Crystal boundary detection method, device, detector case positioning system and medium

Abstract

The present application relates to a crystal boundary detection method, a crystal boundary detection device, a detector event positioning system, and a medium. The method includes: obtaining a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array; determining peak points of each crystal histogram in the two-dimensional distribution histogram, and determining a peak spacing between every two adjacent crystal histograms according to the peak points of each crystal histogram; determining a cutting angle θ of the corresponding two adjacent crystal histograms according to a connection line between peak points in every two adjacent crystal histograms, and respectively determining a first standard deviation and a second standard deviation of a Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms; dividing the peak spacing between the corresponding two adjacent crystal histograms according to a ratio of the first standard deviation to the second standard deviation to obtain a segmentation boundary between the two adjacent crystal histograms. Using this method can more accurately detect the crystal boundary.

Description

Technical Field

[0001] The present application relates to the field of radiation detection, and in particular, to a crystal boundary detection method, a crystal boundary detection device, a detector event positioning system, and a storage medium. Background Art

[0002] For a PET detector (Positron Emission Tomography), signals generated by the interaction of rays with crystals are processed by a positioning algorithm to obtain a two-dimensional distribution histogram of the crystal array. Then, by using the characteristics of the histogram, each bright spot is divided into regions, that is, the crystal boundary is found, and finally, the position of each event is located in the crystal. Related technologies use morphology to preprocess the two-dimensional distribution histogram of the crystal, identify the vertex positions of all crystal histograms, calculate the median values of the coordinates of the four vertices adjacent to each other in the up, down, left, and right directions between adjacent rows and columns, and finally perform curve fitting on all the midpoint coordinates by row and column to complete the detection of the crystal boundary.

[0003] However, the crystal boundary detection method of related technologies has the problem of inaccurate detection. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a crystal boundary detection method, a crystal boundary detection device, a detector event positioning system, and a storage medium that can more accurately detect the crystal boundary.

[0005] In a first aspect, the present application provides a crystal boundary detection method applied to a PET detector, characterized in that the method includes:

[0006] Obtaining a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of respective crystals;

[0007] Determining peak points of the crystal histograms in the two-dimensional distribution histogram, and determining peak spacings between every two adjacent crystal histograms according to the peak points of the crystal histograms;

[0008] Determining a cutting angle θ of two adjacent crystal histograms corresponding to a line connecting peak points between every two adjacent crystal histograms, and determining a first standard deviation and a second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms;

[0009] Dividing the peak spacing between two adjacent crystal histograms corresponding according to a ratio of the first standard deviation and the second standard deviation to obtain a segmentation boundary between the two adjacent crystal histograms.

[0010] In one embodiment, determining the peak points of each crystal histogram in the two-dimensional distribution histogram includes:

[0011] Obtaining the gray values of the pixel points of each crystal histogram in the two-dimensional distribution histogram;

[0012] Determining the maximum gray value of the pixel points of each crystal histogram, and using the position where the maximum gray value of the pixel points is located as the peak point of each crystal histogram.

[0013] In one embodiment, obtaining the two-dimensional distribution histogram obtained after the radiation light source acts on the crystal array includes:

[0014] Obtaining an initial image obtained after the radiation light source acts on the crystal array, sliding the initial image with a preset window at a preset step length, and statistically analyzing the image features within the preset window during each sliding process to obtain a plurality of candidate crystal histograms;

[0015] Performing two-dimensional Gaussian curve fitting on the plurality of candidate crystal histograms to obtain the two-dimensional Gaussian curve fitting degrees of the respective candidate crystal histograms;

[0016] Obtaining the number N of crystals in the crystal array, arranging the fitting degrees of the plurality of candidate crystal histograms in order, and selecting the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the respective crystal positions.

[0017] In one embodiment, obtaining the number N of crystals in the crystal array, arranging the fitting degrees of the plurality of candidate crystal histograms in order, and selecting the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the respective crystal positions includes:

[0018] Judging whether the top N largest fitting degrees selected are all not less than a preset fitting degree threshold;

[0019] In the case where it is judged that there is a fitting degree less than the preset fitting degree threshold among the top N largest fitting degrees selected, adjusting the size of the preset window and / or the preset step length until, when statistically analyzing the image features within the preset window during each sliding process according to the adjusted preset window and / or preset step length, the top N largest fitting degrees among the plurality of obtained crystal histograms are all not less than the preset fitting degree threshold.

[0020] In one embodiment, the dissection angle θ between every two adjacent crystal histograms includes the angle between the line connecting the peak points in every two adjacent crystal histograms and a preset reference direction.

