Method for calibrating physical location of gamma event and related device

Abstract

The invention discloses a calibration method of a physical position of a gamma event and a related device, and relates to the technical field of nuclear medicine gamma photon detection, and the method constructs a frame data image based on an original physical position and an energy value of the gamma event collected by a detector under a preset point mode condition. And segmenting the frame data image by using a watershed algorithm, and adjusting to ensure that the number of the segmented grids is consistent with the number of the dot matrixes of the dot model. And calculating the accurate actual physical position of the gamma event by combining the pixel position and the pixel value of each grid, and calculating the accurate theoretical physical position of the gamma event according to the pixel position and the pixel value of each grid and the row number, the column number and the spacing of the point module. And finally, the accurate physical position deviation of the gamma event is calculated by comparing the theoretical physical position with the actual physical position, and the original physical position is calibrated according to the accurate physical position deviation, so that full-automatic calibration without manual intervention is realized, and the accuracy and efficiency of calibration of the physical position of the gamma event are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of nuclear medicine gamma photon detection technology, and in particular to a method for calibrating the physical position of a gamma event and related devices. BACKGROUND

[0002] Gamma photons are the basic signal carriers in nuclear medicine imaging, released by the decay of radioactive tracers in the body, carrying functional metabolic information. When a gamma photon is absorbed by a detector crystal, it will trigger a series of physical responses, forming an electrical signal pulse that can be recorded by an electronic system, i.e., a gamma event. Due to differences in hardware device characteristics and non-uniform detector responses, systematic deviations and nonlinear distortions exist in the original physical positions of detected gamma events, affecting subsequent analysis. Therefore, calibration of the physical position of a gamma event is particularly important.

[0003] Currently, manual calibration of the original physical position of a gamma event collected by a detector through a preset point model is mainly performed based on experience.

[0004] However, the manual intervention process is tedious and time-consuming, and when the physical position of a gamma event is complex, misjudgments are likely to occur, resulting in poor accuracy and efficiency of the calibration of the physical position of a gamma event. SUMMARY

[0005] In view of the above problems, the present application provides a method for calibrating the physical position of a gamma event and related devices, in order to improve the accuracy and efficiency of the calibration of the physical position of a gamma event, the specific solutions are as follows:

[0006] The first aspect of the present application provides a method for calibrating the physical position of a gamma event, comprising:

[0007] obtaining the original physical position of a gamma event collected by a detector through a preset point model and the energy value of the gamma event;

[0008] constructing a frame data image of the gamma event based on the original physical position of the gamma event and the energy value of the gamma event;

[0009] segmenting the frame data image of the gamma event using a watershed algorithm to obtain a segmented frame data image of the gamma event, and obtaining an adjusted frame data image of the gamma event adjusted from the segmented frame data image of the gamma event, the number of grids of the adjusted frame data image of the gamma event being consistent with the number of dot arrays of the preset point model;

[0010] calculating the actual physical position of the gamma event based on the pixel positions of each grid of the adjusted frame data image of the gamma event and the pixel values of each grid of the adjusted frame data image of the gamma event;

[0011] calculating a theoretical physical position of the gamma event based on the pixel position of each grid of the adjusted frame data image of the gamma event, the pixel value of each grid of the adjusted frame data image of the gamma event, the number of dot array of the preset dot matrix, the number of preset dot array of the preset dot matrix, and the dot pitch of the preset dot matrix;

[0012] calculating a physical position deviation of the gamma event based on the theoretical physical position of the gamma event and the actual physical position of the gamma event, and calibrating the original physical position of the gamma event based on the physical position deviation of the gamma event.

[0013] In a possible implementation, the constructing the frame data image of the gamma event based on the original physical position of the gamma event and the energy value of the gamma event comprises:

[0014] obtaining an energy peak value of the gamma event in the energy value of the gamma event, and calculating an energy window range of the gamma event based on a preset energy window threshold and the energy peak value of the gamma event;

[0015] determining each energy value of the gamma event within the energy window range of the gamma event as an effective energy value of the gamma event, and determining the effective energy value of the gamma event as the pixel value of the gamma event;

[0016] calculating a pixel position corresponding to the pixel value of the gamma event based on each original physical position of the gamma event corresponding to the pixel value of the gamma event, a preset image resolution, and a preset image size;

[0017] constructing the frame data image of the gamma event based on the pixel value of the gamma event and the pixel position corresponding to the pixel value of the gamma event.

[0018] In a possible implementation, the segmenting the frame data image of the gamma event by using the watershed algorithm to obtain the segmented frame data image of the gamma event comprises:

[0019] analyzing the frame data image of the gamma event to obtain an energy gradient amplitude image of the gamma event and an energy brightness marker point of the gamma event;

[0020] segmenting the frame data image of the gamma event by using the watershed algorithm based on the energy gradient amplitude image of the gamma event and the energy brightness marker point of the gamma event to obtain the segmented frame data image of the gamma event.

[0021] In a possible implementation, the calculating the actual physical position of the gamma event based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event comprises:

[0022] calculate a pixel centroid position of each grid of the adjusted frame data image of the gamma event based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event;

[0023] calculate the actual physical position of the gamma event based on the pixel centroid position of each grid of the adjusted frame data image of the gamma event, the preset image resolution and the preset image size.

[0024] In a possible implementation, the calculating the theoretical physical position of the gamma event based on the pixel position of each grid of the adjusted frame data image of the gamma event, the pixel value of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset dot matrix, the preset number of dot array of the preset dot matrix and the dot pitch of the preset dot matrix comprises:

[0025] calculate a pixel centroid position of each grid of the adjusted frame data image of the gamma event based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event;

[0026] assign a corresponding index to each grid of the adjusted frame data image of the gamma event based on the pixel centroid position of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset dot matrix and the preset number of dot array of the preset dot matrix.

