Data processing method and device based on CT equipment, CT equipment and electronic equipment

Abstract

The invention discloses a data processing method and device based on CT equipment, the CT equipment and electronic equipment, the CT equipment comprises a CT detector, the CT detector comprises a detector module with at least two pixel densities, and the data processing method based on the CT equipment comprises the following steps: obtaining initial pixel data corresponding to at least two detection densities based on the CT detector; determining target pixel data corresponding to the target density from the initial pixel data corresponding to the at least one detection density based on the resolution configuration information; and processing the target pixel data to obtain data to be imaged. According to the CT equipment, the pixel data with different detection densities can be obtained, the pixel data are processed according to different resolution requirements to obtain CT images with different resolutions, the user requirements are met, and the cost is reduced.

Description

Technical Field

[0001] This application relates to the technical fields of CT devices, etc., and in particular to a data processing method, device, CT device, and electronic device based on a CT device. Background Art

[0002] CT (Computed Tomography) is a medical imaging technology that generates images of internal body structures by tomographically imaging the human body or the object to be measured using X-rays. The X-ray source emits X-rays that penetrate the human body or the object to be measured. After the X-ray signals received by the CT detector are directly or indirectly converted into electrical signals, the electrical signals are converted into digital signals by an analog / digital converter and then input into a computer for image reconstruction to obtain a CT image.

[0003] The CT detector is an important component of a CT. A CT detector is usually composed of many CT detector modules spliced together. The resolution of a CT includes at least spatial resolution, density resolution, temporal resolution, energy resolution, etc., which are important indicators for judging the mechanical performance and image quality of a CT. Among them, spatial resolution is an important parameter for evaluating a CT device. Methods for improving spatial resolution include focal spot size, geometric shape of the scanning beam, size and spacing of detector elements, flying focal spot technology, etc.

[0004] In related technologies, a high-density hole component can be added between the object to be scanned and the CT detector. There are high-density holes in the component, and the holes re-direct the X-rays, thereby improving the spatial resolution of the X-rays. However, the cost of using the high-density hole component is relatively high, and when using the high-density hole component, the device motor drive and mechanical sliding are required to find the position, which increases the unreliability.

[0005] It is also possible to use all high-density modules installed to form a high-density detector to increase the spatial resolution. Since the integration of high-density pixels is high and the cost of a single detector module is much higher, the total cost of the detector is very high. This method is not applicable to medium- and low-cost models, and the power consumption is high, which is an adverse factor for the heat dissipation of the whole machine. Moreover, for the above two methods of improving spatial resolution, only pixel data of one detection density can be obtained in one scan, that is, when obtaining pixel data, only a CT image with one resolution can be formed according to the obtained pixel data, and it cannot be adjusted subsequently, which has certain limitations. Summary of the Invention

[0006] To this end, the purpose of the embodiments of the present application is to propose a data processing method, device, CT device, electronic device, storage medium and computer program product based on a CT device. The CT device of the present invention can obtain pixel data with different detection densities, and process the pixel data according to different resolution requirements to obtain CT images with different resolutions, which not only meets the user's needs but also reduces costs.

[0007] The embodiments of the present application provide a data processing method based on a CT device. The CT device includes a CT detector, and the CT detector includes detector modules with at least two pixel densities. The method includes: obtaining initial pixel data corresponding to at least two detection densities based on the CT detector; determining target pixel data corresponding to a target density from the initial pixel data corresponding to the at least two detection densities based on resolution configuration information; processing the target pixel data to obtain data to be imaged, and sending the data to be imaged to an imaging device so that the imaging device generates a CT image based on the data to be imaged.

[0008] Exemplarily, the CT detector includes a first preset density detector module and a second preset density detector module. The initial pixel data includes first pixel data obtained based on the first preset density detector module and second pixel data obtained based on the second preset density detector module. Determining target pixel data corresponding to a target density from the initial pixel data corresponding to the at least one detection density based on resolution configuration information includes: when the resolution configuration information indicates that the required resolution of the image is a first preset resolution, determining the first pixel data as the target pixel data; when the resolution configuration information indicates that the required resolution of the image is a second preset resolution, determining the first pixel data and the second pixel data as the target pixel data, or determining the second pixel data as the target pixel data; wherein, the first preset resolution is greater than the second preset resolution, and the first preset density is greater than the second preset density.

[0009] Exemplarily, when the target pixel data includes the first pixel data and the second pixel data, processing the target pixel data to obtain data to be imaged includes: converting the first pixel data to obtain third pixel data so that the pixel size of the third pixel data is the same as that of the second pixel data; obtaining data to be imaged based on the third pixel data and the second pixel data.

[0010] Exemplarily, the processing of the target pixel data to obtain the data to be imaged further includes: when the resolution configuration information indicates that the required resolution of the image is the second preset resolution, processing the target pixel data according to the position information of the object to be scanned to obtain the data to be imaged.

[0011] Exemplarily, the processing of the target pixel data according to the position information of the object to be scanned to obtain the data to be imaged includes: when the target pixel data includes the first pixel data and the second pixel data, and the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the first preset density detector module and not within the scanning field of view corresponding to the second preset density detector module, converting the first pixel data to obtain third pixel data, and obtaining the data to be imaged based on the third pixel data and the second pixel data; when the target pixel data includes the second pixel data, and the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the second preset density detector module, processing the second pixel data to obtain the data to be imaged.

[0012] Exemplarily, converting the first pixel data to obtain third pixel data includes: based on the pixel size of the second pixel data, converting the first pixel data based on calibration data to obtain third pixel data, where the pixel sizes of the third pixel data and the second pixel data are the same.

[0013] Exemplarily, the first pixel data includes the original measurement data corresponding to each pixel point, and the calibration data includes static calibration data and dynamic calibration data. Converting the first pixel data based on the calibration data to obtain third pixel data includes: processing the original measurement data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain third pixel data; or, processing the original measurement data corresponding to each pixel point in the first pixel data based on the static calibration data to obtain a second processing result, and processing the second processing result based on the dynamic calibration data to obtain third pixel data.

[0014] Exemplarily, the dynamic calibration data includes calibration coefficients, the static calibration data includes static calibration values, the original metric data includes original metric values, the third pixel data includes target metric values corresponding to each pixel point, and processing the original metric data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain the third pixel data, includes: multiplying the calibration coefficient corresponding to the pixel point by the original metric value corresponding to the pixel point to obtain a first product; summing the first products corresponding to the first number of pixel points to obtain a first sum value; comparing the first sum value with the static calibration value to obtain the target metric value; wherein, the first number is obtained according to a first ratio of the first preset density and the second preset density, and the static calibration value is obtained according to the pixel area of the first preset density and the first ratio.

[0015] Exemplarily, the dynamic calibration data includes calibration offsets, the static calibration data includes static calibration values, the original metric data includes original metric values, the third pixel data includes target metric values corresponding to each pixel point, and processing the original metric data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain the third pixel data, further includes: adding the calibration offset corresponding to the pixel point to the original metric value corresponding to the pixel point to obtain a second sum value; summing the second sum values corresponding to the first number of pixel points to obtain a third sum value; comparing the third sum value with the static calibration value to obtain the target metric value; wherein, the first number is obtained according to a first ratio of the first preset density and the second preset density, and the static calibration value is obtained according to the pixel area of the first preset density and the first ratio.

