Linear CT imaging system and scanning imaging method

Abstract

The invention provides a linear CT imaging system and a scanning imaging method, and relates to the technical field of radiation imaging and nondestructive testing. The system comprises a linear scanning channel which is used for a scanning object to pass through and extends along a first direction; the scanning assembly comprises a ray source and a detector, the ray source and the detector are arranged on the opposite sides of the scanning channel in the second direction, the ray source is configured to emit ray beams, the detector comprises a plurality of detection units, and the detection units are configured to detect the ray beams emitted by the ray source and passing through a scanned object, projection data are generated based on the detected ray beam, and the scanning assembly and the scanning object are in relative linear motion; the system further comprises a data processing device, the data processing device is electrically connected with the detector, and the data processing device is configured to perform superposition processing on the at least two projection data generated by the at least two detection units.

Description

Technical Field

[0001] This application relates to the fields of radiation imaging and nondestructive testing technology, and more specifically, to a linear CT imaging system and scanning imaging method. Background Technology

[0002] Linear CT is an imaging method different from traditional CT rotational scanning. In linear CT, the X-ray source and detector move in a straight line relative to the object being scanned, rather than rotating as in traditional CT. This scanning method avoids mechanical rotating parts, has a relatively simple structure, and enables flexible and rapid scanning, showing potential applications in industrial non-destructive testing (such as online inspection) and security inspection. Improving the image quality of linear CT scans has always been a key research topic for researchers in this field.

[0003] It should be noted that the information disclosed in this section is only used to understand the background of the inventive concept of this application. Therefore, the above information may include information that does not constitute prior art. Summary of the Invention

[0004] In view of at least one of the above-mentioned technical problems, embodiments of this application provide a linear CT imaging system, comprising: a linear scanning channel for a scanned object to pass through, the linear scanning channel extending along a first direction; a scanning assembly including a radiation source and a detector, the radiation source and the detector being disposed on opposite sides of the scanning channel along a second direction, the radiation source being configured to emit a radiation beam, the detector including a plurality of detection units, the plurality of detection units being respectively configured to: detect the radiation beam emitted by the radiation source and passing through the scanned object, and generate projection data based on the detected radiation beam, wherein the scanning assembly and the scanned object are in relative linear motion; the system further comprising a data processing device electrically connected to the detector, the data processing device being configured to: superimpose at least two projection data generated by at least two detection units.

[0005] According to some embodiments of this application, the detector includes multiple rows of detection units arranged along a first direction, and the data processing device is configured to superimpose at least two projection data generated by at least two detection units located in different rows.

[0006] According to some embodiments of this application, the data processing apparatus is configured to superimpose at least two projection data generated by at least two detection units collected at different times.

[0007] According to some embodiments of this application, the multi-row detection unit includes a first row of detection units and a second row of detection units. In the direction of relative linear motion, the first row of detection units is located upstream of the second row of detection units. The data processing device is configured to superimpose first projection data collected by at least one detection unit in the first row of detection units at a first moment and second projection data collected by at least one detection unit in the second row of detection units at a second moment, wherein the first moment is earlier than the second moment.

[0008] According to some embodiments of this application, the overlay process includes: assigning weight coefficients to at least two projection data to be overlaid; and performing a weighted summation process on the at least two projection data according to the weight coefficients.

[0009] According to some embodiments of this application, at least two projection data to be superimposed are assigned the same weight coefficient; or, at least two of the weight coefficients assigned to at least two projection data to be superimposed are different; or, any two of the weight coefficients assigned to at least two projection data to be superimposed are different.

[0010] According to some embodiments of this application, multiple detection units are respectively configured to: detect ray beams emitted by a ray source and passing through the scanned object along various ray paths, and generate projection data based on the detected ray beams; the data processing device is configured to: superimpose at least two projection data generated by at least two detection units that have similar ray paths, wherein similar ray paths include multiple ray paths whose similarity satisfies a preset condition.

[0011] According to some embodiments of this application, the similarity of ray paths is characterized by at least one of the following: the geometric overlap of multiple ray paths; the difference in projection values ​​of multiple projection data corresponding to multiple ray paths.

[0012] According to some embodiments of this application, the system further includes an imaging device configured to generate a three-dimensional reconstructed image based on target projection data generated from multiple target projection paths.

[0013] According to some embodiments of this application, each of the multiple target projection paths corresponds to multiple similar ray paths; the data processing device is configured to: for each target projection path, superimpose multiple projection data having multiple similar ray paths corresponding to the target projection path to generate target projection data corresponding to the target projection path.

[0014] According to some embodiments of this application, at least one target projection path is one of a plurality of similar ray paths corresponding to the target projection path.

[0015] According to some embodiments of this application, at least one target projection path is a ray path determined based on the geometric relationship between the scanning components, the linear scanning channel, and the scanning object in the system.

[0016] According to some embodiments of this application, the data processing apparatus is configured to: in response to prior information that there is no scanned object, represent similar ray paths using a three-dimensional vector corresponding to a straight path from the ray source to the detection unit.

[0017] According to some embodiments of this application, the data processing apparatus is configured to: in response to prior information about the existence of a scanned object, represent a similar ray path using a three-dimensional vector corresponding to a straight path from the position where the ray beam enters the scanned object to the position where the ray beam leaves the scanned object.

[0018] According to some embodiments of this application, the relative linear motion velocity of the scanned object is v, the sampling frequency of the detector is f, and the minimum interval between two adjacent target projection paths on the central axis of the linear scanning channel is d. The velocity v, frequency f, and interval d satisfy the following relationship: v / f <d。

[0019] According to some embodiments of this application, the similarity of ray paths is characterized by the following function: g(p1,p2)=w1cosθ+w2e -as Where p1 and p2 represent two ray paths respectively, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the interval distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the interval distance, and w1 and w2 are weighting coefficients.

[0020] According to some embodiments of this application, the similarity of ray paths is characterized by the following function: g(p1,p2)=(w1cosθ+w2e -as )*(1 / (|prj(p1)–prj(p2)|+b)), where p1 and p2 represent two ray paths, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the distance between the two ray paths, w1 and w2 are weighting coefficients; prj(p1) represents the projection value of the projection data corresponding to ray path p1, prj(p2) represents the projection value of the projection data corresponding to ray path p2, parameter b is a weighting factor, b>0.