[0021] In one embodiment, determining the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the dissection angle θ in every two adjacent crystal histograms includes:

[0022] Determine the first horizontal standard deviation of the first crystal histogram in the x-axis direction and the first vertical standard deviation in the y-axis direction, as well as the second horizontal standard deviation of the second crystal histogram in the x-axis direction and the second vertical standard deviation in the y-axis direction for every two adjacent crystal histograms according to the two-dimensional Gaussian curve of each crystal histogram;

[0023] Determine the first standard deviation of the one-dimensional Gaussian curve of the first crystal histogram along the cutting angle θ according to the first horizontal standard deviation, the first vertical standard deviation, and the cutting angle θ, and determine the second standard deviation of the one-dimensional Gaussian curve of the second crystal histogram along the cutting angle θ according to the second horizontal standard deviation, the second vertical standard deviation, and the cutting angle θ, where the one-dimensional Gaussian curves are respectively the one-dimensional Gaussian curves along the direction of the line connecting the two peak points of the first crystal histogram and the second crystal histogram.

[0024] In one embodiment, after dividing the peak spacing of two adjacent crystal histograms corresponding to each other according to the ratio of the first standard deviation and the second standard deviation to obtain the segmentation boundary between the two adjacent crystal histograms, the method further includes:

[0025] Determine the segmentation point according to the line connecting the peak points and the segmentation boundary in every two adjacent crystal histograms;

[0026] Obtain the coordinates of the segmentation point, and generate the position table of the crystal array according to the coordinates of the segmentation point.

[0027] In a second aspect, the present application also provides a crystal boundary detection device, and the crystal boundary detection device includes:

[0028] An acquisition module, configured to acquire a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of the crystals;

[0029] A first determination module, configured to determine the peak points of the crystal histograms in the two-dimensional distribution histogram, and determine the peak spacing of every two adjacent crystal histograms according to the peak points of the crystal histograms;

[0030] A second determination module, configured to determine the cutting angle θ of two adjacent crystal histograms corresponding to each other according to the line connecting the peak points in every two adjacent crystal histograms, and determine the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms;

[0031] A partitioning module, configured to partition the peak distance between two adjacent crystal histograms corresponding thereto according to the ratio of the first standard deviation and the second standard deviation, so as to obtain a segmentation boundary between the two adjacent crystal histograms.

[0032] In a third aspect, in this embodiment, a detector event positioning system is provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the crystal boundary detection method described in the first aspect above, and position the crystal where the event occurs according to the detected crystal boundary.

[0033] In a fourth aspect, in this embodiment, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the following steps are implemented:

[0034] Obtain a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to respective crystal positions;

[0035] Determine peak points of the crystal histograms in the two-dimensional distribution histogram, and determine the peak distance between every two adjacent crystal histograms according to the peak points of the crystal histograms;

[0036] Determine a cutting angle θ of two adjacent crystal histograms corresponding thereto according to a connection line between peak points in every two adjacent crystal histograms, and respectively determine a first standard deviation and a second standard deviation of a Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms;

[0037] Partition the peak distance between two adjacent crystal histograms corresponding thereto according to the ratio of the first standard deviation and the second standard deviation, so as to obtain a segmentation boundary between the two adjacent crystal histograms.

[0038] Compared with the related art, in the crystal boundary detection method, device, detector case positioning system, and storage medium provided in this embodiment, by obtaining the two-dimensional distribution histogram obtained after the radiation light source acts on the crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of each crystal; determining the peak points of each crystal histogram in the two-dimensional distribution histogram, and determining the peak spacing between every two adjacent crystal histograms according to the peak points of each crystal histogram; determining the cutting angle θ of the corresponding adjacent two crystal histograms according to the connection line between the peak points of every two adjacent crystal histograms, and respectively determining the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms; dividing the peak spacing of the corresponding adjacent two crystal histograms according to the ratio of the first standard deviation and the second standard deviation to obtain the segmentation boundary between the adjacent two crystal histograms, which solves the problem of low accuracy in crystal boundary detection in the related art and realizes accurate detection of crystal boundaries.

