Fitting method and device of scintillation pulse, computer storage medium, computer program product and radiation detection system

Abstract

The invention discloses a scintillation pulse fitting method and device, a computer storage medium, a computer program product and a radiation detection system. The fitting method comprises the following steps: collecting scintillation pulses to obtain a plurality of sampling points containing time information and voltage information; selecting one or integrating a plurality of noise models from pre-constructed noise models based on the information covered by the sampling points to endow at least part of the sampling points with incompletely same or completely different weight values; and constructing a target function in combination with the weight value, and fitting the sampling points to obtain the waveform of the scintillation pulse. According to the method, the problem of falling into a local optimal solution in the scintillation pulse waveform fitting process can be avoided as much as possible, errors are reduced, the accuracy of pulse parameter estimation is improved, and waveform fitting is more accurate.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method, apparatus, computer storage medium, computer program product, and radiation detection system for fitting scintillation pulses. Background Technology

[0002] In the field of high-energy particle detection, the multiple voltage threshold (MVT) method is widely used as a simple, efficient, and easily scalable method for digitizing scintillation pulses. Unlike traditional equal-interval sampling methods, the MVT method uses a comparator and a time-to-digital converter (TDC) to obtain the timestamps of the scintillation pulse passing through several pre-set voltage thresholds. Combined with a prior model of the scintillation pulse, these obtained time-threshold pairs are fitted to reconstruct the scintillation pulse waveform, thereby obtaining the corresponding time, energy, and position information.

[0003] In practical scintillation detectors, the scintillation pulse waveform can typically be modeled as a double exponential function. Where t0 is the arrival time of the scintillation pulse, r1 and r2 are both related to the time constants of the scintillation crystal and the optoelectronic device, and A is related to the incident gamma photon deposition energy, the light output of the scintillation crystal, and the SiPM gain. In the fitting process, we construct an objective function and use the nonlinear least squares method to fit the waveform. In existing technologies, only the shape model of the scintillation pulse is considered when constructing the objective function, without considering noise. In actual fitting, this can easily lead to getting trapped in local optima, causing the fitted parameters to exceed the physically possible range and affecting the fitting accuracy.

[0004] Therefore, there is an urgent need for a scintillation pulse fitting method that takes noise into account. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer storage medium, computer program product, and radiation detection system for fitting scintillation pulses, addressing at least one technical problem existing in traditional solutions.

[0006] According to a first aspect of this application, a method for fitting a flicker pulse is provided, comprising: acquiring a flicker pulse to obtain a plurality of sampling points, each containing time information and voltage information; selecting one or combining multiple noise models from a pre-built noise model based on the information covered by the sampling points and assigning weight values ​​that are not completely the same or completely different to at least some of the sampling points; constructing an objective function by combining the weight values ​​and fitting the sampling points to obtain the waveform of the flicker pulse.

[0007] According to one embodiment of this application, the noise model is constructed based on the magnitude of the voltage values ​​at different sampling points.

[0008] According to one embodiment of this application, the noise model is constructed following the principle that the weight values ​​and voltage values ​​have a linear relationship.

[0009] According to one embodiment of this application, the noise model is constructed following the principle that the weight values ​​and voltage values ​​have a positive linear relationship.

[0010] According to one embodiment of this application, the noise model is constructed based on the positions of different sampling points on the flicker pulse waveform.

[0011] According to one embodiment of this application, the noise model is constructed based on the principle that the weight value of the sampling point located at the rising edge of the flash pulse waveform is greater than the weight value of the sampling point located at the falling edge of the flash pulse waveform.

[0012] According to one embodiment of this application, constructing an objective function based on the weight values ​​and fitting the sampling points to obtain the flickering pulse includes: during the iterative fitting of the sampling points using the objective function, determining whether there are any outlier sampling points; if so, assigning a weight value less than a predetermined value to the outlier sampling point, and then performing iterative fitting again using the new weight value.

[0013] According to one embodiment of this application, constructing an objective function based on the weight values ​​and fitting the sampling points to obtain the flicker pulse includes: a first step of constructing an objective function based on the assigned weight values ​​and fitting the waveform of the flicker pulse using the objective function; a second step of determining the residual for each sampling point based on the fitted waveform; a third step of determining the standard deviation based on the distribution of all the residuals, and determining outlier sampling points based on the residuals and the standard deviation; a fourth step of assigning a weight value less than the predetermined value to the outlier sampling points; and a fifth step of repeating steps one through four until convergence or iteration to a predetermined number of times.

[0014] According to one embodiment of this application, the noise model assigns weight values ​​to at least a portion of the sampling points based on voltage values ​​at different sampling points and / or based on the positions of different sampling points on the flicker pulse waveform.

[0015] According to one embodiment of this application, a number of sampling points, each containing time information and voltage information, are obtained by acquiring scintillation pulses, including: acquiring scintillation pulses based on an equal time interval sampling method or a multi-voltage threshold sampling method to obtain a number of sampling points.

[0016] According to a second aspect of this application, a fitting device for a flicker pulse is provided, comprising: a acquisition module configured to acquire a flicker pulse to obtain a plurality of sampling points, each containing time information and voltage information; a weighting module configured to assign weight values, which are not entirely the same or completely different, to at least some of the sampling points based on the information covered by the sampling points, selecting one or combining multiple noise models from a pre-built noise model; and a fitting module configured to construct an objective function by combining the weight values ​​and fitting the sampling points to obtain the waveform of the flicker pulse.

[0017] According to one embodiment of this application, the noise model is constructed based on the magnitude of the voltage values ​​at different sampling points.

[0018] According to one embodiment of this application, the noise model is constructed following the principle that the weight values ​​and voltage values ​​have a linear relationship.

[0019] According to one embodiment of this application, the noise model is constructed following the principle that the weight values ​​and voltage values ​​have a positive linear relationship.

