Waveform analysis and example reconstruction algorithm for white light neutron resonance imaging

Abstract

The invention relates to the technical field of neutron imaging and detection, in particular to a data processing method for a neutron imaging detector based on a micro channel plate (MCP), and more particularly relates to a waveform analysis and example reconstruction algorithm for white light neutron resonance imaging. Comprising a waveform analysis step and a case reconstruction step, in the waveform analysis step, an advanced waveform recognition technology is adopted, off-line analysis is carried out on signal waveforms on an anode plate of the neutron imaging detector, and the signal waveform of each electron group is accurately recognized; in the case reconstruction step, the signals are grouped and paired based on a waveform analysis result, and an accurate position reconstruction model is constructed to obtain an accurate coordinate of an electron group hit position; the method not only improves the imaging precision and the counting rate, but also optimizes the data processing flow, adapts to the condition of high counting rate, improves the overall performance of the system, expands the application field, and provides powerful support for the development and application of the neutron imaging technology.

Description

Technical Field

[0001] The present invention relates to the field of neutron imaging and detection technology, in particular to a data processing method for a neutron imaging detector based on microchannel plates (MCP), and more specifically to a waveform analysis and event reconstruction algorithm for white light neutron resonance imaging. Background Art

[0002] As an advanced nondestructive testing technology, neutron resonance imaging technology has shown great application potential in many fields such as material science research, defect detection and archaeological identification. This technology can deeply detect the radionuclide composition and its position distribution inside the sample, providing strong support for research in related fields. However, the development of neutron resonance imaging technology has been limited by the performance of neutron sources and detectors, especially under high counting rate conditions. The problems of signal overlap and difficulty in separation are particularly prominent, which seriously affects the accuracy and efficiency of imaging.

[0003] At present, boron-doped MCP, as the core component of neutron imaging detectors, has become an ideal choice for wide-spectrum neutron resonance imaging because of its significant advantages of high counting rate, high time resolution and high position resolution, combined with the unique energy spectrum characteristics of the reverse-angle white light ray device of the China Spallation Neutron Source. However, the existing technical system still lacks a complete and efficient set of waveform analysis algorithms and event reconstruction algorithms to cope with the complex challenges of signal accumulation and event reconstruction under high counting rates. Therefore, the development of an algorithm that can effectively separate signals and accurately reconstruct events under high counting rate conditions is of great significance to improving the accuracy of neutron imaging and promoting the development of neutron resonance imaging technology. Summary of the invention

[0004] In order to solve the above problems, the present invention aims to provide a data processing method for a neutron imaging detector based on microchannel plates (MCP), and more specifically, a waveform analysis and event reconstruction algorithm for white light neutron resonance imaging.

[0005] The technical solution adopted by the present invention is: a waveform analysis and event reconstruction algorithm for white light neutron resonance imaging, including a waveform analysis step and an event reconstruction step, wherein the waveform analysis step adopts advanced waveform recognition technology to perform offline analysis on the signal waveform on the anode plate of the neutron imaging detector to accurately identify the signal waveform of each electron group; the event reconstruction step groups and pairs the signals based on the waveform analysis results, and constructs an accurate position reconstruction model to obtain the precise coordinates of the electron group hitting position.

[0006] The waveform analysis step specifically includes preprocessing, signal fitting and signal screening sub-steps, wherein the preprocessing is used for baseline subtraction and peak finding, the signal fitting uses a predefined function to perform waveform fitting, and improves the accuracy of signal extraction by combining multi-peak and fitting with residual quadratic fitting, and the signal screening uses the parameters obtained by fitting for further screening.

[0007] The signal grouping in the case reconstruction step is to classify the signals that are adjacent in time and have similar waveforms into the same group according to the waveform recognition results, as signals of the same electronic group; the pairing processing uses a pairing algorithm based on time difference and waveform similarity to pair each group of signals and determine the pairing relationship between the signals.

[0008] The position reconstruction model in the case reconstruction step is constructed based on the geometric structure of the neutron imaging detector and the layout of the anode plate. The model can convert the signal position on the anode plate into a two-dimensional spatial coordinate in the detector, thereby achieving accurate position reconstruction.

[0009] It also includes optimization of the waveform analysis step and the event reconstruction step to improve the accuracy and efficiency of the algorithm and adapt to the needs of neutron imaging at higher counting rates.

[0010] It also includes a neutron imaging system, which includes a microchannel plate detector, an anode plate, and an electronic readout system, and is used to achieve neutron imaging at a high counting rate.

[0011] The detector is an MCP detector, which has the characteristics of high counting rate, high time resolution and high position resolution. Combined with the energy spectrum characteristics of the reverse-angle white light ray device, it is suitable for white light neutron resonance imaging with a wide energy spectrum.

