CT image generation method, device, CT system, equipment, medium and program product

Abstract

The application relates to a CT image generation method, a CT image generation device, a CT system, equipment, a medium and a program product. The method comprises the following steps: acquiring original detection data of each pixel in a CT detector; determining a defective pixel of the CT detector; if the defective pixel is located in a defective row of the CT detector, correcting image data of the defective row pixel by using image data of adjacent row pixels of the defective row to obtain correction data of the defective row pixel. The method can accurately correct the defective pixel in the detector.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a CT image generation method, apparatus, CT system, device, medium, and program product. Background Technology

[0002] Due to manufacturing errors in semiconductor detectors, some pixel units in the pixel array of a semiconductor detector may have abnormal responses, resulting in defective pixels in the pixel array.

[0003] Defective pixels in the pixel array can lead to poor quality of the reconstructed medical image, which in turn affects the doctor's diagnosis.

[0004] Therefore, how to correct defective pixels has become a pressing technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a CT image generation method, apparatus, CT system, device, medium, and program product that can accurately correct defective pixels in the detector.

[0006] Firstly, this application provides a method for generating CT images. The method includes:

[0007] Obtain the raw detection data of each pixel in the CT detector;

[0008] Identify defective pixels in the CT detector;

[0009] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0010] In one embodiment, the image data of the defective row pixels is corrected using the image data of the neighboring row pixels to obtain the corrected data of the defective row pixels. This includes: performing curve fitting processing on the image data of the neighboring row pixels to obtain a function model; and inputting the defective row pixel index information into the function model to obtain the corrected data.

[0011] In one embodiment, the CT image generation method further includes: if the defective pixel is not located in the defect row, then using the original detection data of the pixels other than the defective pixel in the pixel row where the defective pixel is located to perform correction processing on the original detection data of the defective pixel to obtain corrected detection data of the defective pixel; and generating corrected data based on the corrected detection data.

[0012] In one embodiment, before correcting the image data of the defective row pixels using the image data of the neighboring rows of pixels to obtain corrected data, the CT image generation method further includes: generating tomographic image data of each pixel row based on the image reconstruction algorithm and the original detection data of each pixel row.

[0013] In one embodiment, the CT image generation method further includes: determining the number of defective pixels in the pixel row where the defective pixel is located; if the number of defective pixels is greater than a preset threshold, then determining that the defective pixel is located in the defect row of the CT detector; if the number of defective pixels is less than or equal to the preset threshold, then determining that the defective pixel is not located in the defect row of the CT detector.

[0014] In one embodiment, determining defective pixels of a CT detector includes at least one of the following: obtaining statistical boundaries and identifying pixels located outside the statistical boundaries as defective pixels; identifying pixels whose original detection data is outside a preset data range as defective pixels.

[0015] In one embodiment, obtaining the raw detection data of each pixel in the CT detector includes: controlling the CT detector to perform data detection to obtain the raw detection data of each pixel in the CT detector.

[0016] Secondly, this application also provides a CT image generation apparatus. The apparatus includes:

[0017] The acquisition module is used to acquire the raw detection data and raw image data of each pixel in the CT detector;

[0018] The defective pixel determination module is used to determine defective pixels in the CT detector;

[0019] The image domain correction module is used to correct the image data of the defective pixel by using the image data of the neighboring rows of pixels in the defective row if the defective pixel is located in the defective row of the CT detector, so as to obtain the corrected data of the defective pixel.

[0020] Thirdly, this application also provides a CT system for generating CT images, which are obtained using the method described in the first aspect above.

[0021] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0022] Obtain the raw detection data of each pixel in the CT detector;

[0023] Identify defective pixels in the CT detector;

[0024] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0025] Fifthly, this application also provides a computer-readable storage medium. This computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0026] Obtain the raw detection data of each pixel in the CT detector;

[0027] Identify defective pixels in the CT detector;

[0028] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0029] Sixthly, this application also provides a computer program product. This computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0030] Obtain the raw detection data of each pixel in the CT detector;

[0031] Identify defective pixels in the CT detector;

