is compared with the preset concentration threshold value item by item, the voxel frequency characteristics are divided, high-frequency and low-frequency voxels are extracted according to the frequency characteristics, the spatial distribution of the feature data is sorted, and a regional feature concentration map is generated;

[0052] S5: Based on the regional characteristic concentration map, the concentration value difference and frequency difference values within the region are calculated, the concentration mean and frequency changes of each region are compared item by item according to the regional division, the concentration and distribution differences within the region are processed and summarized, and the boron concentration characteristic analysis results are generated.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, by capturing the decay frequency, position and time information of boron 10, the comprehensiveness and spatial accuracy of data acquisition are effectively improved. The spatial positioning processing of decay frequency accumulation is refined in the process, and the frequency value within the voxel is associated with the spatial coordinate, which significantly improves the structural accuracy of the boron 10 concentration distribution, so that the subsequent concentration value mapping can directly reflect the actual concentration distribution status of each voxel position. In the concentration division process, the frequency characteristics are compared by concentration threshold, and the high and low frequency areas are clearly marked to ensure the accurate characterization of the frequency characteristics of different regions, optimize the concentration distribution analysis of boron 10 in each region, and generate a characteristic concentration map by fusing voxel frequency characteristics and concentration information to achieve structured expression of regional characteristic concentration, laying the foundation for subsequent concentration difference and frequency feature calculation. The regional concentration and frequency difference data are refined in an item-by-item operation manner to ensure accurate analysis of concentration distribution characteristics, deepen the understanding of the differences in boron 10 distribution characteristics in different regions, and significantly improve the accuracy of boron 10 distribution detection in the human body and the depth of information interpretation through multi-dimensional difference analysis and regional labeling technology, providing data support for the application optimization of boron 10. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a system flow chart of the present invention;

[0056] Figure 2 is a flow chart of the voxel decay frequency set in the present invention;

[0057] Figure 3 This is a flow chart of the initial distribution data of boron 10 in the present invention;

[0058] Figure 4 Flowchart of the spatial distribution model of boron 10 in the present invention;

[0059] Figure 5 A flow chart of the voxel boron 10 concentration structure diagram in the present invention;

[0060] Figure 6 A flow chart of a characteristic concentration map of a region in the present invention;

[0061] Figure 7 Flow chart of the boron concentration characteristic analysis results in the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0064] See also Figure 1 , the present invention provides a technical solution: a human body boron 10 analysis system based on BNCT includes;

[0065] The Boron-10 distribution detection module detects Boron-10 isotopes in the human body based on BNCT. By capturing the decay frequency, location parameters, and decay event time information of Boron-10 within a voxel, it obtains the voxel decay frequency set. Based on the voxel decay frequency set, it integrates the voxel frequency data and location information to obtain the initial Boron-10 distribution data.

[0066] The voxel reconstruction imaging module accumulates the decay frequencies within each voxel based on the initial distribution data of boron 10, associates and locates the voxel frequency data through spatial coordinates, processes the spatial position and frequency values of the voxels, and generates a boron 10 spatial distribution model. Based on the boron 10 spatial distribution model, the concentration distribution values at the voxel positions are mapped to obtain a voxel boron 10 concentration structure map.

[0067] The concentration region segmentation module is based on the voxel boron 10 concentration structure map, compares the concentration threshold with the voxel, extracts the frequency characteristics of the region, marks the high-frequency and low-frequency regions, and combines the voxel frequency characteristics and concentration information to generate the characteristic concentration map of the region;

[0068] The concentration correlation analysis module calculates the difference value and frequency characteristics based on the characteristic concentration map of the region and the concentration difference of boron 10 in the region. It calculates the concentration mean and frequency difference data in the region item by item, compares the concentration and distribution characteristics of the region in turn, and generates the boron concentration characteristic analysis results.

[0069] The voxel decay frequency set includes decay frequency, position parameters, and decay event time information. The initial distribution data of boron 10 includes the voxel decay frequency set and position information. The boron 10 spatial distribution model includes voxel spatial coordinates, frequency data, and spatial distribution position. The voxel boron 10 concentration structure diagram includes the concentration distribution value of the voxel position, spatial coordinate mapping, and concentration distribution structure. The characteristic concentration diagram of the region includes high-frequency areas, low-frequency areas, frequency characteristics, and concentration information. The boron concentration characteristic analysis results include concentration mean, frequency difference data, and distribution characteristics.