[0016] Exemplarily, the dynamic calibration data includes calibration coefficients, the static calibration data includes static calibration values, the original measurement data includes original measurement values, the third pixel data includes target measurement values corresponding to each pixel point, and processing the original measurement data corresponding to each pixel point in the first pixel data based on the static calibration data to obtain a second processing result, and processing the second processing result based on the dynamic calibration data to obtain the third pixel data, includes: adding the original measurement values corresponding to a first quantity of pixel points to obtain a fourth sum; comparing the fourth sum with the static calibration value to obtain a second ratio; multiplying the calibration coefficient by the second ratio to obtain the target measurement value; wherein, the first quantity is obtained according to a first ratio of a first preset density to a second preset density, and the static calibration value is obtained according to a pixel area of the first preset density and the first ratio.

[0017] Exemplarily, the dynamic calibration data includes calibration offsets, the static calibration data includes static calibration values, the original measurement data includes original measurement values, the third pixel data includes target measurement values corresponding to each pixel point, and processing the original measurement data corresponding to each pixel point in the first pixel data based on the static calibration data to obtain a second processing result, and processing the second processing result based on the dynamic calibration data to obtain the third pixel data, includes: adding the original measurement values corresponding to the first quantity of the pixel points to obtain a fourth sum; comparing the fourth sum with the static calibration value to obtain a second ratio; adding the calibration offset to the second ratio to obtain the target measurement value; wherein, the first quantity is obtained according to a first ratio of a first preset density to a second preset density, and the static calibration value is obtained according to a pixel area of the first preset density and the first ratio.

[0018] Exemplarily, the static calibration data is associated with pixel area and the gap between pixels; the dynamic calibration data is associated with at least one of the maximum current of the ADC, temperature drift, voltage offset, transient response, and frequency response.

[0019] Another embodiment of the present application provides a data processing device, which is applied to a CT device. The CT device includes a CT detector, and the CT detector includes detector modules with at least two pixel densities. The device includes: an acquisition module, configured to acquire initial pixel data corresponding to at least two detection densities based on the CT detector; a determination module, configured to determine target pixel data corresponding to a target density from the initial pixel data corresponding to the at least two detection densities based on resolution configuration information; and a processing module, configured to process the target pixel data to obtain image data to be reconstructed, and send the image data to be reconstructed to an imaging device so that the imaging device generates a CT image based on the image data to be reconstructed.

[0020] Another embodiment of the present application provides a CT device, which includes: an X-ray source; a CT detector, the CT detector at least includes a first preset density detector module and a second preset density detector module, and the CT detector is configured to detect X-rays and generate initial pixel data; and the data processing device according to the above.

[0021] Another embodiment of the present application provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of the above embodiments are implemented.

[0022] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method according to any one of the above embodiments are implemented.

[0023] Another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can execute the steps of the method according to any one of the above embodiments.

[0024] In the above embodiments, the data processing method based on a CT device includes: acquiring initial pixel data corresponding to at least two detection densities based on a CT detector; determining target pixel data corresponding to a target density from the initial pixel data corresponding to the at least two detection densities based on resolution configuration information; and processing the target pixel data to obtain image data to be reconstructed. The CT device of the present invention can acquire pixel data with different detection densities, process the pixel data according to different resolution requirements to obtain CT images with different resolutions, which not only meets the user's needs but also reduces costs. Description of the Drawings

[0025] Figure 1 It is a schematic diagram of a CT detector provided by an embodiment of the present application;

[0026] Figure 2Schematic diagram of a CT detector using a high-density hole component provided by an embodiment of the present application;

[0027] Figure 3 Schematic diagram of a high-density CT detector composed of high-density modules provided by an embodiment of the present application;

[0028] Figure 4 Flowchart of a data processing method based on a CT device provided by an embodiment of the present application;

[0029] Figure 5 Schematic diagram of a CT detector provided by an embodiment of the present application;

[0030] Figure 6 Schematic diagram of a CT device provided by an embodiment of the present application;

[0031] Figure 7 Flowchart of processing target pixel data provided by an embodiment of the present application;

[0032] Figure 8 Structural diagram of data acquisition and processing of a detector device provided by an embodiment of the present application;

[0033] Figure 9 Schematic diagram of a low-density pixel module and a high-density pixel module provided by an embodiment of the present application;

[0034] Figure 10 Flowchart of a data conversion method provided by an embodiment of the present application;

[0035] Figure 11 Schematic diagram of X-ray dose and quantization value provided by an embodiment of the present application;

[0036] Figure 12 Temperature offset curve graph provided by an embodiment of the present application;

[0037] Figure 13 Flowchart of a data conversion method provided by another embodiment of the present application;

[0038] Figure 14 Flowchart of a data conversion method provided by another embodiment of the present application;

[0039] Figure 15 Flowchart of a data conversion method provided by another embodiment of the present application;

[0040] Figure 16 Schematic diagram of a detector data integration unit provided by an embodiment of the present application;

[0041] Figure 17 Data processing flowchart provided by an embodiment of the present application;

[0042] Figure 18 Schematic diagram of the data processing device provided by the embodiment of the present application;

[0043] Figure 19 Block diagram of the electronic device provided by the embodiment of the present application. Specific embodiments

[0044] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0045] Figure 1 Schematic diagram of a CT detector of an embodiment.

[0046] As Figure 1 shown, the CT detector is an important component of the CT device. The CT detector is usually composed of a plurality of CT detector modules spliced together. A detector module is a cube as Figure 1 shown. A plurality of detector modules are spliced together to form a CT detector. Each CT detector module is composed of an X-ray sensor, an analog / digital converter, a data processing unit and its mechanical structure. Usually, the pixel density of all CT detector modules in the CT detector is the same, that is, the CT detector can only collect pixel data of one detection density and cannot meet the requirements of other detection densities.

[0047] Figure 2 Schematic diagram of a CT detector using a high-density hole component of an embodiment.

[0048] As Figure 2 shown, to improve the spatial resolution of the CT device, a high-density hole component can be added between the object to be scanned and the CT detector. There are high-density holes in the middle of the component. Among them, the area of the holes does not exceed the pixel area of the detector. The high-density holes can adjust the area of the X-ray projected onto the detector pixels, thereby effectively improving the spatial resolution of the X-ray. Due to cost limitations (the number of holes is proportional to the manufacturing cost), and the purpose of improving the spatial resolution can only be achieved within the limited width in the layer direction. Therefore, the width in the layer direction is small, and factors such as motor drive and mechanical part sliding to find the position will increase the unreliability during the operation of this component and the instability of the gantry during rotation.

[0049] Figure 3 Schematic diagram of a high-density CT detector composed of high-density modules of an embodiment.

[0050] As Figure 3As shown, the spatial resolution can be improved by installing all high-density detector modules. From Figure 3 the detector modules, it can be seen that the pixel points of each detector module have a higher density compared to the pixel points of the detector modules in Figure 2 . However, due to the high integration of high-density pixels, the relative cost of a single detector module is also high, so the total cost of the detectors is high. For medium- and low-cost models, high-density CT detectors are not the optimal choice. Moreover, the power consumption of high-density CT detectors is also high, which is an adverse factor for the heat dissipation of the whole machine.

[0051] Based on this, the present application proposes a CT detector for improving spatial resolution and its data processing method. This detector can improve the spatial resolution while taking into account the usage cost and performance requirements of the device.

[0052] Figure 4 is a flowchart of a data processing method based on a CT device according to an embodiment of the present application.

[0053] As an example, as Figure 4 shown, the data processing method based on a CT device includes:

[0054] S401, obtaining initial pixel data corresponding to at least two detection densities based on a CT detector.

