High sensitivity x-ray imaging detector system based on photon counting

Abstract

The application discloses a high-sensitivity X-ray imaging detector system based on photon counting, relates to the technical field of X imaging detection, and comprises a photon detection module, a photon processing module, a photon space-time feature integration module and a photon imaging reconstruction module; the photon detection module receives X-ray photons, and collects photon correlation detection data; the photon processing module calculates effective amplitude of photon energy signals and signal-to-noise ratio, distinguishes strong photons from weak photons, calculates correlation degree of weak photons in combination with space-time information and processes the weak photons in a targeted manner; the photon space-time feature integration module carries out partition interpolation according to photon space distribution density, and calculates comprehensive space-time energy values of grid units according to time characteristics of photon counting signals; the photon imaging reconstruction module suppresses noise, calculates a material attenuation coefficient, maps a gray-scale image, enhances edges, and outputs a high-sensitivity X-ray imaging result; the system improves weak photon utilization rate and space-time feature integrity, and enhances imaging definition.

Description

Technical Field

[0001] This invention belongs to the field of X-ray imaging detection technology, specifically, it relates to a high-sensitivity X-ray imaging detector system based on photon counting. Background Technology

[0002] X-ray imaging technology is widely used in medical diagnosis, industrial non-destructive testing and other fields. Its core requirement is to obtain the fine structure or composition distribution inside the target area by accurately detecting X-ray photon information, so as to meet the high requirements of imaging sensitivity and resolution in scenarios such as the identification of small lesions and the detection of minor defects in workpieces.

[0003] Existing X-ray imaging detector systems have the following shortcomings that urgently need to be addressed: limited processing capabilities for low-intensity photon signals, leading to insufficient imaging sensitivity due to inadequate signal extraction, making it difficult to capture weak photon features; lack of targeted data supplementation methods when faced with uneven spatial distribution of photons, resulting in gaps in spatial distribution or distortion of local details, affecting the overall data integrity; and failure to effectively correlate the temporal variation characteristics and spatial distribution information of photons, leading to missing photon feature dimensions and an inability to fully reflect the dynamic photon distribution patterns of the target area. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art.

[0005] Therefore, this invention proposes a high-sensitivity X-ray imaging detector system based on photon counting, which can be achieved through the following technical solutions:

[0006] A high-sensitivity X-ray imaging detector system based on photon counting includes:

[0007] The photon detection module is used to receive X-ray photons from the target detection area and collect photon correlation detection data, including photon energy signals, photon position signals and photon counting signals;

[0008] The photon processing module, connected to the photon detection module, is used to receive photon correlation detection data, extract the effective amplitude of the photon energy signal, and calculate the signal-to-noise ratio by the ratio of the effective amplitude to the inherent noise. Based on the comparison results of the effective amplitude, signal-to-noise ratio and dynamic threshold, the photons are classified into weak photons and strong photons.

[0009] The photon spatiotemporal feature integration module is connected to the photon processing module. It is used to acquire weak photon energy signals, strong photon energy signals and their corresponding photon position signals and photon counting signals. Based on the obtained photon position coordinates, the spatial distribution density is calculated, and the corresponding spatial interpolation processing is applied to the divided density regions. At the same time, the photon time change rate is calculated based on the continuous time series of the obtained photon counting signals, and the fluctuation parameters are extracted to determine the periodic energy contribution value. Finally, the comprehensive spatiotemporal energy value of each grid cell in the target detection area is obtained.

[0010] The photon imaging reconstruction module is connected to the photon spatiotemporal feature integration module. It is used to receive the comprehensive spatiotemporal energy value of each grid cell. After noise suppression, attenuation coefficient calculation, grayscale mapping and edge enhancement processing, the final high-sensitivity X-ray imaging result is obtained.

[0011] Furthermore, the photon processing module performs spatiotemporal correlation analysis on weak photons as follows:

[0012] The location coordinates and timestamps of weak photons are obtained, and the spatial distance d and time interval Δt between any two weak photons are calculated. The number of photon pairs that satisfy d < spatial threshold and Δt < time threshold is counted, and the ratio of the number of photon pairs to the total number of weak photons is calculated as the correlation degree. When the correlation degree is higher than the correlation threshold, the energy signals of these weak photons are accumulated to form an accumulated energy signal. When the correlation degree is lower than the correlation threshold, the weak photon signals with the highest effective amplitude are selected and retained. The spatial threshold, time threshold, and correlation threshold are all preset values.