[0027] calculate the theoretical physical position of the gamma event based on the index of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset dot matrix, the preset number of dot array of the preset dot matrix and the dot pitch of the preset dot matrix.

[0028] In a possible implementation, after the segmenting the frame data image of the gamma event by using the watershed algorithm to obtain the segmented frame data image of the gamma event and obtaining the adjusted frame data image of the gamma event adjusted from the segmented frame data image of the gamma event, the method further comprises:

[0029] based on the number of pixels in each grid of the adjusted frame data image of the gamma event being greater than a preset number of pixels;

[0030] determining a target grid of the adjusted frame data image of the gamma event from each grid of the adjusted frame data image of the gamma event, the number of pixels in the target grid of the adjusted frame data image of the gamma event being greater than a preset number of pixels;

[0031] update each grid of the adjusted frame data image of the gamma event to a target grid of the adjusted frame data image of the gamma event.

[0032] In a possible implementation, after the physical position deviation of the gamma event is calculated based on the theoretical physical position of the gamma event and the actual physical position of the gamma event, the method further includes:

[0033] complement the physical position deviation of the gamma event based on a preset boundary deviation to obtain a complemented physical position deviation of the gamma event;

[0034] perform spatial interpolation on the complemented physical position deviation of the gamma event by using a cubic interpolation algorithm to obtain an interpolated physical position deviation of the gamma event;

[0035] update the physical position deviation of the gamma event to the interpolated physical position deviation of the gamma event.

[0036] The second aspect of the application provides a computer program product, including computer readable instructions, when the computer readable instructions run on an electronic device, the electronic device implements the calibration method of the physical position of the gamma event of the first aspect or any implementation manner of the first aspect.

[0037] The third aspect of the application provides an electronic device, including at least one processor and a memory connected with the processor, wherein:

[0038] The memory is configured to store a computer program;

[0039] The processor is configured to execute the computer program, so that the electronic device can implement the calibration method of the physical position of the gamma event of the first aspect or any implementation manner of the first aspect.

[0040] The fourth aspect of the application provides a computer storage medium, the storage medium carries one or more computer programs, when the one or more computer programs are executed by an electronic device, the electronic device can implement the calibration method of the physical position of the gamma event of the first aspect or any implementation manner of the first aspect.

[0041] By means of the technical scheme, the application provides a gamma event physical position calibration method and related device, the method comprising: constructing a frame data image based on the original physical position and energy value of the gamma event collected by the detector under a preset point source condition, converting discrete events into visual spatial distribution. The frame data image is segmented by using a watershed algorithm, and the number of segmented grids is ensured to be consistent with the number of point source arrays by adjustment, effectively avoiding misjudgment caused by over-segmentation or under-segmentation. On this basis, the accurate actual physical position of the gamma event is calculated in combination with the pixel position and pixel value of each grid, and the accurate theoretical physical position of the gamma event is calculated in combination with the pixel position and pixel value of each grid and the number of rows, the number of columns and the spacing of the point source. Finally, the accurate physical position deviation of the gamma event is calculated by comparing the theoretical physical position with the actual physical position, and the original physical position is calibrated accordingly, realizing fully automatic calibration without manual intervention, and significantly improving the accuracy and efficiency of the gamma event physical position calibration. BRIEF DESCRIPTION OF DRAWINGS

[0042] The above and other features, advantages, and aspects of the present disclosure will become more apparent by referring to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, like or similar reference numerals are used to refer to like or similar elements. It should be understood that the drawings are schematic and elements and features are not necessarily to scale.

[0043] Figure 1 A flowchart of a gamma event physical position calibration method provided by an embodiment of the application;

[0044] Figure 2 A frame data image of a gamma photon event provided by an embodiment of the application;

[0045] Figure 3 A segmented frame data image of a gamma photon event provided by an embodiment of the application;

[0046] Figure 4 A frame data image of a gamma photon event provided by an embodiment of the application before adjustment;

[0047] Figure 5 A frame data image of a gamma photon event provided by an embodiment of the application after adjustment;

[0048] Figure 6 A frame data image of a gamma photon event provided by an embodiment of the application before index allocation;

[0049] Figure 7 A frame data image of a gamma photon event provided by an embodiment of the application after index allocation;

[0050] Figure 8A hardware structure schematic diagram of an electronic device provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described below in conjunction with the accompanying drawings. The terms used in the embodiment part of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0052] The embodiments of the present application are described below in conjunction with the accompanying drawings. The skilled in the art can know that, with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0053] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, and this is only a distinguishing way used in the description of the embodiments of the present application to describe the objects with the same attributes. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, so that the processes, methods, systems, products or equipment containing a series of units do not have to be limited to those units, but can include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0054] In order to improve the accuracy and efficiency of calibrating the physical position of a gamma event, the present application provides a method for calibrating the physical position of a gamma event. The method for calibrating the physical position of a gamma event provided by the present application is described in further detail below in conjunction with the accompanying drawings and the specific embodiments.

[0055] Please refer to the accompanying Figure 1 , Figure 1 A flowchart of a method for calibrating the physical position of a gamma event provided by an embodiment of the present application is provided. The method can include the following steps:

[0056] Step S101: Obtain the original physical position of the gamma event and the energy value of the gamma event collected by the preset point module of the detector.