[0055] S402, determining target pixel data corresponding to a target density from the initial pixel data corresponding to at least two detection densities based on resolution configuration information.

[0056] S403, processing the target pixel data to obtain data to be imaged.

[0057] Exemplarily, the CT device of the present application includes a CT detector, and the CT detector includes detector modules with at least two pixel densities. In order to balance the relationship between spatial resolution and usage cost, the present application balances the cost and the performance advantage of spatial resolution by configuring detector modules with different density resolutions to meet the scanning requirements of different models. The CT detector of the present application is composed of detector modules with multiple detection densities. The number of types can be two, or three or other numbers. Considering the actual application scenario and cost, preferably, detector modules with two detection densities can be used.

[0058] Exemplarily, based on the CT detector, initial pixel data corresponding to at least two detection densities is obtained. The X-ray source in the CT device emits X-rays, which are received by the CT detector after passing through the object to be measured. Each CT detector module includes a plurality of pixel points, and each pixel point corresponds to a photoelectric sensor (also referred to as an X-ray sensor). The photoelectric sensor converts the X-ray signal into an electrical signal to obtain the initial pixel data. Based on the resolution configuration information, the target pixel data corresponding to the target density is determined from the initial pixel data corresponding to at least two detection densities. The resolution configuration information can be obtained by the user through configuration selection. According to different user resolution requirements, the target pixel data corresponding to the target density is determined from the initial pixel data. For example, if the user wants to capture a high-resolution CT image, the high-density pixel data received by the high-density detector module can be selected from the initial pixel data as the target pixel data. Then, the target pixel data is processed to obtain the data to be imaged. The above process can be implemented by a data processing device, which is connected to the CT detector and the imaging device, obtains the initial pixel data through the CT detector, and sends the processed data to be imaged to the imaging device so that the imaging device generates a CT image based on the data to be imaged.

[0059] The data processing method based on the CT device of the present application, by configuring detector modules with different density resolutions, while improving the spatial resolution, also takes into account the usage cost and performance requirements of the device. At the same time, based on the resolution configuration information, the target pixel data is selected from the initial pixel data corresponding to multiple detection densities, and data consistency correction of the set resolution is performed on the target pixel data to obtain the data to be imaged.

[0060] Compared with the method of using mechanical high-density structural components to find positions, it can maximize the absorption of the attenuated X-rays, improve the single-loop field of view of high-density inspection in the layer direction, and eliminate unreliable performance caused by structural actions, such as mechanical vibration and mechanical position positioning fineness.

[0061] Figure 5 It is a schematic diagram of a CT detector according to an embodiment of the present application.

[0062] As Figure 5 shown, high-density detector modules 102 can be configured within the scanning field of view of tissues that require high-density detection. The number of high-density detector modules 102 can be determined according to specific requirements, and low-density detector modules 101 can be configured at other positions. Figure 5In the illustrated example, the CT detector is configured with detector modules of two detection densities. Of course, it can also be configured with detector modules of other numbers and types of detection densities. For example, detector modules of high, medium, and low detection densities can be configured. Hereinafter, a CT detector configured with detector modules of two detection densities will be taken as an example for description. It should be noted that the high-density detector and the low-density detector are relative terms.

[0063] Since the CT detector of the present application is configured with detector modules of different density resolutions, it can not only meet the scanning requirements of high spatial resolution but also meet the scanning requirements of low spatial resolution.

[0064] Figure 6 It is a schematic diagram of a CT device according to an embodiment of the present application.

[0065] As Figure 6 shown, the CT device includes a rotating part and a fixed part. The rotating part includes a high-voltage generating unit, an X-ray tube, and a detector. The detector device at least includes a high-density pixel detector module and a low-density pixel detector module. The high-voltage generator is used to apply a high voltage to the filament of the X-ray tube. The X-ray tube emits rays that penetrate the object to be detected. The detector absorbs the X-rays that have passed through the object to be detected and finally converts the X-rays into digital signals that can be used for evaluation. The fixed part has a motor device that drives the rotating part to rotate. The detector converts the X-rays into digital signals and sends them to the data processing device. The data processing device sends the processed data to the imaging device through transmission media such as slip rings. The imaging device generates a CT image. The CT device also includes a console device (including a computer system, etc.) for interacting with the user and controlling the CT gantry system. Of course, the CT device may also include a bed device, and the bed device can provide functions such as vertical position translation and horizontal position translation.

[0066] As an example, based on the resolution configuration information, determining the target pixel data corresponding to the target density from the initial pixel data corresponding to at least one detection density includes:

[0067] When the resolution configuration information indicates that the required resolution of the image is the first preset resolution, determining the first pixel data as the target pixel data;

[0068] When the resolution configuration information indicates that the required resolution of the image is the second preset resolution, determining the first pixel data and the second pixel data as the target pixel data, or determining the second pixel data as the target pixel data;

[0069] Wherein, the first preset resolution is greater than the second preset resolution, and the first preset density is greater than the second preset density.

[0070] Exemplarily, the CT detector includes a first preset density detector module and a second preset density detector module. The first preset density and the second preset density are different, and the first preset density is greater than the second preset density. It can be understood that the first preset density detector module is a high-density detector module, and the second preset detector module is a low-density detector module. The initial pixel data obtained by the first preset density detector module is the first pixel data, and the initial pixel data obtained by the second preset density detector module is the second pixel data. The first preset resolution is greater than the second preset resolution.

[0071] When the resolution configuration information indicates that the required resolution of the image is the first preset resolution, that is, when the user needs a high-resolution CT image (for example, when scanning blood vessels, etc.), it can be determined that the first pixel data obtained by the high-density detector module is the target pixel data. In this case, the second pixel data obtained based on the low-density detector module cannot meet the high-resolution requirement, and the second pixel data is ignored without being processed, which can save the system computing amount and improve the operation efficiency.

[0072] When the resolution configuration information indicates that the required resolution of the image is the second preset resolution, that is, when the user does not need a high-resolution CT image (for example, when scanning the arm, etc.), both the high-density first pixel data and the low-density second pixel data can be used as the target pixel data, or only the low-density second pixel data can be used as the target pixel data. The target pixel data is processed to obtain the data to be imaged.

[0073] As an example, as Figure 7 shown, when the target pixel data includes the first pixel data and the second pixel data, processing the target pixel data to obtain the data to be imaged includes:

[0074] S701, converting the first pixel data to obtain the third pixel data so that the pixel size of the third pixel data is the same as that of the second pixel data.

[0075] S702, obtaining the data to be imaged based on the third pixel data and the second pixel data.

[0076] Exemplarily, when the resolution configuration information characterizes that the required resolution of the image is the second preset resolution, both the first pixel data and the second pixel data can be used as the target pixel data. When the target pixel data includes the first pixel data and the second pixel data, since the pixel sizes of the first pixel data and the second pixel data are different, it is necessary to convert the pixel size of the first pixel data to the pixel size of the second pixel data. The first pixel data is converted to obtain third pixel data so that the pixel size of the third pixel data is the same as that of the second pixel data, and the data to be imaged is obtained based on the third pixel data and the second pixel data. The data processing device sends the data to be imaged to the imaging device, and the imaging device generates the finally required CT image with the second preset resolution based on the data to be imaged.

[0077] Figure 8 It is a structural diagram of data acquisition and processing of a detector device according to an embodiment of the present application.