[0013] Furthermore, the specific method for dividing density regions based on spatial distribution density is as follows:

[0014] Regions with spatial distribution density higher than a first density value are classified as high-density regions; regions with spatial distribution density between the first and second density values ​​are classified as medium-density regions; and regions with spatial distribution density lower than the second density value are classified as low-density regions. Both the first and second density values ​​are preset spatial distribution density values, and the first density value is greater than the second density value.

[0015] Furthermore, a first-order interpolation algorithm is used for high-density areas; a second-order interpolation algorithm is used for medium-density areas; and a third-order interpolation algorithm is used for low-density areas.

[0016] Further, the first-order interpolation algorithm specifically involves: calculating the distance between adjacent photons, taking the point on the line connecting the position coordinates of adjacent photons as the interpolation point, and linearly weighting the effective amplitudes of the two adjacent photons according to the distance from the interpolation point to the two adjacent photons to obtain the effective amplitude of the interpolation point; the second-order interpolation algorithm specifically involves: using the coordinates of the interpolation point relative to the upper left known photon among the four known photons as normalized coordinates, constructing a second-order interpolation polynomial and substituting the position coordinates and effective amplitudes of the four known photons to solve for the coefficients, and substituting the normalized coordinates of the interpolation point into the polynomial to obtain the effective amplitude of the interpolation point; the third-order interpolation algorithm specifically involves: calculating the Euclidean distance from the interpolation point to each known photon and determining the weights, constructing a third-order interpolation polynomial and substituting the position coordinates, effective amplitudes, and weights of the eight known photons, solving for the coefficients using the weighted least squares method, and substituting the position coordinates of the interpolation point into the polynomial to obtain the effective amplitude of the interpolation point.

[0017] Furthermore, the method for determining the periodic energy contribution value is as follows:

[0018] The periodic units are divided according to the fluctuation period of the photon time change rate. The proportion of weak photons and strong photons in each periodic unit is calculated. The proportion of the proportion of the proportion of the proportion of the proportion of the fluctuation period is multiplied by the fluctuation amplitude of the corresponding fluctuation period to obtain the periodic energy contribution value.

[0019] Furthermore, the formula for calculating the comprehensive spatiotemporal energy value is as follows:

[0020] Et(p,q)=Ew(p,q)×α+Es(p,q)×β,

[0021] In the formula, α is the spatiotemporal weighting coefficient of weak photons, which is positively correlated with the area ratio of low-density regions; β is the spatiotemporal weighting coefficient of strong photons, which is positively correlated with the area ratio of high-density regions, and α+β=1; Ew(p,q) is the sum of the spatiotemporal weighted energy signals of all weak photons in each grid cell; and Es(p,q) is the sum of the spatiotemporal weighted energy signals of all strong photons in each grid cell.

[0022] Furthermore, the noise suppression includes:

[0023] Calculate the difference ΔEt(p,q) between the combined spatiotemporal energy values ​​of adjacent grid cells;

[0024] If ΔEt(p,q) is within the noise determination interval, the current grid cell value is corrected using the neighborhood mean.

[0025] If the value exceeds the noise threshold, the original value is retained.

[0026] Furthermore, the attenuation coefficient is calculated using the following formula:

[0027] ,

[0028] In the formula, k is the calibration coefficient. Et(p,q) is the reference energy value, and Et(p,q) is the comprehensive spatiotemporal energy value of the grid cell.

[0029] Furthermore, the edge enhancement process includes:

[0030] The spatial gradient G(p,q) of the attenuation coefficient of each grid cell is calculated. The spatial gradient is multiplied by the enhancement coefficient to obtain the enhanced edge signal, which is then superimposed onto the initial grayscale image.