[0057] It should be noted that γ refers to the process of detecting one incident γ photon by the detector and forming a complete record. After the γ photon is absorbed by the crystal, the system collects the optical signal through the photomultiplier tube, processes it through the electronic circuit, and finally outputs a set of data containing spatial position, energy, time, etc. This data record is called a γ event, which is the basic data unit for subsequent image reconstruction and calibration. The original physical position refers to the two-dimensional coordinate of the γ event on the detector plane calculated by the positioning circuit of the detector according to the relative intensity distribution of the photomultiplier tube signals (such as using the center of gravity method or the maximum likelihood method), usually represented as (raw x , raw y ), unit: millimeter (mm). This position is not corrected by any non-linear distortion, and reflects the actual response of the detector in the current state. The energy value refers to the value obtained by measuring the total pulse height of the γ event by the energy circuit of the detector, usually represented as E, unit: kiloelectronvolt (keV). The preset point module is a standard module used for detector performance calibration, usually made of high-density materials (such as lead or tungsten), and has multiple small holes arranged according to precise rules, allowing only γ photons in specific directions to pass through.

[0058] In this application, first, by placing the preset point module in front of the detector, the regular hole array structure is used to constrain the space of γ rays, so that each small hole corresponds to a certain theoretical incident direction and ideal response position. Under this condition, the γ photons released by the decay of radionuclides (such as 99m Tc) enter the detector after passing through the small holes of the point module, interact with the scintillation crystal and produce visible light flashes. Multiple photomultiplier tubes receive the light signal and output corresponding voltage pulses. The positioning circuit calculates the occurrence position of this interaction according to the relative intensity distribution of the photomultiplier tube signals, using algorithms such as the center of gravity method, to obtain the original physical position of the γ event (raw x , raw y ). At the same time, the energy circuit sums all the photomultiplier tube signals and measures their total pulse height to obtain the energy value E of the event. These data reflect the spatial response characteristics of the detector in the real working state.

[0059] For ease of understanding, the following example is given: in the actual calibration process, a lead preset point module with 55 rows × 42 columns of regularly arranged small holes is used. The 99m Tc radioactive source is placed behind the point module, and the γ photons enter the detector crystal one after another after passing through each small hole. When the γ photons hit the crystal, they trigger the scintillation light, and the light signal is received by multiple photomultiplier tubes and converted into voltage pulses. The positioning circuit calculates the original physical position of the event (raw x , raw y) = (-148.6mm, -162.4mm), and its energy value E = 139.2keV.

[0060] Step S102: Construct a frame data image of the γ event based on the original physical location and energy value of the γ event.

[0061] In this application, the peak energy value of the gamma event is first obtained, and the energy window range of the gamma event is calculated based on a preset energy window threshold and the peak energy value of the gamma event. Then, the energy values ​​of each gamma event within the energy window range of the gamma event are determined as the effective energy values ​​of the gamma event, and the effective energy values ​​of the gamma event are determined as the pixel values ​​of the gamma event.

[0062] Then, based on the original physical location of each γ event corresponding to its pixel value, the preset image resolution, and the preset image size, the pixel position corresponding to the pixel value of the γ event can be calculated. Finally, based on the pixel value of the γ event and the pixel position corresponding to its pixel value, the frame data image of the γ event can be constructed.

[0063] Specifically, the peak energy of a gamma event is the maximum energy deposited for each gamma event, determined by the pulse height output by the photomultiplier tube. 99m Taking Tc as an example of a radioactive source, the gamma photons emitted by this nuclide have a characteristic energy peak of 140.5 keV. This means that, ideally, all gamma photons emitted by Tc can reach a peak energy peak of 140.5 keV. 99m A gamma event emitted by Tc and recorded by a detector should have an energy value close to 140.5 keV. However, in practice, the actual energy of a gamma event may deviate due to factors such as scattering and differences in detector efficiency. To filter out the data most likely representing the true gamma event, we calculate an energy window range based on the energy peak and a preset energy window threshold. This energy window range is used to filter out non-target events that may be caused by scattering or noise, ensuring that only the energy values ​​of gamma events falling within this energy window range are determined as valid gamma event energy values. For example, for 99m Tc is set with an energy window threshold of ±10%, meaning it fluctuates around 10% of 140.5 keV, resulting in an energy window range of 126-154 keV. The original physical location of the γ event is raw. x =-148.6mm, raw y =162.4mm, its energy value E=139.2keV. Since this energy value is within the set energy window range, the energy value of this gamma event is the effective energy value.

[0064] You can first calculate the center pixel of the image based on the image dimensions (including image length and image width). Refer to the following formula for details: x center_px= (image length - 1) / 2 and y center_px = (image width - 1) / 2, the image center pixel corresponds to the physical coordinate origin (0, 0) mm, and the image center pixel can be taken as the center pixel offset. Then the original physical position (raw x , raw y ) of the gamma event can be converted into the pixel position (row, col) in the image based on the center pixel offset and the image resolution resolutionraw through a simple mathematical transformation, that is, the original physical position raw x and raw y of the gamma event can be divided by the pixel resolution respectively and the corresponding center pixel offset is added to obtain the pixel position of the gamma event in the image, and the specific formula can be referred to the following formula: and . For ease of understanding, the following example is given: the pixel resolution is 2.0 mm / pixel, the image size is 500x500 pixels, the image center pixel is x center_px = (500 - 1) / 2 = 249.5 and y center_px = (500 - 1) / 2 = 249.5, the image center pixel (249.5, 249.5) corresponds to the physical coordinate origin (0, 0) mm, and the image center pixel (249.5, 249.5) can be taken as the center pixel offset. Then the original physical position (-148.6 mm, -162.4 mm) of the gamma event can be converted to obtain the pixel position .