[0032] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0033] This application provides a CT image generation method, apparatus, CT system, device, medium, and program product. The CT image generation method identifies defective pixels in a pixel array based on the original detection data of each pixel in a CT detector. Then, it identifies pixel rows with a large number of defective pixels as defective rows. Next, it performs image reconstruction based on the pixel array to obtain the CT image corresponding to each pixel row. Finally, it corrects the pixels in the CT image corresponding to the defective row based on the pixels in the CT images of neighboring rows, obtaining corrected data for the defective row pixels. The method provided in this application corrects the image data corresponding to defective row pixels based on the image data of neighboring rows, avoiding the problem of low accuracy obtained by using normal pixels with a small proportion in the same row. Furthermore, this application performs pixel correction in an image domain with more data, achieving more precise correction and further improving the accuracy of the correction results. Attached Figure Description

[0034] Figure 1This is an application environment diagram of a CT image generation method in one embodiment;

[0035] Figure 2 This is a flowchart illustrating a CT image generation method in one embodiment;

[0036] Figure 3 This is a schematic diagram of a pixel array in one embodiment;

[0037] Figure 4 This is a schematic diagram of another pixel array in one embodiment;

[0038] Figure 5 This is a schematic diagram of another pixel array in one embodiment;

[0039] Figure 6 This is another flowchart illustrating the CT image generation method in one embodiment;

[0040] Figure 7 This is another flowchart illustrating the CT image generation method in one embodiment;

[0041] Figure 8 This is a schematic diagram of a CT image in one embodiment;

[0042] Figure 9 This is another flowchart illustrating the CT image generation method in one embodiment;

[0043] Figure 10 This is another flowchart illustrating the CT image generation method in one embodiment;

[0044] Figure 11 This is a structural block diagram of a CT image generation device in one embodiment;

[0045] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] The CT image generation method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, the CT detector 102 communicates with the terminal 104 via a network. A data storage system can store the data that the terminal 104 needs to process. The data storage system can be integrated into the terminal 104 or located in the cloud or on another network server. The CT detector 102 can send the acquired pixel array to the terminal 104, and the terminal 104 can perform defective pixel correction processing on the pixel array. The CT detector can also be a CT device containing a detector; the terminal 104 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc.

[0048] In one embodiment, such as Figure 2 As shown, a CT image generation method is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps:

[0049] Step 101: Obtain the raw detection data of each pixel in the CT detector.

[0050] Among them, the CT detector can be a semiconductor detector in the CT equipment, and can be of the energy integration type, photon counting type, etc.

[0051] In this embodiment of the application, the CT detector can perform CT detection processing on the object to be detected, converting the physiological parameters of the object to be detected into pixel values, and displaying them as follows: Figure 3 The data is stored in the form of a pixel array. After the CT detector completes its scan, it sends the pixel array to the terminal, allowing the terminal to obtain the raw scan data of each pixel in the CT detector.

[0052] In a pixel array, a row is called a slice, and a column is called a channel.

[0053] Step 102: Identify defective pixels in the CT detector.

[0054] Among them, defective pixels can be pixels with obviously abnormal pixel values ​​in the pixel array, or pixels with pixel values ​​that exceed the range of pixel values ​​preset by the user.

[0055] In this embodiment, a screening criterion for normal pixels can be preset manually. Pixels in the pixel array are then screened according to this criterion, and all pixels that do not meet the screening criteria are identified as defective pixels of the CT detector. Alternatively, a screening criterion for defective pixels can be directly preset. Pixels in the pixel array are then screened according to this criterion, and pixels that meet the screening criteria are directly identified as defective pixels of the CT detector. The pixel array after the defective pixels are identified is as follows: Figure 4 As shown.

[0056] In one embodiment, without setting additional filtering conditions, all pixels in the pixel array can be automatically analyzed through data processing methods such as fitting, and abnormal pixels in the pixel array can be directly identified as defective pixels of the CT detector.

[0057] Step 103: If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row to obtain the corrected data of the defective pixel.

[0058] The pixel data in the row of pixels is corrected by using neighboring pixels with a smaller percentage of defective pixels. Furthermore, the pixel data can be corrected in the image domain. For example, the image data corresponding to the neighboring pixels can be used to correct the image data corresponding to the pixel data in the row of pixels, thereby improving the accuracy of the correction results.

[0059] In this embodiment, after acquiring the pixel array of the CT detector and identifying all defective pixels in the pixel array, the terminal can first determine the pixel row with a higher proportion of defective pixels and designate it as the defective row. Then, image reconstruction processing is performed on the pixel array to obtain the CT image corresponding to each row of pixels. Next, the CT image layer corresponding to the defective row in the multi-layer CT image is determined and denoted as the defective layer. Finally, neighboring layer pixels are used to correct the pixels in the defective layer to obtain the corrected data for the defective row pixels.