[0012] The electronic readout system includes a front-end amplification module, a waveform digitization module and a data aggregation module, which are used to amplify, digitize and aggregate the signals output by the MCP detector to provide them to the waveform analysis and case reconstruction algorithm for further processing.

[0013] This technical solution also includes the overall integrated design of the neutron imaging system, integrating the MCP detector, anode plate, electronic readout system, waveform analysis and event reconstruction algorithm into a whole, so as to reduce the connection between components and the loss of signal transmission, and improve the overall performance and stability of the system.

[0014] This technical solution also includes expanding the application field, applying the neutron imaging system to safety inspection, environmental monitoring and other fields, and providing accurate and efficient detection means.

[0015] The beneficial effects of the present invention are as follows: the waveform analysis and event reconstruction algorithm for white light neutron resonance imaging of the present invention not only improves the imaging accuracy and counting rate, but also optimizes the data processing flow, adapts to high counting rate conditions, improves the overall performance of the system, and expands the application field. This innovative achievement will provide strong support for the development and application of neutron imaging technology. Compared with the existing technology, it has significant beneficial effects, which are specifically manifested in the following aspects:

[0016] (1) Improving imaging accuracy and counting rate: The present invention uses advanced waveform recognition technology to accurately analyze the signal waveform on the anode plate, effectively solving the problem of signal overlap and difficulty in separation under high counting rates; through signal grouping, pairing processing and position reconstruction algorithm, the present invention can accurately identify and reconstruct the impact position of each electron group, thereby significantly improving the counting rate and imaging accuracy of neutron imaging; this improvement is crucial to the high-precision requirements of wide-spectrum neutron resonance imaging, and helps to obtain clearer and more accurate imaging results.

[0017] (ii) Optimizing data processing flow: The waveform analysis algorithm of the present invention adopts the steps of preprocessing, signal fitting and signal screening, and improves the accuracy of signal extraction by combining multi-peak fitting with residual quadratic fitting. This refined data processing flow not only reduces noise interference, but also improves the efficiency and stability of signal processing, providing a reliable foundation for subsequent case reconstruction.

[0018] (III) Adaptability to high counting rate conditions: Under high counting rate conditions, traditional neutron imaging detectors often face the challenges of signal accumulation and difficulty in separation. The present invention effectively solves this problem by constructing a new event reconstruction algorithm to group and pair the anode strip signals. This innovation enables the present invention to maintain excellent imaging performance under high counting rate conditions, meeting the high requirements in practical applications.

[0019] (IV) Improving the overall performance of the system: The present invention integrates the MCP detector, anode plate, electronic readout system, waveform analysis and event reconstruction algorithm into a complete neutron imaging system; by optimizing the connection method and signal transmission method between the various components, signal loss and interference are reduced, and the overall performance and stability of the system are improved; this integrated design makes the present invention more convenient and efficient in practical applications.

[0020] (V) Expanding application areas: The neutron imaging system of the present invention is not only applicable to the fields of material science research, defect detection and archaeological identification, but can also be expanded to more fields such as safety inspection and environmental monitoring. This wide applicability provides accurate and efficient means for non-destructive testing in various fields, which helps to promote the progress and development of related technologies.

[0021] (VI) Promoting technological innovation and development: The proposal and implementation of the present invention not only solves the key problems in neutron imaging technology, but also provides strong support for the continuous innovation and development in this field. By continuously optimizing and improving system performance, the present invention will be able to adapt to higher imaging requirements and promote the continuous advancement and application expansion of neutron imaging technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the MCP detector structure in the present invention.

[0023] Figure 2 It is an example of the waveform analysis and event reconstruction algorithm in the present invention.

[0024] Figure 3 This is a schematic diagram for comparing the results after reconstruction. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described in detail below in conjunction with specific embodiments. These embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0026] Example 1: Application to China Spallation Neutron Source

[0027] The specific implementation results of the waveform analysis algorithm are as follows Figure 1-Figure 3 As shown, Figure 1 The specific structure of the MCP detector is demonstrated, including key components such as the microchannel plate and anode plate, as well as their connection methods and signal transmission paths; Figure 2 It shows the key parts such as waveform analysis and case reconstruction algorithm, as well as the relationship between them; this diagram clearly shows the process of the algorithm; Figure 3 The comparison results of neutron beam spot distribution before and after using the event reconstruction algorithm were compared. After using the event reconstruction algorithm, the imaging effect was significantly improved; in terms of counting: the number of reconstructed events in the new version is about 13% higher than the original number of reconstructed events, which is due to the new event position reconstruction code that can separate and reconstruct high-energy events that were previously inseparable; in terms of event distribution: compared with the previous event reconstruction imaging, the event distribution of the new version of event reconstruction imaging is more uniform and more in line with the actual beam spot distribution.