[0021] According to some embodiments of this application, the similarity of ray paths meets the preset conditions including at least one of the following: the similarity calculated by the function is greater than a preset threshold; the similarity calculated by the function ranks in the top N, where N is the preset number of similar ray paths.

[0022] According to some embodiments of this application, the superposition process includes: for a target ray path p m The function calculates the relationship between multiple ray paths and the target ray path p. m Similarity degree; select the top N similar ray paths with the highest similarity degree calculated by the function, where the j-th ray path p among the N similar ray paths j With the target ray path p m The similarity is g(p) m ,p j ), where 1≤j≤N; obtain N projection values ​​corresponding to N similar ray paths, where the j-th ray path p in the N similar ray paths j The corresponding projection value is prj(p) j ); Assign weight coefficients to each of the N projected values, where the projected value prj(p j The corresponding weight coefficient is q(p) j The following formula is used to perform a weighted summation of the N projection values ​​to obtain the result relative to the target ray path p. m The corresponding target projection value prj(p) m ): .

[0023] According to some embodiments of this application, the weighting coefficient q(pj) is calculated using the following formula: .

[0024] According to some embodiments of this application, the system includes multiple detectors arranged at intervals along a first direction, each detector including a single row of detection units, each row of detection units including multiple detection units arranged along a third direction, the third direction being perpendicular to the first and second directions; or, the system includes multiple detectors arranged at intervals along the first direction, each detector including multiple rows of detection units, each row of detection units including multiple detection units arranged along a third direction, the third direction being perpendicular to the first and second directions; or, the system includes a single detector, the single detector being an area array detector.

[0025] According to some embodiments of this application, the system includes a single radiation source, which is disposed opposite to the intermediate row of detection units in a first direction.

[0026] According to some embodiments of this application, the system includes multiple radiation sources, and the data processing device is configured to separately superimpose at least two projection data generated by at least two detection units for each radiation source's radiation beam.

[0027] A second aspect of this application provides a scanning imaging method using any of the above systems, comprising: controlling a scanning object to move along a straight scanning channel; controlling a scanning component to scan the scanning object during the movement of the scanning object along the straight scanning channel to obtain multiple projection data generated by multiple detection units; and superimposing at least two projection data generated by at least two detection units. Attached Figure Description

[0028] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0029] Figure 1 The schematic diagram illustrates the principle of the linear CT imaging system and scanning imaging method according to embodiments of this application;

[0030] Figure 2 A schematic diagram of a linear CT imaging system according to an embodiment of this application is shown.

[0031] Figure 3 This illustration schematically shows a projection data overlay principle diagram of a linear CT imaging system according to an embodiment of this application;

[0032] Figure 4 This illustration schematically shows a second linear CT imaging system projection data overlay principle according to an embodiment of this application;

[0033] Figure 5 This illustration schematically shows a ray path diagram according to an embodiment of the present application;

[0034] Figure 6A A schematic diagram of a first detector according to an embodiment of this application is shown;

[0035] Figure 6B A schematic diagram of a second detector according to an embodiment of this application is shown.

[0036] Figure 6C A schematic diagram of a third detector according to an embodiment of this application is shown.

[0037] Figure 6D This schematic diagram illustrates a side view of a linear CT imaging system according to an embodiment of the present application;

[0038] Figure 7AA schematic diagram of a multi-ray source linear CT imaging system according to an embodiment of this application is shown.

[0039] Figure 7B A schematic diagram of another multi-ray source linear CT imaging system according to an embodiment of this application is shown.

[0040] Figure 8 A flowchart illustrating a scanning imaging method according to an embodiment of this application is shown schematically;

[0041] Figure 9 A block diagram of an electronic device according to an embodiment of the present application is shown schematically. Detailed Implementation

[0042] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0044] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0045] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0046] It should be noted that, in this application, computed tomography (CT) imaging refers to the process of using X-rays to perform a tomographic scan of the object, converting the analog signals received by the detector into digital signals, calculating the attenuation coefficient of each pixel by a computer, and then reconstructing the image to display the tomographic structure of each part of the scanned object.

[0047] The embodiments of this application are described in detail below. It should be understood that the embodiments of this application can be applied to various scanning imaging scenarios. For example, it can be applied to scanning imaging scenarios that include various different scanning objects, including but not limited to vehicle scanning imaging, luggage / parcel scanning imaging, human or animal scanning imaging, organ / tissue scanning imaging, small object scanning imaging, and large object scanning imaging such as containers. It should be noted that the description of scanning imaging scenarios here is not exhaustive, and the exemplary descriptions below should not be construed as limiting the scope of protection of this application.

[0048] Figure 1 The schematic diagram illustrates the principle of the linear CT imaging system and scanning imaging method according to embodiments of this application.

[0049] Reference Figure 1 The object being scanned, 3, moves along a straight trajectory between the radiation source 1 and the detector 2. During this movement, radiation emitted from the radiation source 1 (e.g., X-rays, gamma rays) is incident on the object being scanned, and the radiation transmitted through the object being scanned is detected by the detector 2. A spatial point on the object being scanned is transmitted from the radiation source 1 to an image point on the detector 2.

[0050] For example, X-ray source 1 can be a cone-beam X-ray source. The X-rays emitted by the cone-beam X-ray source are distributed in a cone shape from the point source, which can cover a certain volume of scanning area at one time, and can realize the acquisition of two-dimensional projection data of a certain area of ​​the scanned object in a single projection.

[0051] Detector 2 may include a detector array for acquiring transmission projection data of rays by receiving rays that pass through the scanned object. Detector 2 may also include readout circuitry and a logic control unit for reading the projection data on the detector array. The detector array may include multiple solid-state detection units, multiple gas detection units, or multiple semiconductor detection units. Depending on the detector's structure, it may include flat-panel detectors and multi-row detectors, etc.

[0052] For example, a flat panel detector is a two-dimensional array-type X-ray detection device, composed of a large number of detection units (each corresponding to a pixel) arranged in a matrix. It can directly receive X-rays and convert them into electrical signals, outputting a two-dimensional digital projection image. The combination of a cone-beam X-ray source and a flat panel detector is a typical linear CT layout. Flat panel detectors typically have small pixel sizes, enabling them to clearly capture details of the scanned object and offering the advantage of high projection resolution.