[0065] The effective energy value of a gamma event not only indicates the energy level of the gamma event, but also serves as a basis for constructing a frame data image in subsequent steps. The effective energy value of each gamma event contributes a certain gray value to the pixel of the frame data image of the gamma event. For each valid event, its corresponding energy value (i.e., pixel value) can be accumulated into the corresponding position in the image array. This means that if multiple events are mapped to the same pixel or adjacent pixel area, the gray value of these pixels will increase, reflecting a higher energy density or count rate. This process is repeated until all valid events are processed, and a frame data image is finally formed. For ease of understanding, the following example is given: there are 2310 bright spots in this 55 row x 42 column image, each corresponding to a response area of a point hole, and the brightness reflects the total energy or count rate of the gamma events in the area. Multiple 139.2 keV events are accumulated at the (175, 168) position of the image array, and due to the large number of gamma events gathered here, the gray value of this pixel can be very high, reaching about 5000 keV, forming a significant bright spot. This bright spot not only demonstrates the spatial response characteristics of the detector, but also provides a high-quality data basis for subsequent watershed segmentation algorithms, facilitating accurate image segmentation and position calibration. For details, please refer to Figure 2 , Figure 2 A schematic diagram of a frame data image of a gamma event is provided for the embodiments of the present application. Through the above steps, the discrete gamma events are converted into a structured two-dimensional frame data image, laying a solid foundation for further analysis.

[0066] Step S103: using a watershed algorithm to segment the frame data image of the gamma event to obtain a segmented frame data image of the gamma event, and obtaining an adjusted frame data image of the gamma event from the segmented frame data image of the gamma event, the number of grids of the adjusted frame data image of the gamma event being consistent with the number of dot arrays of the preset point module.

[0067] In the present application, the frame data image of the gamma event can be analyzed to obtain an energy gradient amplitude image of the gamma event and an energy brightness marker point of the gamma event. Based on the energy gradient amplitude image of the gamma event and the energy brightness marker point of the gamma event, the frame data image of the gamma event can be segmented using a watershed algorithm to obtain a segmented frame data image of the gamma event.

[0068] The energy frame image is a distribution map of photons released after gamma rays interact with matter captured by a detector. The gray value of each point reflects the energy deposition level at that position. These images often contain noise, overlapping areas and other interference factors, so they need to be processed to extract useful information.

[0069] To identify the boundaries or edges between different regions. These places are usually where the energy changes most dramatically. Apply a gradient operator (such as the Sobel operator, Prewitt operator, etc.) to the original energy frame image. This step will calculate the energy change rate along the x-axis and y-axis directions at each pixel point. According to the change rate in these two directions, a comprehensive gradient vector can be calculated, whose size (i.e. gradient amplitude) represents the degree of energy change at that point. The gradient direction provides additional information about the boundary, but in this scenario, the main focus is on the gradient amplitude image. To find potential seed points for the initial segmentation of the watershed algorithm. These points should correspond to local maximum points with the highest energy, as they are likely to be located at the center of the point-like hole. Peak detection algorithms (such as non-maximum suppression techniques) can be used to find local maximum points in the energy frame image. This process may include smoothing to reduce the impact of noise. Verify each candidate point to ensure it meets certain threshold conditions, such as minimum energy intensity, to exclude false positive results caused by noise. Take the resulting energy gradient amplitude image as input and execute the watershed algorithm using the previously determined energy intensity marker points as seed points. This stage may cause over-segmentation problems, generating too many small regions (such as 2350 regions). The generated label_map[x][y] is a two-dimensional array, where each element represents the segmentation region number to which the corresponding position (x, y) on the image belongs. label_map[x][y]=k (k=1~2350) means that the pixel belongs to the region corresponding to the kth point-like hole, while label_map[x][y]=0 represents a background pixel. Finally, the segmented frame data image of the gamma photon event is obtained, which can be referred to in detail Figure 3 , Figure 3 A segmented frame data image of a gamma photon event provided by an embodiment of the present application. Based on the energy gradient amplitude image of the gamma event and the energy intensity marker points of the gamma event, the frame data image of the gamma event is segmented using the watershed algorithm to obtain the segmented frame data image of the gamma event.

[0070] Since the initial segmentation can lead to over-segmentation (i.e. an actual point hole is mistakenly segmented into multiple small regions), further processing is needed. This usually includes merging small regions (which can be false segmentation caused by noise) and using morphological operations (such as closing) to fix region boundaries. In order to make the grid number of the segmented frame data image consistent with the dot matrix number of the preset point module, an accurate alignment step needs to be performed. This means determining the correspondence between each segmented region and a specific point in the preset point module. This can be achieved in the following ways: geometric transformation: if the layout of the point module hole is known, appropriate geometric transformations (such as translation, rotation, scaling) can be applied to align the segmentation result and the preset point module. Template matching: use template matching techniques to find the best alignment so that the segmented regions correspond to the point module holes as accurately as possible. After correct alignment, a grid can be created that matches the dot matrix number of the preset point module. Each grid cell represents a point module hole and contains the energy deposition information of that position. For those cases where there is no direct correspondence to the segmented region, interpolation or assignment can be made according to the information of the adjacent region. Finally, the adjusted frame data image is verified to ensure that its grid number is indeed completely consistent with the dot matrix number of the preset point module. This involves checking whether each grid cell is correctly associated with a specific point module hole and whether all expected point module holes are appropriately represented in the final image. At this point, label_map[x][y]=k (k=1~2310) means that the pixel belongs to the region corresponding to the kth point module hole. The clear and accurate point module response pattern is extracted from the complex gamma photon event frame data image, and the adjusted frame data image of the gamma photon event is obtained, which can be referred to in detail Figure 4 and Figure 5 , Figure 4 a gamma photon event frame data image before adjustment provided by an embodiment of the present application, Figure 4 a gamma photon event frame data image after adjustment provided by an embodiment of the present application.