[0031] The beneficial effects of this invention are:

[0032] (1) Effectively improve the utilization rate of weak photon signals. By selectively accumulating or filtering the spatiotemporal correlation of weak photon combinations, the loss of low-intensity photon signals can be avoided, thereby enhancing the integrity and effectiveness of the overall photon signal.

[0033] (2) Achieve precise fusion of photon spatiotemporal features, fill the distribution gaps by using differentiated interpolation algorithms according to photon spatial distribution density, and at the same time associate the temporal variation features of photon counting signals to provide more comprehensive spatiotemporal data support for imaging, thereby improving imaging resolution and information accuracy;

[0034] (3) Significantly optimize imaging quality by eliminating invalid noise components through noise suppression processing and improving the clarity of target structure boundaries by combining edge enhancement, so as to ensure that the output X-ray imaging results can clearly present the fine internal structure or defects of the target, and meet the high sensitivity requirements of medical diagnosis and industrial inspection. Attached Figure Description

[0035] The invention will now be further described with reference to the accompanying drawings.

[0036] Figure 1 This is a schematic diagram of the overall system architecture of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see Figure 1 This invention provides a high-sensitivity X-ray imaging detector system based on photon counting;

[0039] As an embodiment of the present invention, the system specifically includes:

[0040] The photon detection module is used to receive X-ray photons from the target detection area and collect photon correlation detection data, including photon energy signals, photon position signals and photon counting signals;

[0041] The photon processing module, connected to the photon detection module, is used to receive photon correlation detection data, extract the effective amplitude of the photon energy signal, and calculate the signal-to-noise ratio by the ratio of the effective amplitude to the inherent noise. Based on the comparison results of the effective amplitude, signal-to-noise ratio and dynamic threshold, the photons are classified into weak photons and strong photons.

[0042] The photon spatiotemporal feature integration module is connected to the photon processing module. It is used to acquire weak photon energy signals, strong photon energy signals and their corresponding photon position signals and photon counting signals. Based on the obtained photon position coordinates, the spatial distribution density is calculated, and the corresponding spatial interpolation processing is applied to the divided density regions. At the same time, the photon time change rate is calculated based on the continuous time series of the obtained photon counting signals, and the fluctuation parameters are extracted to determine the periodic energy contribution value. Finally, the comprehensive spatiotemporal energy value of each grid cell in the target detection area is obtained.

[0043] The photon imaging reconstruction module is connected to the photon spatiotemporal feature integration module. It is used to receive the comprehensive spatiotemporal energy value of each grid cell. After noise suppression, attenuation coefficient calculation, grayscale mapping and edge enhancement processing, the final high-sensitivity X-ray imaging result is obtained.

[0044] As a second embodiment of the present invention, based on the first embodiment:

[0045] The photon detection module is used to receive X-ray photons from the target detection area and collect core data directly related to the photon properties, which is referred to as photon-related detection data.

[0046] The target detection area is a specific spatial region that needs to obtain internal structure or composition information through X-ray imaging. This region has a clear detection boundary, which can be determined according to the application scenario, such as the spatial range corresponding to the part of the human body to be inspected in medical imaging, or the spatial range corresponding to the workpiece to be inspected in industrial inspection.

[0047] The photon correlation detection data includes photon energy signals, photon position signals, and photon count signals;

[0048] Among them, the photon energy signal refers to the electrical signal that is linearly related to the energy of the incident X-ray photon. Its amplitude increases with the increase of photon energy and is used to reflect the energy properties of a single X-ray photon.

[0049] Photon position signal refers to an electrical signal that corresponds one-to-one with the incident position of an X-ray photon within the target detection area, and is used to locate the specific incident point of a single X-ray photon within the target detection area;

[0050] A photon counting signal is an electrical signal that is directly proportional to the number of X-ray photons received by the target detection area per unit time. It is used to count the total number of photons during the detection period.

[0051] The photon processing module, connected to the photon detection module, is used to receive photon correlation detection data and analyze and process it. The specific processing procedure is as follows:

[0052] The effective amplitude and inherent noise of the photon energy signal within the target detection area are obtained, and the ratio of the effective amplitude to the inherent noise is marked as the signal-to-noise ratio.

[0053] If the effective amplitude is lower than the dynamic threshold and the signal-to-noise ratio is within the specified range, the photon energy signal is marked as a weak photon energy signal and the corresponding photon is marked as a weak photon.