[0053] For example, a multi-row detector can use multiple linear array detectors arranged along the direction of motion of the object to be scanned. The system cost is relatively low, the scanning range is large, and it is suitable for scenarios where large-sized objects are rapidly imaged.

[0054] Figure 2 A schematic diagram of a linear CT imaging system according to an embodiment of this application is shown.

[0055] like Figure 2 As shown, the linear CT imaging system according to an embodiment of this application may include: a linear scanning channel 4, a scanning component, and a data processing device 5.

[0056] The linear scanning channel 4 is used for the scanning object 3 to pass through. For example, the linear scanning channel 4 can extend along the first direction z.

[0057] The scanning assembly may include a radiation source 1 and a detector 2, which may be arranged on opposite sides of the linear scanning channel 4 along a second direction x. The radiation source 1 is configured to emit a radiation beam, and the detector 2 includes multiple detection units, which are respectively configured to detect the radiation beam emitted by the radiation source 1 and passing through the scanned object 3, and generate projection data based on the detected radiation beam. The scanning assembly and the scanned object 3 are in relative linear motion.

[0058] The data processing device 5 is electrically connected to the detector 2, and the data processing device 5 is configured to superimpose at least two projection data generated by at least two detection units.

[0059] The linear scanning channel 4 can serve as a passageway for the scanned object 3, allowing it to pass stably along a fixed direction through the scanning area formed by the X-ray source 1 and the detector 2, thus avoiding detection errors caused by positional shifts. The linear scanning channel 4 can have sufficient space to allow the scanned object 3 to pass along the first direction z. For example, in industrial applications, a conveyor rail can be installed within the linear scanning channel, allowing the scanned object to pass through the channel along the first direction.

[0060] The first direction z can be the extension direction of the linear scanning channel 4, or the main direction of movement of the scanned object 3. The first direction z can also have a certain angle with the movement of the scanned object 3. For example, a vehicle carrying goods (the scanned object) travels along the linear scanning channel 4 along the first direction z, and the movement of the vehicle is the same as that of the first direction z; as another example, materials (the scanned object) are transported through the linear scanning channel in an industrial assembly line, and the direction of movement of the materials can have an angle of 10° with the first direction z.

[0061] Detector 2 can contain multiple independent detection units, which can be arranged in arrays, multiple columns, or with angular intervals, and the interval size can be set as needed. Multiple detection units can form a group and be arranged as a whole, such as forming a detector arm. Each detection unit can independently detect the X-ray beam after it has passed through the scanned object and convert the X-ray signal into digital projection data (such as X-ray attenuation value, light intensity value, etc.). Each detection unit can generate a set of independent projection data.

[0062] The second direction x can be the direction opposite to the radiation source 1 and the detector 2, such as the direction x being perpendicular to the plane of the detector array. The second direction x and the first direction z can have an angle, and this angle can be greater than zero. For example, the second direction x and the first direction z can be perpendicular to each other, or, for another example, the second direction x and the first direction z can have an angle of 60°.

[0063] Relative linear motion can be: the scanning object 3 moves along the first direction z while the scanning component remains fixed; the scanning object 3 remains fixed while the scanning component translates along the first direction z; and the scanning object 3 and the scanning component move towards each other along the first direction z. For example, under normal circumstances or to improve detection efficiency when the number of scanning objects 3 is large, the scanning object 3 can move along the first direction z while the scanning component remains fixed. For scanning objects that are difficult to move, the scanning object 3 can be fixed while the scanning component translates along the first direction z; to improve scanning speed, the scanning object 3 and the scanning component can also move towards each other along the first direction z.

[0064] The data processing device 5 may include a hardware system capable of receiving, storing, and processing data, such as an industrial computer, an embedded computing module, a field-programmable gate array (FPGA), or a dedicated image processing chip. It can superimpose projection data generated by at least two detection units. In the embodiments of this application, by superimposing the projection data, the random noise of a single detection unit can be canceled, thereby improving the signal-to-noise ratio of the projection data.

[0065] For example, in a security inspection scenario, the object being scanned 3 moves along a first direction z through a straight scanning channel. The scanning assembly includes an X-ray source 1 and a detector 2, which are positioned on both sides of the straight scanning channel along a second direction x. The detector 2 has multiple detection units, and the data processing device 5 superimposes the projection data of multiple detection units of the same inspected item (scanned object 3) to make the image of the inspected item clearer and reduce misjudgments caused by noise.

[0066] In the embodiments of this application, the linear scanning channel extends along the first direction, the scanning component moves in a linear motion relative to the scanning object, and the multi-detection unit structure of the detector can synchronously collect the projection data of the scanning object from different angles. The projection data generated by at least two detection units are superimposed using a data processing device, which can cancel the random noise of the data collected by a single detection unit, improve the signal-to-noise ratio of the projection data, and thus optimize the subsequent imaging quality.

[0067] According to some embodiments of this application, such as Figure 6A As shown, the detector includes multiple single-row detection units 21 arranged along a first direction, and the data processing device 5 is configured to superimpose at least two projection data generated by at least two detection units 20 located in different rows.

[0068] The detector units 20 can be arranged in rows. For example, if the first direction of the scanned object's movement is horizontal, the detector units can be arranged in the first direction or in the vertical direction. Different rows of detector units can be independent X-ray receiving units, and their arrangement positions can differ spatially, enabling the acquisition of X-ray signals from the same scanning area from different spatial positions or angles.

[0069] Different rows of detector units can acquire projection data generated by rays penetrating the same or similar areas of the scanned object. Data overlay processing can be performed by aggregating the data using methods such as weighting, averaging, and summing. Overlaying the projection data from different rows of detector units can cancel out random noise from individual detector units, supplement data dimensions, improve the signal-to-noise ratio of the projection data, and thus optimize the final imaging quality.