[0071] Further, based on the number of pixels in each grid of the adjusted frame data image of the gamma event being greater than the preset number of pixels, a target grid of the adjusted frame data image of the gamma event is determined from each grid of the adjusted frame data image of the gamma event, and the number of pixels in the target grid of the adjusted frame data image of the gamma event is greater than the preset number of pixels. Each grid of the adjusted frame data image of the gamma event is updated to the target grid of the adjusted frame data image of the gamma event.

[0072] To further optimize and validate the gamma event-adjusted frame data image, a series of meticulous processing steps are required. These steps include filtering target grids based on pixel count, updating grids, and ensuring connectivity of segmentation lines and identifying valid regions. Here is a detailed explanation: First, set a pre-defined pixel count threshold, which can be determined according to the actual application scenario. For example, assume we set the pre-defined pixel count to 50 pixels, meaning only those grids containing more than 50 pixels will be considered valid target grids. For each grid (i.e., the area corresponding to a point module hole), calculate the total number of pixels it contains. This can be achieved by traversing the label_map array and counting all pixels corresponding to each label. For example, if label_map[x][y]=k, count the number of all pixels equal to k. Compare the pixel count of each grid with the pre-defined pixel count threshold. If the pixel count of a grid is greater than or equal to the pre-defined value, mark it as a target grid. These target grids represent valid gamma event clustering areas, which may correspond to real point module hole responses. Update all grids in the adjusted frame data image to target grids. This means that non-target grids (i.e., those with pixel counts below the pre-defined value) may be ignored or merged into adjacent target grids to ensure that the final result only contains significant gamma event clustering areas.

[0073] To ensure the quality of the segmentation result, it is necessary to ensure that each pixel on each segmentation line has at least two adjacent pixels (up, down, left, right). This avoids discontinuous boundaries caused by isolated noise points, thereby improving the accuracy of segmentation. You can traverse each pixel on the segmentation line and check if there are other pixels belonging to the same segmentation line in the four directions above, below, left and right. If there are no adjacent pixels that meet the conditions, the pixel is considered isolated and may be a false segmentation caused by noise. Once an isolated pixel is detected, it can be reassigned to the nearest adjacent large area, or techniques such as morphological closing operation can be used to fill small gaps and enhance the continuity of the segmentation line.

[0074] Perform attribute analysis on each region after segmentation, focusing mainly on area size. By setting an area threshold, you can effectively remove those small areas that may be caused by noise. According to the pre-set area threshold (e.g., minimum area of 100 pixels), filter out valid regions that meet the conditions. Any small area smaller than the threshold will be removed, while the remaining areas are the valid areas that truly reflect gamma event clustering. After filtering, the remaining regions should accurately reflect the response of the detector to different point module hole positions. These regions not only help subsequent physical analysis, but also can be used to evaluate the spatial resolution and uniformity of the detector and other performance indicators.

[0075] Step S104: Based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event, the actual physical position of the gamma event is calculated.

[0076] In the present application, the pixel centroid position of each grid of the adjusted frame data image of the gamma event can be calculated based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event. The actual physical position of the gamma event can be calculated based on the pixel centroid position of each grid of the adjusted frame data image of the gamma event, the preset image resolution and the preset image size.

[0077] After the image segmentation and the effectiveness screening are completed, each target grid that is retained represents a gamma event response region corresponding to a point-like hole site. Due to the nonlinear distortion or positioning error of the detector, the center of the response region does not necessarily fall accurately on the theoretical grid position. Therefore, the actual response center of each response region, i.e., the pixel centroid position, can be accurately estimated by means of weighted average.

[0078] The pixel centroid (referring to a spatial center point obtained by weighted average of all pixels in a connected region according to their gray value (here, the energy deposition value, i.e., the pixel value). The pixel centroid is more capable of reflecting the true center of gravity of the energy distribution than the simple geometric center, and is particularly suitable for the case where the response intensity is uneven. For the kth effective grid, let the set of all pixels contained in the grid be Ω k , where each pixel i has a pixel position (x i , y i ) and a pixel value I i . The pixel centroid position (C x k , C y k ) of the grid is calculated according to the following formula:

[0079] C x k = [∑(x i × I i )] / ∑I i

[0080] C y k = [∑(y i × I i )] / ∑I i

[0081] , where C x k represents the weighted average position of the centroid in the column direction (unit: pixel), and C yk represents the weighted average position of the centroid in the row direction (unit: pixel), the denominator is the sum of all pixel values in the region, representing the total energy; the numerator is the sum of energy weighted by position.

[0082] For ease of understanding, the following example is given: there are three pixels in a certain grid participating in the calculation: pixel position (x i , y i )=(168, 175), pixel value I i =1300keV; pixel position (x i , y i )=(169, 175), pixel value I i =1200keV; pixel position (x i , y i )=(168, 176), pixel value I i =500keV, total energy =1300+1200+500=3000keV. x =(175×1300+175×1200+176×500) / 3000≈175.17px, C y =(168×1300+169×1200+168×500) / 3000≈168.40px, the pixel centroid of the grid is finally obtained as (C x k ,C y k )=(175.17, 168.40) pixels.

[0083] It is not enough to obtain only the pixel coordinates, it is also necessary to restore them to the actual physical position (x_actual, y_actual) on the detector plane, in millimeters (mm), in order to compare them with the theoretical position of the preset point phantom, and then evaluate the system deviation and implement correction. The image center pixel coordinates and image resolution are needed, the pixel position is subtracted from the image center offset to obtain the offset pixel number relative to the image center, and then multiplied by the resolution to convert it to a physical position. For reference, the following formulas can be used: x actual =(C x -xcenter_px)×resolution and y actual =(C y -y center_px )×resolution. For ease of understanding, the following example is given: when (C x k ,C y k )=(175.17, 168.40) pixels, the physical position is x actual= (175.17 - 249.5) x 2.0 = -148.66 mm and y actual = (168.40 - 249.5) x 2.0 = -162.20 mm, so the actual physical position of this gamma event is (-148.66, -162.20) mm.