[0070] See also Figure 2 , the specific steps for obtaining the voxel decay frequency set are:

[0071] Based on BNCT, the boron-10 isotope in the human body is detected, and the frequency parameters, location parameters, and time parameters of the boron-10 isotope decay events are recorded. The frequency information of the decay events is used to sort and screen voxels with potential analytical value to establish an initial voxel frequency distribution set.

[0072] Detect and record the frequency parameters, position parameters and time parameters of each event in the decay event of boron-10 isotope, establish a frequency set of decay events within the voxel based on the collected decay event information, sort according to the frequency data of the decay events, classify the decay frequency values according to the fluctuation range and mark them as voxels to be analyzed, classify the frequency data in each category, and identify the distribution position of high-frequency decay events by analyzing the change trend and frequency peak position of the decay frequency values within the voxel. Further eliminate extreme frequency values that are not in a reasonable range, establish a data parameter set for the effective voxel area, and provide preliminary screening data for subsequent steps. It is necessary to comprehensively consider the decay event frequency, position change and time information to ensure that the preliminary screening of voxels is more in line with the actual distribution, and establish an initial parameter set of voxel decay frequency for voxel frequency calibration in subsequent steps.

[0073] The initial voxel frequency distribution set is calibrated to remove abnormal data that deviate from the distribution, and weighted correction is performed using position, time, and frequency weight parameters using the formula:

[0074] ,

[0075] Generate voxel decay frequency calibration results, where represents the voxel decay frequency calibration result, represents the initial voxel decay frequency, and are the spatial and temporal parameters of the voxel, Weight parameter for controlling decay frequency correction;

[0076] The benefit of the formula is that by introducing a collaborative correction method of spatial position Pa, time parameter Ta and weight parameter Wa, the accuracy of voxel decay frequency data is improved, the spatial and temporal resolution of the data is enhanced, and the frequency calibration is more consistent with the actual decay characteristics;

[0077] Set the initial voxel decay frequency data Fa to 5.4 Hz, the position parameter Pa to 3.2 mm, the time parameter Ta to 0.8 s, and the weight parameter Wa to 2.5. Substitute them into the formula to obtain the calibration frequency data The specific calculation process is as follows:

[0078] ,

[0079] Expand the calculation to get:

[0080] ,

[0081] The results show that the calibrated voxel decay frequency data is 3.69 Hz, which is used to represent the corrected frequency value and lays the data foundation for subsequent frequency data distribution analysis.

[0082] Combined with the distribution of voxel decay frequency calibration results, the matching degree of decay frequency distribution parameters is analyzed. According to the peak position of voxel decay, the decay frequency data that meets the standard distribution is determined to obtain the voxel decay frequency set.

[0083] Combined with the calibrated voxel decay frequency data generated in the previous step, a distribution analysis is performed based on the variation range of the spatial position and time parameters of the calibrated data. The calibrated frequency data is classified based on the numerical range of Ff and its rationality is further judged. The reference range of the distribution is set, and the voxel decay frequency data set is compared with other distributions in turn. The values that deviate from the standard distribution are identified and eliminated. The decay frequency peaks that meet the characteristics of the standard distribution are extracted, and the corresponding peak voxel positions are used to construct the final decay frequency distribution results. In this process, the peak positions in the distribution results need to be compared for rationality to ensure that the control frequency is typical and meets the detection requirements, and finally the voxel decay frequency data that meets the expected standards are obtained.

[0084] See also Figure 3 The specific steps for obtaining the initial distribution data of boron 10 are as follows:

[0085] Based on the voxel decay frequency set, the decay frequency and position parameter combination of each voxel is calculated, the frequency data of each voxel is called, and the frequency and position correlation matrix is generated;

[0086] Based on the voxel decay frequency set, the voxel decay frequency is first quantified. The frequency and spatial position data of each voxel are combined, and the distribution characteristics of the frequency of each voxel are extracted. The set operation of decay characteristics and spatial position is calculated, and each frequency result is combined with its corresponding voxel position parameter. The distribution of frequency data is distinguished by frequency amplitude and the position is marked to form the frequency amplitude information corresponding to the voxel position. The frequency information is integrated with the position coordinates to obtain the frequency and position information integration matrix.