[0070] For example, the detector has an array structure of 5 rows * 20 columns. The 5 rows of detector units are arranged along the first direction of the scanned object, and each row has 20 columns arranged vertically. When the scanned object moves into the scanned area, the detector units in the 8th column of the 1st to 3rd rows first collect the rays penetrating a certain area of ​​the scanned object, generating projection data d1, d2, and d3 respectively. After receiving the projection data d1, d2, and d3, the data processing device performs weighted superposition processing on the projection data of these three detector units in the 8th column of the 1st to 3rd rows. For example, the weights are assigned according to their positional relationship: d1 accounts for 30%, d2 accounts for 40%, and d3 accounts for 30%, resulting in the optimized superimposed projection data d = d1 × 30% + d2 × 40% + d3 × 30%.

[0071] In the embodiments of this application, the projection data generated by different rows of detection units can be superimposed. Multiple sets of projection data of the same area of ​​the scanned object can be acquired by using different rows of detection units. By superimposing the data, the random noise of a single detection unit is reduced and the effective ray signal is enhanced, thereby improving the signal-to-noise ratio and imaging clarity of the projection data. No additional hardware is required to improve data quality. The utilization rate of the original projection data can be improved, taking into account both imaging quality improvement and economy.

[0072] According to some embodiments of this application, the data processing apparatus can be configured to superimpose at least two projection data generated by at least two detection units collected at different times.

[0073] Linear CT scanning components move in a straight line relative to the scanned object. At different times, the detection units can acquire projection data of the same or similar areas of the scanned object. The noise of a single detection unit at different times is random noise. By superimposing the projection data acquired at different times, random noise can be reduced, the effective X-ray signal intensity can be enhanced, the signal-to-noise ratio of the projection data can be improved, and the subsequent reconstructed image can be clearer.

[0074] Different times can refer to different points in time when the detector performs ray detection on the scanned object and generates projection data. These different times can be points with varying intervals or consecutive points generated during the scanning process. For example, if the detector's sampling frequency is 500Hz, it can complete 500 projection data acquisitions per second, with an interval of approximately 2 milliseconds between two adjacent acquisition times; if the scanned object passes through the scanning channel at a constant speed, time t1 can be the 0th millisecond, time t2 can be the 2nd millisecond, and time t3 can be the 4th millisecond.

[0075] Figure 3 The illustration shows a schematic diagram of the projection data overlay principle of a linear CT imaging system according to an embodiment of this application.

[0076] For example, the detector comprises an array structure of 5 rows * 20 columns. The 5 rows of detector units are arranged along the first direction of the scanned object, and each row has 20 columns arranged vertically. When the scanned object moves within the scanned area, at time t1, the detector unit in the 8th column of the 1st row first collects the rays penetrating a certain area of ​​the scanned object, generating the first set of projection data d1; at time t2, a certain area of ​​the scanned object is directly opposite the detector unit in the 8th column of the 2nd row, and this unit collects the rays from the same area, generating the second set of projection data d2; at time t3, the detector unit in the 8th column of the 3rd row collects the rays from the same area, generating the third set of projection data d3. After receiving d1, d2, and d3, the data processing device determines that these three sets of data are all projection data of the same area of ​​the scanned object. It then performs weighted superposition processing on the projection data of these three detector units in different rows, such as assigning weights according to signal quality: d1 accounts for 30%, d2 accounts for 40%, and d3 accounts for 30%, resulting in the superimposed optimized projection data d = d1 × 30% + d2 × 40% + d3 × 30%.

[0077] In the embodiments of this application, projection data from at least two detection units acquired at different times can be superimposed. For multiple sets of projection data of the same or similar areas, superposition can reduce random noise generated by a single detection unit at different times, enhance the effective X-ray signal, improve the signal-to-noise ratio of the projection data, and optimize the subsequent imaging quality. Superimposing projection data acquired at different times, adapted to a linear CT imaging system, can uncover the patterns in the projection data over time and improve data utilization.

[0078] According to some embodiments of this application, in conjunction with reference to... Figure 3 and Figure 6A The multi-row detection unit may include a first row of detection units 211 and a second row of detection units 212. In the relative linear motion direction, the first row of detection units 211 is located upstream of the second row of detection units 212. The data processing device is configured to superimpose first projection data collected by at least one detection unit 20 in the first row of detection units 211 at a first moment and second projection data collected by at least one detection unit 20 in the second row of detection units 212 at a second moment, wherein the first moment is earlier than the second moment.

[0079] The first row of detection units 211 can be located closer to the starting position of the linear motion, and the second row of detection units 212 can be located further away from the starting position of the linear motion. Alternatively, the first row of detection units 211 can be located closer to the entrance of the linear scanning channel, and the second row of detection units 212 can be located closer to the exit of the linear scanning channel. When the scanned object 3 moves in the linear scanning channel 4, it can pass through the first row of detection units 211 and the second row of detection units 212 in sequence.

[0080] For example, the detector has an array structure of 5 rows * 20 columns. The 5 rows of detection units are arranged along the first direction of the scanned object, and each row has 20 columns arranged vertically. When the scanned object 3 moves into the scanned area, the detection unit in the 8th column of the first row of the detector first collects the rays penetrating a certain area of ​​the scanned object, generating the first set of projection data d1. As the scanned object moves at a constant speed of 2mm along the first direction (corresponding to one row spacing), a certain area of ​​the scanned object is directly facing the detection unit in the 8th column of the second row of the detector. This unit collects the rays from the same area, generating the second set of projection data d2. After receiving d1 and d2, the data processing device determines that both sets of data are projection data from the same area. It performs weighted superposition processing on the projection data of the detection unit in the 8th column of the first row and the detection unit in the 8th column of the second row, such as averaging the weights: d1 accounts for 50% and d2 accounts for 50%, to obtain the superimposed optimized projection data d = d1 × 50% + d2 × 50%.

[0081] In the embodiments of this application, the first projection data collected by the first row of detector units upstream in the direction of relative linear motion can be superimposed with the second projection data collected by the second row of detector units downstream. Based on the spatial arrangement of the detector rows upstream and downstream and the temporal relationship of scanning, projection data from different rows and at different times in the same area of ​​the scanned object can be collected and superimposed. Data superposition can reduce the random noise of individual detector units, enhance the effective X-ray signal, and improve the signal-to-noise ratio of the projection data and the clarity of subsequent imaging. By utilizing the spatial arrangement dimension of the detectors and the temporal dimension of scanning, the inherent correlation of the original data can be explored, adapting to the linear CT imaging system and improving imaging quality.