[0084] Step S105: Based on the pixel position of each grid of the adjusted frame data image of the gamma event, the pixel value of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset point module, the preset dot array number of the preset point module, and the dot spacing of the preset point module, the theoretical physical position of the gamma event is calculated.

[0085] In the present application, first, the pixel centroid position of each grid of the adjusted frame data image of the gamma event can be calculated based on the pixel position of each grid of the adjusted frame data image of the gamma event and the pixel value of each grid of the adjusted frame data image of the gamma event. Then, the corresponding index of each grid of the adjusted frame data image of the gamma event can be assigned based on the pixel centroid position of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset point module, and the preset dot array number of the preset point module. Finally, the theoretical physical position of the gamma event can be calculated based on the index of each grid of the adjusted frame data image of the gamma event, the number of dot array rows of the preset point module, the preset dot array number of the preset point module, and the dot spacing of the preset point module.

[0086] For each effective gamma photon event response region after segmentation, screening, and centroid calculation, i.e., each grid of the adjusted frame data image, its theoretical physical position in an ideal non-distorted detection system is determined. This process does not depend on the response deviation of the actual detector, but is based on the geometric design parameters of the preset point module to construct an ideal coordinate reference system for subsequent comparison with the actual physical position, thereby realizing spatial distortion correction.

[0087] First, for each effective segmented region (i.e., a connected region with a label k), the weighted centroid method is used to calculate its center position in the image, i.e., the pixel centroid position (C x ,C y ). Since the detector may have nonlinear distortion (such as edge stretching, rotation, tilt, etc.), the physical position of the centroid cannot be directly used to determine which row and column it belongs to. Therefore, a unique logical number (i, j) must be assigned to each response region using an automatic sorting method based on the relative spatial distribution, indicating that it is the response corresponding to the hole of the i-th row and j-th column of the point module. A one-to-one mapping relationship is established from the "detected response region in the image" to the "logical grid of the preset point module".

[0088] Because in the image coordinate system, C yThe smaller, the closer to the top, so this is arranged from the topmost response area. For the 2310 response areas and the 55x42 logical grid, the pixel centroids of all 2310 grids can be sorted by their C y values (row direction coordinates) from small to large. Then the sorted list is evenly divided into 55 groups, each containing 2310 / 55=42 grids, each group corresponding to a row of point modules. Assign each group a row index i=0,1,2,...,54, increasing from top to bottom. The first group (the topmost): i=0; the middle row: i=27; the bottommost group: i=54. Even if a row of response areas is slightly skewed or misaligned in the actual image, as long as the overall trend is arranged from top to bottom, this grouping method can still correctly identify the row structure. For each row, the pixel centroids of the 42 grids it contains are sorted by their C x values (column direction coordinates) from small to large. C x The smaller, the closer to the left. Assign column indices j=0,1,2,...,41 from left to right. Finally, each grid now has a unique logical coordinate (i,j) indicating its theoretical attribution in the point module array. For ease of understanding, refer to Figure 6 and Figure 7 , Figure 6 a frame data image before assigning indices to a gamma photon event provided by an embodiment of the present application, Figure 7 a frame data image after assigning indices to a gamma photon event provided by an embodiment of the present application.

[0089] Once each response area is assigned a logical index (i,j), the physical coordinates that the position should have in an ideal detection system can be calculated according to the design parameters of the point module. The preset point array spacing grid_spacing is 10.0 mm, at which time the center index (i center , j center ) of the logical grid is i center =(row-1) / 2 and j center =(col-1) / 2, and an ideal coordinate system is constructed with the logical center as the origin (0,0): x theory =(j-j center )×grid_spacing and y theory =(i-i center )×grid_spacing, x theory is the theoretical horizontal physical position (unit: mm); y theory is the theoretical vertical physical position (unit: mm). For ease of understanding, take the following example: for a grid assigned an index (10,5), i center =(55-1) / 2=27, j center =(42-1) / 2=20.5, xtheory = (5 - 20.5) x 10.0 = -155.0 mm, y theory = (10 - 27) x 10.0 = -170.0 mm, the theoretical longitudinal physical position is (-155.170.0) mm, which means that in the ideal detector, the (10, 5)th point hole should produce a response on the detector plane, which is located 155.0 mm to the left of the center and 170.0 mm above the center. The theoretical physical position is the ideal, non-distorted position calculated based on the geometry of the point hole.

[0090] Step S106: Based on the theoretical physical position of the gamma event and the actual physical position of the gamma event, the physical position deviation of the gamma event is calculated, and the original physical position of the gamma event is calibrated based on the physical position deviation of the gamma event.

[0091] In this application, for each valid segmentation region (i.e. each identified point hole response), the positioning error in two directions, i.e. the physical position deviation (Δx, Δy) of the gamma event, is calculated, which can be specifically referred to the following formula: Δx = x actual - x theory and Δy = y actual - y theory . The positive deviation indicates that the detector response deviates to the right or below; the negative deviation indicates that the response deviates to the left or above. Taking the (i = 10, j = 5) grid as an example, Δx = -148.6 - (-155.0) = +6.4 mm, Δy = -162.8 - (-170.0) = +7.2 mm, there is a significant positive deviation in this region, i.e. the detector incorrectly records the response that should appear at (-155.0, -170.0) to a position more to the right and more below.

[0092] The deviation of a single point is not enough to represent the performance of the entire detector. It is necessary to repeat the above calculation for all 2310 valid response regions to construct a complete two-dimensional distortion field (Distortion Field). Each (x theory , y theory ) and its corresponding (Δx, Δy) can be stored as a key-value pair, which is suitable for subsequent interpolation or fast retrieval, or because the distortion of the detector is usually continuous and smooth (such as radial stretching, tangential twisting, etc.), a mathematical function can be used to model the overall deviation trend.