[0078] As Figure 8 shown, Figure 8 represents a combination method of at least one high-density module (simplified diagram) and one low-density module (simplified diagram). Other high-density detection modules and low-density detection modules are the same as Figure 8 shown. For simplicity of illustration, Figure 8 only shows the structure of several channels for the high-density module and the low-density module in

[0079] The photoelectric sensor is used to convert the X-ray signal into an electrical signal. Figure 8 The photoelectric sensor in

[0080] The data processing device includes a detector module signal processing unit (also referred to as an integration unit). The detector module signal processing unit is used to process signals at the module level. For a scintillation detector, it processes the integral value, and for a photon counting detector, it processes the count value. After integrating the signals at the module level, the detector module signal processing unit outputs them to the detector data integration unit. The detector integration unit can be formed by the operation of a firmware (software) program of an electronic circuit machine with an FPGA or CPU or ASIC as the processing center, and has functions such as compressing, integrating, filtering, and storing the pre-stage digital signals. The detector data integration unit is used to form image frame data and then transmit it to the imaging device for imaging.

[0081] As an example, processing the target pixel data to obtain the data to be imaged further includes:

[0082] When the resolution configuration information indicates that the required resolution of the image is the second preset resolution, the target pixel data is processed according to the position information of the object to be scanned to obtain the data to be imaged.

[0083] Exemplarily, both the high-density detector module and the low-density detector module have their respective corresponding scanning field of view ranges, and the detector module can only acquire data of objects within its own scanning field of view. When the required resolution of the image is the second preset resolution, in addition to collecting data within the scanning field of view range formed by combining the high-density pixel module and the low-density pixel module, the present application can also process the target pixel data according to the position information of the object to be scanned to obtain the data to be imaged. The position information of the object to be scanned can be obtained according to the positioning film in the CT device.

[0084] As an example, processing the target pixel data according to the position information of the object to be scanned to obtain the data to be imaged includes:

[0085] When the target pixel data includes first pixel data and second pixel data, and the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the first preset density detector module and not within the scanning field of view corresponding to the second preset density detector module, the first pixel data is converted to obtain third pixel data, and the data to be imaged is obtained based on the third pixel data and the second pixel data;

[0086] When the target pixel data includes second pixel data, and the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the second preset density detector module, the second pixel data is processed to obtain the data to be imaged.

[0087] Exemplarily, when the required resolution is the second preset resolution, the first pixel data and the second pixel data can be collected as the target pixel data. If at this time, the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the first preset density detector module and not within the scanning field of view corresponding to the second preset density detector module, that is, the object to be scanned is only located within the scanning field of view corresponding to the first preset density detector module. In this case, only the first pixel data can be converted to obtain the third pixel data, and the data to be reconstructed is obtained based on the third pixel data and the second pixel data.

[0088] Exemplarily, when the required resolution is the second preset resolution and the target pixel data includes the second pixel data, if at this time, the position information of the object to be scanned indicates that the object to be scanned is within the scanning field of view corresponding to the second preset density detector module, that is, the scanning of the object to be scanned can be completed using the second pixel data obtained by the second preset density detector module. To reduce the system calculation amount and improve the imaging efficiency, preferably, only the second pixel data can be processed at this time to obtain the data to be reconstructed, and the first pixel data is not retained. Of course, in this case, the first pixel data can also be retained, the first pixel data is converted to obtain the third pixel data, and the data to be reconstructed is obtained based on the third pixel data and the second pixel data. For example, when collecting an arm or a finger, the data of the low-density pixel module can be used. However, the finger may not be within the scanning field of view of the low-density pixel module but within the scanning field of view of the high-density pixel module, and pixel size conversion needs to be performed on the data collected by the high-density module. The finger may also be within the scanning field of view of the low-density pixel module, and in this case, pixel size conversion may not be required, and the data of the low-density pixel module can be used.

[0089] It should be noted that when processing the target pixel data to obtain the data to be reconstructed, it may not be based on the position information of the object to be scanned. For example, in the high-resolution acquisition mode: only the data within the FOV scanning field of view corresponding to the high-density pixel module is collected or gathered, and the data within the corresponding range of the low-density pixel module is ignored. In the low-resolution acquisition mode, the data within the scanning field of view formed by combining the high-density pixel module and the low-density pixel module is collected.

[0090] It should also be noted that the above example is given for the case of two density detector modules and is also applicable to other numbers and types of detector modules. For example, a CT detector includes high, medium, and low density detector modules. When the required resolution is high resolution, the data obtained by the high density detector module is used as the target pixel data. When the required resolution is medium resolution, either the data obtained by the high density detector module and the data obtained by the medium density detector module can be used as the target pixel data, or only the data obtained by the medium density detector module can be used as the target pixel data. If both the data obtained by the high density detector module and the data obtained by the medium density detector module are used as the target pixel data, the data obtained by the high density detector module needs to be converted in pixel size to the pixel size of the data obtained by the medium density detector module. When the required resolution is low resolution, either the data obtained by the high density detector module, the data obtained by the medium density detector module, and the data obtained by the low density detector module can be used as the target pixel data, or the data obtained by the medium density detector module and the data obtained by the low density detector module can be used as the target pixel data, or only the data obtained by the low density detector module can be used as the target pixel data. When the target pixel data includes data obtained by other density detector modules, it needs to be converted in pixel size to unified pixel data with consistent resolution.

[0091] The following will detail the pixel size conversion of pixel data.

[0092] As an example, converting the first pixel data to obtain the third pixel data includes:

[0093] For the pixel size of the second pixel data, the first pixel data is converted based on the calibration data to obtain the third pixel data, where the third pixel data and the second pixel data have the same pixel size.

[0094] Exemplarily, for the pixel size conversion of the first pixel data, the pixel size of the first pixel data is converted to the pixel size of the second pixel data. The first pixel data can be converted through the calibration data, and the calibration data can be pre-configured. The calibration data can also be customized according to specific requirements.

[0095] As an example, the first pixel data includes the original metric data corresponding to each pixel point, the calibration data includes static calibration data and dynamic calibration data, and converting the first pixel data based on the calibration data to obtain the third pixel data includes:

[0096] Processing the original metric data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain the first processing result, and processing the first processing result based on the static calibration data to obtain the third pixel data;

[0097] And / or, process the original metric data corresponding to each pixel point in the first pixel data based on static calibration data to obtain a second processing result, and process the second processing result based on dynamic calibration data to obtain third pixel data.

[0098] Exemplarily, the first pixel includes the original metric data corresponding to each pixel point. The present application proposes a data conversion method, which combines the influencing factors of static and dynamic pixel data to perform format conversion on the pixel data. Among them, the calibration data includes static calibration data and dynamic calibration data. The dynamic calibration data includes, for example, calibration data obtained by considering factors such as temperature drift and voltage offset. The static calibration data includes, for example, calibration data obtained by considering factors such as different pixel areas and possible differences in the gaps between pixels.

[0099] Exemplarily, to convert the first pixel data into third pixel data based on the calibration data, the original metric data corresponding to each pixel point in the first pixel data can be first processed based on the dynamic calibration data, and then processed based on the static calibration data to obtain the third pixel data. This processing method has higher estimation accuracy because the original metric data is dynamically calibrated first. It is also possible to first perform static calibration processing and then dynamic calibration processing. This processing method is simpler and faster in calculation.

[0100] Figure 9 It is a schematic diagram of a low-density pixel module and a high-density pixel module according to an embodiment of the present application.

[0101] Figure 9 In the illustrated example, the pixel density ratio of the low-density pixel module to the high-density pixel module is 1:4. The present application uses the pixel density ratio of 1:4 as an example to illustrate data conversion. Of course, it is also applicable to other pixel density ratios, such as 1:9, etc.

