PEPT tracer particle trajectory reconstruction algorithm

Abstract

The invention discloses a PEPT tracer particle trajectory reconstruction algorithm, which is characterized in that framing and positioning processing is carried out on response line (LoRs) data captured by a PEPT detector, and overlapping segmentation is carried out on the data through a sliding window method to improve the utilization rate of the LoRs data, so that the positioning frequency of PEPT tracer particles is increased; meanwhile, the center point of the shortest line segment of two straight lines in different planes is introduced into the positioning process to serve as an approximate position of a twice positron annihilation emission response line (LoRs), so that the positioning process is closer to an actual physical process, and higher PEPT tracer particle positioning frequency is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of multiphase flow measurement, and in particular relates to a trajectory reconstruction algorithm of a PEPT tracing example. Background Art

[0002] PEPT (positron emission particle tracer) technology is a new type of non-intrusive and non-destructive measurement technology for complex multiphase flows in industrial processes. It can image the three-dimensional dynamics of various liquid, powder and particle systems. The basic principle of PEPT is to use the γ-photon 3D imaging method to detect the position of radioactive tracer particles. Since the radioactive nuclides in the tracer particles are very unstable, positron emission and positron annihilation events are very likely to occur. At the same time, γ-rays are radiated outward from the tracer particles and captured by the detector. Then the PEPT tracer particle positioning algorithm converts the captured γ-ray data into the position data of the tracer particles. Therefore, PEPT technology can measure the motion trajectory of tracer particles. Further dynamic solution can obtain the physical parameters such as the three-dimensional velocity field, turbulence field, density field and dispersion of PEPT tracer particles, which can be applied to the study of systems and flow phenomena of various scales, geometries and physical states.

[0003] Among them, the trajectory reconstruction of PEPT tracer particles is the core step of PEPT technology, which converts the gamma ray (also known as response line, Line of response, LoR) data captured by the PEPT detector into the trajectory of the tracer particle. Therefore, the PEPT tracer particle trajectory reconstruction algorithm needs to first frame the LoRs data stream, and then convert the single-frame LoRs data into a tracer particle spatial position. In theory, ignoring the movement distance of the tracer particle in a very short time, each response line (LoR) mathematically corresponds to a spatial straight line passing through the position of the tracer particle, and the intersection of two ideal straight lines (LoRs) is the spatial position of the tracer particle. However, due to the influence of scattering noise, environmental noise, etc., most LoRs are non-planar straight lines and cannot intersect at a point in space. Therefore, dozens or hundreds of LoRs are usually used to determine a spatial position of a PEPT tracer particle to ensure that there is enough valid data in a single frame of LoRs data.

[0004] At present, the main PEPT tracer particle trajectory reconstruction algorithms include: Birmingham algorithm, line density algorithm and clustering algorithm. These algorithms generally use direct segmentation methods. After segmentation, each frame of data is connected end to end to form the original data stream, which makes the utilization rate of LoRs data low, and then leads to the problem of low PEPT tracer particle positioning frequency. At the same time, the Birmingham algorithm simplifies the single-frame PEPT positioning problem to solving a point in space so that the sum of its distances to all straight lines is minimized, also known as the minimum distance point; the line density algorithm and clustering algorithm convert the single-frame LoRs data into the distribution of straight lines in a three-dimensional grid space, and then approximate the position with higher line density as the position of the tracer particle. These methods simplify the positioning process without considering the actual physical process of positron annihilation. Summary of the invention

[0005] In view of the above problems, the object of the present invention is to provide a trajectory reconstruction algorithm for PEPT tracing examples.

[0006] The specific technical solutions are as follows:

[0007] A trajectory reconstruction algorithm for a PEPT tracing example includes the following steps:

[0008] Step 1: Read the LoRs (Lines of Response) data stream collected by the PEPT detector, where the data format corresponding to each LoR is [t, x A ,y A , z A , x B ,y B , z B ], where t represents the data collection time of the LoR, x A ,y A , z A and x B ,y B , z B Represent the coordinates of the space where points A and B are located, and calculate the sampling rate N of LoRs data s =N all / t all , in Hz, where N all , t all Respectively represent the total number of LoRs data and the total acquisition time, in seconds;

[0009] Step 2: According to the measurement conditions of PEPT, input the estimated effective LoRs ratio f, which represents the proportion of effective data in all LoRs data, generally set in the range of 20% to 60%; estimate the single-frame LoRs sample length Where Δ is the intrinsic resolution of the detector, in mm, and v is the estimated speed of the tracer particles, in m / s; setting the moving step k of the frame sequence (moving step of the sample window per frame) determines the data overlapping area during subsequent data segmentation. Generally, the smaller the k value, the higher the utilization rate of LoRs, that is, the higher the final PEPT tracer particle positioning frequency. However, infinitely reducing the value of k will also lead to a large amount of redundancy in the positioning data and a huge amount of calculation. Usually, k will be selected from (0.1~0.5)w.