[0093] Further, after the physical position deviation of the gamma event is calculated based on the theoretical physical position of the gamma event and the actual physical position of the gamma event, the physical position deviation of the gamma event can be supplemented based on a preset boundary deviation to obtain a supplemented physical position deviation of the gamma event. The supplemented physical position deviation of the gamma event is subjected to spatial interpolation by using a cubic interpolation algorithm to obtain an interpolated physical position deviation of the gamma event. The physical position deviation of the gamma event is updated to the interpolated physical position deviation of the gamma event.

[0094] In the present application, after the physical position deviation is calculated based on the theoretical physical position and the actual physical position of the gamma photon event, a boundary constraint mechanism is further introduced to further improve the integrity and stability of spatial correction. Since the effective response region of the detector is usually slightly larger than the point module coverage range, especially in the edge and corner regions of the imaging plane, the measured deviation data is sparse or even missing, and direct spatial interpolation may lead to inaccurate extrapolation, boundary distortion amplification or artifacts in the corrected image. Therefore, the system supplements the original physical position deviation based on a preset boundary deviation, and adds a group of virtual deviation points on the peripheral boundary of the detector imaging region. The deviation values are set according to the prior characteristics of the system, for example, using zero deviation, gradual transition or mirror continuation strategies to ensure that the deviation distribution in the entire effective region has reasonable boundary conditions. Through this supplement process, the original discrete deviation data is expanded into an enhanced deviation set covering the entire field of view, which not only contains internal measured points, but also contains peripheral constraint points, thereby providing complete spatial support for subsequent high-precision interpolation.

[0095] On this basis, the supplemented physical position deviation is subjected to spatial interpolation by using a cubic interpolation algorithm. The cubic interpolation has the characteristics of second-order continuous derivability, which can generate a smooth and non-mutated deviation field function, effectively avoiding the gradient discontinuity and local oscillation that may be caused by low-order interpolation methods. The algorithm takes the supplemented deviation data as input, constructs a high-resolution two-dimensional interpolation grid on the entire detector plane, calculates the predicted deviation value of each position point by point, and finally generates a continuous and fine interpolated physical position deviation field. The deviation field accurately reflects the spatial distortion trend of the detector at any position, especially in the edge and intermediate unsampled regions, and has good generalization ability. Subsequently, the original discrete physical position deviation obtained based on the point module response is updated to the interpolated continuous deviation field, so that the subsequent calibration of the gamma photon event is no longer limited to the measured point position, but can achieve high-precision and consistent deviation compensation in the entire field of view, significantly improving the accuracy of spatial positioning and imaging quality.

[0096] In summary, the application provides a calibration method for the physical position of a gamma event, which comprises: constructing a frame data image based on the original physical position and energy value of the gamma event collected by the detector under a preset point source condition, converting discrete events into visual spatial distribution. The frame data image is segmented by using a watershed algorithm, and the number of grids after segmentation is ensured to be consistent with the number of dot matrix of the point source by adjustment, effectively avoiding misjudgment caused by over-segmentation or under-segmentation. On this basis, the accurate actual physical position of the gamma event is calculated in combination with the pixel position and pixel value of each grid, and the accurate theoretical physical position of the gamma event is calculated according to the pixel position and pixel value of each grid and the number of rows, the number of columns and the spacing of the point source. Finally, the accurate physical position deviation of the gamma event is calculated by comparing the theoretical physical position with the actual physical position, and the original physical position is calibrated accordingly, realizing fully automatic calibration without manual intervention, and significantly improving the accuracy and efficiency of the calibration of the physical position of the gamma event.

[0097] The application also provides an electronic device. Figure 8 The electronic device in the embodiments of the application can include, but is not limited to, a fixed terminal such as a mobile phone, a notebook computer, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a desktop computer, and the like. Figure 8 The electronic device shown is only an example, and should not impose any limitation on the functions and use range of the embodiments of the application.

[0098] As shown in Figure 8 The electronic device can include a processing device (for example, a central processor, a graphics processor, and the like) 801, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 802 or loaded from a storage device 808 to a random access memory (RAM) 803. In the state that the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0099] Generally, the following devices can be connected to the I / O interface 805: an input device 806 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; an output device 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage device 808 including, for example, a memory card, a hard disk, and the like; and a communication device 809. The communication device 809 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 8Electronic devices having various apparatuses are shown, but it should be understood that not all of the illustrated apparatuses are required to implement or be present in a particular implementation. More or fewer apparatuses can alternatively be implemented or present.

[0100] The embodiment of the present application further provides a computer program product comprising computer readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the calibration methods of the physical location of a gamma event provided by the embodiments of the present application.

[0101] The embodiment of the present application further provides a computer readable storage medium carrying one or more computer programs, which, when executed by an electronic device, can cause the electronic device to implement any of the calibration methods of the physical location of a gamma event provided by the embodiments of the present application.

[0102] In addition, it should be noted that the apparatus embodiments described above are merely illustrative, and the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. In addition, in the apparatus embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines.

[0103] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary general hardware, and of course can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. Generally, functions completed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better embodiment. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, training device, or network device, etc.) execute the methods described in various embodiments of the present application.

[0104] In the above embodiments, the implementation can be wholly or partially by software, hardware, firmware, or any combination thereof. When implemented by software, the implementation can be wholly or partially in the form of a computer program product.