[0054] A photon pair can be formed between any two photons;

[0055] If the effective amplitude is higher than the dynamic threshold, the photon energy signal is marked as a strong photon energy signal, and the corresponding photon is marked as a strong photon.

[0056] The dynamic threshold is a preset amplitude that is dynamically adjusted based on the inherent noise level and signal characteristics of the photon detection module. It is used to compare with the effective amplitude to distinguish the strength of the photon energy signal.

[0057] The specified range is a preset allowable interval for the signal-to-noise ratio, used to determine whether the signal-to-noise ratio of the photon energy signal meets the labeling condition for weak photons.

[0058] For weak photon energy signals, the corresponding photon position signals and timestamp information are obtained. The position coordinates of the weak photon can be obtained based on the photon position signals. The spatial distance d and time interval Δt between any two weak photons are calculated, where the spatial distance d is the Euclidean distance between the position coordinates of the two photons, and the time interval Δt is the absolute difference between the timestamps of the two photons.

[0059] Count the number of photon pairs among all weak photons whose spatial distance d is less than the spatial threshold and whose time interval Δt is less than the time threshold, and mark the ratio of this number to the total number of weak photons as the correlation degree.

[0060] When the correlation degree is higher than the correlation threshold, it is determined that the weak photons in the region have spatiotemporal correlation. The energy signals of these weak photons are accumulated to obtain the accumulated energy signal, the amplitude of which is the sum of the effective amplitudes of the energy signals of each weak photon.

[0061] When the correlation is lower than the correlation threshold, select a few weak photons with the highest effective amplitude of the energy signal, retain their energy signals, and discard the energy signals of the remaining weak photons.

[0062] The correlation threshold is the threshold for determining the correlation degree, used to distinguish whether a weak photon population has significant spatiotemporal correlation.

[0063] As a third embodiment of the present invention, based on the first embodiment:

[0064] The photon spatiotemporal feature integration module, connected to the photon processing module, is used to acquire weak photon energy signals, strong photon energy signals, and their corresponding photon position signals and photon counting signals from the photon processing module. It then processes and analyzes the spatial distribution characteristics of the photon position signals and the temporal variation characteristics of the photon counting signals. The specific process is as follows:

[0065] The position coordinates of all weak and strong photons are obtained, and the spatial distribution density of the position coordinates of each photon in the two-dimensional coordinate system of the target detection area is calculated. The spatial distribution density is the number of photons contained in a unit area, and its value is obtained by statistically analyzing the ratio of the total number of photons in a region with the position as the center and a preset area to the area of ​​the region.

[0066] Based on the magnitude of spatial distribution density, the target detection area is divided into high-density area, medium-density area and low-density area. The high-density area is the area with spatial distribution density higher than the first density value, the medium-density area is the area with spatial distribution density between the first density value and the second density value, and the low-density area is the area with spatial distribution density lower than the second density value. The first density value is greater than the second density value, and the first density value and the second density value are preset spatial distribution density values.

[0067] Spatial interpolation is performed on the photon position signals in different density regions. Different interpolation algorithms are used for each spatial interpolation. By combining the known photon position coordinates and energy signal amplitude, the photon energy signals in areas where the photon position was not directly detected are supplemented, so as to achieve differentiated data supplementation for different density regions.

[0068] In the high-density region, a first-order interpolation algorithm is used to obtain the photon position signals and photon energy signals of adjacent known photons in the high-density region. The position coordinates of the photons in the position signals and the effective amplitudes in the photon energy signals are extracted. Then, the distance between any two adjacent photons is calculated. Any point on the line connecting the position coordinates of these two photons is taken as the interpolation point. Then, the effective amplitudes of the two photons are linearly weighted according to the distance from the interpolation point to the two photons. The result is the effective amplitude of the interpolation point. This avoids the distortion of detail smoothing caused by higher-order calculations.