[0082] According to some embodiments of this application, the overlay process may include: assigning weight coefficients to at least two projection data to be overlaid; and performing a weighted summation process on the at least two projection data according to the weight coefficients.

[0083] Weighting coefficients can be used to assign the contribution of different projection data to the superposition result, and the sum of the weighting coefficients can be 1. The weighting coefficients of different projection data can be assigned values ​​based on the quality of the projection data, the positional relationship between the detection units, or other methods.

[0084] For example, the corresponding weighting coefficients can be determined based on the signal quality of the projection data itself. The quality of the projection data can be judged based on factors such as signal stability, signal-to-noise ratio, and signal effectiveness. The better the signal quality, the larger the weighting coefficient can be, in order to weaken the influence of data with high noise and large deviations. The weight of obviously abnormal projection data can be assigned to 0, while normal projection data is weighted according to its quality.

[0085] The corresponding weighting coefficients can be determined based on the hardware characteristics of the detection units. The weighting coefficients can also be determined based on the performance parameters of the detection units during periodic calibration. For example, if a detection unit in the first row has higher calibration accuracy and stability, its collected projection data will be assigned a higher weight (60%), while if a detection unit in the second row has lower calibration accuracy and stability, its collected projection data will be assigned a corresponding weight (30%).

[0086] The corresponding weighting coefficient can be determined based on the acquisition time of the projection data. The smaller the interval between the acquisition time of the data to be superimposed and the target time, the higher the weight, and vice versa. For example, the weighting coefficient of projection data with an interval of 0.01 seconds is higher than that of projection data with an interval of 0.02 seconds.

[0087] The corresponding weighting coefficients can also be determined based on the spatial characteristics of the projection data. The higher the spatial alignment between the detection unit and the target area of ​​the scanned object, the higher the weighting coefficient. For example, the detection unit in the 6th column of the second row is directly aligned with the target area of ​​the scanned object, and its weighting coefficient is higher than that of the detection units in the 5th column of the second row and the 4th column of the second row.

[0088] In the embodiments of this application, corresponding weighting coefficients can be assigned to the projection data to be superimposed before weighted summation. This highlights the differences in the projection data, allowing high-quality, highly matched effective projection data to play a greater role in the superimposed result, weakening the interference of low-quality data with high noise and low matching degree. This improves the accuracy of the superimposed projection data, more realistically reflects the actual characteristics of the scanned object, and further optimizes the subsequent imaging quality. The method of assigning weighting coefficients can be adjusted according to the arrangement of detector units, the motion state of the scanned object, the actual detection scenario, etc., to improve the scene adaptability of the linear CT imaging system.

[0089] According to some embodiments of this application, at least two projection data to be superimposed are assigned the same weight coefficient; or, at least two of the weight coefficients assigned to at least two projection data to be superimposed are different; or, any two of the weight coefficients assigned to at least two projection data to be superimposed are different.

[0090] The projected data to be overlaid can be at least two, for example, 2, 3, 4, 5 or more. The weighting coefficients assigned to different projected data can be the same or different.

[0091] For example, the projection data to be superimposed includes d1, d2, d3, d4, and d5. The weighting coefficients can be all the same, such as average weighting coefficients of 20%, 20%, 20%, 20%, and 20%; or at least two weighting coefficients can be different, such as 8%, 12%, 15%, 15%, and 50% based on the spatial relationship of the detection units; or any two weighting coefficients can be different, such as 8%, 12%, 15%, 25%, and 40% determined based on signal quality. Same weights enable efficient superimposition processing and improve computational efficiency; different weights enable differentiated weighting, highlighting the role of high-quality data, weakening interference from low-quality data, and improving the accuracy of the projection data.

[0092] In the embodiments of this application, multiple weighting coefficient assignment methods are provided, which can support the use of the same weight, some different weights, or any two different weights for the projection data to be superimposed. The methods can be flexibly selected according to different scanning acquisition scenarios of linear CT and the actual imaging accuracy requirements of the detection. Multiple methods take into account the efficiency and accuracy of data processing, and can adapt to different detection requirements without the need for additional hardware, thus having strong adaptability.

[0093] According to some embodiments of this application, multiple detection units can be configured to: detect ray beams emitted by a ray source and passing through the scanned object along various ray paths, and generate projection data based on the detected ray beams; the data processing device can be configured to: superimpose at least two projection data generated by at least two detection units that have similar ray paths, wherein similar ray paths include multiple ray paths whose similarity satisfies a preset condition.

[0094] Each detection unit on the detector receives rays emitted from the ray source that pass through the scanned object along different paths and generates corresponding projection data. The rays received by different detection units differ due to their different paths and the different areas they pass through on the scanned object.

[0095] Similar ray paths can be ray paths that are similar to or match the target projection path. Similar ray paths can be ray paths in CT imaging that have similar spatial orientations, penetrate the same or similar areas of the scanned object, and have a high degree of overlap with the target projection path. Based on similar ray paths, the data processing device can select and superimpose projection data with similar paths that pass through the same area of ​​the scanned object.

[0096] Similarity score can be used to quantify the degree of similarity between two or more ray paths during linear CT imaging. Higher similarity scores indicate higher similarity, and lower similarity scores indicate lower similarity. The value of similarity score can range from 0 to 1, or it can be any other value range.

[0097] Figure 4 The illustration shows a schematic diagram of the projection data overlay principle of a second linear CT imaging system according to an embodiment of this application.

[0098] like Figure 4 As shown, a coordinate system is established with a point on the scanned object as the origin, and multiple detectors are represented by D. n Let p(n,t) represent the ray path of the nth detector at time t (t=1,2,...,T) during data acquisition (n=1,2,...,T), which is a three-dimensional vector. The detector can detect ray beams emitted from the ray source that pass through the scanned object along various ray paths p(n,t), and generate projection data based on the detected ray beams. p(1,t) and p(2,t) are ray paths with similarity satisfying preset conditions. The data processing device can then process the D... 1、 The projection data generated by the detection units on the two detectors of D2, which have similar ray paths, are superimposed.

[0099] In the embodiments of this application, multiple detection units can independently collect rays passing through the scanned object along different ray paths and generate projection data. Then, the data processing device can superimpose the projection data of similar paths that meet the preset conditions. This can superimpose the projection data that penetrate the same or similar areas of the scanned object, thereby reducing random noise and improving the signal-to-noise ratio and imaging quality of the projection data.