[0028] The specific implementation of the waveform analysis algorithm mainly includes the following steps:

[0029] Data preprocessing: First, the original signal obtained from the MCP detector in the reverse-angle white-light device of the China Spallation Neutron Source is subjected to baseline subtraction and peak-finding processing; this step provides accurate basic data for subsequent waveform fitting by removing baseline drift and noise in the signal and locating the signal peak.

[0030] Signal fitting: Use predefined functions (such as Gaussian function, exponential decay function, etc.) to fit the waveform of the preprocessed signal; use optimization algorithms (such as least squares method, genetic algorithm, etc.) to fit the parameters of each waveform (such as amplitude, time offset, attenuation constant, etc.); in particular, for complex waveforms, use multi-peak fitting combined with residual quadratic fitting to represent the waveform as a superposition of multiple basic waveforms, so as to more accurately identify and separate accumulated signals.

[0031] Signal screening: According to the fitted parameters, set a reasonable threshold range to further screen the signal; remove signals with too small amplitude, too large time offset or too fast decay to retain reliable signal data for subsequent analysis.

[0032] Secondly, regarding signal grouping and pairing processing, it mainly includes the following steps:

[0033] Signal grouping: Based on the waveform recognition results, signals that are adjacent in time and have similar waveforms are grouped together; this step is achieved by calculating the time difference and waveform similarity between signals to ensure that signals in the same group belong to hit events of the same electron group.

[0034] Pairing processing: On the basis of signal grouping, a pairing algorithm based on time difference and waveform similarity is used to pair each group of signals; by fine-tuning the pairing parameters, the pairing relationship between each pair of signals is ensured to be accurate, thereby determining the hitting position of each electron group.

[0035] Finally, the specific implementation of the position reconstruction algorithm includes the following steps:

[0036] Constructing a position reconstruction model: Based on the geometric structure of the MCP detector and the layout of the anode plate, an accurate position reconstruction model is constructed; this model converts the signal position on the anode plate into two-dimensional spatial coordinates in the MCP detector, providing an accurate mathematical basis for position calculation.

[0037] Position calculation: Use the position reconstruction model to calculate the position of the grouped and paired signals; combine the position of the signal on the anode plate, the signal strength and the characteristic parameters of the MCP detector (such as channel width, anode strip spacing, etc.) to calculate the two-dimensional spatial coordinates of the impact position of each electron group in the MCP detector.

[0038] Result output: The position reconstruction results are output as image files for subsequent analysis and processing; by comparing the neutron beam spot distribution images before and after using the event reconstruction algorithm, the improvement in imaging effect and the uniformity of event distribution can be clearly seen.

[0039] Example 2: Optimizing the signal accumulation problem at high counting rates

[0040] This embodiment mainly optimizes the signal accumulation problem under high counting rate, and the specific steps are as follows:

[0041] First, optimize the waveform analysis algorithm: perform more refined peak-finding processing on the preprocessed signal, and use more advanced peak detection algorithms (such as adaptive threshold method, wavelet transform method, etc.) to more accurately locate the signal peak; in the signal fitting process, introduce more complex function models (such as double exponential decay function, Lorentz function, etc.) to better describe the waveform characteristics of the actual signal; at the same time, use more efficient optimization algorithms (such as simulated annealing algorithm, particle swarm optimization algorithm, etc.) to improve fitting speed and accuracy.

[0042] Then, improve the signal grouping and pairing processing: when grouping signals, consider introducing more characteristic parameters (such as signal width, rise time, etc.) to more accurately judge the similarity between signals; at the same time, adopt more advanced clustering algorithms (such as K-means algorithm, DBSCAN algorithm, etc.) to improve grouping accuracy; when pairing, adopt more sophisticated pairing strategies, such as considering factors such as the phase relationship and amplitude ratio between signals to ensure the accuracy of the pairing relationship.

[0043] Finally, the robustness of the position reconstruction algorithm is enhanced: the position reconstruction model is calibrated and verified more precisely to ensure that the model can accurately reconstruct the impact position of the electron group under different conditions; in the position calculation process, more correction factors and error compensation mechanisms are introduced to reduce the influence of various factors (such as detector nonlinearity, signal noise, etc.) on the position reconstruction accuracy. Through the above optimization measures, this embodiment achieves better imaging effects under high counting rate conditions, effectively solves the signal accumulation problem, and improves the counting rate and imaging accuracy of neutron imaging.