[0087] Combine the frequency and position correlation matrix with the voxel position parameter, select the attenuation frequency and position parameter for multiplication, call the frequency weight set, and use the formula:

[0088] ,

[0089] Get the frequency and position weight results of the voxels, where represents the frequency and position weight results of the voxels, represents the frequency weight parameter, represents the voxel frequency, represents the voxel position parameter;

[0090] The benefit of the formula is that by introducing absolute value and square root operations, the calculation of frequency-position weights is smoother, while the stability and flexibility of the results are improved;

[0091] In the formula, the frequency weight parameter is the weighted value of the voxel frequency extracted from the frequency-position association matrix, and the position parameter is the position coordinate of each voxel, the frequency It is obtained by sampling the frequency of each voxel;

[0092] The calculation is performed using the following specific values: =2.5, =1.8, =3.2;

[0093] Substitute into the formula:

[0094] ,

[0095] ,

[0096] ,

[0097] ;

[0098] The results show that the frequency-position weight is 5.37, which will be used in subsequent voxel high-frequency integration processing to obtain the initial distribution data of boron 10.

[0099] Based on the frequency and position weight results of the voxels, the high-frequency weight position data of the voxels are called, and the distribution of boron 10 is identified by density weighting to obtain the initial distribution data of boron 10;

[0100] Based on the frequency-position weight results, the frequency and position parameters of the voxels are called, and the density-weighted calculation method is selected. The frequency values and position data of the voxels are combined one by one. The frequency proportion of the voxels is determined through successive iterations to obtain the voxel set that best conforms to the density distribution. The density-weighted synthesis is performed on the frequency parameters and position parameters of each voxel. After integrating the frequency proportion parameters in the density distribution, the voxel clusters with high frequency distribution are further screened out. The data of this cluster are summarized to obtain the initial distribution data of boron 10.

[0101] See also Figure 4 The specific steps for obtaining the spatial distribution model of boron 10 are as follows:

[0102] Based on the initial distribution data of boron 10, the decay frequency of each voxel is accumulated item by item, and the average frequency of the voxel frequency array is calculated according to the spatial position of the voxel to obtain the initial voxel frequency distribution result;

[0103] Based on the initial distribution data of boron 10, the decay frequency of each voxel is accumulated item by item to generate a decay frequency array containing all voxel frequencies. The elements in the array represent the frequency of each voxel. The data in the decay frequency array are numbered according to the spatial position of the voxel to achieve accurate correspondence between the frequency value and the spatial position of the specific voxel. The generated frequency array contains complete spatial frequency information, which is sorted according to a specific spatial position to form the initial voxel frequency distribution. The overall average value of the initial frequency distribution is calculated based on the decay frequency data, which represents the statistical mean of all voxel frequencies. Through this association between spatial position and frequency data, the decay frequency of each voxel is linked to the initial distribution of boron 10 in the area, and then the initial voxel frequency distribution result is obtained, providing basic data support for the subsequent calculation of frequency deviation.

[0104] Based on the initial voxel frequency distribution results, the voxel frequency array and the average frequency are used, using the formula:

[0105] ,

[0106] The frequency deviation of the voxel is calculated, where represents the frequency deviation of the voxel, is the total number of voxels, is the decay frequency of each voxel, is the average frequency, is the frequency weight parameter, is the frequency adjustment coefficient;

[0107] The benefit of this formula is that by introducing adjustment coefficients and weight parameters based on the original voxel frequency deviation calculation, the dynamic adjustment capability of the frequency deviation result is improved, making the frequency deviation calculation adaptable between different voxels.

[0108] For the convenience of calculation, the number of voxels is set =5, the decay frequency of each voxel They are 2, 3, 4, 5, and 6 respectively, with an average frequency of Set the frequency weight to 4.0 =0.8 and adjustment coefficient =0.2, the specific calculation process of the formula is as follows:

[0109] Calculate the adjustment result for each frequency:

[0110] ,

[0111] ;

[0112] Compute the sum of squares and take the square root:

[0113] ,

[0114] Expand and calculate:

[0115] ,

[0116] The results show that the voxel frequency deviation in the spatial distribution of boron 10 is 2.33, which represents the decay frequency deviation level in this area and is used for deviation data calling in subsequent distribution model construction.

[0117] Combined with the frequency deviation of the voxels, the frequency deviation value of each voxel is calibrated by analyzing the correspondence between the spatial coordinate set and the frequency deviation value, the frequency deviation is associated with the voxel spatial position, and the spatial coordinates are corresponded with the frequency deviation to generate the boron 10 spatial distribution model;

[0118] Combined with the initial Boron-10 distribution data, the researchers used the frequency deviation Δ of each voxel and associated the frequency deviation Δ with the voxel position in spatial coordinates, forming a bidirectional mapping relationship between voxel coordinates and frequency deviations. This model was then used to establish a comprehensive Boron-10 spatial distribution model. The model generation process involved quantifying the frequency deviation Δ of each voxel and mapping it to the corresponding spatial coordinate position, while also establishing a comprehensive distribution structure for the frequency deviation Δ of all voxels. This model accurately represents the distribution of Boron-10 at different spatial locations, allowing the decay frequency of each voxel to be fully reflected in the overall spatial distribution model.