[0100] According to some embodiments of this application, the similarity of ray paths can be characterized by at least one of the following: the geometric overlap of multiple ray paths; the difference in projection values ​​of multiple projection data corresponding to multiple ray paths.

[0101] Geometric coincidence can be considered the degree of spatial proximity between two ray paths. For example, the closer the path directions, the smaller the path spacing, and the more consistent the areas they pass through on the scanned object, the higher the geometric coincidence. For instance, if two rays originate from the same ray source and pass almost through the same point on the scanned object, their similarity is relatively high.

[0102] The similarity between any two ray paths p1(n1,t1) and p2(n2,t2) can be represented by a function g(p1,p2). Ray path p j The superimposed weighting coefficients can be q(p,p) j The weighting coefficients can be superimposed based on the overlap function g(p,p). j ) or ray path p j The projection value and other numerical values ​​are used to determine this.

[0103] g(p1,p2) can be represented by the degree of overlap of the straight line segments within the intersection range of the two rays p1 and p2 with the scanned object. The range of the straight line segments can be obtained by pre-reconstructing the data to obtain the contour of the scanned object or by obtaining the contour information of the scanned object in advance through other prior information.

[0104] The similarity of ray paths can also be determined based on the degree of difference between the projection values ​​of multiple projection data corresponding to multiple ray paths. The superposition weighting coefficient q(p,p) can be determined based on the degree of difference in the projection values. j The degree of difference in projection values ​​can be the magnitude of the difference in projection data corresponding to the ray.

[0105] For example, three rays correspond to three ray paths passing through the same part of the scanned object, and the multiple projection data corresponding to the three ray paths are 50, 84, and 85, respectively. If the difference between the first ray path and the second and third ray paths is relatively large, the similarity is low. If the difference between the second ray path and the third ray path is relatively small, the similarity is high.

[0106] In the embodiments of this application, the similarity of ray paths can be characterized by at least one of the geometric overlap of the ray path or the difference in the projection values ​​of the corresponding projection data, which can make the selection of similar paths more accurate and reliable, improve the effectiveness of the superimposed data, and improve the imaging quality; the similarity characterization method can be used alone or in combination, and has strong adaptability.

[0107] According to some embodiments of this application, such as Figure 2 As shown, the system also includes an imaging device 6, which is configured to generate a three-dimensional reconstructed image based on target projection data generated from multiple target projection paths.

[0108] The target projection path can be a reference path selected by the system for final imaging. Each target projection path can correspond to a sampling direction or position in the final image. The target projection path can be a three-dimensional spatial vector, and a three-dimensional reconstructed image of the scanned object can be formed based on multiple target projection paths.

[0109] Imaging device 6 can be a component in the system responsible for generating images and can be connected to data processing device 5. Imaging device 6 can perform image reconstruction and output three-dimensional reconstructed images based on multiple sets of target projection data. Imaging device 6 can be an embedded imaging processing module integrated inside the system that directly completes reconstruction and outputs images, or it can be an image processing unit (such as a graphics processor) for three-dimensional reconstruction, or it can be a computer or host computer equipped with three-dimensional reconstruction software that receives target projection data and generates and displays three-dimensional images.

[0110] The imaging device 6 may also have a matching output or display section. For example, the imaging device 6 may have an image output interface or a display screen, which can output the generated three-dimensional reconstructed image as data from the output interface or display it on the display screen in a visual form.

[0111] In the embodiments of this application, an imaging device can be set up, and a three-dimensional reconstructed image can be generated based on target projection data generated by multiple target projection paths. The superimposed projection data can be used to present the internal structure and external shape of the scanned object, improve the accuracy and comprehensiveness of the detection, and enhance the practicality of the linear CT imaging system.

[0112] According to some embodiments of this application, each of the multiple target projection paths corresponds to multiple similar ray paths; the data processing device is configured to: for each target projection path, superimpose multiple projection data having multiple similar ray paths corresponding to the target projection path to generate target projection data corresponding to the target projection path.

[0113] In actual scanning, for each target projection path, multiple ray paths may be close to it, forming multiple similar ray paths. Different detection units at different times can collect multiple sets of projection data corresponding to these similar paths. The data processing device can reduce random noise and enhance the effective signal by superimposing these multiple sets of data to generate the target projection data corresponding to the target projection path. For example, based on imaging requirements, the target projection path p required for imaging is selected. The ray paths obtained by different detection units at different time series are evaluated by the function g, and the path p with the highest degree of overlap with path p is selected. j (j=1,2,...,J) are weighted and superimposed, and the result of the weighted superposition is used as the projection value of the target path p. The above process is repeated for each target projection path, and finally, target projection data corresponding to multiple target projection paths can be obtained for 3D imaging.

[0114] The embodiments of this application provide that each target projection path can match multiple similar ray paths and superimpose the corresponding data, which can make full use of the effective data collected by multiple paths and multiple times, improve the signal-to-noise ratio and accuracy of a single target projection data, thereby improving the clarity and reliability of the subsequent three-dimensional reconstruction image, adapting to the scanning mode of linear CT relative to linear motion, and improving the overall imaging quality and detection accuracy without increasing hardware costs.

[0115] According to some embodiments of this application, at least one target projection path is one of a plurality of similar ray paths corresponding to the target projection path.

[0116] The target projection path can be selected from existing similar ray paths without needing to be redefined or recalculated. Among multiple similar ray paths corresponding to a target projection path, one of the similar ray paths can be directly used as the target projection path itself.

[0117] For example, when using a linear CT scanner to inspect a scanned object, the X-ray source emits multiple rays during scanning. These rays pass through the workpiece and are received by multiple rows of detection units, forming multiple ray paths: ray path A, ray path B, ray path C, ray path D, ray path E, and ray path F. Ray paths A, B, C, and D are determined to be similar to each other. That is, similar ray paths include ray path A, B, C, and D. From these four similar ray paths, ray path A can be selected as the target projection ray path. The data processing device superimposes the projection data corresponding to these four ray paths to generate the target projection data corresponding to ray path A.