[0060] Among them, the pixel array after the defect sorting is determined is as follows: Figure 5 As shown. x in the pixel array index The corresponding pixel rows are defect rows. The pixel rows corresponding to x1, x2, x3, and x4 are neighboring pixels with a smaller proportion of defective pixels, which are used to correct the defective pixels.

[0061] It should be noted that the adjacent CT images for which defective layer pixels are corrected are non-defective layer CT images.

[0062] In one embodiment, criteria for determining defective rows can be preset, and then defective rows can be determined from the pixel array according to these criteria. For example, a pixel array in which the proportion of defective pixels is greater than a preset threshold can be determined as a defective row.

[0063] In one embodiment, linear fitting, least squares method or other methods can be used to correct the defective layer pixels, as long as the correction of the defective layer pixels can be achieved. This application does not limit this.

[0064] The CT image generation method provided in this application can determine defective pixels in the pixel array based on the original detection data of each pixel in the CT detector, then identify the pixel row with more defective pixels as the defective row, and then perform image reconstruction based on the pixel array to obtain the CT image corresponding to each pixel row. Furthermore, it corrects the pixels in the CT image corresponding to the defective row based on the pixels in the CT images of neighboring rows, obtaining the corrected data for the defective row pixels. The method provided in this application can correct the image data corresponding to the defective row pixels based on the image data of neighboring rows, avoiding the problem of low accuracy obtained by using normal pixels with a small proportion in the same row for correction. Moreover, this application performs pixel correction in an image domain with more data, achieving more precise correction and further improving the accuracy of the correction results.

[0065] The embodiments described above introduced a scheme for correcting image data with defective pixels. In another embodiment of this application, pixel correction can be achieved through curve fitting, including methods such as... Figure 6 The steps shown are as follows:

[0066] Step 201: Perform curve fitting on the image data of neighboring rows of pixels to obtain the function model.

[0067] In this embodiment, after determining the CT image (defect layer image) corresponding to the defect row, multiple neighboring layer images can be selected first. Then, curve fitting is performed on the pixels at the corresponding positions in the multiple neighboring layer images to obtain the function model corresponding to that position.

[0068] For example, the two layers above and below the flawed layer in the CT image, which are also non-flawed layers, can be identified as neighboring layer images. Then, curve fitting is performed on the first pixel in each of the four neighboring layer images to obtain the function model corresponding to the first pixel; curve fitting is also performed on the second pixel in each of the four neighboring layer images to obtain the function model corresponding to the second pixel, and so on, to obtain the function models corresponding to each pixel in the CT image.

[0069] Step 202: Input the index information of the defective pixels into the function model to obtain the correction data.

[0070] The image data for the defective pixel row can include index information, that is, the position of the CT image corresponding to the defective pixel row within multiple neighboring image layers. For example, if the CT image corresponding to the defective pixel row is located in the middle layer of four neighboring image layers, then the position of the CT image corresponding to the defective pixel row is the third layer.

[0071] In this embodiment, the pixel values ​​of each pixel in the CT image corresponding to the defective pixel can be calculated using a function model based on the position (i.e., index information) of the CT image in multiple neighboring layers. This yields the corrected data after correction. Specifically, the position of the CT image corresponding to the defective pixel is input into each function model to obtain the corrected pixel values ​​output by each model. These corrected pixel values ​​then constitute the corrected data.

[0072] The method provided in this application embodiment can perform curve fitting processing on CT images of adjacent rows of pixels to obtain a function model corresponding to each pixel in the CT image. Then, based on multiple function models, calculations are performed to obtain the corrected data of the pixels in the CT image corresponding to the defective row of pixels. The method provided in this application embodiment can achieve the image data correction processing of defective row of pixels through data fitting, avoiding the problem of low accuracy obtained by using normal pixels with a small proportion in the same row for correction processing, thus improving the accuracy of the correction results. Furthermore, this application performs pixel correction processing on pixels in an image domain with more data, which can achieve more precise correction, further improving the accuracy of the correction results.