[0039] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects, and advantages of this application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of this application and form a part of this application. The illustrative embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0041] Figure 1 is the hardware structure block diagram of the terminal of the crystal boundary detection method according to an embodiment of this application;

[0042] Figure 2 is the flowchart of the crystal boundary detection method according to an embodiment of this application;

[0043] Figure 3 is the flowchart of the crystal histogram determination method according to an embodiment of this application;

[0044] Figure 4 is the two-dimensional scatter plot of the crystal according to an embodiment of this application;

[0045] Figure 5 is the one-dimensional Gaussian curve graph along the cutting angle θ according to an embodiment of this application;

[0046] Figure 6 is the flowchart of the crystal boundary detection method according to a preferred embodiment of this application;

[0047] Figure 7 is the structure block diagram of the crystal boundary detection device according to an embodiment of this application. DETAILED DESCRIPTION

[0048] To more clearly understand the purpose, technical solution and advantages of this application, the following describes and explains this application in conjunction with the accompanying drawings and embodiments.

[0049] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by those with ordinary skills in the technical field to which this application belongs. In this application, words such as "a", "one", "a kind of", "the", "these" and the like do not indicate a limitation in quantity, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products or devices. The terms "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly connected. The "multiple" involved in this application means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the objects associated before and after are in an "or" relationship. The terms "first", "second", "third" and the like involved in this application only distinguish similar objects and do not represent a specific sorting for the objects.

[0050] The method embodiment provided in this embodiment can be executed on a terminal, a computer or a similar computing device. For example, running on a terminal Figure 1 is the hardware structure block diagram of the terminal of the crystal boundary detection method in this embodiment. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in Figure 1 the figure) processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown Figure 1 is only schematic and does not limit the structure of the above terminal. For example, the terminal may also include more or fewer components than

[0051] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the crystal boundary detection method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above-mentioned method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0052] The transmission device 106 is used to receive or send data via a network. The above-mentioned network includes a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0053] When generating a position table according to the crystal array histogram, the quality of the histogram has an obvious impact on finding the crystal boundary, and there are many factors affecting the quality of the histogram. For example: the crystal array structure, the differences between crystal materials, the packaging process of the crystal array, and noise superposition will all cause the crystal array histogram to be "strange-shaped". The most common situation is that the positions of adjacent crystal histograms are too close, resulting in too much overlap of crystal histograms and causing blurred boundaries. Therefore, if the boundary position between crystal histograms cannot be accurately found and the correct position table is generated, it will lead to incorrect case positioning. It has been found through research that the related technology only uses the median of the vertex coordinates in the crystal histogram as the crystal boundary method, without considering the distribution characteristics of the crystal histogram itself. When one of the two adjacent crystal histograms is "fatter" and the other is "thinner", the error of the determined crystal boundary will be amplified and the accuracy will be greatly reduced.

[0054] To solve the above problems, a crystal boundary detection method is provided in this embodiment, which is applied to a PET detector. Figure 2 It is a flowchart of the crystal boundary detection method in this embodiment, as Figure 2 shown. The process includes the following steps:

[0055] Step S201: Obtain a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array. The two-dimensional distribution histogram includes crystal histograms corresponding to the positions of respective crystals.

[0056] Among them, the two-dimensional distribution histogram may be an image obtained by a PET detector through a positioning algorithm for signals generated by the interaction of rays with crystals. The two-dimensional distribution histogram includes image features such as pixel point coordinates and gray values. Through the image features, the crystal histograms corresponding to the positions of respective crystals can be located.

[0057] Step S202: Determine the peak points of the crystal histograms in the two-dimensional distribution histogram, and determine the peak spacing between every two adjacent crystal histograms according to the peak points of the crystal histograms.

[0058] Among them, the peak points of the crystal histograms can be determined through image features such as pixel point coordinates and gray values. The peak spacing is the distance between the pixel point coordinates where the peak points are located.

[0059] Step S203: Determine the cutting angle θ of the corresponding two adjacent crystal histograms according to the connection line between the peak points in every two adjacent crystal histograms, and determine the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms.

[0060] Among them, two adjacent crystals can be determined according to the peak point coordinates of the crystal histogram. The cutting angle θ is the angle between the straight line connected by the peak points of two adjacent crystal histograms and a preset reference direction (such as the x-axis of the coordinate system). The first standard deviation is the standard deviation of the one-dimensional Gaussian curve of one crystal histogram in two adjacent crystal histograms along the cutting angle θ direction, and the second standard deviation is the standard deviation of the one-dimensional Gaussian curve of the other crystal histogram in two adjacent crystal histograms along the cutting angle θ direction.

[0061] Step S204: Divide the peak spacing of the corresponding two adjacent crystal histograms according to the ratio of the first standard deviation to the second standard deviation to obtain the segmentation boundary between the two adjacent crystal histograms.