[0105] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, training device or data center to another website, computer, training device or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. integrated with one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

Claims

1. A method for calibrating the physical location of a gamma event, characterized in that, include: The original physical location and energy value of the gamma event are obtained by the detector through a preset point model; Based on the original physical location and energy value of the γ event, a frame data image of the γ event is constructed; The frame data image of the γ event is segmented using the watershed algorithm to obtain the segmented frame data image of the γ event, and the adjusted frame data image of the γ event is obtained after adjusting the segmented frame data image of the γ event. The number of grids in the adjusted frame data image of the γ event is consistent with the number of dots in the preset dot matrix. The actual physical location of the γ event is calculated based on the pixel positions of each grid in the adjusted frame data image of the γ event and the pixel values ​​of each grid in the adjusted frame data image of the γ event. The theoretical physical location of the γ event is calculated based on the pixel position of each grid in the adjusted frame data image of the γ event, the pixel value of each grid in the adjusted frame data image of the γ event, the number of dot matrix rows of the preset dot matrix, the number of preset dot arrays of the preset dot matrix, and the dot matrix spacing of the preset dot matrix. Based on the theoretical physical location and the actual physical location of the γ event, the physical location deviation of the γ event is calculated, and the original physical location of the γ event is calibrated based on the physical location deviation of the γ event.

2. The calibration method for the physical location of a gamma event according to claim 1, characterized in that, The construction of the frame data image of the gamma event based on its original physical location and energy value includes: The peak energy value of the γ event is obtained from the energy value of the γ event, and the energy window range of the γ event is calculated based on the preset energy window threshold and the peak energy value of the γ event. The energy value of each γ event within the energy window range of the γ event is determined as the effective energy value of the γ event, and the effective energy value of the γ event is determined as the pixel value of the γ event; Based on the original physical location of each γ event corresponding to the pixel value of the γ event, the preset image resolution and preset image size, the pixel position corresponding to the pixel value of the γ event is calculated; Based on the pixel value of the γ event and the pixel position corresponding to the pixel value of the γ event, a frame data image of the γ event is constructed.

3. The method for calibrating the physical location of a gamma event according to claim 1, characterized in that, The step of segmenting the frame data image of the γ event using the watershed algorithm to obtain the segmented frame data image of the γ event includes: The frame data image of the γ event is analyzed to obtain the energy gradient amplitude image of the γ event and the energy brightness marker points of the γ event; Based on the energy gradient magnitude image and the energy brightness markers of the γ event, the frame data image of the γ event is segmented using the watershed algorithm to obtain the segmented frame data image of the γ event.

4. The method for calibrating the physical location of a gamma event according to claim 1, characterized in that, The actual physical location of the γ event is calculated by using the pixel positions of each grid in the adjusted frame data image based on the γ event and the pixel values ​​of each grid in the adjusted frame data image based on the γ event, including: Based on the pixel positions of each grid in the adjusted frame data image of the γ event and the pixel values ​​of each grid in the adjusted frame data image of the γ event, the centroid positions of the pixels in each grid of the adjusted frame data image of the γ event are calculated. The actual physical location of the γ event is calculated based on the pixel centroid positions of each grid in the adjusted frame data image of the γ event, the preset image resolution, and the preset image size.

5. The method for calibrating the physical location of a gamma event according to claim 1, characterized in that, The theoretical physical location of the γ event is calculated using the pixel positions of each grid in the adjusted frame data image based on the γ event, the pixel values ​​of each grid in the adjusted frame data image based on the γ event, the number of dot matrix rows of the preset dot matrix, the number of preset dot matrix arrays of the preset dot matrix, and the dot matrix spacing of the preset dot matrix, including: Based on the pixel positions of each grid in the adjusted frame data image of the γ event and the pixel values ​​of each grid in the adjusted frame data image of the γ event, the centroid positions of the pixels in each grid of the adjusted frame data image of the γ event are calculated. Based on the pixel centroid position of each grid in the adjusted frame data image of the γ event, the number of dot matrix rows of the preset dot matrix, and the number of preset dot arrays, a corresponding index is assigned to each grid in the adjusted frame data image of the γ event. The theoretical physical location of the γ event is calculated based on the index of each grid in the adjusted frame data image of the γ event, the number of rows of the preset dot matrix, the number of preset dot arrays of the preset dot matrix, and the dot spacing of the preset dot matrix.

6. The method for calibrating the physical location of a gamma event according to claim 1, characterized in that, After segmenting the frame data image of the γ event using the watershed algorithm to obtain the segmented frame data image of the γ event, and obtaining the adjusted frame data image of the γ event after adjusting the segmented frame data image of the γ event, the method further includes: The number of pixels in each grid of the adjusted frame data image based on the γ event is greater than the preset number of pixels; The target grid of the adjusted frame data image of the γ event is determined from each grid of the adjusted frame data image of the γ event, wherein the number of pixels in the target grid of the adjusted frame data image of the γ event is greater than a preset number of pixels; Update each grid of the adjusted frame data image of the γ event to the target grid of the adjusted frame data image of the γ event.

7. The method for calibrating the physical location of a gamma event according to claim 1, characterized in that, After calculating the physical position deviation of the γ event based on its theoretical physical position and actual physical position, the method further includes: The physical position deviation of the γ event is supplemented based on the preset boundary deviation to obtain the supplemented physical position deviation of the γ event; A cubic interpolation algorithm is used to spatially interpolate the supplemented physical position deviation of the γ event to obtain the interpolated physical position deviation of the γ event. The physical position deviation of the γ event is updated to the interpolated physical position deviation of the γ event.

8. A computer program product, characterized in that, Includes computer-readable instructions that, when executed on an electronic device, cause the electronic device to implement the calibration method for the physical location of a gamma event as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program to enable the electronic device to implement the calibration method for the physical location of the gamma event as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The storage medium carries one or more computer programs that, when executed by an electronic device, enable the electronic device to implement the calibration method for the physical location of a gamma event as described in any one of claims 1 to 7.

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