[0073] The embodiments described above introduced a correction method when the defective pixel is located in the defect row. In another embodiment of this application, a correction method is provided when the defective pixel is not located in the defect row, including, for example... Figure 7 The steps shown are as follows:

[0074] Step 301: If the defective pixel is not located in the defective pixel row, then use the original detection data of the pixels in the pixel row where the defective pixel is located, excluding the defective pixel, to correct the original detection data of the defective pixel and obtain the corrected detection data of the defective pixel.

[0075] In this embodiment, if a defective pixel is not located in a defective row, the proportion of normal pixels in that row is relatively high. Therefore, the defective pixel can be corrected using pixels other than the defective pixel (i.e., normal pixels) in the same row. For example, curve fitting can be performed on the normal pixels in the row containing the defective pixel to obtain a second function model. Then, the relevant parameters of the defective pixel (e.g., the position of the defective pixel among the normal pixels) are input into the second function model to obtain the corrected pixel value, i.e., the correction detection data.

[0076] Step 302: Generate correction data for defective pixels based on the correction detection data.

[0077] In this embodiment of the application, defective pixels are replaced according to the corrected detection data to obtain a row of corrected detection data. Based on the corrected detection data, image reconstruction processing is performed to obtain the CT image of the pixel row, that is, the corrected data of the defective pixel row.

[0078] The method provided in this application addresses defective pixels in non-defective rows by using normal pixels in the same row for correction processing, and then reconstructs the image based on the corrected pixel row to obtain the target image data. In pixel rows with a high proportion of normal pixels, the method provided in this application can directly use normal pixels in the same row for correction processing in the data domain. Compared to the correction processing in the image domain, the data domain reduces the amount of data involved in the processing and improves data processing efficiency.

[0079] The embodiments described above introduced a scheme for image reconstruction of pixel rows. In another embodiment of this application, image reconstruction of pixel rows can be achieved based on a reconstruction algorithm, including the following steps:

[0080] Based on the image reconstruction algorithm and the original detection data of each pixel row, tomographic image data of each pixel row is generated.

[0081] In this embodiment, an image reconstruction algorithm can be used to process the original detection data of each pixel row, or the corrected detection data of each pixel row, to obtain, as shown below. Figure 8 The image shown represents the CT image corresponding to each pixel row, i.e., the tomographic image data of each pixel row. Here, Height and Width are the height and width of a CT image, respectively. index This represents the CT image corresponding to the pixel row of defects, img1, img2, img index ,img 511 ,img 512 The subscript indicates the position of the corresponding CT image among all CT images, and the maximum value of the subscript is the same as the number of rows of the CT detector.

[0082] The method provided in this application embodiment can process the original detection data of each pixel row according to the image reconstruction algorithm to obtain the image data corresponding to each pixel row, so that the terminal can correct the CT image corresponding to the defect row in the image domain, thereby improving the accuracy of the correction result.

[0083] The embodiments described above introduced a method for correcting defective pixels in defective rows and non-defective rows. In another embodiment of this application, a method for determining defective rows and non-defective rows is provided, including, for example... Figure 9 The steps shown are as follows:

[0084] Step 401: Determine the number of defective pixels in the pixel row where the defective pixel is located.

[0085] Step 402: If the number of defective pixels is greater than a preset threshold, then the defective pixels are determined to be located in the defect row of the CT detector.

[0086] Step 403: If the number of defective pixels is less than or equal to a preset threshold, then it is determined that the defective pixels are not located in the defect row of the CT detector.

[0087] In this embodiment, after the terminal obtains the pixel array of the CT detector and determines all defective pixels in the pixel array, it can first determine the number of defective pixels in each pixel row, and then compare the number with a preset threshold. If the number of defective pixels in the pixel row is greater than the preset threshold, it is determined that the proportion of defective pixels in the pixel row is relatively high, and the pixel row is identified as a defective row. The defective pixels in the pixel row are located in the defective row of the CT detector. If the number of defective pixels in the pixel row is less than or equal to the preset threshold, it is determined that the proportion of normal pixels in the pixel row is relatively high, and the pixel row is a non-defective row. The defective pixels in the pixel row are not located in the defective row of the CT detector.

[0088] The method provided in this application embodiment can determine the defective row based on the number of defective pixels in the pixel row, and then determine whether the defective pixels are located in the defective row. This application embodiment can distinguish between defective rows and non-defective rows, and perform different correction processes for defective rows and non-defective rows according to their characteristics, thus offering high flexibility.