[0020] According to one embodiment of this application, the noise model is constructed based on the positions of different sampling points on the flicker pulse waveform.

[0021] According to one embodiment of this application, the noise model is constructed based on the principle that the weight value of the sampling point located at the rising edge of the flash pulse waveform is greater than the weight value of the sampling point located at the falling edge of the flash pulse waveform.

[0022] According to one embodiment of this application, the fitting module is configured to determine whether there are any outlier sampling points during the iterative fitting of the sampling points using the objective function. If so, the outlier sampling points are assigned a weight value less than a predetermined value, and the iterative fitting is performed again using the new weight value.

[0023] According to one embodiment of this application, the fitting module is configured to perform fitting using the following process: First, constructing an objective function based on assigned weight values ​​and fitting the waveform of the flashing pulse using the objective function; Second, determining the residual for each sampling point based on the fitted waveform; Third, determining the standard deviation based on the distribution of all residuals, and determining outlier sampling points based on the residuals and the standard deviation; Fourth, assigning weight values ​​less than predetermined values ​​to the outlier sampling points; Fifth, repeating steps one through four until convergence or iteration to a predetermined number of times.

[0024] According to one embodiment of this application, the weighting module is configured to determine weight values ​​for at least a portion of the sampling points based on voltage values ​​at different sampling points and / or based on the positions of different sampling points on the flicker pulse waveform.

[0025] According to one embodiment of this application, the acquisition module is configured to acquire scintillation pulses to obtain several sampling points based on an equal time interval sampling method or a multi-voltage threshold sampling method.

[0026] According to a third aspect of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the steps of the fitting method.

[0027] According to a fourth aspect of this application, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the steps of the fitting method.

[0028] According to a fifth aspect of this application, a radiation detection system is provided, including the aforementioned fitting device.

[0029] The scintillation pulse fitting method, apparatus, computer storage medium, and radiation detection system provided in this application no longer treat all sampling points equally. Instead, they are based on the noise conditions existing in different scenarios, covering the comprehensive noise of the entire signal chain from scintillation light generation to digitization. They consider different signal-to-noise ratios under different pulse amplitudes and detector operating conditions, accurately analyze the sampling points before fitting, and flexibly select noise models to assign different weights to each sampling point. This can avoid getting trapped in local optima during the scintillation pulse waveform fitting process as much as possible, reduce errors, improve the accuracy of pulse parameter estimation, and fit the waveform more accurately. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating a method for fitting a flicker pulse in one embodiment of this application;

[0032] Figure 2 This is a schematic diagram of a flash pulse waveform in one embodiment of this application;

[0033] Figure 3 This is a schematic diagram of a flash pulse waveform in another embodiment of this application;

[0034] Figure 4 This is a flowchart illustrating a method for fitting a flicker pulse in another embodiment of this application;

[0035] Figure 5 This is a schematic diagram of the structure of a scintillation pulse fitting device in one embodiment of this application;

[0036] Figure 6 This is a schematic diagram of the structure of a radiation detection system in one embodiment of this application;

[0037] Figure 7 This is an internal structural diagram of a computer device in one embodiment of this application. Detailed Implementation

[0038] To make the above-mentioned objectives, features, and advantages of this application more readily understood, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0039] It should be noted that when an element is said to be "fixed to" another element, it can be directly fixed to the other element or there may be an intervening element. When an element is said to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "substantially equal" or "substantially equal to" as used herein mean that the difference between the two lies within a range of errors considered equivalent in the art. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The terms “and / or” or “and / or” as used herein include one or more of the associated listed items and combinations of all of them.

[0041] As described in the background section, in existing technologies, when fitting scintillation pulse waveforms, only the shape model is considered, without taking noise into account. However, in the actual scintillation pulse acquisition process, from the process of gamma photons incident on the scintillation crystal to generate visible light, to the process of visible light being transported in the scintillation crystal, to the process of visible light being converted into photoelectric signals by photoelectric conversion devices, and finally to the process of sampling electrical signals to obtain sampling points, various noises are involved. For example, noise caused by different threshold setting accuracy of digital-to-analog converters, dispersion noise caused by overdrive of voltage comparators, electronic baseline noise, interference from tiny dark pulses caused by dark counting, dark pulse superposition, signal accumulation, circuit disturbances, etc., all affect the accuracy of the final sampling points, and thus affect the accuracy of the fitted scintillation pulse waveform.

[0042] In view of the technical problems existing in the prior art, this application proposes a method, device and supporting application for fitting scintillation pulses that can at least improve the fitting accuracy.

[0043] In some embodiments, the method for fitting the scintillation pulse can be executed by a scintillation pulse fitting device. For example, the scintillation pulse fitting method can be partially or wholly stored in a storage device (such as the built-in storage module of the detection device or an external storage device) in the form of a program or instructions, which, when executed, can implement the scintillation pulse fitting method. The fitting device disclosed in this application for implementing the above-described fitting method can be either a device with abundant computing resources (e.g., a computer, server, cloud computing, etc.) or a device with limited computing resources (e.g., FPGA (Field Programmable Gate Array) chip board, ASIC (Application-Specific Integrated Circuit) chip board, and other hardware circuits).

[0044] The following description, with reference to the accompanying drawings, illustrates some preferred embodiments of the present application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application.

[0045] Figure 1 This is a flowchart illustrating a method for fitting a flicker pulse in one embodiment of this application. In one embodiment, the method for fitting a flicker pulse may include the following steps S100 to S300.

[0046] S100: Collects scintillation pulses to obtain several sampling points, each containing time and voltage information.

[0047] Regarding the source of scintillation pulses, scintillation detectors are typically used to capture gamma photons. A scintillation detector generally includes a scintillation crystal and a photoelectric conversion device. During the detection of gamma photons using a scintillation detector, after the gamma photon enters the scintillation crystal, many visible photons are generated inside the crystal. These visible photons are then collected by the photoelectric conversion device. Taking a silicon photomultiplier tube (SiPM) as an example, the current generated by several single-photon avalanche diodes (SPADs) within it converges to form a scintillation pulse.