[0102] In terms of static, (1) the pixel areas between the high-density pixel module and the low-density pixel module are different. (2) The gaps between pixels are also different. Therefore, the present application sets the static calibration data to be associated with the pixel area and the gaps between pixels.

[0103] In terms of dynamics, (1) since the more pixels a high-density module has, the greater the pixel density, although more detailed information can be recorded, the photosensitive area of a single pixel will decrease, which will affect the maximum current incident on the ADC, thus increasing the inconsistency between high-density modules and low-density modules. (2) Since the density of high-density modules is higher than that of low-density modules, for the same spatial range, the number of ADCs used in high-density modules is more than that in low-density modules. Due to the increase in spatial density, the temperature rise (temperature drift) of high-density modules will be greater than that of low-density modules. Temperature rise is one of the main factors affecting the dynamic performance of ADCs. (3) Other factors: voltage offset, transient response, frequency response, etc. Therefore, the present application sets the dynamic calibration data to be associated with at least one of the ADC maximum current, temperature drift, voltage offset, transient response, and frequency response.

[0104] As an example, the dynamic calibration data includes calibration coefficients, the static calibration data includes static calibration values, the original metric data includes original metric values, and the third pixel data includes target metric values corresponding to each pixel point, such as Figure 10 shown, processing the original metric data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain the third pixel data, including:

[0105] S1001, multiplying the calibration coefficient corresponding to the pixel point by the original metric value corresponding to the pixel point to obtain a first product.

[0106] S1002, summing the first products corresponding to the first number of pixel points to obtain a first sum value.

[0107] S1003, comparing the first sum value with the static calibration value to obtain the target metric value.

[0108] Among them, the first number is obtained according to the first ratio of the first preset density and the second preset density, and the static calibration value is obtained according to the pixel area of the first preset density and the first ratio.

[0109] Exemplarily, the dynamic calibration data can be in the form of calibration coefficients. For the case of first performing dynamic calibration and then static calibration, the calibration coefficient corresponding to each pixel point can be multiplied by the original metric value corresponding to the pixel point to obtain a first product. Each pixel point corresponds to a first product, and then the first products corresponding to the first number of pixel points are summed to obtain a first sum value, where the first number is obtained according to the first ratio of the first preset density and the second preset density. For example, as Figure 9 shown in the example, the first number is 4. Then, comparing the first sum value with the static calibration value to obtain the target metric value.

[0110] Exemplarily, in order to calibrate the area and pixel gap inconsistency factors in the static aspect, the static calibration value can be set as shown in the following formula: 1 - S1 / (S1 + 4*S2), where, as Figure 9 shown, S1 is the area of the shaded part (the gap area between 4 pixel points), S2 is the area of each high-density pixel (assuming that the areas of high-density pixels are the same), and S3 is the area of the low-density module. The number 4 in 1 - S1 / (S1 + 4*S2) represents the first quantity. The static calibration value is not limited to the above value. For different pixel density ratios between high-density pixel modules and low-density pixel modules, different static calibration values can be set. For example, if the pixel density ratio between high-density pixel modules and low-density pixel modules is 1:9, the static calibration value can be set as 1 - S1 / (S1 + 9*S2). The static calibration value is to compensate for the missing X-ray photons caused by the gaps between high-density pixel points.

[0111] Exemplarily, denote the original measurement value corresponding to each pixel point as An, the target measurement value as Ax, and the calibration coefficient as Bn. The formula for calculating the target measurement value Ax is as shown below:

[0112]

[0113] Exemplarily, as Figure 9 shown in the example when n = 4, An includes A1, A2, A3, A4, and Bn includes B1, B2, B3, B4. At this time the calibration coefficient Bn can be obtained by looking up a table, where the table can be configured in advance. The calibration coefficient includes the product of at least one or more of the maximum current calibration coefficient, temperature drift calibration coefficient, voltage offset calibration coefficient, transient response calibration coefficient, and frequency response calibration coefficient. The following takes the calibration coefficient including the product of the maximum current calibration coefficient and the temperature drift calibration coefficient as an example for illustration.

[0114] Exemplarily, denote the maximum current calibration coefficient as Fn (n is 1, 2, 3...). The maximum current calibration coefficient can determine the calibration coefficient in the previous calibration stage. As Figure 11 shown in the schematic diagram of X-ray dose and quantization value, a reasonable step can be taken to traverse the X-ray dose, and the actual value received by each pixel can be obtained through pre-scanning to form a corresponding relationship similar to the values in Table 1 below (Table 1 is only for clearly explaining the problem and lists some specific values). When formally scanning, first receive the actual quantization value A, then index the corresponding interval range in the A column of the following table, find the corresponding F in the F column, and then A*F is the relatively ideal quantization value obtained for one pixel. For example, when the actual value received is 420, the final quantization value is 420 * 1.010 = 424 (rounded).

[0115] Table 1

[0116] Column A (Measure) Column F 0～200 0.990 201～400 1.000 401～600 1.010 601～800 1.015 …… ……

[0117] Exemplarily, the temperature drift calibration coefficient can be denoted as Kn (n is 1, 2, 3...). The temperature drift calibration coefficient can also determine the calibration coefficient in the previous calibration stage. The temperature drift calibration coefficient characterizes the offset behavior of each channel (pixel) relative to the temperature degree, as follows Figure 12 shown in the temperature offset curve graph. According to the temperature offset curve, a corresponding relationship similar to the values in Table 2 below is formed (Table 2 only lists some specific values for the sake of clear illustration). When performing formal scanning, the corresponding coefficient can be obtained according to the temperature value range and converted into a more ideal value. For example, the ideal operating temperature value of the device is 24 °C, that is, the performance of the device is optimal at 24 °C. When the temperature value is 24.5 °C during formal scanning, the corresponding coefficient is 1.006. Assuming the input value is 424, the value after temperature coefficient calibration is 424 * 1.006 = 427 (rounded).

[0118] Table 2

[0119] Column B Column K Below 23° 0.990 23.1°～23.5° 1.994 23.6°～24° 0.998 24.1°～24.5° 1.006 …… ……

[0120] Exemplarily, for example, the calibration coefficient is the product of the maximum current calibration coefficient and the temperature drift calibration coefficient. When n = 4, the calculation formula for the target metric value is shown as follows

[0121] Ax = (A1 * F1 * K1 + A2 * F2 * K2 + A3 * F3 * K3 + A4 * F4 * K4) / (1 - S1 / (S1 + 4 * S2))

[0122] It should be noted that for other calibration coefficients (such as voltage offset calibration coefficient, transient response calibration coefficient, frequency response calibration coefficient), the calibration coefficient configuration can be carried out in a similar way as the configuration method of the maximum current calibration coefficient described above.

[0123] The above data conversion method is more suitable for floating-point operations based on the CPU or GPU, and based on the density of the original pixels first, with high estimation accuracy, but the calculation process is relatively complex.

[0124] As an example, the dynamic calibration data includes calibration offsets, as Figure 13 shown, processing the original metric data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain the third pixel data, further including

[0125] S1301, adding the calibration offset corresponding to the pixel point to the original metric value corresponding to the pixel point to obtain a second sum value.

[0126] S1302. Sum the second sums corresponding to the first quantity of pixel points to obtain a third sum.

[0127] S1303. Compare the third sum with the static calibration value to obtain the target metric value.