[0119] See also Figure 5 The specific steps for obtaining the voxel boron 10 concentration structure map are as follows:

[0120] Mapping the initial concentration value of the voxel position according to the boron 10 spatial distribution model, calculating and mapping the concentration distribution parameter of each voxel position to the concentration distribution value, performing the mapping calculation on the voxel position, and generating an initial voxel position concentration map;

[0121] Voxel position concentration data is mapped based on the boron 10 spatial distribution model, and the concentration value is calculated for each voxel position using the concentration distribution parameters. During the calculation process, the voxel position coordinate value is used as input, and the corresponding concentration distribution function is called. The concentration value of each voxel is mapped according to the voxel position and distribution model. The concentration distribution value of each voxel is corrected based on the parameter structure of the model to form an initial concentration distribution map. The concentration value is then verified for each voxel position. The concentration values that obviously deviate from the distribution are eliminated through specific concentration matching rules to ensure the numerical integrity and accuracy of the initial voxel position concentration map, and thus an initial voxel position concentration map is generated.

[0122] Based on the initial voxel position concentration map, the concentration difference is refined and analyzed, and the concentration difference of adjacent voxels is calculated by difference weights. The concentration difference formula is applied:

[0123] ,

[0124] Calculate the difference between adjacent voxels and generate a concentration difference parameter matrix, where represents the difference between adjacent voxels, represents the concentration value at the voxel location, is the assigned concentration difference weight, is the mapping value of the voxel position, is the total number of pixels, is the average concentration distribution value;

[0125] The formula is useful because it takes the concentration value at the voxel location and distribution weights Combined with the above, the average difference offset is used to correct the concentration difference, which improves the adaptability and accuracy of the concentration difference calculation;

[0126] For each parameter in the formula, Represents the concentration value of each voxel position, which is obtained by model distribution mapping calculation, The correction weight representing the concentration distribution difference needs to be calculated and determined based on the concentration difference fluctuation range of each voxel and its surrounding voxels. is the concentration map value of the voxel position, which is directly generated by the spatial distribution model. is the total number of voxels, obtained through data statistics, is the average concentration distribution value, which is obtained by averaging the concentration values of all voxels in the initial mapping. Let’s use the numerical example:

[0127] set up is 0.8, is 1.2, is 0.6, is 0.5, is 1000, the calculation is as follows:

[0128] ;

[0129] This result shows that Pd is the relative concentration difference value of each voxel, which can be used in the subsequent voxel concentration difference structure optimization.

[0130] Using the concentration difference parameter matrix as the judgment standard, the voxel positions that do not meet the local concentration standard are eliminated and screened, and the voxel distribution is gradually screened and optimized to obtain the voxel boron 10 concentration structure map;

[0131] Using the concentration difference parameter matrix as the judgment criterion, the voxel position concentration difference is gradually screened, and conditional judgment is made on the concentration difference between each voxel position and the adjacent voxel. By setting a concentration difference threshold, voxel positions that do not meet the requirements are gradually eliminated, and voxel positions with difference values below the threshold are retained. If the voxel concentration exceeds the set concentration difference upper limit, the voxel concentration is adjusted. By correcting and screening one by one, a set of voxel positions that meet the standards is formed, and a voxel boron 10 concentration structure map is generated.

[0132] See also Figure 6 , the steps for obtaining the characteristic concentration map of the region are as follows:

[0133] Based on the voxel boron 10 concentration structure map, the concentration of each voxel is compared with the preset concentration threshold, the high-frequency and low-frequency voxel distribution areas are extracted, the voxel frequency characteristics of each area are calculated, and the areas are marked to obtain the high-frequency and low-frequency area extraction results;

[0134] Based on the voxel boron 10 concentration structure diagram, the concentration of each voxel is detected in turn and compared with the preset concentration threshold one by one. The voxel distribution information above or below the threshold is detected voxel by voxel. During the comparison process, the voxel concentration is higher or lower than the threshold, and it is marked as a high-frequency or low-frequency voxel area, forming a preliminary high-frequency and low-frequency voxel area classification. The frequency characteristics of each voxel are separately counted, and the voxel frequency characteristics of each area are recorded and calculated to determine the high-frequency or low-frequency attributes of each voxel area, and obtain the high-frequency and low-frequency area extraction results.