[0010] Step 3: Calculate the shortest line segment length d0 and the line segment center point (x0, y0, z0) of each pair of LoRs (non-coplanar straight lines) between the fth LoR and the k LoRs before and after it. At this time, each LoR data corresponds to 2k shortest line segments and their center points, and the unit of d0 is m.

[0011] Step 4: Based on the sliding window method, the LoRs sequence data is framed, and w LoRs and their corresponding shortest line segments and center points (2kw) are obtained in each frame. The average acquisition time of the LoRs data in this frame is calculated as the timestamp t′ of the data in this frame. i , unit is s;

[0012] Step 5: Before locating the PEPT tracer particles in a single frame of data, remove the center points of the shortest line segments outside the detector field of view and corresponding to the shortest line segment length greater than 2R to eliminate the influence of some environmental noise, where R is the equivalent radius of the detection field of view, in meters.

[0013] Step 6: The remaining shortest line center points are grouped into a point group, the least squares method is used to fit the centroid of the point group, and the distance d from the center points of all remaining shortest line segments to the centroid is calculated. j , unit is m.

[0014] Step 7: Calculate the RMS of all distances And D use Remove the farthest position (d j >D use )’s shortest line segment center point, the number of remaining valid shortest line segment center points is N use .

[0015] Step 8: Return to step 6 and re-execute steps 6 and 7 until the remaining effective shortest line segment center points account for N use / 2kw is not higher than the preset ratio f.

[0016] Step 9: Output the time and position [t′, x′, y′, z′] of the PEPT tracer particle in the current frame, and enter the next frame to perform the calculation in a loop starting from step 5.

[0017] Step 10: After all frames are calculated, the complete motion trajectory of the PEPT tracer particles is output.

[0018] The beneficial effects of the present invention are:

[0019] The present invention provides a trajectory reconstruction algorithm for a PEPT tracing example, the positioning process of which is closer to the positron annihilation physical process and has a higher PEPT tracing particle positioning frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of LoRs data segmentation based on sliding window method;

[0021] Figure 2 It is a detailed flow chart of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited thereto.

[0023] like Figure 2 As shown, a trajectory reconstruction algorithm for a PEPT tracing example includes the following steps:

[0024] Step 1: Read the LoRs data stream collected by the PEPT detector, where the data format corresponding to each LoR is [t, x A ,y A , z A , x B ,y B , z B ], where t represents the data collection time of the LoR, x A ,y A , z A and x B ,y B , z B Represent the coordinates of the space where points A and B are located, and calculate the sampling rate N of LoRs data s =N al1 / t all , where N a11 , t all Respectively represent the total number of LoRs data and the total acquisition time;

[0025] Step 2: According to the measurement conditions of PEPT, input the estimated effective LoRs ratio f, which represents the proportion of effective data in all LoRs data, generally set in the range of 20% to 60%; estimate the single-frame LoRs sample length w is the number of samples in a single frame, where Δ is the intrinsic resolution of the detector and v is the estimated speed of the tracer particles. Setting the moving step k of the frame sequence determines the data overlapping area during subsequent data segmentation. Generally, a smaller k value will lead to a higher utilization rate of LoRs, that is, a higher frequency of PEPT tracer particle positioning. However, infinitely reducing the value of k will also lead to a large amount of redundancy in the positioning data and a huge amount of calculation. Usually, k will be selected from (0.1 to 0.5)w.

[0026] Step 3: Calculate the shortest line segment length d0 and the line segment center point (x0, y0, z0) of each pair of LoRs (non-coplanar straight lines) between the i-th LoR and the k LoRs before and after it. At this time, each LoR data corresponds to 2k shortest line segments and their center points.

[0027] Step 4: Based on the sliding window method, the LoRs sequence data is framed, and w LoRs and their corresponding shortest line segments and center points (2kw) are obtained in each frame. The average acquisition time of the LoRs data in this frame is calculated as the timestamp t′ of the data in this frame. i , such as Figure 1 shown.

[0028] Step 5: Before locating the PEPT tracer particles in a single frame of data, remove the center point of the shortest line segment outside the detector field of view to eliminate the influence of some environmental noise.