[0128] Exemplarily, in addition to being in the form of a calibration coefficient, the dynamic calibration data can also be in the form of a calibration offset. For the case where dynamic calibration is performed first and then static calibration, the calibration offset corresponding to each pixel point can be added to the original metric value corresponding to the pixel point to obtain the second sum. Each pixel point corresponds to a first and a second sum. Then, sum the second sums corresponding to the first quantity of pixel points to obtain the third sum. The first quantity is obtained according to the first ratio of the first preset density to the second preset density. For example, as Figure 9 shown in the example, the first quantity is 4. Then, compare the third sum with the static calibration value to obtain the target metric value. The static calibration value is the same as above and will not be elaborated here.

[0129] Exemplarily, denote the original metric value corresponding to each pixel point as An, denote the target metric value as Ax, and denote the calibration offset as ΔCn. The formula for calculating the target metric value Ax is shown as follows:

[0130]

[0131] Exemplarily, as Figure 9 shown in the example, when n = 4, An includes A1, A2, A3, A4, and ΔCn includes ΔC1, ΔC2, ΔC3, ΔC4. At this time the calibration offset ΔCn can be obtained by looking up a pre-configured table. Among them, the calibration offset includes, for example, the sum of at least one or more of the maximum current calibration offset, temperature drift calibration offset, voltage offset calibration offset, transient response calibration offset, and frequency response calibration offset. Taking the calibration offset including the sum of the maximum current calibration offset and the temperature drift calibration offset as an example for illustration.

[0132] Exemplarily, denote the maximum current calibration offset as ΔFn (n is 1, 2, 3...), denote the temperature drift calibration offset as ΔKn (n is 1, 2, 3...). When n = 4, the formula for calculating the target metric value is shown as follows:

[0133] Ax = ((A1 + ΔF1 + ΔK1) + (A2 + ΔF2 + ΔK2) + (A3 + ΔF3 + ΔK3) + (A4 + ΔF4 + ΔK4)) / (1 - S1 / (S1 + 4 * S2))

[0134] It should be noted that calibration offset configuration is also performed for other calibration offsets (voltage offset calibration offset, transient response calibration offset, frequency response calibration offset), and during the actual scanning process, the corresponding offsets are summed up.

[0135] The above pixel size conversion method performs addition and subtraction based on offsets, and is more suitable for calculation on FPGA - like carriers. The parallel processing advantage of FPGA calculation is obvious, which can save the time of single - thread (or a small number of threads) operation of the CPU or GPU.

[0136] As an example, dynamic calibration data includes calibration coefficients, static calibration data includes static calibration values, original metric data includes original metric values, and third pixel data includes target metric values corresponding to each pixel point, such as Figure 14 shown. Based on the static calibration data, the original metric data corresponding to each pixel point in the first pixel data is processed to obtain a second processing result, and based on the dynamic calibration data, the second processing result is processed to obtain the third pixel data, including:

[0137] S1401, adding the original metric values corresponding to the first number of pixel points to obtain a fourth sum.

[0138] S1402, comparing the fourth sum with the static calibration value to obtain a second ratio.

[0139] S1403, multiplying the calibration coefficient by the second ratio to obtain the target metric value.

[0140] Among them, the first number is obtained according to the first ratio of the first preset density and the second preset density, and the static calibration value is obtained according to the pixel area of the first preset density and the first ratio.

[0141] Exemplarily, the dynamic calibration data can be in the form of calibration coefficients. For the case of first performing static calibration and then dynamic calibration, the original metric values corresponding to the first number of pixel points can be added first to obtain a fourth sum, where the first number is obtained according to the first ratio of the first preset density and the second preset density. For example, as Figure 9 shown in the example, the first number is 4. Then the fourth sum is compared with the static calibration value to obtain a second ratio, that is, static calibration processing is first performed, and then the calibration coefficient is multiplied by the second ratio to obtain the target metric value. This data calculation method of first converting the intermediate value of the low - density pixel area and then estimating the final ideal value is relatively simple to run and improves the processing efficiency.

[0142] Exemplarily, the setting of the static calibration value is the same as the above method, which will not be elaborated here. Denote the original measurement value corresponding to each pixel as An, the target measurement value as Ax, and the calibration coefficient as B. The formula for calculating the target measurement value Ax is as follows:

[0143]

[0144] It should be noted that in this data conversion method, the calibration coefficient B does not correspond to each pixel, but corresponds to an entire S3 (i.e., the pixels of the low-density module). Of course, B can also be obtained by pre-configuration, or can be obtained from the original calibration coefficient Bn corresponding to each pixel. For example, take the average value of Bn as B. The calibration coefficient includes, for example, the product of at least one or more of the maximum current calibration coefficient, temperature drift calibration coefficient, voltage offset calibration coefficient, transient response calibration coefficient, and frequency response calibration coefficient. The following takes the calibration coefficient including the product of the maximum current calibration coefficient and the temperature drift calibration coefficient as an example for illustration.

[0145] Exemplarily, denote the maximum current calibration coefficient as F and the temperature drift calibration coefficient as K. When n = 4, the formula for calculating the target measurement value is as follows:

[0146] Ax = ((A1 + A2 + A3 + A4) / (1 - S1 / (S1 + 4*S2)))*F*K

[0147] It should be noted that for other calibration coefficients (voltage offset calibration coefficient, transient response calibration coefficient, frequency response calibration coefficient), the calibration coefficient configuration can also be carried out in a similar manner to the above configuration method.

[0148] The above pixel size conversion method first calculates an intermediate value of the low-density pixel area through a static formula, and then obtains the ideal value by multiplying coefficients. This method reduces the amount of calculation and is relatively simple to operate.

[0149] As an example, the dynamic calibration data includes a calibration offset. As Figure 15 shown, based on the static calibration data, the original measurement data corresponding to each pixel point in the first pixel data is processed to obtain a second processing result, and based on the dynamic calibration data, the second processing result is processed to obtain third pixel data, including:

[0150] S1501, add the original measurement values corresponding to the first number of pixel points to obtain a fourth sum.

[0151] S1502, compare the fourth sum with the static calibration value to obtain a second ratio.

[0152] S1503, add the calibration offset to the second ratio to obtain the target measurement value.

[0153] Exemplarily, the dynamic calibration data can be in the form of calibration offsets in addition to the form of calibration coefficients. For the case where dynamic calibration is performed first and then static calibration, the raw measurement values corresponding to the first number of pixel points can be added first to obtain a fourth sum value. The first number is obtained according to the first ratio of the first preset density to the second preset density. For example, as shown in the example, the first number is 4. Then, the fourth sum value is compared with the static calibration value to obtain a second ratio. That is, static calibration processing is performed first, and then the calibration offset is added to the second ratio to obtain the target measurement value. This data calculation method that first converts the intermediate value of the low-density pixel area and then estimates the final ideal value is relatively simple to run and improves the processing efficiency. Figure 9 As shown, the example, the first number is 4. Then, the fourth sum value is compared with the static calibration value to obtain a second ratio. That is, static calibration processing is performed first, and then the calibration offset is added to the second ratio to obtain the target measurement value. This data calculation method that first converts the intermediate value of the low-density pixel area and then estimates the final ideal value is relatively simple to run and improves the processing efficiency.

