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

Abstract

The present application relates to the technical field of neutron imaging and detection, in particular to a data processing method for a neutron imaging detector based on microchannel plates (MCP), and more particularly to a waveform analysis and event reconstruction algorithm for white-light neutron resonance imaging; comprising a waveform analysis step and an event reconstruction step, wherein the waveform analysis step uses advanced waveform recognition technology to perform offline analysis on the signal waveforms on the anode plate of the neutron imaging detector, accurately identifying the signal waveform of each electron group; the event reconstruction step is based on the waveform analysis results, grouping and pairing the signals, and constructing an accurate position reconstruction model to obtain the accurate coordinates of the electron group hit position; not only improves the imaging accuracy and count rate, but also optimizes the data processing flow, adapts to high count rate conditions, improves the overall performance of the system, and expands the application field, which will provide strong support for the development and application of neutron imaging technology.

Description

Technical Field

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

[0002] Neutron resonance imaging (NRI) is an advanced non-destructive testing technique that has shown great application potential in various fields such as materials science research, defect detection, and archaeological identification. This technique can deeply probe the nuclide composition and its location distribution inside samples, providing strong support for research in related fields. However, the development of NRI has been limited by the performance of neutron sources and detectors. Especially under high count rate conditions, the problems of signal overlap and difficulty in separation are particularly prominent, which seriously affect the accuracy and efficiency of imaging.

[0003] Currently, boron-doped MCPs, as the core component of neutron imaging detectors, have become an ideal choice for wide-spectrum neutron resonance imaging due to their significant advantages of high count rate, high temporal resolution, and high positional resolution, combined with the unique energy spectrum characteristics of the China Spallation Neutron Source's anti-angle white light ray facility. However, the existing technology system lacks a complete and efficient waveform analysis algorithm and event reconstruction algorithm to address the complex challenges of signal accumulation and event reconstruction under high count rates. Therefore, developing an algorithm that can effectively separate signals and accurately reconstruct events under high count rate conditions is of great significance for improving the accuracy of neutron imaging and promoting the development of neutron resonance imaging technology. Summary of the Invention

[0004] To address the aforementioned problems, this invention aims to provide a data processing method for neutron imaging detectors based on microchannel plates (MCPs), and more specifically, a waveform analysis and event reconstruction algorithm for white-light neutron resonance imaging.

[0005] The technical solution adopted in this 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. The waveform analysis step uses advanced waveform recognition technology to perform offline analysis on the signal waveform on the anode plate of the neutron imaging detector, accurately identifying the signal waveform of each electron swarm. The event reconstruction step, based on the waveform analysis results, groups and pairs the signals, and constructs an accurate position reconstruction model to obtain the precise coordinates of the electron swarm's impact position.

[0006] The waveform analysis steps specifically include preprocessing, signal fitting, and signal filtering sub-steps. Preprocessing is used for baseline subtraction and peak finding. Signal fitting uses a predefined function to fit the waveform and improves the accuracy of signal extraction by combining multi-peak fitting with residual quadratic fitting. Signal filtering uses the parameters obtained from the fitting for further filtering.

[0007] The signal grouping in the case reconstruction step is based on the waveform recognition results, grouping signals that are temporally adjacent and have similar waveforms into the same group as signals of the same electron group; the pairing process 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 geometry of the neutron imaging detector and the layout of the anode plate. This model can convert the signal position on the anode plate into two-dimensional spatial coordinates in the detector, thereby achieving accurate position reconstruction.

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

[0010] It also includes a neutron imaging system, which comprises a microchannel plate detector, an anode plate, and an electronic readout system, for achieving neutron imaging at high count rates.

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

[0012] The electronic readout system includes a front-end amplification module, a waveform digitization module, and a data aggregation module, which amplify, digitize, and aggregate the signal output by the MCP detector to provide it for further processing by waveform analysis and event reconstruction algorithms.

[0013] This technical solution also includes the overall integrated design of the neutron imaging system, which integrates the MCP detector, anode plate, electronic readout system and 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 areas, applying the neutron imaging system to fields such as security inspection and environmental monitoring, providing accurate and efficient detection methods.