[0089] The embodiments described above mainly introduce a scheme for correcting defective pixels. In another embodiment of this application, a scheme for determining defective pixels is provided, including, for example... Figure 10 The steps shown are as follows:

[0090] Step 501: Obtain the statistical boundary and identify pixels outside the statistical boundary as defective pixels.

[0091] Step 502: Pixels whose original detection data are outside the preset data range are identified as defective pixels.

[0092] In this embodiment of the application, based on the characteristics of the CT detector, the pixel values ​​(raw detection data) of the pixels in the CT detector have statistical boundaries. The pixel values ​​of the pixels in the CT detector are all within the range of these statistical boundaries. Therefore, pixels outside the range of statistical boundaries are abnormal pixels, and pixels outside the statistical boundaries can be identified as defective pixels.

[0093] In one embodiment, a data range can be preset based on factors such as the specific object to be detected and the detection location of the object to be detected, and pixels outside the preset data range are determined to be defective pixels.

[0094] The method provided in this application embodiment can identify pixels whose pixel values ​​are outside the statistical boundary of the CT detector and outside the preset data range as defective pixels. By identifying defective pixels through multiple standards, the accuracy of the identified defective pixels is improved.

[0095] The foregoing embodiments described a scheme for obtaining raw detection data. In another embodiment of this application, a scheme for obtaining raw detection data is described, including the following steps:

[0096] The CT detector is controlled to perform data detection, and the raw detection data of each pixel in the CT detector is obtained.

[0097] Among them, the CT detector can be a semiconductor detector in the CT equipment, and can be of the energy integration type, photon counting type, etc.

[0098] In this embodiment, the terminal can control the CT detector to perform data detection on the object to be detected, convert the detected physiological parameters of the object into pixel values, and store them in the form of a pixel array. After the CT detector completes the detection, it sends the pixel array to the terminal, thereby enabling the terminal to obtain the raw detection data of each pixel in the CT detector.

[0099] In this embodiment, the raw detection data is obtained through the data detection function of the CT detector, and then the correction processing of defective pixels is performed based on the raw detection data to obtain the correction data of defective pixels.

[0100] In one embodiment, for defective pixels, data domain correction and image domain correction can be combined to generate correction data, as specifically implemented below:

[0101] After acquiring the raw detection data of each pixel in the CT detector, the terminal first performs image reconstruction directly based on the raw detection data to obtain the raw image data.

[0102] Then, for the defective pixel row, the defective pixel row is corrected by using the neighboring pixel row to obtain the correction detection data, and the image is reconstructed based on the correction detection data to obtain the auxiliary image data.

[0103] Next, the CT image corresponding to the defective row pixels is identified as the defective layer, and the defective layer pixels are corrected using the pixels of neighboring layers to obtain corrected image data;

[0104] Finally, the auxiliary image data and the corrected image data are weighted and summed to obtain the corrected data.

[0105] The method provided in this application combines data domain correction and image domain correction to generate correction data. Data domain correction processing can correct the image data corresponding to defective pixels based on the image data of neighboring rows of pixels, avoiding the problem of low accuracy of correction results obtained by using normal pixels with a small proportion in the same row. Correction processing is performed in the image domain with more data, which can achieve more accurate correction. The combination of the two further improves the accuracy of the correction results.

[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides a CT image generation apparatus for implementing the CT image generation method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more CT image generation apparatus embodiments provided below can be found in the limitations of the CT image generation method described above, and will not be repeated here.

[0108] In one embodiment, such as Figure 11 As shown, a CT image generation device is provided, including: an acquisition module 601, a defective pixel determination module 602, and an image domain correction module 603, wherein:

[0109] The acquisition module 601 is used to acquire the raw detection data and raw image data of each pixel in the CT detector;

[0110] The defective pixel determination module 602 is used to determine defective pixels of the CT detector;

[0111] The image domain correction module 603 is used to correct the image data of the defective pixel by using the image data of the neighboring rows of pixels in the defective row if the defective pixel is located in the defective row of the CT detector, so as to obtain the correction data of the defective pixel.

[0112] In one embodiment, the image domain correction module 603 is specifically used to perform curve fitting processing on the image data of neighboring rows of pixels to obtain a function model; and input the index information of the defective rows of pixels into the function model to obtain correction data.