[0048] The method for acquiring scintillation pulses can be an equal-interval sampling method or a multi-voltage threshold (MVT) method, or a method derived from the equal-interval sampling method or the MVT method, such as the peak-multi-voltage threshold (PP-MVT) method. The equal-interval sampling method and the MVT method are relatively mature sampling methods, and several existing technologies are available for reference; therefore, they will not be elaborated upon here due to space limitations. For the PP-MVT method, please refer to Y. Ling et al., "A Novel Peak Picking Multi-Voltage Threshold Digitizer for Pulse Sampling," in IEEE Transactions on Radiation and Plasma Medical Sciences, vol.8, no.3, pp.248-256, March 2024, doi:10.1109 / TRPMS.2024.3359241. Preferably, the scintillation pulse is acquired using the MVT method or the PP-MVT method to obtain several sampling points, each containing both time and voltage information. Taking the MVT method as an example, the PP-MVT method is similar; the general representation of the sampling point is (t... i v i ), where i represents the sampling point number, which can usually be represented by numbers 1 to n, t i represents the time of the i-th sampling point, and v represents the amplitude of the i-th sampling point, which is usually voltage. Figure 2 The waveform of the scintillation pulse obtained by sampling and fitting using the MVT method is shown. Figure 3 The waveform of the flicker pulse obtained by fitting after sampling using the PP-MVT method is shown, with an example of sampling using four voltage thresholds, t p This indicates the peak time of the flash pulse.

[0049] S200: Based on the information covered by the sampling points, select one or combine multiple pre-built noise models to assign weight values ​​that are not exactly the same or completely different to at least some of the sampling points.

[0050] It is understandable that the larger the weight value of a sampling point, the greater its impact on the fitting result.

[0051] In this embodiment of the application, the information covered by the acquired sampling points is analyzed, covering the entire process of sampling point generation, including, for example, the scene in which the sampling points are collected, the type of detector used, the method of collection, the threshold used, etc., to determine the noise model.

[0052] In this application embodiment, based on the noise conditions existing in different scenarios, covering the comprehensive noise of the entire signal chain from the generation of flicker light to digitization, and considering the different signal-to-noise ratios under different pulse amplitudes and detector operating conditions, a variety of methods for constructing noise models are provided.

[0053] In one scenario, at the low voltage threshold, the signal amplitude is low, making it more susceptible to interference from electronic baseline noise and tiny dark pulses caused by SiPM dark counting. Furthermore, the comparator exhibits greater dispersion in its propagation delay at low overdrive voltages. Therefore, the timestamp t at the low threshold... i The uncertainty is higher. In this case, the noise model can be constructed based on the magnitude of the voltage values ​​at different sampling points. For example, the weight values ​​can be set to be linearly related to the voltage values. In different application scenarios, this linear relationship can be positive or negative. For example, when sampling points with lower voltage values ​​provide more important information for fitting the flicker pulse waveform, the weight values ​​of each sampling point can be determined by the principle that the weight values ​​are negatively linearly related to the voltage values. Conversely, the weight values ​​of each sampling point can also be determined by the principle that the weight values ​​are positively linearly related to the voltage values. Specifically, in one example, the weight value of each sampling point can be the ratio of the voltage value of the sampling point to the maximum voltage value of all sampling points. When sampling using the MVT method or PP-MVT method, the weight value can be the threshold / maximum threshold of the sampling point. In another example, hierarchical weights can also be used. For example, when sampling using the MVT method or PP-MVT method, the weight value of the sampling point corresponding to the lowest threshold is 0.5, the weight value of the sampling point corresponding to the middle threshold is 1.0, and the weight value of the sampling point corresponding to the highest threshold is 1.5. It should be understood that the values ​​here are only examples and should not be considered as limitations of this application. By assigning weights to each sampling point using a noise model, the negative impact of noise data in low signal-to-noise ratio regions on the fitting results can be effectively suppressed, thereby improving the robustness of the fitting results.

[0054] Furthermore, when determining the weight value based on the magnitude of the voltage value in different sampling points, weights can be assigned to all sampling points. For example, in some cases, sampling points have already been screened during the sampling process, and the remaining sampling points have different degrees of value in fitting the flicker pulse. In this case, weights are assigned to all sampling points. Of course, weights can also be assigned to only some sampling points as needed. For some sampling points that are obviously noise, for example, if the time difference between a sampling point and other sampling points of the flicker pulse is much greater than the normal pulse width range, then the sampling point is considered obviously noise. Or, if the time difference between a sampling point on the rising edge of the flicker pulse and other sampling points on the rising edge is much greater than the rise time range, then the sampling point is considered obviously noise. Such sampling points can be discarded. Of course, sampling points that are obviously noise can also be assigned 0 as a weight. It can be understood that, from the result, discarding and assigning 0 as a weight are the same, but from the process, the discarded sampling points do not participate in the fitting calculation, while the sampling points assigned 0 as a weight participate in the fitting calculation.

[0055] In another scenario, the contribution of sampling points on the rising, peak, and falling edges of the flash pulse to the fitting of the flash pulse waveform varies depending on the specific circumstances. For example, in some cases, the rising edge of the flash pulse contains the most critical information about the trigger time t0, thus the sampling points on the rising edge contribute more to the fitting of the flash pulse waveform. The falling edge, on the other hand, primarily reflects the decay time constant, and its information value decreases relatively when the time constant is pre-calibrated. Therefore, in the embodiments of this application, the noise model can be constructed based on the positions of the different sampling points on the flash pulse waveform. Specifically, in one example, the weight value of sampling points located on the rising edge of the flash pulse waveform is greater than the weight value of sampling points located on the falling edge of the flash pulse waveform. By assigning weights to the sampling points using the above noise model, the fitting algorithm can be guided to focus more on the data regions that contribute the most to the key parameters, accelerating convergence and improving the accuracy of key parameters (such as t0).