[0062] Among them, the peak spacing of two adjacent crystal histograms is divided proportionally according to the ratio of the first standard deviation to the second standard deviation. For example, the peak spacing between the first crystal histogram and the second crystal histogram is L, the first standard deviation is A, the second standard deviation is B, and A:B = 3:4. Then the peak spacing L is divided according to the ratio of 3:4.

[0063] In this step, determining the segmentation boundary through the standard deviation of the crystal histogram takes into account the distribution characteristics of the crystal histogram itself. In the case where the distributions of crystal histograms are inconsistent, it is still possible to find a relatively accurate segmentation boundary to segment two adjacent crystal histograms.

[0064] In steps S201 to S204, by obtaining the crystal histograms corresponding to the positions of each crystal in the two-dimensional distribution histogram, then determining the peak points of the crystal histograms, and finally dividing the peak distance between two adjacent crystal histograms according to the ratio of the standard deviations of the Gaussian curves in the direction of the line connecting the peak points of the two adjacent crystal histograms, the segmentation boundary between two adjacent crystal histograms is determined, which fully considers the distribution characteristics and properties of the crystal histogram itself, solves the problem of inaccurate crystal boundary detection in the related art, and improves the accuracy of crystal boundary detection.

[0065] In some of these embodiments, determining the peak points of each crystal histogram in the two-dimensional distribution histogram includes: obtaining the gray values of the pixel points of each crystal histogram in the two-dimensional distribution histogram; determining the maximum gray value of each crystal histogram, and using the position where the maximum gray value is located as the peak point of each crystal histogram.

[0066] In this embodiment, the gray histogram of each crystal can be used as the crystal histogram to find the coordinates of the pixel point with the largest gray value, thereby determining the peak point.

[0067] In some of these embodiments, when obtaining the two-dimensional distribution histogram obtained after the radiation light source acts on the crystal array, a method for determining the crystal histogram corresponding to each crystal position is also provided, such as Figure 3 The flowchart of this crystal histogram determination method is shown as follows, including the following steps:

[0068] Step S301, obtaining the initial image obtained after the radiation light source acts on the crystal array, sliding the initial image with a preset window at a preset step length, and statistically analyzing the image features within the preset window during each sliding process to obtain a plurality of candidate crystal histograms.

[0069] Such as Figure 4 The initial image obtained after the radiation light source acts on the crystal array is shown, which is also called the two-dimensional scatter plot of the crystal. Each bright spot in the figure represents a crystal. In practical applications, use the preset window to slide row by row and column by column in the two-dimensional scatter plot shown in Figure 4 and obtain the image area located by each window sliding, and obtain the crystal histogram within this area as the candidate crystal histogram. It should be noted that the number of candidate crystal histograms obtained by the method of this embodiment is often greater than the actual number of crystals.

[0070] Among them, the size of the preset window can be determined according to the pixel size set for the crystal histogram in the actual situation. The preset window is generally square. For example, when the crystal histogram is represented by a pixel size of 256*256, windows with pixel sizes of 256*256, 128*128, 64*64, and 32*32 can be used as the preset window.

[0071] Step S302: Perform two-dimensional Gaussian curve fitting on multiple candidate crystal histograms to obtain the two-dimensional Gaussian curve fitting degrees of the candidate crystal histograms.

[0072] Specifically, perform two-dimensional Gaussian function fitting on the image features in the multiple candidate crystal histograms obtained in step S301, and determine the two-dimensional Gaussian curve fitting degree through the fitting degree calculation method.

[0073] Step S303: Obtain the number N of crystals in the crystal array, arrange the fitting degrees of the multiple candidate crystal histograms in order, and select the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the positions of the respective crystals.

[0074] Specifically, arrange the fitting degrees corresponding to the multiple candidate crystal histograms obtained in step S301 in ascending or descending order, and determine the candidate crystal histograms corresponding to the top N higher fitting degree values as the crystal histograms corresponding to the actual crystal positions.

[0075] In some of these embodiments, another method for determining the crystal histogram is also provided. The difference from the above method for determining the crystal histogram is that: before selecting the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the positions of the respective crystals, it is determined whether all of the selected top N largest fitting degrees are not less than a preset fitting degree threshold; in the case where it is determined that there is a top N largest fitting degree less than the preset fitting degree threshold, adjust the size of the preset window and / or the preset step length until, when counting the image features within the preset window during each sliding process according to the adjusted preset window and / or preset step length, the top N largest fitting degrees in the obtained multiple crystal histograms are all not less than the preset fitting degree threshold.