[0113] In one embodiment, the CT image generation apparatus further includes a data domain correction module, which is used to correct the original detection data of the defective pixel by using the original detection data of the pixels other than the defective pixel in the pixel row where the defective pixel is located if the defective pixel is not located in the defect row, so as to obtain corrected detection data of the defective pixel; and generate corrected data of the defective pixel based on the corrected detection data.

[0114] In one embodiment, prior to the image domain correction module 603, the CT image generation apparatus further includes a reconstruction module, which generates tomographic image data for each pixel row based on the image reconstruction algorithm and the original detection data for each pixel row.

[0115] In one embodiment, the CT image generation apparatus further includes a defect row determination module, which is used to determine the number of defective pixels in the pixel row where the defective pixel is located; if the number of defective pixels is greater than a preset threshold, the defective pixel is determined to be located in the defect row of the CT detector; if the number of defective pixels is less than or equal to the preset threshold, the defective pixel is determined not to be located in the defect row of the CT detector.

[0116] In one embodiment, the defective pixel determination module 602 is specifically used to obtain statistical boundaries, determine pixels located outside the statistical boundaries as defective pixels, and determine pixels whose original detection data is outside a preset data range as defective pixels.

[0117] In one embodiment, the acquisition module 601 is specifically used to control the CT detector to perform data detection and obtain the raw detection data of each pixel in the CT detector.

[0118] Each module in the aforementioned CT image generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0119] In one embodiment, a CT system is provided for generating CT images, which are obtained using the method described in the above embodiments.

[0120] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a CT image generation method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0121] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0122] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0123] Obtain the raw detection data of each pixel in the CT detector;

[0124] Identify defective pixels in the CT detector;

[0125] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0126] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing curve fitting processing on the image data of neighboring rows of pixels to obtain a function model; and inputting the index information of the defective rows of pixels into the function model to obtain correction data.

[0127] In one embodiment, when the processor executes the computer program, it further performs the following steps: if the defective pixel is not located in the defect row, the original detection data of the defective pixel is corrected using the original detection data of the pixels other than the defective pixel in the pixel row where the defective pixel is located, to obtain corrected detection data of the defective pixel; and the corrected data of the defective pixel is generated based on the corrected detection data.

[0128] In one embodiment, when the processor executes the computer program, it further performs the following steps: generating tomographic image data for each pixel row based on the image reconstruction algorithm and the original detection data for each pixel row.

[0129] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the number of defective pixels in the pixel row where the defective pixel is located; if the number of defective pixels is greater than a preset threshold, determining that the defective pixel is located in the defect row of the CT detector; if the number of defective pixels is less than or equal to the preset threshold, determining that the defective pixel is not located in the defect row of the CT detector.

[0130] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining statistical boundaries, identifying pixels located outside the statistical boundaries as defective pixels; and identifying pixels whose original detection data is outside a preset data range as defective pixels.

[0131] In one embodiment, when the processor executes the computer program, it also performs the following steps: controlling the CT detector to perform data detection to obtain the raw detection data of each pixel in the CT detector.

[0132] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0133] Obtain the raw detection data of each pixel in the CT detector;

[0134] Identify defective pixels in the CT detector;

[0135] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0136] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing curve fitting processing on the image data of neighboring rows of pixels to obtain a function model; and inputting the index information of the defective rows of pixels into the function model to obtain correction data.

[0137] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the defective pixel is not located in the defect row, the original detection data of the defective pixel is corrected using the original detection data of the pixels other than the defective pixel in the pixel row where the defective pixel is located, to obtain corrected detection data of the defective pixel; and the corrected data of the defective pixel is generated based on the corrected detection data.

[0138] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: generating tomographic image data for each pixel row based on the image reconstruction algorithm and the original probe data for each pixel row.

[0139] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the number of defective pixels in the pixel row where the defective pixel is located; if the number of defective pixels is greater than a preset threshold, determining that the defective pixel is located in the defect row of the CT detector; if the number of defective pixels is less than or equal to the preset threshold, determining that the defective pixel is not located in the defect row of the CT detector.

[0140] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining statistical boundaries, identifying pixels located outside the statistical boundaries as defective pixels; and identifying pixels whose original detection data is outside a preset data range as defective pixels.

[0141] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: controlling the CT detector to perform data detection to obtain the raw detection data of each pixel in the CT detector.