[0029] Step 6: The remaining shortest line center points are grouped into a point group, the least squares method is used to fit the centroid of the point group, and the distance d from the center points of all remaining shortest line segments to the centroid is calculated. j .

[0030] Step 7: Calculate the RMS of all distances And D use Remove the farthest position (d i >D use )’s shortest line segment center point, the number of remaining valid shortest line segment center points is N use .

[0031] Step 8: Return to step 6 and re-execute steps 6 and 7 until the remaining effective shortest line segment center points account for N use / 2kw is not higher than the preset ratio f.

[0032] Step 9: Output the time and position [t′, x′, y′, z′] of the PEPT tracer particle in the current frame, and enter the next frame to perform the calculation in a loop starting from step 5.

[0033] Step 10: After all frames are calculated, the complete motion trajectory of the PEPT tracer particles is output.

[0034] Specific example: For a typical PEPT detection system, the inherent resolution of the detector is Δ = 6mm, the effective detection efficiency f is about 20%, and the number of response lines LoRs collected in 10 minutes is 60,000,000, so the average sampling rate of LoRs is N s =100kHz; PEPT tracer particles move within the detector field of view at a speed of 1m / s.

[0035] For the LoRs data collected by this typical detection system, the PEPT positioning frequency of the traditional positioning method (Birmingham algorithm) is about 250 times / second.

[0036] Using the positioning algorithm in the patent of this invention, according to the single frame LoRs data length estimation formula Usually the value of k ranges from 0.1 to 0.5w. In this example, the average value k = 0.25w ≈ 48 is selected, which can achieve a speed of 100 ms per second for PEPT tracer particles. The positioning frequency is about 8 times that of traditional methods.

Claims

1. A trajectory reconstruction algorithm for PEPT tracer particles, characterized in that: The steps include: Step 1: Read the LoRs data stream collected by the PEPT detector and calculate the sampling rate N of the LoRs data s , unit is Hz; Step 2: According to the measurement conditions of PEPT, input the estimated effective LoRs ratio f, estimate the single-frame LoRs sample length w and the moving step k of the frame sequence; Step 3: Calculate the shortest line segment length d0 and the line segment center point (x0, y0, z0) of each pair of LoRs between the i-th LoR and its previous and next k LoRs. At this time, each LoR data corresponds to 2k shortest line segments and their center points. The unit of d0 is m. Step 4: Based on the sliding window method, the LoRs sequence data is framed and processed. Each frame obtains w LoRs and their corresponding shortest line segments and 2kw center points, and the average acquisition time of the LoRs data of this frame is calculated as the timestamp t of this frame data. i ′ , unit is s; Step 5: Before locating the PEPT tracer particles in a single frame of data, remove the center points of the shortest line segments outside the detector field of view and corresponding to the shortest line segment length greater than 2R to eliminate the influence of some environmental noise, where R is the equivalent radius of the detection field of view, in meters; Step 6: Use the minimum multiplication method to fit the centroid of the remaining shortest line segment center point group, and calculate the distance d from all remaining center points to the centroid j , unit is m; Step 7: Take the root mean square of all distances and D use As a guideline, remove d j >D use The number of remaining valid shortest line segment center points is N use ; Step 8: Return to step 6 and re-execute steps 6 and 7 until the remaining effective shortest line segment center points account for N. use / 2kw is not higher than the preset ratio f; Step 9: Output the time and position of the PEPT tracer particle in the current frame [t ′ ,x ′ ,y ′ ,z ′ ] and enter the next frame and start the calculation loop from step 5; Step 10: After all frames are calculated, the complete motion trajectory of the PEPT tracer particles is output.

2. A PEPT tracer particle trajectory reconstruction algorithm as claimed in claim 1, characterized in that: The data format of the LoRs data stream collected in the first step is [t,x A ,y A ,z A ,x B ,y B ,z B ], t represents the data acquisition time of LoR, x A ,y A ,z A and x B ,y B ,z B Respectively represent the coordinates of the space where points A and B are located; the sampling rate N s =N all / t all , where N all ,t all Respectively represent the total number of LoRs data and the total acquisition time, in seconds.

3. A trajectory reconstruction algorithm for PEPT tracer particles as claimed in claim 2, characterized in that: The length of LoRs samples in a single frame in the second step Where Δ is the intrinsic resolution of the detector in mm, v is the estimated velocity of the tracer particles in m / s, f is set to 20-60%, and k is selected to be 0.1-0.5w.

4. A PEPT tracer particle trajectory reconstruction algorithm as claimed in claim 3, characterized in that: The root mean square sum of all distances in step 7

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