[0056] Similarly, when determining the weight value based on the position of different sampling points on the flicker pulse waveform, weights can be assigned to all sampling points. For example, in some cases, the sampling points have already been screened during the sampling process, and the remaining sampling points have different degrees of value for fitting the flicker pulse. In this case, weights are assigned to all sampling points. Of course, weights can also be assigned to only some sampling points as needed. Some sampling points that are obviously noise (examples of obviously noise sampling points include the above example of determining their weight value based on the magnitude of the voltage value in different sampling points) can be discarded. Of course, obviously noise sampling points can also be assigned 0 as a weight. It can be understood that, from the result, discarding and assigning 0 as a weight are the same, but from the process, the discarded sampling points do not participate in the fitting calculation, while the sampling points assigned 0 as a weight participate in the fitting calculation.

[0057] In addition to the two scenarios mentioned above, another scenario considered in this application can be further refined based on the waveform of the flicker pulse. For example, during the construction of the noise model, different or completely different weight values ​​can be assigned to the sampling points of the starting segment, rapid rising segment, rising edge near peak segment, peak point, falling edge near peak segment, rapid falling segment, gentle falling segment, and ending segment of the flicker pulse. It is understood that the aforementioned segments / points can be divided, selected, or further added based on different flicker pulse waveforms.

[0058] Furthermore, the various noise models mentioned above can be used in combination. It is understood that two or three models can be combined to fit a better flicker pulse waveform.

[0059] The noise model mentioned in this application can form different prior information based on different noises in different application scenarios. In application, different standard weighting methods can be selected from the prior information to assign weights to the sampling points according to the application scenario or noise type before performing waveform fitting of the flicker pulse.

[0060] S300: Construct an objective function by combining the weight values ​​and fit the sampling points to obtain the waveform of the flashing pulse.

[0061] For methods of fitting sampling points, existing methods for fitting scintillation pulse waveforms can be referenced. Generally, a target function is constructed based on a known waveform model for fitting. Known waveform models can be classified based on physical processes, empirical mathematical functions, circuit functions, etc. For example, models based on physical processes include single-exponential models, double-exponential models, and multi-exponential models; models based on empirical mathematical functions include Gaussian models, quasi-Gaussian models, and convolutional models; and models based on circuit functions include CR-(RC). nFilter models. Among them, the double exponential model is the most common. The scintillation pulse shape of most inorganic scintillators (such as NaI(Tl), LaBr3(Ce)) and organic scintillators (such as plastic scintillators) can be well approximated by this model, and its expression is: Where t0 is the arrival time of the scintillation pulse, r1 and r2 are both related to the time constants of the scintillation crystal and the optoelectronic device (which can also be directly called time constants), and A is related to the incident gamma photon deposition energy, the light output of the scintillation crystal, and the SiPM gain.

[0062] Taking an LYSO / SiPM detector as an example, the objective function is constructed using a double exponential model. The objective functions constructed using the MVT method and the PP-MVT method for sampling will be listed below.

[0063] Without considering noise, the objective function constructed using the MVT method is as follows:

[0064]

[0065] Among them, L(A) ′ r ′ 1. r ′ 2. t ′ 0) represents the numerical solution, A ′ It is a parameter related to the deposition energy of photons, the light output of the scintillation crystal, and the gain of optoelectronic devices, t ′ 0 represents the photon arrival time, r ′ 1. r ′ 2 represents the time constant, i represents the i-th sampling point, and t i v represents the time contained in the i-th sampling point. i This represents the voltage contained in the i-th sampling point.

[0066] In this embodiment, considering noise, the objective function constructed using the MVT method becomes:

[0067]

[0068] Where, ω i This represents the weight value of the i-th sampling point, and the remaining letters are the same as those in the existing objective function.

[0069] Without considering noise, the objective function constructed using the PP-MVT method is as follows:

[0070]

[0071] Among them, t p v represents the peak time of the flash pulse. pThis represents the peak voltage of the scintillation pulse; the remaining letters have the same meaning as in the objective function of the MVT method described above.

[0072] In this embodiment, considering noise, the objective function constructed using the PP-MVT method becomes:

[0073]

[0074] Where, ω p This represents the weight value of the peak sampling point; the other letters are the same as those in the other functions mentioned above.

[0075] Although the above only illustrates the application of the fitting method provided in this application to a scenario where a target function is constructed using a double exponential model, those skilled in the art should be able to apply the fitting method of this application to scenarios where other models are used to construct target functions for fitting.

[0076] To illustrate the fitting process, let's take the objective function constructed using the MVT method mentioned above as an example. For example, using the least squares method for iterative solution, generally, the iteration continues until the optimal solution is found when the function converges. In some special cases, the optimal solution cannot be found. In this case, a maximum number of iterations can be set as a convergence condition. When the iteration reaches the maximum number of iterations and has not yet converged normally, it will automatically stop and the last result will be used as the solution of the objective function.

[0077] In a preferred embodiment of this application, considering that some sampling points may become "outliers" due to dark pulse superposition, signal accumulation, or circuit disturbances, severely deviating from the theoretical model, the weight values ​​of the sampling points are corrected during the iterative fitting process, dynamically identifying and adjusting the weight values ​​of outliers. For example, step S300 includes: during the iterative fitting of the sampling points using the objective function, determining whether any sampling points are outliers; if so, assigning a weight value less than a predetermined value to the outlier sampling point, and then performing iterative fitting again using the new weight value. Thus, by continuously iteratively correcting outliers, the algorithm's anti-interference capability can be greatly enhanced, maintaining robust performance even under non-ideal working environments (such as high count rates and high dark noise rates).