[0076] Through the method for determining the crystal histogram in this embodiment, it is possible to ensure that the finally selected crystal histogram has a high degree of matching with the actual crystal distribution, thereby improving the accuracy of crystal boundary detection.

[0077] In some of these embodiments, determining the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms includes: determining the first horizontal standard deviation in the x-axis direction and the first vertical standard deviation in the y-axis direction of the first crystal histogram in every two adjacent crystal histograms according to the two-dimensional Gaussian curve of each crystal histogram, and the second horizontal standard deviation in the x-axis direction and the second vertical standard deviation in the y-axis direction of the second crystal histogram; determining the first standard deviation of the one-dimensional Gaussian curve of the first crystal histogram along the cutting angle θ according to the first horizontal standard deviation, the first vertical standard deviation and the cutting angle θ, and determining the second standard deviation of the one-dimensional Gaussian curve of the second crystal histogram along the cutting angle θ according to the second horizontal standard deviation, the second vertical standard deviation and the cutting angle θ, where the one-dimensional Gaussian curves are respectively the one-dimensional Gaussian curves along the direction connecting the two peak points of the first crystal histogram and the second crystal histogram.

[0078] Specifically, the crystal histogram is statistically fitted with a two-dimensional Gaussian function as shown in formula (1):

[0079]

[0080] where A is the amplitude, (x 0 , y 0 ) is the peak coordinate of the crystal histogram, σ x is the standard deviation of the two-dimensional Gaussian function in the x direction, σ y is the standard deviation of the two-dimensional Gaussian function in the y direction, and the standard deviation represents the broadening size of the one-dimensional Gaussian curve passing through the peak point and along a certain profile direction.

[0081] By fitting the crystal histogram with a two-dimensional Gaussian function to obtain a two-dimensional Gaussian curve, the first horizontal standard deviation in the x-axis direction and the first vertical standard deviation in the y-axis direction of the first crystal histogram in every two adjacent crystal histograms are obtained, as well as the second horizontal standard deviation in the x-axis direction and the second vertical standard deviation in the y-axis direction of the second crystal histogram; then the geometric relationship between the x-axis, the y-axis and the cutting angle θ is used to determine the first standard deviation and the second standard deviation of the one-dimensional Gaussian curve of two adjacent crystal histograms along the cutting angle θ. As Figure 5 shown is the one-dimensional Gaussian curve diagram of the two-dimensional Gaussian curve of the crystal histogram along the cutting angle θ, which shows the geometric relationship between the x-axis, the y-axis and the cutting angle θ, and the fitting standard deviation σ θ of the one-dimensional Gaussian curve in the direction of the θ angle as the profile can be specifically determined by formula 2:

[0082]

[0083] In some of these embodiments, after dividing the peak spacing between two adjacent crystal histograms corresponding thereto according to the ratio of the first standard deviation and the second standard deviation to obtain the segmentation boundary between the two adjacent crystal histograms, the method further includes: determining a segmentation point according to the line connecting the peak points in each pair of adjacent crystal histograms and the segmentation boundary; obtaining the coordinates of the segmentation point, and generating a position table of the crystal array according to the coordinates of the segmentation point.

[0084] Specifically, the segmentation point is the intersection point of the line connecting the peak points and the segmentation boundary. Connecting the segmentation points in sequence can generate the position table of the crystal array.

[0085] The following describes and illustrates this embodiment through preferred embodiments.

[0086] Taking the histograms of crystal A and crystal B as an example, where the peak coordinates of crystal A are (x A , y A ), and the standard deviations in the x and y directions are σ A,x and σ A,y ; the peak coordinates of crystal B are (x B , y B ), and the standard deviations in the x and y directions are σ B,x and σ B,y

[0087] Figure 6 is the flowchart of the crystal boundary detection method of this preferred embodiment. As Figure 6 shown, the crystal boundary detection method includes the following steps:

[0088] Step S601, respectively perform two-dimensional Gaussian function formula fitting on all crystal histograms in the crystal array histogram to obtain the standard deviations σ i,x and σ i,y in the x and y directions and the peak coordinates (x i , y i ) of each crystal histogram;

[0089] Step S602, determine the profile direction angle θ through the peak coordinates of crystal A and crystal B, and further obtain the standard deviations σ A,θ and σ B,θ of the one-dimensional Gaussian function of crystal A and crystal B in the profile direction;

[0090] Step S603, determine the peak spacing L between crystal A and crystal B;

[0091] Step S604, through the proportion of the sizes of the standard deviations σ A,θ and σ B,θ of crystal A and crystal B in the θ angle direction, divide the peak spacing L into L 1 and L 2Two segments, and determine the coordinates P(x P , y P ) of the separation point;

[0092] Step S605: Sequentially perform the loop operations of steps S602 to S604 on all adjacent crystals in the row and column order to determine the coordinates of all P points;

[0093] Step S606: Connect the coordinates of the P points obtained in step 605 in the row and column order to generate the corresponding position table.