[0154] Exemplarily, the setting of the static calibration value is the same as the above method and will not be elaborated here. Denote the raw measurement value corresponding to each pixel point as An, the target measurement value as Ax, and the calibration offset as ΔC. The formula for calculating the target measurement value Ax is shown as follows:

[0155]

[0156] It should be noted that in this data conversion method, the calibration offset ΔC does not correspond to each pixel point, but corresponds to an entire S3 (i.e., the pixel points of the low-density module). Of course, ΔC can also be obtained by prior configuration or by the original calibration offset ΔCn corresponding to each pixel. For example, the average value of ΔCn is taken as ΔC. The calibration offset includes, for example, at least one or a sum of multiple ones among the maximum current calibration coefficient, temperature drift calibration coefficient, voltage offset calibration coefficient, transient response calibration coefficient, and frequency response calibration coefficient. The following takes the sum of the maximum current calibration coefficient and the temperature drift calibration coefficient as an example for illustration.

[0157] Exemplarily, denote the maximum current calibration offset as ΔF, the temperature drift calibration offset as ΔK. When n = 4, the formula for calculating the target measurement value is shown as follows:

[0158] Ax = ((A1 + A2 + A3 + A4) / (1 - S1 / (S1 + 4*S2))) + ΔF + ΔK

[0159] It should be noted that for other calibration offsets (voltage offset calibration offset, transient response calibration offset, frequency response calibration offset), the calibration offset configuration can be performed in a similar manner to the above configuration method.

[0160] The above pixel size conversion method first calculates an intermediate value of the low-density pixel area through a static formula and then obtains the ideal value by multiplying coefficients. This method reduces the amount of calculation and is relatively simple to run.

[0161] It should be noted that all the above pixel size conversion methods can be executed by carriers such as CPU, GPU, and FPGA. The high-density pixels or low-density pixels are all uniform (i.e., the pixel areas of the high-density modules are the same, and the pixel areas of the low-density modules are the same). For non-uniform pixels (such as there are certain slight differences between the edges and the middle of the module) or the gaps between pixels are different (such as the gaps between the high-density module and the low-density module splicing are different from other gaps), it is necessary to perform a combined calculation according to the actual area and gap.

[0162] Figure 16 It is a schematic diagram of the detector data integration unit according to an embodiment of the present application.

[0163] As Figure 16 shown, the high-density module data receiving unit is used to receive the data of the high-density module, and the low-density module data receiving unit is used to receive the data of the low-density module. The high-density module to low-density module data function module determines whether to convert the high-density data into low-density data according to the enable signal. If not enabled, the data of the high-density module is directly passed through. The storage part is used to cache the collected data, and the data sending unit is used to interface with the backend device to send the corresponding data out. Among them, the form used by the storage part is not fixed. It can be an external storage chip (such as DDR, QDR, etc.), or built into FPGA, CPU, etc. For the sake of illustration, Figure 16 the block diagram shown is only one of them, and this block diagram should not be regarded as a patent limiting factor. Moreover, the functional unit block diagram shown in the following figure does not limit the carrier used. For example, the function can be implemented by a single electronic circuit board, or some functions can be implemented in the front-end detector module.

[0164] Figure 17 It is a data processing flow chart according to an embodiment of the present application.

[0165] As Figure 17 shown, during the scanning of the CT device, it is judged whether high-density resolution scanning is required (whether the required image resolution is high resolution). If the required image resolution is high resolution, the pixel data collected by the high-density detector module is collected as the target pixel data and directly output to the data processing device. If the required image resolution is low resolution, the pixel data collected by the high-density detector module can be collected, or the pixel data collected by the low-density detector module can be collected. If both the pixel data collected by the high-density detector module and the pixel data collected by the low-density detector module are used as the target pixel data, it is necessary to convert the pixel data collected by the high-density detector module into low-density resolution pixel data. If only the pixel data collected by the low-density detector module is used as the target pixel data, no data conversion is required.

[0166] According to the method of configuring detector modules with different density resolutions in the present application, by configuring high-density detector modules within the effective scanning area (FOV), images with higher resolution of fine parts of tissue structures (such as the inner ear and bones and joints) can be obtained, thereby expanding the application range of mid- to low-end models.

[0167] The present application also proposes a data processing device.

[0168] As an example, as Figure 18 shown, the data processing device is applied to a CT device. The CT device includes a CT detector. The device includes: an acquisition module 1801, configured to acquire initial pixel data corresponding to at least two detection densities based on the CT detector; a determination module 1802, configured to determine target pixel data corresponding to a target density from the initial pixel data corresponding to at least two detection densities based on resolution configuration information; and a processing module 1803, configured to process the target pixel data to obtain data to be imaged, and send the data to be imaged to an imaging device so that the imaging device generates a CT image based on the data to be imaged.

[0169] The present application also proposes a CT device.

[0170] As an example, as Figure 6 shown, the CT device includes: an X-ray source; a CT detector, where the CT detector at least includes a first preset density detector module and a second preset density detector module, and the CT detector is configured to detect X-rays and generate initial pixel data; and the data processing device according to the above.

[0171] The present application also proposes a computer-readable storage medium.

[0172] In this embodiment, a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above data processing method based on the CT device are implemented.

[0173] Figure 19 It is a block diagram of an electronic device provided by an embodiment of the present application.

[0174] An embodiment of the present application provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above data processing method based on the CT device is implemented.

[0175] As Figure 19 shown, for ease of understanding, an embodiment of the present application shows a specific electronic device.

[0176] The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, personal digital processors, cellular telephones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0177] As Figure 19 shown, the device includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the electronic device can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other through a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0178] Multiple components in the electronic device are connected to the I / O interface 1905, and the multiple components include: an input unit 1906, such as a keyboard, a mouse, etc.; an output unit 1907, such as various types of displays, speakers, etc.; a storage unit 1908, such as a magnetic disk, an optical disk, etc.; and a communication unit 1909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1909 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0179] The computing unit 1901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1901 executes the various methods described above, such as the data processing method based on the CT device. For example, in some embodiments, the data processing method based on the CT device can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 1908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 1902 and / or the communication unit 1909. When the computer program is loaded into the RAM 1903 and executed by the computing unit 1901, the data processing method based on the CT device described above can be executed. Alternatively, in other embodiments, the computing unit 1901 can be configured to execute the data processing method based on the CT device in any other suitable way (e.g., by means of firmware).

[0180] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this application, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0181] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0182] In the description of the present application, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0183] In the description of the present application, it should be understood that the orientation or positional relationships indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present application.

[0184] In addition, the terms "first", "second", etc. used in the embodiments of the present application are only for descriptive purposes, and cannot be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in this embodiment. Thus, the features defined with terms such as "first", "second", etc. in the embodiments of the present application can explicitly or implicitly indicate that at least one such feature is included in this embodiment. In the description of the present application, the meaning of the word "plurality" is at least two or more than two, such as two, three, four, etc., unless otherwise specifically defined in the embodiments.

[0185] In this application, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "coupled" and "fixed" etc. appearing in the embodiments shall be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or integrated. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the communication inside two elements, or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific implementation circumstances.

[0186] In this application, unless otherwise clearly specified and limited, the first feature being "on" or "under" the second feature can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature can be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below" and "beneath" the second feature can be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0187] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A data processing method based on CT equipment, characterized in that: The CT device includes a CT detector, the CT detector includes detector modules with at least two pixel densities, and the method includes: Acquiring initial pixel data corresponding to at least two detection densities based on the CT detector; Based on the resolution configuration information, determining target pixel data corresponding to the target density from the initial pixel data corresponding to the at least two detection densities; The target pixel data is processed to obtain image data to be constructed.