[0078] More specifically, see Figure 4 In this embodiment of the application, step S300 includes:

[0079] S310: Construct an objective function based on the assigned weight values ​​and use the objective function to fit the waveform of the flashing pulse;

[0080] S320: Based on the fitting results, determine the residual for each of the sampling points;

[0081] S330: Determine the standard deviation based on the distribution of all the residuals, and determine outlier sampling points based on the residuals and the standard deviation;

[0082] S340: Assign a weight value to the outlier sampling point that is less than the predetermined value;

[0083] S350: Repeat S310 to S340 until convergence or iteration to the predetermined number of times.

[0084] More specifically, the noise model in step S310 above can be constructed using a combination of or one of the following: voltage values ​​at different sampling points, the position of different sampling points on the flicker pulse waveform, or both.

[0085] The scintillation pulse fitting method provided in this application no longer treats all sampling points equally. Instead, it is based on the noise situation existing in different scenarios, covering the comprehensive noise of the entire signal chain from scintillation light generation to digitization. It considers the different signal-to-noise ratios under different pulse amplitudes and detector operating conditions, accurately analyzes the sampling points before fitting, and flexibly selects the noise model to assign different weights to each sampling point. This can avoid getting trapped in local optima during the scintillation pulse waveform fitting process as much as possible, reduce errors, improve the accuracy of pulse parameter estimation, and fit the waveform more accurately.

[0086] Based on the description of the above-described methods for fitting flicker pulses, this application also provides a device for fitting flicker pulses. The device may include an apparatus (including a distributed system), software (application), module, component, server, client, etc., using the methods described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the devices in one or more embodiments provided in this application are as described in the following embodiments. Since the implementation schemes and methods for solving the problem are similar, the implementation of specific devices in the embodiments of this specification can refer to the implementation of the foregoing methods, and repeated details will not be repeated. As used below, the terms "module" or "module group" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible.

[0087] Figure 5 This is a schematic diagram of the structure of a scintillation pulse fitting device in one embodiment of the present application. In one embodiment, the scintillation pulse fitting device 500 may include a data acquisition module 510, a weighting module 520, and a fitting module 530.

[0088] Specifically, the acquisition module 510 is configured to acquire scintillation pulses and obtain several sampling points, each containing time and voltage information. Regarding the source of the scintillation pulses, a scintillation detector is typically used to capture gamma photons. A scintillation detector generally includes a scintillation crystal and a photoelectric conversion device. During the detection of gamma photons using a scintillation detector, after the gamma photons enter the scintillation crystal, many visible photons are generated inside the crystal, and these visible photons are collected by the photoelectric conversion device. Taking a silicon photomultiplier tube (SiPM) as an example, the current generated by several single-photon avalanche diodes (SPADs) within it converges to form a scintillation pulse.

[0089] The method for acquiring scintillation pulses can be an equal-interval sampling method or a multi-voltage threshold (MVT) method, or a method derived from the equal-interval sampling method or the MVT method, such as the peak-multi-voltage threshold (PP-MVT) method. The equal-interval sampling method and the MVT method are relatively mature sampling methods, and several existing technologies are available for reference; therefore, they will not be elaborated upon here due to space limitations. For the PP-MVT method, please refer to Y. Ling et al., "A Novel Peak Picking Multi-Voltage Threshold Digitizer for Pulse Sampling," in IEEE Transactions on Radiation and Plasma Medical Sciences, vol.8, no.3, pp.248-256, March 2024, doi:10.1109 / TRPMS.2024.3359241. Preferably, the scintillation pulse is acquired using the MVT method or the PP-MVT method to obtain several sampling points, each containing time signal and voltage information. Taking the MVT method as an example, the PP-MVT method is similar; the general representation of the sampling point is (t... i v i ), where i represents the sampling point number, which can usually be represented by numbers 1 to n, t i represents the time of the i-th sampling point, and v represents the amplitude of the i-th sampling point, which is usually voltage.

[0090] Specifically, the weighting module 520 is configured to select one or combine multiple noise models from a pre-built noise model based on the information covered by the sampling points, and assign weight values ​​that are not exactly the same or completely different to at least some of the sampling points. It is understood that for a sampling point, the larger its weight value, the greater its impact on the fitting result.

[0091] In this embodiment of the application, the information covered by the acquired sampling points is analyzed, covering the entire process of sampling point generation, including, for example, the scene in which the sampling points are collected, the type of detector used, the method of collection, the threshold used, etc., to determine the noise model.

[0092] In this application embodiment, based on the noise conditions existing in different scenarios, covering the comprehensive noise of the entire signal chain from the generation of flicker light to digitization, and considering the different signal-to-noise ratios under different pulse amplitudes and detector operating conditions, a variety of methods for constructing noise models are provided.

[0093] In one scenario, at the low voltage threshold, the signal amplitude is low, making it more susceptible to interference from electronic baseline noise and tiny dark pulses caused by SiPM dark counting. Furthermore, the comparator exhibits greater dispersion in its propagation delay at low overdrive voltages. Therefore, the timestamp t at the low threshold... i The uncertainty is higher. In this case, the noise model can be constructed based on the magnitude of the voltage values ​​at different sampling points. For example, the weight values ​​can be set to be linearly related to the voltage values. In different application scenarios, this linear relationship can be positive or negative. For example, when sampling points with lower voltage values ​​provide more important information for fitting the flicker pulse waveform, the weight values ​​of each sampling point can be determined by the principle that the weight values ​​are negatively linearly related to the voltage values. Conversely, the weight values ​​of each sampling point can also be determined by the principle that the weight values ​​are positively linearly related to the voltage values. Specifically, in one example, the weight value of each sampling point can be the ratio of the voltage value of the sampling point to the maximum voltage value of all sampling points. When sampling using the MVT or PP-MVT method, the weight value can be the threshold / maximum threshold of the sampling point. In another example, a hierarchical weight can also be used. For example, when sampling using the MVT or PP-MVT method, the weight value of the sampling point corresponding to the lowest threshold is 0.5, the weight value of the sampling point corresponding to the middle threshold is 1.0, and the weight value of the sampling point corresponding to the highest threshold is 1.5. It should be understood that the values ​​here are only examples and should not be considered as limitations of this application. By assigning weights to each sampling point using a noise model, the negative impact of noise data in low signal-to-noise ratio regions on the fitting results can be effectively suppressed, thereby improving the robustness of the fitting results.