[0135] Based on the extraction results of high-frequency and low-frequency regions, the concentration characteristic values of voxels in the high-frequency and low-frequency regions are calculated using the formula:

[0136] ,

[0137] The concentration feature extraction results are obtained, where represents the concentration characteristic value, represents the voxel frequency in the high-frequency region, represents the voxel concentration threshold, is the low frequency region frequency, represents the difference in voxel distribution, is the adjustment factor;

[0138] The benefit of the formula is that it uses frequency characteristics and adjustment coefficients to effectively extract concentration characteristic values, achieving the accuracy of concentration differences between high-frequency and low-frequency regions;

[0139] The voxel frequency of the high-frequency area can be obtained by detecting the ratio of the number of voxels above the threshold to the total number of voxels in the area;

[0140] is the voxel concentration threshold, which is directly introduced into the calculation according to the preset threshold and is set to 40;

[0141] The frequency of the low-frequency region was calculated by the ratio of the number of voxels below the threshold to the total number of voxels and was set to 0.2;

[0142] is the voxel distribution difference, which is calculated based on the standard deviation of the voxel distribution in the region and is set to 5;

[0143] The adjustment coefficient is used to adjust the concentration difference and is dynamically set to 2 according to the fluctuation range of frequency characteristics and distribution differences;

[0144] Specific operation process:

[0145] Substitute into numerical calculations:

[0146] ;

[0147] Calculate the absolute difference: ;

[0148] The final result is: ;

[0149] The result shows that the concentration characteristic value is 33.13, which is the key basis for subsequent concentration feature extraction.

[0150] Combined with the concentration feature extraction results, the frequency characteristics and concentration feature data of the region are used for comparison and classification to generate a characteristic concentration map of the region;

[0151] Combined with the concentration feature extraction results, the frequency features and concentration feature data are compared and classified, and the high-frequency and low-frequency concentration feature values are input into the comparison system. The comparison operation is performed region by region based on the distribution of characteristic concentration in each region. The high- and low-frequency concentration division of the region is further verified by the concentration distribution of each voxel in the region. In this way, the characteristic concentration information of each region is confirmed, the high-frequency and low-frequency voxel concentrations in different regions are marked and classified, and the characteristic concentration map of the region is generated.

[0152] See also Figure 7 The specific steps for obtaining the boron concentration characteristic analysis results are as follows:

[0153] Based on the characteristic concentration map of the region, the Boron 10 concentration values in each region were compared item by item, the concentration data of the difference values were extracted, the concentration differences within the region were recorded, the mean of the difference items was calculated, and preliminary concentration difference data were obtained by comparing the frequency characteristics of each region;

[0154] Based on the characteristic concentration map of the region, the boron 10 concentration value in each region is detected one by one. The voxels with larger difference values are extracted from each region through the difference detection of concentration values, and a concentration difference data set is constructed for each region to facilitate separate analysis of high difference values in subsequent comparisons. In the specific operation, the boron 10 concentration value of each voxel is extracted in turn, and the difference between it and the mean concentration value in the region is calculated to ensure the accuracy of the difference value calculation. The concentration difference data are recorded and summarized one by one. By comparing the concentration difference values, the regions with obvious frequency characteristics are further screened out, and the high-frequency or low-frequency voxels are marked to form preliminary concentration difference data. By summarizing the concentration difference characteristic data of each region, the overall difference in the concentration characteristics of the voxels in each region is analyzed to establish the concentration distribution structure of each region, laying the foundation for further frequency characteristic comparison, obtaining preliminary concentration difference data, and laying a data foundation for the next step of frequency characteristic analysis.

[0155] Based on the preliminary concentration difference data, the concentration mean and frequency difference of each area were calculated region by region using the formula:

[0156] ,

[0157] The concentration mean and frequency difference characteristic data are calculated, where represents the concentration mean and frequency difference eigenvalues, is the concentration value of a voxel in the region, Indicates the average concentration value in the area. is the frequency value of the current area, is the adjustment coefficient of the mean concentration;

[0158] The benefit of the formula is that it introduces concentration difference and frequency characteristic adjustment coefficients during calculation, so that concentration difference and frequency characteristics are comprehensively balanced and the key characteristics of frequency and concentration differences within the region are effectively extracted.