[0094] The crystal boundary detection method of the present application determines the boundary position between crystal histograms by the ratio of the magnitudes of the respective standard deviations of the one-dimensional Gaussian curves along the direction of the line connecting the two peak points of the adjacent crystal histograms, taking into account the distribution characteristics of each crystal histogram itself. It can not only solve the problem of uneven distribution of crystal histograms, but also solve the problems of different shapes of crystal histograms and serious boundary overlap, effectively improving the accuracy of crystal boundary division and reducing the probability of case positioning errors.

[0095] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0096] In this embodiment, a crystal boundary detection device is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. The following terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0097] Figure 7 is the structural block diagram of the crystal boundary detection device of this embodiment, as shown in Figure 7As shown in the figure, the device includes: an acquisition module, configured to acquire a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of respective crystals; a first determination module, configured to determine peak points of the crystal histograms in the two-dimensional distribution histogram, and determine peak spacings between every two adjacent crystal histograms according to the peak points of the crystal histograms; a second determination module, configured to determine a cutting angle θ of two adjacent crystal histograms corresponding to a connection line between peak points of every two adjacent crystal histograms, and determine a first standard deviation and a second standard deviation of a Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms; a division module, configured to divide the peak spacing between two adjacent crystal histograms corresponding to the first standard deviation and the second standard deviation to obtain a division boundary between the two adjacent crystal histograms.

[0098] In some embodiments, the device includes Figure 7 all the modules shown in the figure, and further includes: a second acquisition module, configured to acquire pixel grayscale values of the crystal histograms in the two-dimensional distribution histogram; a third determination module, configured to determine a maximum pixel grayscale value of each crystal histogram, and use the position where the maximum pixel grayscale value is located as the peak point of each crystal histogram.

[0099] In some embodiments, the device includes Figure 7 all the modules shown in the figure, and further includes: a third acquisition module, configured to acquire an initial image obtained after a radiation light source acts on a crystal array, slide the initial image with a preset window at a preset step length, and count image features within the preset window during each sliding process to obtain a plurality of candidate crystal histograms; a function fitting module, configured to perform two-dimensional Gaussian curve fitting on the plurality of candidate crystal histograms; a fitting degree calculation module, configured to calculate a two-dimensional Gaussian curve fitting degree of each candidate crystal histogram; a fourth acquisition module, configured to acquire the number N of crystals in the crystal array; a sorting module, configured to sort the fitting degrees of the plurality of candidate crystal histograms in sequence; a selection module, configured to select the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the positions of respective crystals.

[0100] In some embodiments, the device includes Figure 7 all the modules shown in the figure, and further includes: a judgment module, configured to judge whether the top N largest fitting degrees selected are all not less than a preset fitting degree threshold; an adjustment module, configured to, when it is judged that there is a top N largest fitting degree less than the preset fitting degree threshold, adjust the size of the preset window and / or the preset step length until, when counting image features within the preset window during each sliding process according to the adjusted preset window and / or preset step length, the top N largest fitting degrees in the plurality of obtained crystal histograms are all not less than the preset fitting degree threshold.

[0101] In some of these embodiments, the device includes Figure 7 all the modules shown, and further includes: a fourth determination module, configured to determine a first lateral standard deviation of a first crystal histogram in the x-axis direction and a first longitudinal standard deviation in the y-axis direction, and a second lateral standard deviation of a second crystal histogram in the x-axis direction and a second longitudinal standard deviation in the y-axis direction for every two adjacent crystal histograms according to the two-dimensional Gaussian curves of the respective crystal histograms; a fifth determination module, configured to determine a first standard deviation of the one-dimensional Gaussian curve of the first crystal histogram along the cutting angle θ according to the first lateral standard deviation, the first longitudinal standard deviation, and the cutting angle θ, and determine a second standard deviation of the one-dimensional Gaussian curve of the second crystal histogram along the cutting angle θ according to the second lateral standard deviation, the second longitudinal standard deviation, and the cutting angle θ, where the one-dimensional Gaussian curves are respectively the one-dimensional Gaussian curves along the direction of the line connecting the two peak points of the first crystal histogram and the second crystal histogram.