[0094] Furthermore, when determining the weight value based on the magnitude of the voltage value in different sampling points, weights can be assigned to all sampling points. For example, in some cases, the sampling points have already been screened during the sampling process, and the remaining sampling points have different degrees of value for fitting the flicker pulse. In this case, weights are assigned to all sampling points. Of course, weights can also be assigned to only some sampling points as needed. Some sampling points that are obviously noise (examples of obviously noise sampling points can be found in the examples in the method section of this application) can be discarded. Of course, obviously noise sampling points can also be assigned 0 as a weight. It is understood that, from the result, discarding and assigning 0 as a weight are the same, but from the process, the discarded sampling points do not participate in the fitting calculation, while the sampling points assigned 0 as a weight participate in the fitting calculation.

[0095] In another scenario, the contribution of sampling points on the rising, peak, and falling edges of the flash pulse to the fitting of the flash pulse waveform varies depending on the specific circumstances. For example, in some cases, the rising edge of the flash pulse contains the most critical information about the trigger time t0, thus the sampling points on the rising edge contribute more to the fitting of the flash pulse waveform. The falling edge, on the other hand, primarily reflects the decay time constant, and its information value decreases relatively when the time constant is pre-calibrated. Therefore, in the embodiments of this application, the noise model can be constructed based on the positions of the different sampling points on the flash pulse waveform. Specifically, in one example, the weight value of sampling points located on the rising edge of the flash pulse waveform is greater than the weight value of sampling points located on the falling edge of the flash pulse waveform. By assigning weights to the sampling points using the above noise model, the fitting algorithm can be guided to focus more on the data regions that contribute the most to the key parameters, accelerating convergence and improving the accuracy of key parameters (such as t0).

[0096] Similarly, when determining the weight value based on the position of different sampling points on the flicker pulse waveform, weights can be assigned to all sampling points. For example, in some cases, the sampling points have already been screened during the sampling process, and the remaining sampling points have different degrees of value for fitting the flicker pulse. In this case, weights are assigned to all sampling points. Of course, weights can also be assigned to only some sampling points as needed. Some sampling points that are obviously noise (examples of obviously noise sampling points can be found in the examples in the method section of this application) can be discarded. Of course, obviously noise sampling points can also be assigned 0 as a weight. It is understood that, from the result, discarding and assigning 0 as a weight are the same, but from the process, the discarded sampling points do not participate in the fitting calculation, while the sampling points assigned 0 as a weight participate in the fitting calculation.

[0097] In addition to the two cases mentioned above, in another case considered in this application, the waveform of the flicker pulse can be further divided in a more detailed manner. For example, in the process of constructing the noise model, the sampling points of the starting segment, the rapid rising segment, the rising edge near the peak segment, the peak point, the falling edge near the peak segment, the rapid falling segment, the smooth falling segment, and the ending segment of the flicker pulse can be assigned different or completely different weight values.

[0098] Furthermore, the various noise models mentioned above can be used in combination. It is understood that two or three models can be combined to fit a better flicker pulse waveform.

[0099] The noise model mentioned in this application can form different prior information based on different noises in different application scenarios. In application, different standard weighting methods can be selected from the prior information to assign weights to the sampling points according to the application scenario or noise type before performing waveform fitting of the flicker pulse.

[0100] Specifically, the fitting module 530 is configured to construct an objective function by combining the weight values ​​and fit the sampling points to obtain the flicker pulse. The method for fitting the sampling points can refer to existing methods for fitting flicker pulse waveforms. Generally, an objective function is constructed based on a known waveform model for fitting. Known waveform models can be categorized based on physical processes, empirical mathematical functions, circuit functions, etc. For example, based on physical processes, there are single-exponential models, double-exponential models, and multi-exponential models; based on empirical mathematical functions, there are Gaussian models, quasi-Gaussian models, and convolutional models; and based on circuit functions, there are CR-(RC) models. n Filter models. Among them, the double exponential model is the most common. The scintillation pulse shape of most inorganic scintillators (such as NaI(Tl), LaBr3(Ce)) and organic scintillators (such as plastic scintillators) can be well approximated by this model, and its expression is: Where t0 is the arrival time of the scintillation pulse, r1 and r2 are both related to the time constants of the scintillation crystal and the optoelectronic device (which can also be directly called time constants), and A is related to the incident gamma photon deposition energy, the light output of the scintillation crystal, and the SiPM gain.

[0101] Taking an LYSO / SiPM detector as an example, the objective function is constructed using a double exponential model. The objective functions constructed using the MVT method and the PP-MVT method for sampling will be listed below.

[0102] Without considering noise, the objective function constructed using the MVT method is as follows:

[0103]

[0104] Among them, L(A) ′ r ′ 1. r ′ 2. t ′ 0) represents the numerical solution, A ′ It is a parameter related to the deposition energy of photons, the light output of the scintillation crystal, and the gain of optoelectronic devices, t ′ 0 represents the photon arrival time, r ′ 1. r ′ 2 represents the time constant, i represents the i-th sampling point, and t i v represents the time contained in the i-th sampling point. i This represents the voltage contained in the i-th sampling point.

[0105] In this embodiment, considering noise, the objective function constructed using the MVT method becomes:

[0106]

[0107] Where, ω i This represents the weight value of the i-th sampling point, and the remaining letters are the same as those in the existing objective function.