[0044] Example 3: Neutron imaging in different fields

[0045] In this embodiment, the present invention is mainly applied to neutron imaging needs in different fields. The specific steps are as follows:

[0046] First, adjust the waveform analysis algorithm parameters: adjust the parameter settings of the waveform analysis algorithm according to the neutron imaging requirements in different fields; in the field of security inspection, more emphasis may be placed on imaging speed and real-time performance, so the complexity of peak finding and fitting can be appropriately reduced.

[0047] Secondly, optimize the signal grouping and pairing processing strategies: optimize the signal grouping and pairing processing strategies according to the characteristics of neutron imaging in different fields; for example, in the field of material science research, since samples may produce complex signal waveforms, more advanced clustering algorithms and pairing strategies are needed to accurately identify the hit events of each electron group; and in the field of archaeological identification, since samples are usually more stable and the signal waveforms are relatively simple, a simpler grouping and pairing method can be used.

[0048] Then, customize the position reconstruction algorithm: customize the appropriate position reconstruction algorithm according to the application requirements of different fields; for example, in the field of defect detection, it may be necessary to build a more sophisticated position reconstruction model to accurately identify tiny defects in the sample; while in the field of environmental monitoring, more attention may be paid to the overall imaging effect and macroscopic features.

[0049] Finally, system integration and testing: Integrate the adjusted waveform analysis algorithm, optimized signal grouping and pairing processing strategy, and customized position reconstruction algorithm into the neutron imaging system, and conduct comprehensive testing and verification to ensure that the system can work stably and accurately under the neutron imaging requirements in different fields.

[0050] Through the above-mentioned implementation steps, the present invention has been successfully applied to the neutron imaging needs in different fields, demonstrating wide applicability and powerful imaging capabilities; it not only improves the counting rate and imaging accuracy of neutron imaging, but also provides an accurate and efficient means for non-destructive testing in various fields.

Claims

1. A waveform analysis and event reconstruction algorithm for white light neutron resonance imaging, characterized in that: It includes a waveform analysis step and an event reconstruction step. The waveform analysis step uses advanced waveform recognition technology to perform offline analysis of the signal waveform on the anode plate of the neutron imaging detector to accurately identify the signal waveform of each electron group. The event reconstruction step groups and pairs the signals based on the waveform analysis results, and constructs an accurate position reconstruction model to obtain the precise coordinates of the electron group's impact position.

2. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 1, characterized in that: The waveform analysis step specifically includes preprocessing, signal fitting and signal screening sub-steps, wherein the preprocessing is used for baseline subtraction and peak finding, the signal fitting uses a predefined function to perform waveform fitting, and improves the accuracy of signal extraction by combining multi-peak and fitting with residual quadratic fitting, and the signal screening uses the parameters obtained by fitting for further screening.

3. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 1, characterized in that: The signal grouping in the case reconstruction step is to group the signals that are adjacent in time and have similar waveforms into the same group as signals of the same electronic group according to the waveform recognition result; The pairing process uses a pairing algorithm based on time difference and waveform similarity to perform pairing processing on each group of signals and determine the pairing relationship between the signals.

4. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 1, characterized in that: The position reconstruction model in the case reconstruction step is constructed based on the geometric structure of the neutron imaging detector and the layout of the anode plate. The model can convert the signal position on the anode plate into a two-dimensional spatial coordinate in the detector, thereby achieving accurate position reconstruction.

5. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 1, characterized in that: It also includes optimization of the waveform analysis step and the event reconstruction step to improve the accuracy and efficiency of the algorithm and adapt to the needs of neutron imaging at higher counting rates.

6. A waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to any one of claims 1 to 5, characterized in that: It also includes a neutron imaging system, which includes a microchannel plate detector, an anode plate, and an electronic readout system, and is used to achieve neutron imaging at a high counting rate.

7. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 6, characterized in that: The detector is an MCP detector, which has the characteristics of high counting rate, high time resolution and high position resolution. Combined with the energy spectrum characteristics of the reverse-angle white light ray device, it is suitable for white light neutron resonance imaging with a wide energy spectrum.

8. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 6, characterized in that: The electronic readout system includes a front-end amplification module, a waveform digitization module and a data aggregation module, which are used to amplify, digitize and aggregate the signals output by the MCP detector to provide them to the waveform analysis and case reconstruction algorithm for further processing.

9. A waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to any one of claims 1 to 5, characterized in that: It also includes the overall integrated design of the neutron imaging system, integrating the MCP detector, anode plate, electronic readout system, waveform analysis and event reconstruction algorithm into a whole to reduce the connection between components and the loss of signal transmission, and improve the overall performance and stability of the system.

10. The waveform analysis and event reconstruction algorithm for white light neutron resonance imaging according to claim 6, characterized in that: It also includes expanding the application areas and applying neutron imaging systems to safety inspections, environmental monitoring and other fields to provide accurate and efficient detection methods.

Images