[0142] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0143] Obtain the raw detection data of each pixel in the CT detector;

[0144] Identify defective pixels in the CT detector;

[0145] If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected by using the image data of the neighboring rows of pixels in the defective row, and the corrected data of the defective pixel is obtained.

[0146] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing curve fitting processing on the image data of neighboring rows of pixels to obtain a function model; and inputting the index information of the defective rows of pixels into the function model to obtain correction data.

[0147] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the defective pixel is not located in the defect row, the original detection data of the defective pixel is corrected using the original detection data of the pixels other than the defective pixel in the pixel row where the defective pixel is located, to obtain corrected detection data of the defective pixel; and the corrected data of the defective pixel is generated based on the corrected detection data.

[0148] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: generating tomographic image data for each pixel row based on the image reconstruction algorithm and the original probe data for each pixel row.

[0149] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the number of defective pixels in the pixel row where the defective pixel is located; if the number of defective pixels is greater than a preset threshold, determining that the defective pixel is located in the defect row of the CT detector; if the number of defective pixels is less than or equal to the preset threshold, determining that the defective pixel is not located in the defect row of the CT detector.

[0150] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining statistical boundaries, identifying pixels located outside the statistical boundaries as defective pixels; and identifying pixels whose original detection data is outside a preset data range as defective pixels.

[0151] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: controlling the CT detector to perform data detection to obtain the raw detection data of each pixel in the CT detector.

[0152] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating CT images, characterized in that, The method includes: Obtain the raw detection data of each pixel in the CT detector; Identify defective pixels in the CT detector; If the defective pixel is located in the defect row of the CT detector, the image data of the defective pixel is corrected using the image data of the neighboring rows of pixels in the defect row to obtain the corrected data of the defective pixel; wherein, if the number of defective pixels in the pixel row where the defective pixel is located is greater than a preset threshold, it is determined that the defective pixel is located in the defect row of the CT detector.

2. The method according to claim 1, characterized in that, The step of correcting the image data of the defective row pixels by using the image data of the neighboring rows of pixels to obtain the corrected image data of the defective row pixels includes: The image data of the neighboring rows of pixels are subjected to curve fitting to obtain a function model; The index information of the defective pixels is input into the function model to obtain the correction data.

3. The method according to claim 1, characterized in that, The method further includes: If the defective pixel is not located in the defective row, the original detection data of the defective pixel is corrected by using the original detection data of the pixels in the pixel row where the defective pixel is located, excluding the defective pixel, to obtain the corrected detection data of the defective pixel. The correction data is generated based on the correction detection data.

4. The method according to any one of claims 1-3, characterized in that, Before performing correction processing on the image data of the defective row pixels using image data of neighboring rows of pixels to obtain corrected data, the method further includes: Based on the image reconstruction algorithm and the original detection data of each pixel row, tomographic image data of each pixel row is generated.

5. The method according to any one of claims 1-3, characterized in that, The method further includes: Determine the number of defective pixels in the pixel row where the defective pixel is located; If the number of defective pixels is less than or equal to the preset threshold, then it is determined that the defective pixels are not located in the defect row of the CT detector.

6. The method according to claim 1, characterized in that, The determination of defective pixels in the CT detector includes at least one of the following: Obtain the statistical boundary, and determine the pixels located outside the statistical boundary as the defective pixels; Pixels whose original detection data falls outside the preset data range are identified as defective pixels.

7. The method according to claim 1, characterized in that, The acquisition of raw detection data for each pixel in the CT detector includes: The CT detector is controlled to perform data detection, and the raw detection data of each pixel in the CT detector is obtained.

8. A CT image generation device, characterized in that, The device includes: The acquisition module is used to acquire the raw detection data and raw image data of each pixel in the CT detector; A defective pixel determination module is used to determine defective pixels of the CT detector; An image domain correction module is used to correct the image data of the defective pixel by using the image data of the neighboring rows of pixels in the defective row of the CT detector if the defective pixel is located in the defective row of the CT detector, so as to obtain the corrected data of the defective pixel; wherein, if the number of defective pixels in the pixel row where the defective pixel is located is greater than a preset threshold, it is determined that the defective pixel is located in the defective row of the CT detector.

9. A CT system, characterized in that, The CT system is used to generate CT images, which are obtained using the method described in any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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