[0069] In the medium-density region, a second-order interpolation algorithm is used to obtain the photon position signals and photon energy signals of four adjacent known photons around the interpolation point in the medium-density region. The position coordinates of the photons in the photon position signals and the effective amplitude in the photon energy signals are extracted. The coordinates of the interpolation point relative to the upper left known photon among the four known photons are normalized coordinates, where the horizontal and vertical coordinates of the normalized coordinates are the distance between the interpolation point and the upper left known photon in the corresponding direction, and the ratio of the distance between adjacent known photons in that direction. A second-order interpolation polynomial is constructed and the position coordinates and effective amplitude of the four known photons are substituted to solve for the polynomial coefficients. The normalized coordinates of the interpolation point are then substituted into the second-order interpolation polynomial after solving for the polynomial coefficients to calculate the effective amplitude of the interpolation point. This balances the transition of details with the overall smoothness.

[0070] In the low-density region, a third-order interpolation algorithm is used to obtain the photon position signals and photon energy signals of the eight adjacent known photons in a 3×3 matrix around the interpolation point in the low-density region. The position coordinates of the photons in the position signals and the effective amplitudes in the photon energy signals are extracted. The Euclidean distance from the interpolation point to each known photon is calculated, and the weights are determined based on the distances. A third-order interpolation polynomial is constructed, and the position coordinates, effective amplitudes, and corresponding weights of the eight known photons are substituted into the polynomial. The polynomial coefficients are solved using the weighted least squares method. The position coordinates of the interpolation point are substituted into the third-order interpolation polynomial after solving the polynomial coefficients, and the result is the effective amplitude of the interpolation point. This fills the distribution gaps and improves continuity.

[0071] Then, a continuous time series of the photon counting signal is obtained, which contains the cumulative difference in the number of photons at each sampling time. The photon increment per unit time is obtained, and the ratio of the photon increment to the corresponding time interval is marked as the photon time change rate.

[0072] The fluctuation characteristics of the photon time change rate were analyzed, and the fluctuation period and fluctuation amplitude were extracted. The fluctuation period is the time interval from the previous peak to the next peak of the photon time change rate, and the fluctuation amplitude is the difference between the maximum and minimum values ​​of the photon time change rate within the fluctuation period.

[0073] The entire detection period is divided into several periodic units based on the fluctuation period. Each periodic unit corresponds to a complete fluctuation period. The ratio of weak photon energy signals to strong photon energy signals in each periodic unit is counted, that is, the ratio of the number of weak photons to the total number of photons and the ratio of the number of strong photons to the total number of photons.

[0074] The periodic energy contribution value is obtained by multiplying the proportion of different periodic units by the fluctuation amplitude of the corresponding period, which is used to characterize the contribution of weak photons and strong photons in each periodic unit to the overall energy distribution.

[0075] The spatially interpolated photon position signal is correlated with the periodic energy contribution value, that is, each spatial position point corresponds to an energy contribution coefficient that varies with the periodic unit.

[0076] The effective amplitudes of the weak photon energy signal and the strong photon energy signal are multiplied by their respective energy contribution coefficients to obtain the spatiotemporally weighted energy signal.

[0077] The target detection area is divided into grids, with the coordinates of each grid cell denoted as (p,q). The sum of the spatiotemporal weighted energy signals of all weak photons within each grid cell is denoted as Ew(p,q), and the sum of the spatiotemporal weighted energy signals of all strong photons within each grid cell is denoted as Es(p,q).

[0078] Through the formula:

[0079] Et(p,q)=Ew(p,q)×α+Es(p,q)×β,

[0080] The comprehensive spatiotemporal energy value Et(p,q) of each grid cell is calculated, where α is the spatiotemporal weighting coefficient for weak photons, which is positively correlated with the area ratio of low-density regions, and β is the spatiotemporal weighting coefficient for strong photons, which is positively correlated with the area ratio of high-density regions, and α+β=1.