[0108] Without considering noise, the objective function constructed using the PP-MVT method is as follows:

[0109]

[0110] Among them, t p v represents the peak time of the flash pulse. p This represents the peak voltage of the scintillation pulse; the remaining letters have the same meaning as in the objective function of the MVT method described above.

[0111] In this embodiment, considering noise, the objective function constructed using the PP-MVT method becomes:

[0112]

[0113] Where, ω p This represents the weight value of the peak sampling point; the other letters are the same as those in the other functions mentioned above.

[0114] Although the above only illustrates the application of the fitting method provided in this application to a scenario where a target function is constructed using a double exponential model, those skilled in the art should be able to apply the fitting method of this application to scenarios where other models are used to construct target functions for fitting.

[0115] To illustrate the fitting process, let's take the objective function constructed using the MVT method mentioned above as an example. For example, using the least squares method for iterative solution, generally, the iteration continues until the optimal solution is found when the function converges. In some special cases, the optimal solution cannot be found. In this case, a maximum number of iterations can be set as a convergence condition. When the iteration reaches the maximum number of iterations and has not yet converged normally, it will automatically stop and the last result will be used as the solution of the objective function.

[0116] In a preferred embodiment of this application, considering that some sampling points may become "outliers" due to dark pulse superposition, signal accumulation, or circuit disturbances, severely deviating from the theoretical model, the weight values ​​of the sampling points are corrected during the iterative fitting process, dynamically identifying and adjusting the weight values ​​of outliers. For example, the fitting module 530 is configured to: determine whether any sampling points are outliers during the iterative fitting of the sampling points using the objective function; if so, assign a weight value less than a predetermined value to the outlier sampling point, and then perform iterative fitting again using the new weight value. Thus, by continuously iteratively correcting outliers, the algorithm's anti-interference capability can be greatly enhanced, maintaining robust performance even under non-ideal working environments (such as high count rates and high dark noise rates).

[0117] More specifically, in this embodiment of the application, the fitting module 530 is configured to perform fitting using the following steps:

[0118] The first step is to construct an objective function based on the assigned weight values ​​and then use the objective function to fit the waveform of the flickering pulse;

[0119] The second step is to determine the residual for each sampling point based on the fitting results;

[0120] The third step is to determine the standard deviation based on the distribution of all the residuals, and then determine the outlier sampling points based on the residuals and the standard deviation.

[0121] The fourth step is to assign a weight value to the outlier sampling point that is less than the predetermined value;

[0122] Fifth, repeat steps one through four until convergence or the predetermined number of iterations is reached.

[0123] More specifically, the noise model in the first step above can be constructed using a combination of two factors: voltage values ​​at different sampling points, the position of different sampling points on the flicker pulse waveform, or one of these factors.

[0124] The scintillation pulse fitting device provided in this application no longer treats all sampling points equally. Instead, it considers the noise conditions existing in different scenarios, covering the comprehensive noise of the entire signal chain from scintillation light generation to digitization. It takes into account the different signal-to-noise ratios under different pulse amplitudes and detector operating conditions, accurately analyzes the sampling points before fitting, and flexibly selects the noise model to assign different weights to each sampling point. This can avoid getting stuck in local optima during the scintillation pulse waveform fitting process as much as possible, reduce errors, improve the accuracy of pulse parameter estimation, and fit the waveform more accurately.

[0125] It should be understood that Figure 5 The apparatus and modules shown can be implemented in various ways. For example, in some embodiments, the apparatus and modules can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution device, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and apparatus described above can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The apparatus and modules described in this application can be implemented not only with hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also with software, for example, executed by various types of processors, or with a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0126] It should be noted that the above description of the modules is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principle of the device, may arbitrarily combine the modules or construct subsystems connected to other modules without departing from this principle. For example, the modules may share a single storage module, or each module may have its own separate storage module. Such modifications are all within the scope of this specification.

[0127] Figure 6 This is a schematic diagram of a radiation detection system for implementing a scintillation pulse fitting method in one embodiment of this application. (Refer to...) Figure 6The radiation detection system S00 may include a processing component S20, which further includes one or more processors, and memory resources represented by a memory S22 for storing instructions, such as application programs, that can be executed by the processors of the processing component S20. The application programs stored in the memory S22 may include one or more instructions, with each module corresponding to a set of instructions. Furthermore, the processing component S20 is configured to execute instructions to perform the aforementioned fitting method.

[0128] The operations and / or methods described in the embodiments of this specification, implemented by a single processor, may also be implemented jointly or independently by multiple processors. For example, if, in this application specification, the processor of the processing device executes steps S100 to S300, it should be understood that steps S100 to S300 may also be executed jointly or independently by two different processors of the processing device (e.g., the first processor executes step S100, the second processor executes steps S200 to S300, or the first and second processors jointly execute steps S100 to S300).

[0129] The radiation detection system S00 may further include: a power supply component S24 configured to perform power management of the radiation detection system S00; a wired or wireless network interface S26 configured to connect the radiation detection system S00 to a network; and an input / output (I / O) interface S28. The radiation detection system S00 can operate on an operating system stored in memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or similar.

[0130] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory S22 including instructions, which can be executed by the processor of the radiation detection system S00 to perform the above method. The storage medium may be a computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0131] In an exemplary embodiment, a computer program product is also provided, the computer program product including instructions that can be executed by the processor of the radiation detection system S00 to perform the above method.

[0132] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, Figure 7This is an internal structural diagram of a computer device according to one embodiment of this application. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores user- and task-related data used in the aforementioned fitting method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for fitting blinking pulses.

[0133] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0135] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0136] It should be noted that the devices, electronic devices, servers, etc., described above according to the method embodiments may also include other implementation methods, and specific implementation methods can be referred to the description of the relevant method embodiments. Furthermore, new embodiments formed by the combination of features between various methods, devices, and server embodiments still fall within the scope of this application, and will not be elaborated upon here.