[0159] It represents the concentration value of a single voxel detected in the area. The actual measured value is 45, which is obtained by voxel-by-voxel concentration detection.

[0160] is the mean value of the voxel concentration in the region, which is calculated based on the average value of the concentration values of all voxels in the region and is set to 42;

[0161] is the voxel frequency value in the region, which is 1.2 based on the ratio of the number of voxels in each region to the total number of voxels;

[0162] is the concentration mean adjustment coefficient, which is obtained by calculating the standard deviation of the concentration difference of voxels in each region and is set to 2;

[0163] The specific operation process is as follows:

[0164] Calculate the concentration difference term: ;

[0165] Calculate the frequency difference term: ;

[0166] Combine the two results: ;

[0167] The results show that the concentration frequency characteristic value is 7.77, which reflects the differences in concentration differences and frequency distribution characteristics within the region, and is used as the input characteristic value for subsequent boron concentration characteristic analysis.

[0168] Combine the concentration mean and frequency difference characteristic data, compare the frequency difference distribution of voxels in the region, calculate the concentration mean and frequency difference characteristic value of the voxels in each region, compare the concentration and frequency characteristic data of the differentiated regions item by item, and obtain the boron concentration characteristic analysis results;

[0169] Combined with the concentration-frequency characteristic results, the concentration and frequency distribution characteristic differences of the region are further analyzed. First, the frequency difference characteristic data is input into the analysis system to screen out the regional frequency difference values that meet the analysis conditions. The concentration distribution of each region is compared and the concentration frequency characteristics of each voxel are marked to ensure the comprehensiveness of the results. During the specific processing, the concentration distribution of the high-frequency area and the low-frequency area are extracted and summarized respectively by calling the concentration-frequency characteristic data, and the concentration characteristics of the high-frequency and low-frequency areas are marked according to the frequency difference to generate the boron concentration characteristic analysis results.

[0170] The BNCT-based human body boron 10 analysis method is performed based on the above-mentioned BNCT-based human body boron 10 analysis system, and includes the following steps:

[0171] S1: Based on BNCT, the boron-10 isotope in the human body is detected, capturing the decay frequency and location parameters of boron-10 within the voxel, recording the time information of the decay event, matching the voxel decay frequency data with the location parameters, integrating the voxel frequency data with the spatial coordinates, classifying and sorting each data item, and generating the initial boron-10 distribution data;

[0172] S2: Based on the initial distribution data of boron 10, the decay frequencies of the voxels are accumulated item by item, the frequency information and position are associated through spatial coordinates, the frequency values are aggregated and compared by position, and the frequency data of each voxel is bound to the spatial coordinates to generate the spatial distribution model of boron 10;

[0173] S3: Based on the B-10 spatial distribution model, the B-10 concentration parameters of the voxel position are mapped, the spatial coordinates of the voxel are matched with the concentration value, and the frequency value and coordinate of each position are correlated item by item by summarizing the concentration distribution data of the voxel to generate the B-10 concentration structure map of the voxel;

[0174] S4: Based on the voxel boron 10 concentration structure map, the voxel concentration value is compared with the preset concentration threshold one by one, the voxel frequency characteristics are divided, high-frequency and low-frequency voxels are extracted according to the frequency characteristics, the spatial distribution of the feature data is sorted out, and a regional feature concentration map is generated;

[0175] S5: Based on the regional characteristic concentration map, calculate the concentration value difference and frequency difference value within the region, compare the concentration mean and frequency change of each region item by item according to the regional division, process and summarize the concentration and distribution differences within the region, and generate the boron concentration characteristic analysis results.