[0102] In some of these embodiments, the device includes Figure 7 all the modules shown, and further includes: a sixth determination module, configured to determine a segmentation point according to the line connecting the peak points and the segmentation boundary for every two adjacent crystal histograms; a fifth acquisition module, configured to acquire the coordinates of the segmentation point; a generation module, configured to generate a position table of the crystal array according to the coordinates of the segmentation point.

[0103] It should be noted that the above-mentioned respective modules can be functional modules or program modules, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned respective modules can be located in the same processor; or the above-mentioned respective modules can also be located in different processors in any combined form.

[0104] In this embodiment, a detector event positioning system is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the crystal boundary detection methods in the above embodiments, and locate the crystal position where the event occurs according to the detected crystal boundary.

[0105] Through this detector event positioning system, the crystal position where each event occurs can be accurately located, avoiding detection errors caused by irregular crystal histogram distributions and severe edge overlaps, and effectively improving the spatial resolution of the PET detector.

[0106] Optionally, the above-mentioned electronic device may further include a transmission device and an input / output device, where the transmission device is connected to the above-mentioned processor, and the input / output device is connected to the above-mentioned processor.

[0107] Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

[0108] Step S201: Obtain a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of respective crystals;

[0109] Step S202: Determine the peak points of the crystal histograms in the two-dimensional distribution histogram, and determine the peak spacing between every two adjacent crystal histograms according to the peak points of the crystal histograms;

[0110] Step S203: Determine the cutting angle θ of the corresponding two adjacent crystal histograms according to the connection line between the peak points in every two adjacent crystal histograms, and determine the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms;

[0111] Step S204: Divide the peak spacing between the corresponding two adjacent crystal histograms according to the ratio of the first standard deviation to the second standard deviation to obtain the segmentation boundary between the two adjacent crystal histograms.

[0112] It should be noted that the specific examples in this embodiment may refer to the examples described in the above-mentioned embodiment and the optional implementation manners, and will not be elaborated in this embodiment.

[0113] In addition, in combination with the crystal boundary detection method provided in the above-mentioned embodiment, a storage medium may also be provided in this embodiment to implement it. A computer program is stored on the storage medium; when the computer program is executed by a processor, any one of the crystal boundary detection methods in the above-mentioned embodiment is implemented.

[0114] It should be understood that the specific embodiments described here are only used to explain this application, rather than to limit it. According to the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0115] Obviously, the drawings are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can also be applied to other similar situations according to these drawings without creative work. In addition, it can be understood that although the work done during the development process here may be complex and time-consuming, for those of ordinary skill in the art, certain design, manufacturing, or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be regarded as insufficient disclosure of the present application.

[0116] As used in this application, the term "embodiment" means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. Those of ordinary skill in the art can clearly or implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0117] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all fall within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A crystal boundary detection method applied to a PET detector, characterized in that, the method includes: Obtain a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of each crystal; Determine the peak points of each crystal histogram in the two-dimensional distribution histogram, and determine the peak spacing between every two adjacent crystal histograms according to the peak points of each crystal histogram; Determine the cutting angle θ of the corresponding two adjacent crystal histograms according to the connection line between the peak points of every two adjacent crystal histograms, and determine the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms; Divide the peak spacing of the corresponding two adjacent crystal histograms according to the ratio of the first standard deviation to the second standard deviation to obtain the segmentation boundary between the two adjacent crystal histograms; Determining the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the cutting angle θ in every two adjacent crystal histograms includes: Determine the first horizontal standard deviation in the x-axis direction and the first vertical standard deviation in the y-axis direction of the first crystal histogram in every two adjacent crystal histograms according to the two-dimensional Gaussian curve of each crystal histogram, and the second horizontal standard deviation in the x-axis direction and the second vertical standard deviation in the y-axis direction of the second crystal histogram; Determine the first standard deviation of the one-dimensional Gaussian curve of the first crystal histogram along the cutting angle θ according to the first horizontal standard deviation, the first vertical standard deviation and the cutting angle θ, and determine the second standard deviation of the one-dimensional Gaussian curve of the second crystal histogram along the cutting angle θ according to the second horizontal standard deviation, the second vertical standard deviation and the cutting angle θ, where the one-dimensional Gaussian curves are respectively one-dimensional Gaussian curves along the direction of the connection line of the two peak points of the first crystal histogram and the second crystal histogram.

2. The method according to claim 1, characterized in that, Determining the peak points of each crystal histogram in the two-dimensional distribution histogram includes: Obtain the pixel gray values of each crystal histogram in the two-dimensional distribution histogram; Determine the maximum pixel gray value of each crystal histogram, and use the position of the maximum pixel gray value as the peak point of each crystal histogram.