[0118] In the embodiments of this application, one of multiple similar ray paths can be used as the target projection path, eliminating the need to define or calculate a new reference path. This simplifies the determination process of the target projection path and reduces data processing complexity and computational costs. Since the target projection path originates from the actual acquired ray path, it better matches the actual detection data, ensuring the accuracy and consistency of similar path selection and data overlay, and improving the reliability of the imaging data.

[0119] According to some embodiments of this application, at least one target projection path can be a ray path determined based on the geometric relationship between the scanning components, the linear scanning channel, and the scanning object in the system.

[0120] Geometric relationships can include at least one of the spatial relationships between the X-ray source, detector, scanning channel, and scanned object, such as position, angle, distance, and size. Based on the geometric relationships between the linear CT imaging system itself and the scanned object, at least a portion of the target projection path can be determined.

[0121] For example, the X-ray source can be installed directly above the center of a linear scanning channel, and the detector array can be installed below the linear scanning channel, directly opposite the X-ray source. The object being scanned moves horizontally along a straight line in the middle of the linear scanning channel. Based on the object's movement time and speed, initial placement position, and orientation within the linear scanning channel, the object's position in the channel at a given moment, as well as its geometric relationships such as its distances to the scanning components, the linear scanning channel, and the object itself, can be determined. This allows for the determination of at least one target projection path. For instance, through geometric calculations, a ray can be directly derived that originates from the X-ray source focus, passes through the channel, a certain area on the object being scanned, and reaches the center of a certain unit of the detector. This ray path can be set as the target projection path, and by superimposing data from all similar ray paths, target projection data can be obtained for 3D reconstruction.

[0122] In the embodiments of this application, the target projection path can be determined based on the geometric relationship between the scanning component, the linear scanning channel and the scanning object. Determining the target projection path based on geometric methods can improve the matching accuracy of similar ray paths and the reliability of data superposition. The path determination method is easy to implement and can improve imaging stability and reconstruction accuracy.

[0123] According to some embodiments of this application, the data processing apparatus is configured to: in response to prior information that there is no scanned object, represent similar ray paths using a three-dimensional vector corresponding to a straight path from the ray source to the detection unit.

[0124] Prior information can be information known before scanning begins or before data overlay processing. For example, it may include the shape and size of the scanned object, as well as at least one of the material or internal structure of the scanned object. If there is no prior known shape information of the scanned object, the data processing device can represent and determine which are similar ray paths using a three-dimensional vector of the straight path from the ray source to the detection unit, without relying on the structure of the scanned object.

[0125] For example, when there is absolutely no prior information available for the scanned area, such as Figure 4 As shown, p1 and p2 can be three-dimensional vectors from the target point of X-ray source 1 to detector 2. By comparing the two three-dimensional vectors p1 and p2, it can be determined whether the two X-ray paths are similar. The methods for comparing the two three-dimensional vectors p1 and p2 can include: calculating the angle between the two vectors (the smaller the angle, the closer the directions); calculating the shortest spatial distance between the two X-rays (the smaller the distance, the closer the positions); or simultaneously comparing the angle and spatial distance between the two X-rays.

[0126] For example, the angle between the direction vectors of ray path A and ray path B is 0.3°, which is less than the threshold of 0.5°, and the distance between the ray paths is approximately 0.12 mm, which is less than the threshold of 0.2 mm. Since ray path A and ray path B are close in direction and position, they can be identified as similar ray paths, and their corresponding projection data can be superimposed.

[0127] In the embodiments of this application, in the absence of prior information about the scanned object, similar ray paths can be filtered by the three-dimensional vector of the straight path from the ray source to the detection unit, thereby achieving the adaptability of scanning imaging for scanned objects without prior information. Even in scenarios without prior information, similar paths can still be accurately identified and data superposition can be completed. The three-dimensional vector judgment based on the geometric straight path can reduce the amount of computation and improve the system's data processing speed.

[0128] According to some embodiments of this application, the data processing apparatus is configured to: in response to prior information about the existence of a scanned object, represent a similar ray path using a three-dimensional vector corresponding to a straight path from the position where the ray beam enters the scanned object to the position where the ray beam leaves the scanned object.

[0129] The positions where the X-ray beam enters and exits the scanned object are the entry and exit points of the beam as it passes through the object. The straight-line path from the entry point to the exit point is the line segment through which the beam actually passes through the interior of the scanned object, and is closely related to the shape of the object.

[0130] Figure 5 A schematic diagram of a ray path according to an embodiment of this application is shown.

[0131] like Figure 5 As shown, when the contour information of the scanned object is available, p1 and p2 can be three-dimensional vectors from the position where the ray enters the scanned object to the position where it leaves the scanned object. This method can better measure the correlation between the ray and the scanned object.

[0132] A beam P is emitted from a source and strikes the detector; its entry point into the scanned object is P1(x1,y1,z1); its exit point is P2(x2,y2,z2); the straight-line path of beam P can be represented as P1→P2. A beam Q is emitted from a source and strikes the detector; its entry point into the scanned object is Q1(x3,y3,z3); its exit point is Q2(x4,y4,z4); the straight-line path of beam Q can be represented as Q1→Q2. By comparing these two three-dimensional vectors represented by points on the surface contour of the scanned object, it can be determined whether the two beam paths are similar.

[0133] The contour information of a scanned object can be obtained through pre-reconstruction using a small number of rays. Before reconstruction, a rough, rapid pre-reconstruction can be performed using a subset of rays to obtain information such as the object's outline, approximate size, and position. This information can then be used to form the object's contour information. Based on this contour information, similar ray paths can be more accurately identified, allowing for projection data overlay and image reconstruction.

[0134] In the embodiments of this application, when prior information about the scanned object is available, the three-dimensional vector of the straight-line path between the incident point and the exit point of the ray beam inside the scanned object is used to represent the similar ray path. The prior information is used to achieve more accurate and targeted path matching, eliminate the interference of invalid paths outside the scanned object, and make the similar ray path selection more closely match the actual scanned object. This can improve the rationality and accuracy of the projection data superposition, thereby improving the quality and detection accuracy of the three-dimensional reconstructed image.