[0137] In the description of this specification, the references to "one embodiment," "an embodiment," and / or "some embodiments," "some embodiments," "other embodiments," "ideal embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiment or example, and certain features, structures, or characteristics in one or more embodiments of this specification may be appropriately combined.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

[0140] The basic concepts have been described herein. It is obvious that the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0141] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways, including any new and useful combinations of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “module,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0142] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0143] The computer program code required for the operation of each part of this manual can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran 3003, Perl, COBOL 3002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0144] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0145] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0146] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0147] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0148] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for fitting scintillation pulses, characterized in that, include: Several sampling points, each containing time and voltage information, were obtained by collecting scintillation pulses. Based on the information covered by the sampling points, one or a combination of multiple pre-built noise models are selected to assign weight values ​​that are not exactly the same or completely different to at least some of the sampling points; The objective function is constructed by combining the weight values, and the waveform of the flickering pulse is obtained by fitting the sampling points.

2. The fitting method according to claim 1, characterized in that, The noise model is constructed based on the magnitude of the voltage values ​​at different sampling points.

3. The fitting method according to claim 2, characterized in that, The noise model is constructed following the principle that the weight values ​​and voltage values ​​have a linear relationship.

4. The fitting method according to claim 2, characterized in that, The noise model is constructed following the principle that the weight values ​​and voltage values ​​have a positive linear relationship.

5. The fitting method according to claim 1, characterized in that, The noise model is constructed based on the positions of the different sampling points on the flicker pulse waveform.

6. The fitting method according to claim 5, characterized in that, The noise model is constructed based on the principle that the weight value of the sampling point located at the rising edge of the flicker pulse waveform is greater than the weight value of the sampling point located at the falling edge of the flicker pulse waveform.

7. The fitting method according to claim 1, characterized in that, The process of constructing an objective function by combining the weight values ​​and fitting it to the sampling points to obtain the flicker pulse includes: During the iterative fitting of the sampling points using the objective function, it is determined whether there are any outlier sampling points. If so, the outlier sampling points are assigned a weight value less than a predetermined value, and the iterative fitting is performed again using the new weight value.

8. The fitting method according to claim 7, characterized in that, The process of constructing an objective function by combining the weight values ​​and fitting it to the sampling points to obtain the flicker pulse includes: The first step is to construct an objective function based on the assigned weight values ​​and then use the objective function to fit the waveform of the flickering pulse; The second step is to determine the residual for each sampling point based on the fitted waveform. The third step is to determine the standard deviation based on the distribution of all the residuals, and then determine the outlier sampling points based on the residuals and the standard deviation. The fourth step is to assign a weight value to the outlier sampling point that is less than the predetermined value; Fifth, repeat steps one through four until convergence or the predetermined number of iterations is reached.

9. The fitting method according to claim 8, characterized in that, The noise model assigns weight values ​​to at least some of the sampling points based on the voltage values ​​at different sampling points and / or based on the positions of different sampling points on the flicker pulse waveform.

10. The fitting method according to claim 1, characterized in that, Several sampling points, each containing time and voltage information, were obtained by collecting scintillation pulses, including: Several sampling points are obtained by collecting scintillation pulses using an equal time interval sampling method or a multi-voltage threshold sampling method.

11. A device for fitting scintillation pulses, characterized in that, include: The acquisition module is configured to acquire flicker pulses to obtain several sampling points, each containing time and voltage information. The weighting module is configured to select one or combine multiple noise models from a pre-built noise model based on the information covered by the sampling points and assign weight values ​​that are not exactly the same or completely different to at least some of the sampling points; The fitting module is configured to construct an objective function by combining the weight values ​​and fit the sampling points to obtain the waveform of the flickering pulse.

12. The fitting device according to claim 11, characterized in that, The noise model is constructed based on the magnitude of the voltage values ​​at different sampling points.

13. The fitting device according to claim 12, characterized in that, The noise model is constructed following the principle that the weight values ​​and voltage values ​​have a linear relationship.

14. The fitting device according to claim 12, characterized in that, The noise model is constructed following the principle that the weight values ​​and voltage values ​​have a positive linear relationship.

15. The fitting device according to claim 11, characterized in that, The noise model is constructed based on the positions of the different sampling points on the flicker pulse waveform.

16. The fitting device according to claim 15, characterized in that, The noise model is constructed based on the principle that the weight value of the sampling point located at the rising edge of the flicker pulse waveform is greater than the weight value of the sampling point located at the falling edge of the flicker pulse waveform.

17. The fitting device according to claim 11, characterized in that, The fitting module is configured to: during the iterative fitting of the sampling points using the objective function, determine whether there are any outlier sampling points; if so, assign a weight value less than a predetermined value to the outlier sampling point, and then perform iterative fitting again using the new weight value.

18. The fitting device according to claim 12, characterized in that, The fitting module is configured to perform fitting using the following process: The first step is to construct an objective function based on the assigned weight values ​​and then use the objective function to fit the waveform of the flickering pulse; The second step is to determine the residual for each sampling point based on the fitted waveform. The third step is to determine the standard deviation based on the distribution of all the residuals, and then determine the outlier sampling points based on the residuals and the standard deviation. The fourth step is to assign a weight value to the outlier sampling point that is less than the predetermined value; Fifth, repeat steps one through four until convergence or the predetermined number of iterations is reached.

19. The fitting device according to claim 18, characterized in that, The weighting module is configured to determine weight values ​​for at least a portion of the sampling points based on voltage values ​​at different sampling points and / or based on the positions of different sampling points on the flicker pulse waveform.

20. The fitting device according to claim 11, characterized in that, The acquisition module is configured to acquire scintillation pulses to obtain several sampling points based on an equal time interval sampling method or a multi-voltage threshold sampling method.

21. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the fitting method according to any one of claims 1 to 10.

22. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the fitting method according to any one of claims 1 to 10.

23. A radiation detection system, characterized in that, Includes the fitting device as described in any one of claims 11 to 20.

Images