2. The data processing method based on CT equipment according to claim 1, characterized in that: The CT detector includes a first preset density detector module and a second preset density detector module, the initial pixel data includes first pixel data acquired based on the first preset density detector module and second pixel data acquired based on the second preset density detector module, and the step of determining target pixel data corresponding to the target density from the initial pixel data corresponding to the at least one detection density based on the resolution configuration information includes: In a case where the resolution configuration information indicates that the required resolution of the image is a first preset resolution, determining the first pixel data as the target pixel data; In a case where the resolution configuration information indicates that the required resolution of the image is a second preset resolution, determining the first pixel data and the second pixel data to be the target pixel data, or determining the second pixel data to be the target pixel data; The first preset resolution is greater than the second preset resolution, and the first preset density is greater than the second preset density.

3. The data processing method based on CT equipment according to claim 2, characterized in that: In a case where the target pixel data includes the first pixel data and the second pixel data, the processing of the target pixel data to obtain the image data to be constructed includes: Converting the first pixel data to obtain third pixel data, so that the third pixel data has the same pixel size as the second pixel data; The image data to be constructed is obtained based on the third pixel data and the second pixel data.

4. The data processing method based on CT equipment according to claim 3, characterized in that: The processing of the target pixel data to obtain the image data to be constructed also includes: When the resolution configuration information indicates that the required resolution of the image is the second preset resolution, the target pixel data is processed according to the position information of the object to be scanned to obtain the image data to be constructed.

5. The data processing method based on CT equipment according to claim 4, characterized in that: The step of processing the target pixel data according to the position information of the object to be scanned to obtain the image data to be constructed includes: When the target pixel data includes the first pixel data and the second pixel data, in the case where the position information of the object to be scanned indicates that the object to be scanned is in the scanning field of view corresponding to the first preset density detector module and is not in the scanning field of view corresponding to the second preset density detector module, the first pixel data is converted to obtain third pixel data, and the image data to be constructed is obtained based on the third pixel data and the second pixel data; When the target pixel data includes the second pixel data, and when the position information of the object to be scanned indicates that the object to be scanned is in the scanning field of view corresponding to the second preset density detector module, the second pixel data is processed to obtain the image data to be constructed.

6. The data processing method based on CT equipment according to any one of claims 3 to 5, characterized in that: Converting the first pixel data to obtain third pixel data includes: With respect to a pixel size of the second pixel data, the first pixel data is converted based on calibration data to obtain third pixel data, wherein the third pixel data has the same pixel size as the second pixel data.

7. The data processing method based on CT equipment according to claim 6, characterized in that: The first pixel data includes original measurement data corresponding to each pixel point, the calibration data includes static calibration data and dynamic calibration data, and the first pixel data is converted based on the calibration data to obtain third pixel data, including: Processing original measurement data corresponding to each pixel point in the first pixel data based on the dynamic calibration data to obtain a first processing result, and processing the first processing result based on the static calibration data to obtain third pixel data; Alternatively, the original measurement data corresponding to each pixel point in the first pixel data is processed based on the static calibration data to obtain a second processing result, and the second processing result is processed based on the dynamic calibration data to obtain third pixel data.

8. The data processing method based on CT equipment according to claim 7, characterized in that: The dynamic calibration data includes a calibration coefficient, the static calibration data includes a static calibration value, the original measurement data includes an original measurement value, the third pixel data includes a target measurement value corresponding to each pixel point, and the original measurement data corresponding to each pixel point in the first pixel data is processed based on the dynamic calibration data to obtain a first processing result, and the first processing result is processed based on the static calibration data to obtain the third pixel data, including: Multiplying the calibration coefficient corresponding to the pixel point by the original measurement value corresponding to the pixel point to obtain a first product; Adding first products corresponding to a first number of pixel points to obtain a first sum value; Comparing the first sum value with the static calibration value to obtain a target metric value; The first number is obtained according to a first ratio of the first preset density to the second preset density, and the static calibration value is obtained according to a pixel area of ​​the first preset density and the first ratio.

9. The data processing method based on CT equipment according to claim 7, characterized in that: The dynamic calibration data includes a calibration offset, the static calibration data includes a static calibration value, the original measurement data includes an original measurement value, the third pixel data includes a target measurement value corresponding to each pixel point, the original measurement data corresponding to each pixel point in the first pixel data is processed based on the dynamic calibration data to obtain a first processing result, and the first processing result is processed based on the static calibration data to obtain the third pixel data, and further includes: Adding the calibration offset corresponding to the pixel point to the original measurement value corresponding to the pixel point to obtain a second sum value; Adding the second sum values ​​corresponding to the first number of pixel points to obtain a third sum value; Comparing the third sum value with the static calibration value to obtain the target metric value; The first number is obtained according to a first ratio of the first preset density to the second preset density, and the static calibration value is obtained according to a pixel area of ​​the first preset density and the first ratio.

10. The data processing method based on CT equipment according to claim 7, characterized in that: The dynamic calibration data includes a calibration coefficient, the static calibration data includes a static calibration value, the original measurement data includes an original measurement value, the third pixel data includes a target measurement value corresponding to each pixel point, and the original measurement data corresponding to each pixel point in the first pixel data is processed based on the static calibration data to obtain a second processing result, and the second processing result is processed based on the dynamic calibration data to obtain the third pixel data, including: Adding the original measurement values ​​corresponding to the first number of pixel points to obtain a fourth sum value; Comparing the fourth sum with the static calibration value to obtain a second ratio; multiplying the calibration coefficient by the second ratio to obtain the target measurement value; The first number is obtained according to a first ratio of the first preset density to the second preset density, and the static calibration value is obtained according to a pixel area of ​​the first preset density and the first ratio.

11. The data processing method based on CT equipment according to claim 7, characterized in that: The dynamic calibration data includes a calibration offset, the static calibration data includes a static calibration value, the original measurement data includes an original measurement value, the third pixel data includes a target measurement value corresponding to each pixel point, and the original measurement data corresponding to each pixel point in the first pixel data is processed based on the static calibration data to obtain a second processing result, and the second processing result is processed based on the dynamic calibration data to obtain the third pixel data, including: Adding the original measurement values ​​corresponding to the first number of pixel points to obtain a fourth sum; Comparing the fourth sum with the static calibration value to obtain a second ratio; Adding the calibration offset to the second ratio to obtain the target metric value; The first number is obtained according to a first ratio of the first preset density to the second preset density, and the static calibration value is obtained according to a pixel area of ​​the first preset density and the first ratio.

12. The data processing method based on CT equipment according to claim 7, characterized in that: The static calibration data is associated with the pixel area and the gap between pixels; The dynamic calibration data is associated with at least one of ADC maximum current, temperature drift, voltage offset, transient response, and frequency response.

13. A data processing device, characterized in that: The device is applied to a CT device, the CT device includes a CT detector, the CT detector includes detector modules with at least two pixel densities, and the device includes: An acquisition module, configured to acquire initial pixel data corresponding to at least two detection densities based on the CT detector; A determination module, configured to determine target pixel data corresponding to the target density from the initial pixel data corresponding to the at least two detection densities based on the resolution configuration information; The processing module is used to process the target pixel data to obtain the data to be imaged, and send the data to be imaged to an imaging device so that the imaging device generates a CT image based on the data to be imaged.

14. A CT device, characterized in that: The CT device comprises: X-ray source; A CT detector, the CT detector comprising at least a first preset density detector module and a second preset density detector module, the CT detector being used to detect X-rays and generate initial pixel data; A data processing apparatus according to claim 13.

15. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 12 are implemented.

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