[0081] As a fourth embodiment of the present invention, based on the first embodiment:

[0082] The photon imaging reconstruction module, connected to the photon spatiotemporal feature integration module, receives the integrated spatiotemporal energy value Et(p,q) of each grid cell output by the module and performs high-sensitivity X-ray image reconstruction based on Et(p,q). The specific process is as follows:

[0083] After obtaining the combined spatiotemporal energy value Et(p,q) of all grid cells, the difference between adjacent grid cells is calculated using the following formula:

[0084] ΔEt(p,q)=Et(p,q)-Et(p±1,q±1),

[0085] In the formula, (p±1,q±1) are the coordinates of the adjacent grid cells of the current grid cell;

[0086] If ΔEt(p,q) is within the noise determination interval, the corresponding difference is determined to be caused by noise, and the mean of Et(p,q) of the current grid cell is used to correct Et(p,q);

[0087] If ΔEt(p,q) exceeds the noise judgment interval, the corresponding difference is determined to reflect the structural differences in the target detection area. The original value of Et(p,q) of the current grid cell is retained to complete the noise suppression of the original imaging data.

[0088] The noise determination range is a numerical range determined based on the inherent noise level of the photon detection module and the spatial interpolation error, used to distinguish between noise components and effective structural components in ΔEt(p,q).

[0089] According to the formula:

[0090] ,

[0091] The attenuation coefficient of the material corresponding to the grid cell (p,q) is calculated and denoted as μ(p,q), which is used to characterize the absorption capacity of the material for X-rays within the target detection area; where k is the calibration coefficient, the value of which is determined by the known attenuation coefficient of the standard material and the experimentally measured attenuation coefficient. Et(p,q) is calculated;

[0092] The range of values ​​for the attenuation coefficient μ(p,q) is linearly mapped to the range of grayscale values ​​in the image, thus forming a preliminary X-ray grayscale image.

[0093] Edge enhancement processing is performed on the initial X-ray grayscale image.

[0094] According to the formula:

[0095] ,

[0096] The spatial gradient of the attenuation coefficient μ(p,q) of each grid cell is calculated and denoted as G(p,q), where, , are the partial derivatives of μ(p,q) in the x and y directions, respectively;

[0097] The enhanced edge signal, denoted as G'(p,q), is obtained by multiplying the spatial gradient G(p,q) by the enhancement coefficient γ. This enhanced edge signal is then superimposed onto the initial X-ray grayscale image to improve the clarity of the target structure edges in the image. The enhancement coefficient γ is determined based on the spatial resolution requirements of the target detection area; the higher the resolution requirement, the larger the value of γ.

[0098] After the above processing, the final high-sensitivity X-ray imaging result is output.