[0176] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The human body boron 10 analysis system based on BNCT is characterized by: The system comprises: The Boron 10 distribution detection module detects the Boron 10 isotope in the human body based on BNCT. By capturing the decay frequency, location parameters and decay event time information of Boron 10 in the voxel, the module obtains the voxel decay frequency set. Based on the voxel decay frequency set, the module integrates the frequency data and location information of the voxel to obtain the initial Boron 10 distribution data. The voxel reconstruction imaging module accumulates the decay frequencies within each voxel item by item based on the initial B10 distribution data, associates and locates the voxel frequency data through spatial coordinates, processes the spatial position and frequency values of the voxels, generates a B10 spatial distribution model, and maps the concentration distribution values of the voxel positions according to the B10 spatial distribution model to obtain a voxel B10 concentration structure diagram; The concentration region division module compares the concentration threshold with the voxel based on the voxel boron 10 concentration structure map, extracts the frequency characteristics of the region, marks the high-frequency and low-frequency regions, and combines the voxel frequency characteristics and concentration information to generate a characteristic concentration map of the region; The concentration correlation analysis module calculates the difference value and frequency characteristics based on the characteristic concentration map of the region and the concentration difference of boron 10 in the region. It calculates the concentration mean and frequency difference data in the region one by one, compares the concentration and distribution characteristics of the region in turn, and generates the boron concentration characteristic analysis results. The voxel decay frequency set includes decay frequency, position parameters, and decay event time information; the boron 10 initial distribution data includes voxel decay frequency set and position information; the boron 10 spatial distribution model includes voxel spatial coordinates, frequency data, and spatial distribution position; the voxel boron 10 concentration structure diagram includes the concentration distribution value of the voxel position, spatial coordinate mapping, and concentration distribution structure; the characteristic concentration diagram of the region includes high-frequency region, low-frequency region, frequency characteristics, and concentration information; and the boron concentration characteristic analysis results include concentration mean, frequency difference data, and distribution characteristics.

2. The BNCT-based human body boron 10 analysis system according to claim 1, characterized in that: The steps for obtaining the voxel decay frequency set are specifically as follows: Based on BNCT, the boron-10 isotope in the human body is detected, and the frequency parameters, location parameters, and time parameters of the boron-10 isotope decay events are recorded. The frequency information of the decay events is used to sort and screen voxels with potential analytical value to establish an initial voxel frequency distribution set. The initial voxel frequency distribution set is calibrated to remove abnormal data that deviate from the distribution, and weighted correction is performed using position, time, and frequency weight parameters using the formula: ， Generate voxel decay frequency calibration results, where represents the voxel decay frequency calibration result, represents the initial voxel decay frequency, and are the spatial and temporal parameters of the voxel, Weight parameter for controlling decay frequency correction; Combined with the distribution of the voxel decay frequency calibration results, the matching degree of the decay frequency distribution parameters is analyzed, and based on the peak position of the voxel decay, the decay frequency data that meets the standard distribution is determined to obtain the voxel decay frequency set.

3. The human body boron 10 analysis system based on BNCT according to claim 2, characterized in that: The steps for obtaining the initial distribution data of boron 10 are specifically as follows: Based on the voxel decay frequency set, calculating the decay frequency and position parameter combination of each voxel, calling the frequency data of each voxel, and generating a frequency and position correlation matrix; The frequency and position correlation matrix is combined with the voxel position parameter, the attenuation frequency is selected and multiplied with the position parameter, and the frequency weight set is called using the formula: ， Get the frequency and position weight results of the voxels, where represents the frequency and position weight results of the voxels, represents the frequency weight parameter, represents the voxel frequency, represents the voxel position parameter; Based on the frequency and position weight results of the voxels, the high-frequency weight position data of the voxels are called, and the distribution of boron 10 is identified through density weighting to obtain the initial distribution data of boron 10.

4. The BNCT-based human body boron 10 analysis system according to claim 3, characterized in that: The steps for obtaining the boron 10 spatial distribution model are specifically as follows: Based on the initial boron 10 distribution data, the decay frequency of each voxel is accumulated item by item, and the average frequency of the voxel frequency array is calculated according to the spatial position of the voxel to obtain the initial voxel frequency distribution result; Based on the initial voxel frequency distribution result, the voxel frequency array and the average frequency are used to calculate the formula: ， The frequency deviation of the voxel is calculated, where represents the frequency deviation of the voxel, is the total number of voxels, is the decay frequency of each voxel, is the average frequency, is the frequency weight parameter, is the frequency adjustment coefficient; Combined with the frequency deviation of the voxel, the spatial coordinate set and the frequency deviation value are analyzed item by item, the frequency deviation value of each voxel is calibrated, the frequency deviation is associated with the voxel spatial position, and the spatial coordinates and the frequency deviation are operated in correspondence to generate a boron 10 spatial distribution model.