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

[0016] (i) Improved imaging accuracy and count rate: This 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 count rates; through signal grouping, pairing processing and position reconstruction algorithms, this invention can accurately identify and reconstruct the hit position of each electron group, thereby significantly improving the count rate and imaging accuracy of neutron imaging; this improvement is crucial for the high precision requirements of wide-spectrum neutron resonance imaging and helps to obtain clearer and more accurate imaging results.

[0017] (II) Optimized data processing flow: The waveform analysis algorithm of this invention adopts steps such as preprocessing, signal fitting and signal screening. By combining multi-peak and fitting with residual quadratic fitting, the accuracy of signal extraction is improved. 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) Adapting to high count rate conditions: Under high count rate conditions, traditional neutron imaging detectors often face the challenges of signal accumulation and difficulty in separation; this invention effectively solves this problem by constructing a new event reconstruction algorithm to group and pair the anode strip signals; this innovation enables the invention to maintain excellent imaging performance under high count rate conditions, meeting the high requirements of practical applications.

[0019] (iv) Improve overall system performance: This invention integrates the MCP detector, anode plate, electronic readout system and waveform analysis and event reconstruction algorithm into a complete neutron imaging system; by optimizing the connection method and signal transmission method between the components, signal loss and interference are reduced, and the overall performance and stability of the system are improved; this integrated design makes the 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 materials science research, defect detection and archaeological identification, but can also be extended to more fields such as security 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 this invention not only solves the key problems in neutron imaging technology, but also provides strong support for the continuous innovation and development of this field; by continuously optimizing and improving system performance, this invention will be able to adapt to higher imaging requirements and promote the continuous progress and application expansion of neutron imaging technology. Attached Figure Description

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

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

[0024] Figure 3 This is a diagram showing the comparison of the results after reconstruction. Detailed Implementation

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

[0026] Example 1: Application at the China Spallation Neutron Source

[0027] The specific implementation results of the waveform analysis algorithm are as follows: Figures 1-3 As shown, where Figure 1 The specific structure of the MCP detector is shown, including key components such as the microchannel plate and the anode plate, as well as the connection methods and signal transmission paths between them; Figure 2 The diagram illustrates key components such as waveform analysis and event reconstruction algorithms, as well as the relationships between them; it clearly demonstrates the algorithm's flow. Figure 3 The comparison results of neutron beam spot distribution before and after using the event reconstruction algorithm show that the imaging effect is significantly improved after using the event reconstruction algorithm. In terms of counting, the number of reconstructed events in the new version is about 13% higher than that in the original version. This is due to the new event location reconstruction code, which can separate and reconstruct high-energy events that could not be separated before. 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 consistent with the actual beam spot distribution.

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

[0029] Data preprocessing: First, baseline subtraction and peak finding are performed on the raw signal obtained from the MCP detector of the reverse angle white light ray device at the China Spallation Neutron Source. This step provides accurate basic data for subsequent waveform fitting by removing baseline drift and noise in the signal and locating signal peaks.

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

[0031] Signal filtering: Based on the fitted parameters, set a reasonable threshold range to further filter the signals; 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, the main steps include:

[0033] Signal grouping: Based on the waveform recognition results, signals that are temporally adjacent and have similar waveforms are grouped into the same group. This step is achieved by calculating the time difference and waveform similarity between signals to ensure that signals in the same group belong to the same electron group's hit event.

[0034] Pairing Processing: Based on signal grouping, a pairing algorithm based on time difference and waveform similarity is used to pair each group of signals. By finely adjusting the pairing parameters, the pairing relationship between each pair of signals is ensured to be accurate, thereby determining the hit position of each electron swarm.

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

[0036] Constructing a position reconstruction model: Based on the geometry 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: Using the position reconstruction model, the position of the grouped and paired signals is calculated; combining 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 bar spacing, etc.), the two-dimensional spatial coordinates of the impact position of each electron group in the MCP detector are calculated.

[0038] Output results: The location 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: Optimization is mainly performed to address the signal accumulation problem at high count rates.

[0040] This embodiment primarily addresses the signal accumulation problem at high count rates by optimizing the processing. The specific steps are as follows:

[0041] First, optimize the waveform analysis algorithm: perform more refined peak finding on the preprocessed signal, and adopt more advanced peak detection algorithms (such as adaptive thresholding, wavelet transform, etc.) to more accurately locate the signal peaks; 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, adopt more efficient optimization algorithms (such as simulated annealing algorithm, particle swarm optimization algorithm, etc.) to improve the fitting speed and accuracy.