[0099] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A high-sensitivity X-ray imaging detector system based on photon counting, characterized in that, include: The photon detection module is used to receive X-ray photons from the target detection area and collect photon correlation detection data, including photon energy signals, photon position signals and photon counting signals; The photon processing module, connected to the photon detection module, is used to receive photon correlation detection data, extract the effective amplitude of the photon energy signal, and calculate the signal-to-noise ratio by the ratio of the effective amplitude to the inherent noise. Based on the comparison results of the effective amplitude, signal-to-noise ratio and dynamic threshold, the photons are classified into weak photons and strong photons. The photon spatiotemporal feature integration module is connected to the photon processing module. It is used to acquire weak photon energy signals, strong photon energy signals and their corresponding photon position signals and photon counting signals. Based on the obtained photon position coordinates, the spatial distribution density is calculated, and the corresponding spatial interpolation processing is applied to the divided density regions. At the same time, the photon time change rate is calculated based on the continuous time series of the obtained photon counting signals, and the fluctuation parameters are extracted to determine the periodic energy contribution value. Finally, the comprehensive spatiotemporal energy value of each grid cell in the target detection area is obtained. The photon imaging reconstruction module is connected to the photon spatiotemporal feature integration module. It is used to receive the comprehensive spatiotemporal energy value of each grid cell. After noise suppression, attenuation coefficient calculation, grayscale mapping and edge enhancement processing, the final high-sensitivity X-ray imaging result is obtained. The specific method for dividing density regions based on spatial distribution density is as follows: Regions with spatial distribution density higher than a first density value are classified as high-density regions; regions with spatial distribution density between the first and second density values ​​are classified as medium-density regions; regions with spatial distribution density lower than the second density value are classified as low-density regions; both the first and second density values ​​are preset spatial distribution density values, and the first density value is greater than the second density value. First-order interpolation algorithm is used for high-density areas; second-order interpolation algorithm is used for medium-density areas; and third-order interpolation algorithm is used for low-density areas. The first-order interpolation algorithm is as follows: calculate the distance between adjacent photons, take the point on the line connecting the position coordinates of adjacent photons as the interpolation point, and linearly weight the effective amplitude of the two adjacent photons according to the distance from the interpolation point to the two adjacent photons to obtain the effective amplitude of the interpolation point; the second-order interpolation algorithm is as follows: take the coordinates of the interpolation point relative to the upper left known photon among the four known photons as normalized coordinates, construct a second-order interpolation polynomial and substitute the position coordinates and effective amplitude of the four known photons to solve for the coefficients, and substitute the normalized coordinates of the interpolation point into the polynomial to obtain the effective amplitude of the interpolation point; the third-order interpolation algorithm is as follows: calculate the Euclidean distance from the interpolation point to each known photon and determine the weight, construct a third-order interpolation polynomial and substitute the position coordinates, effective amplitude and weight of the eight known photons, solve for the coefficients by weighted least squares method, and substitute the position coordinates of the interpolation point into the polynomial to obtain the effective amplitude of the interpolation point. The method for determining the periodic energy contribution value is as follows: The periodic units are divided according to the fluctuation period of the photon time change rate. The proportion of weak photons and strong photons in each periodic unit is calculated. The proportion of the number of photons is multiplied by the fluctuation amplitude of the corresponding fluctuation period to obtain the periodic energy contribution value. The formula for calculating the comprehensive spatiotemporal energy value is: Et(p,q)=Ew(p,q)×α+Es(p,q)×β, In the formula, α is the spatiotemporal weighting coefficient of weak photons, which is positively correlated with the area ratio of low-density regions, β is the spatiotemporal weighting coefficient of strong photons, which is positively correlated with the area ratio of high-density regions, and α+β=1, Ew(p,q) is the sum of the spatiotemporal weighted energy signals of all weak photons in each grid cell, and Es(p,q) is the sum of the spatiotemporal weighted energy signals of all strong photons in each grid cell.

2. The high-sensitivity X-ray imaging detector system based on photon counting according to claim 1, characterized in that, The process of spatiotemporal correlation analysis of weak photons by the photon processing module is as follows: The location coordinates and timestamps of weak photons are obtained, and the spatial distance d and time interval Δt between any two weak photons are calculated. The number of photon pairs that satisfy d < spatial threshold and Δt < time threshold is counted, and the ratio of the number of photon pairs to the total number of weak photons is calculated as the correlation degree. When the correlation degree is higher than the correlation threshold, the energy signals of these weak photons are accumulated to form an accumulated energy signal. When the correlation degree is lower than the correlation threshold, the weak photon signals with the highest effective amplitude are selected and retained. The spatial threshold, time threshold, and correlation threshold are all preset values.

3. The high-sensitivity X-ray imaging detector system based on photon counting according to claim 1, characterized in that, The noise suppression process includes: Calculate the difference ΔEt(p,q) between the combined spatiotemporal energy values ​​of adjacent grid cells; If ΔEt(p,q) is within the noise determination interval, the current grid cell value is corrected using the neighborhood mean. If the value exceeds the noise threshold, the original value is retained.

4. The high-sensitivity X-ray imaging detector system based on photon counting according to claim 1, characterized in that, The attenuation coefficient is calculated using the following formula: μ(p,q)=k×ln(Et0 / Et(p,q)), In the formula, k is the calibration coefficient, which is calculated from the known attenuation coefficient of the standard material and the experimentally measured Et0 and Et(p,q). Et(p,q) is the comprehensive spatiotemporal energy value of the grid cell.

5. The high-sensitivity X-ray imaging detector system based on photon counting according to claim 1, characterized in that, The edge enhancement process includes: The spatial gradient G(p,q) of the attenuation coefficient of each grid cell is calculated. The spatial gradient is multiplied by the enhancement coefficient to obtain the enhanced edge signal. The enhanced edge signal is then superimposed onto the initial grayscale image. The enhancement coefficient γ is determined based on the spatial resolution requirements of the target detection area; the higher the resolution requirement, the larger the value of γ.

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