5. The BNCT-based human body boron 10 analysis system according to claim 4, characterized in that: The steps for obtaining the voxel boron 10 concentration structure map are specifically as follows: Mapping the initial concentration value of the voxel position according to the boron 10 spatial distribution model, calculating and mapping the concentration distribution parameter of each voxel position to the concentration distribution value, performing the mapping calculation on the voxel position, and generating an initial voxel position concentration map; Based on the initial voxel position concentration map, the concentration difference is refined and analyzed, and the concentration difference of adjacent voxels is calculated by difference weights, and the concentration difference formula is applied: ， Calculate the difference between adjacent voxels and generate a concentration difference parameter matrix, where represents the difference between adjacent voxels, represents the concentration value at the voxel location, is the assigned concentration difference weight, is the mapping value of the voxel position, is the total number of pixels, is the average concentration distribution value; The concentration difference parameter matrix is used as a judgment standard to eliminate and screen voxel positions that do not meet the local concentration standard, and the voxel distribution is gradually screened and optimized to obtain a voxel boron 10 concentration structure map.

6. The BNCT-based human body boron 10 analysis system according to claim 5, characterized in that: The steps for obtaining the characteristic concentration map of the region are specifically as follows: Based on the voxel boron 10 concentration structure diagram, the concentration of each voxel is compared with a preset concentration threshold, the high-frequency and low-frequency voxel distribution areas are extracted, the voxel frequency characteristics of each area are calculated, and the area is marked to obtain the high-frequency and low-frequency area extraction results; Based on the high-frequency and low-frequency region extraction results, the concentration characteristic values of the voxels in the high-frequency and low-frequency regions are calculated using the formula: ， The concentration feature extraction results are obtained, where represents the concentration characteristic value, represents the voxel frequency in the high-frequency region, represents the voxel concentration threshold, is the low frequency region frequency, represents the difference in voxel distribution, is the adjustment factor; Combined with the concentration feature extraction results, the frequency features and concentration feature data of the region are used for comparison and classification to generate a characteristic concentration map of the region.

7. The BNCT-based human body boron 10 analysis system according to claim 6, characterized in that: The steps for obtaining the boron concentration characteristic analysis results are specifically as follows: Based on the characteristic concentration map of the region, the Boron 10 concentration values in each region are compared item by item, the concentration data of the difference values are extracted, the concentration differences within the regions are recorded, the mean of the difference items is calculated, and preliminary concentration difference data are obtained by comparing the frequency characteristics of each region; Based on the preliminary concentration difference data, the concentration mean and frequency difference of each area were calculated area by area using the formula: ， The concentration mean and frequency difference characteristic data are calculated, where represents the concentration mean and frequency difference eigenvalues, is the concentration value of a voxel in the region, Indicates the average concentration value in the area. is the frequency value of the current area, is the adjustment coefficient of the mean concentration; Combined with the concentration mean and frequency difference characteristic data, the frequency difference distribution of voxels in the region is compared, the concentration mean and frequency difference characteristic value of the voxels in each region are calculated, and the concentration and frequency characteristic data of the differentiated regions are compared item by item to obtain the boron concentration characteristic analysis results.

8. A method for analyzing boron 10 in the human body based on BNCT, characterized in that: The BNCT-based human body boron 10 analysis system according to any one of claims 1 to 7 comprises the following steps: Based on BNCT, the boron-10 isotope in the human body is detected, the decay frequency and location parameters of boron-10 in the voxel are captured, the time information of the decay event is recorded, the decay frequency data of the voxel is matched with the location parameter, the frequency data of the voxel is integrated with the spatial coordinates, each data item is classified and sorted, and the initial distribution data of boron-10 is generated. Based on the initial distribution data of boron 10, the decay frequencies of the voxels are accumulated item by item, the frequency information and the position are associated through the spatial coordinates, the frequency values are aggregated and compared by position, the frequency data of each voxel is bound to the spatial coordinates, and a boron 10 spatial distribution model is generated; Based on the B10 spatial distribution model, the B10 concentration parameters of the voxel positions are mapped, the spatial coordinates of the voxels are matched with the concentration values, and the frequency values and coordinates of each position are correlated item by item by summarizing the concentration distribution data of the voxels to generate a voxel B10 concentration structure map; Based on the voxel boron 10 concentration structure map, the voxel concentration values are compared with the preset concentration thresholds one by one, the voxel frequency characteristics are divided, high-frequency and low-frequency voxels are extracted according to the frequency characteristics, the spatial distribution of the feature data is sorted out, and a regional feature concentration map is generated; Based on the regional characteristic concentration map, the concentration value difference and frequency difference values within the region are calculated, the concentration mean and frequency changes of each region are compared item by item according to the regional division, the concentration and distribution differences within the region are processed and summarized, and the boron concentration characteristic analysis results are generated.

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