3. The method according to claim 1, characterized in that, Obtaining the two-dimensional distribution histogram obtained after the radiation light source acts on the crystal array includes: Obtain the initial image obtained after the radiation light source acts on the crystal array, slide the initial image with a preset window at a preset step length, and count the image features in the preset window during each sliding process to obtain a plurality of candidate crystal histograms; Perform two-dimensional Gaussian curve fitting on the plurality of candidate crystal histograms to obtain the two-dimensional Gaussian curve fitting degrees of each candidate crystal histogram; Obtain the number of crystals N of the crystal array, arrange the fitting degrees of the plurality of candidate crystal histograms in order, and select the candidate crystal histograms corresponding to the top N large fitting degrees as the crystal histograms corresponding to the positions of each crystal.

4. The method according to claim 3, wherein, obtaining the number N of crystals in the crystal array, arranging the fitting degrees of the plurality of candidate crystal histograms in sequence, and selecting the candidate crystal histograms corresponding to the top N largest fitting degrees as the crystal histograms corresponding to the positions of the respective crystals includes: judging whether the top N largest fitting degrees selected are all not less than a preset fitting degree threshold; in the case where it is judged that there is a fitting degree among the top N largest fitting degrees selected that is less than the preset fitting degree threshold, adjusting the size of the preset window and / or the preset step length until, when statistically analyzing the image features within the preset window during each sliding process according to the adjusted preset window and / or preset step length, the top N largest fitting degrees among the obtained plurality of crystal histograms are all not less than the preset fitting degree threshold.

5. The method according to claim 1, wherein, the dissection angle θ between every two adjacent crystal histograms includes the angle between the line connecting the peak points in every two adjacent crystal histograms and a preset reference direction.

6. The method according to claim 1, wherein, after dividing the peak distance between two adjacent crystal histograms corresponding thereto according to the ratio of the first standard deviation and the second standard deviation to obtain a segmentation boundary between the two adjacent crystal histograms, the method further includes: determining a segmentation point according to the line connecting the peak points in every two adjacent crystal histograms and the segmentation boundary; obtaining the coordinates of the segmentation point, and generating a position table of the crystal array according to the coordinates of the segmentation point.

7. A crystal boundary detection device, wherein, it includes: an acquisition module, configured to acquire a two-dimensional distribution histogram obtained after a radiation light source acts on a crystal array, where the two-dimensional distribution histogram includes crystal histograms corresponding to the positions of the respective crystals; a first determination module, configured to determine the peak points of the crystal histograms in the two-dimensional distribution histogram, and determine the peak distance between every two adjacent crystal histograms according to the peak points of the crystal histograms; a second determination module, configured to determine the dissection angle θ between two adjacent crystal histograms corresponding thereto according to the line connecting the peak points in every two adjacent crystal histograms, and determine the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the dissection angle θ in every two adjacent crystal histograms; a division module, configured to divide the peak distance between two adjacent crystal histograms corresponding thereto according to the ratio of the first standard deviation and the second standard deviation to obtain a segmentation boundary between the two adjacent crystal histograms; determining the first standard deviation and the second standard deviation of the Gaussian curve of each crystal histogram along the dissection angle θ in every two adjacent crystal histograms includes: determining, according to the two-dimensional Gaussian curve of each crystal histogram, the first horizontal standard deviation in the x-axis direction and the first vertical standard deviation in the y-axis direction of the first crystal histogram, and the second horizontal standard deviation in the x-axis direction and the second vertical standard deviation in the y-axis direction of the second crystal histogram in every two adjacent crystal histograms; Determine a first standard deviation of the one-dimensional Gaussian curve of the first crystal histogram along the cutting angle θ according to the first horizontal standard deviation, the first vertical standard deviation, and the cutting angle θ, and determine a second standard deviation of the one-dimensional Gaussian curve of the second crystal histogram along the cutting angle θ according to the second horizontal standard deviation, the second vertical standard deviation, and the cutting angle θ, where the one-dimensional Gaussian curves are respectively the one-dimensional Gaussian curves along the direction of the line connecting the two peak points of the first crystal histogram and the second crystal histogram.

8. A detector event positioning system, comprising a memory and a processor, characterized in that, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the crystal boundary detection method according to any one of claims 1 to 6, and locate the crystal position where the event occurs according to the detected crystal boundary.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the crystal boundary detection method according to any one of claims 1 to 6 are implemented.

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