[0042] Then, improve signal grouping and pairing processing: when grouping signals, consider introducing more feature parameters (such as signal width, rise time, etc.) to more accurately determine 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 refined pairing strategies, such as considering the phase relationship and amplitude ratio between signals, to ensure the accuracy of pairing relationships.

[0043] Finally, the robustness of the position reconstruction algorithm is enhanced: the position reconstruction model is calibrated and validated more finely to ensure that it can accurately reconstruct the impact position of the electron swarm under different conditions; during the position calculation process, more correction factors and error compensation mechanisms are introduced to reduce the impact 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 results under high count rate conditions, effectively solves the signal accumulation problem, and improves the count rate and imaging accuracy of neutron imaging.

[0044] Example 3: Neutron Imaging Applied to Different Fields

[0045] In this embodiment, the main focus is on exploring the application of this invention 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 of 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 signal grouping and pairing processing strategies: optimize signal grouping and pairing processing strategies according to the characteristics of neutron imaging in different fields; for example, in the field of materials science research, since samples may generate complex signal waveforms, more advanced clustering algorithms and pairing strategies are needed to accurately identify the hit events of each electron group; while in the field of archaeological identification, since samples are usually relatively stable and signal waveforms are relatively simple, simpler grouping and pairing methods can be used.

[0048] Then, customize the location reconstruction algorithm: customize the appropriate location reconstruction algorithm according to the application needs of different fields; for example, in the field of defect detection, it may be necessary to build a more refined location 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 effect and macroscopic features of the imaging.

[0049] Finally, system integration and testing: The adjusted waveform analysis algorithm, optimized signal grouping and pairing processing strategy, and customized position reconstruction algorithm are integrated into the neutron imaging system and comprehensively tested and verified to ensure that the system can work stably and accurately under the neutron imaging requirements of different fields.

[0050] Through the above implementation steps, this invention has been successfully applied to 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 a precise 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 waveform analysis steps and event reconstruction steps, and also includes a neutron imaging system, which includes a microchannel plate detector, an anode plate, and an electronic readout system, for realizing neutron imaging at high count rates; The waveform analysis step employs waveform recognition technology to perform offline analysis of the signal waveforms on the anode plate of the neutron imaging detector, accurately identifying the signal waveforms of each electron swarm. The event reconstruction step, based on the waveform analysis results, groups and pairs the signals and constructs an accurate position reconstruction model to obtain the precise coordinates of the electron swarm's impact location. The waveform analysis steps specifically include preprocessing, signal fitting, and signal filtering sub-steps. Preprocessing is used for baseline subtraction and peak finding. Signal fitting uses a predefined function to fit the waveform and improves the accuracy of signal extraction by combining multi-peak fitting with residual quadratic fitting. Signal filtering uses the parameters obtained from the fitting for further filtering. The signal grouping in the case reconstruction step is based on the waveform recognition results, grouping temporally adjacent and waveform-similar signals into the same group as signals of the same electron group; The pairing process employs a pairing algorithm based on time difference and waveform similarity to pair each group of signals and determine the pairing relationship between the signals.

2. 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 geometry of the neutron imaging detector and the layout of the anode plate. This model can convert the signal position on the anode plate into two-dimensional spatial coordinates in the detector, thereby achieving accurate position reconstruction.

3. The waveform analysis and event reconstruction algorithm for white-light neutron resonance imaging according to claim 1, characterized in that: The detector described is an MCP detector, which features high count rate, high time resolution, and high position resolution. Combined with the energy spectrum characteristics of the anti-angle white light ray device, it is suitable for wide-spectrum white light neutron resonance imaging.

4. The waveform analysis and event reconstruction algorithm for white-light neutron resonance imaging according to claim 1, characterized in that: The electronic readout system includes a front-end amplification module, a waveform digitization module, and a data aggregation module, which amplify, digitize, and aggregate the signal output by the MCP detector to provide it for further processing by waveform analysis and event reconstruction algorithms.

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

6. The waveform analysis and event reconstruction algorithm for white-light neutron resonance imaging according to claim 1, characterized in that: It also includes expanding application areas, applying neutron imaging systems to security inspections and environmental monitoring, providing accurate and efficient detection methods.

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