[0135] According to some embodiments of this application, the relative linear motion velocity of the scanned object is v, the sampling frequency of the detector is f, and the minimum interval between two adjacent target projection paths on the central axis of the linear scanning channel is d. The velocity v, frequency f, and interval d satisfy the following relationship: v / f <d。

[0136] The detector's sampling frequency is the number of times the detector acquires projection data per second. v / f represents the distance the scanned object moves between two samples. If the distance between two adjacent samples is less than the minimum interval between adjacent target projection paths, the sampling density is sufficient, providing enough projection data for overlay. If the distance the scanned object moves between two samples is greater than the minimum interval between target projection paths, for a given target projection path, the scanned object's movement speed is too fast, causing it to deviate too far from the target projection path. Consequently, there are not enough similar ray paths near each target projection path, making projection data overlay impossible and affecting the imaging effect.

[0137] For example, the detector consists of 40 detector arms, each with a pixel size of 3*500 pixels (3mm*3mm). A single detector arm is 9mm wide and 1500mm high. The X-ray source and detector are positioned opposite each other, with a distance of 3m from the nearest arm. The arms and X-ray sources are arranged at equidistant angles of 2°. The target movement speed is 500mm / s, and the detector sampling frequency can be 500Hz. The v / f ratio is 1mm. Each pixel on the detector corresponds to a target projection path between itself and the X-ray source. The minimum distance d between two adjacent target projection paths on the central axis of the linear scanning channel is approximately 1.5mm, satisfying the v / f ratio. <d。

[0138] In the embodiments of this application, the moving distance of the scanned object between two adjacent samples can be configured to be less than the minimum interval of the target projection path, so that a sufficient number of similar ray paths can be obtained around each target projection path. This allows the expected number of similar path data to be selected for superposition processing, improving the signal-to-noise ratio and imaging quality of the projection data. It avoids problems such as insufficient matching of similar ray paths, failure of superposition effect, increased image noise and decreased reconstruction accuracy caused by excessive sampling interval, and enables the system to maintain stable and reliable superposition imaging capability during straight-line scanning.

[0139] like Figure 4 As shown, according to some embodiments of this application, the similarity of ray paths can be characterized by the following function:

[0140] g(p1,p2)=w1cosθ+w2e -as

[0141] Where p1 and p2 represent two ray paths respectively, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the interval distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the interval distance, and w1 and w2 are weighting coefficients.

[0142] A larger value for the similarity function g(p1,p2) indicates that the two paths p1 and p2 are more similar, and the corresponding projection data are more suitable for superposition. The closer cosθ is to 1, the more consistent the directions are, and the higher the similarity. The smaller the distance s between the two ray paths p1 and p2 on the central axis of the linear scanning channel, the closer their positions are when passing through the scanned object, and the higher the similarity.

[0143] The weighting coefficients w1 and w2 can be preset based on empirical values, or adjusted according to factors such as system structure, scanning scene, imaging requirements, and characteristics of the scanned object.

[0144] For example, with preset weighting coefficients w1=0.6, w2=0.4, and sensitivity a=2.0, if the angle θ between ray paths p1 and p2 is 0.1 radians and the central axis interval s is 0.2 mm, the calculated value g(p1,p2)=0.865 can be used to determine whether p1 and p2 are similar ray paths.

[0145] In the embodiments of this application, the similarity of the ray paths can be determined based on the included angle of the ray paths and the interval distance of the ray paths on the central axis of the linear scanning channel. This can improve the accuracy of screening similar ray paths, provide a reliable basis for subsequent projection data superposition, and thus improve the imaging quality.

[0146] According to some embodiments of this application, the similarity of ray paths is characterized by the following function:

[0147] g(p1,p2)=(w1cosθ+w2e -as )*(1 / (|prj(p1)–prj(p2)|+b))

[0148] Where p1 and p2 represent two ray paths, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the distance between the two ray paths, w1 and w2 are weighting coefficients; prj(p1) represents the projection value of the projection data corresponding to ray path p1, prj(p2) represents the projection value of the projection data corresponding to ray path p2, parameter b is a weighting factor, b>0.

[0149] In addition to considering the overlap of geometric paths, g(p1,p2) can also consider the difference in the projected values ​​of the two rays. g(p1,p2) can be weighted using the correlation between the projected data prj(p1) and prj(p2) of the two paths. |prj(p1)–prj(p2)| can represent the absolute difference between the projected values ​​of paths p1 and p2; the smaller the difference, the more consistent the signals. b can be used to avoid a denominator of 0 and to adjust the influence of the projection difference term.

[0150] For example, with preset weighting coefficients w1=0.5, w2=0.5, sensitivity a=2.0, and parameter b=0.1, if the angle θ between ray paths p1 and p2 is 0.1 radians, and the central axis interval s is 0.2 mm, the projection values ​​prj(p1)=85 and prj(p2)=87. The similarity g(p1,p2) is calculated to be 0.397. Based on this value, it can be determined whether p1 and p2 are similar ray paths.

[0151] In the embodiments of this application, the similarity of ray paths can be determined based on the included angle of the ray paths, the interval distance of the ray paths on the central axis of the linear scanning channel, and the difference in projection values. This makes the similarity calculable and improves the accuracy of similar ray path selection. Multiple constraints make the selection of similar paths more rigorous and closer to the physical meaning of imaging. At the same time, the influence of geometric and projection factors can be flexibly adjusted by weighting coefficients w1 and w2 and parameters a and b to adapt to different scanning objects and imaging accuracy requirements. This provides a reliable basis for subsequent projection data superposition, thereby improving imaging quality.

[0152] According to some embodiments of this application, the similarity of ray paths meets the preset conditions including at least one of the following: the similarity calculated by the function is greater than a preset threshold; the similarity calculated by the function ranks in the top N, where N is the preset number of similar ray paths.

[0153] After determining the similarity of different ray paths, similar ray paths of the target projection path can be selected from different ray paths based on preset thresholds and similarity ranking.

[0154] For example, for ray path A, the similarity scores of multiple candidate ray paths are calculated, resulting in similarity scores of 0.75 for ray path B, 0.68 for ray path C, 0.60 for ray path D, 0.52 for ray path E, 0.45 for ray path F, and 0.38 for ray path G. If the preset threshold is 0.5, ray paths B, C, D, and E can be selected as similar ray paths to ray path A. If the top 4 similarity scores are taken (N=4), ray paths B, C, D, and E can be selected as similar ray paths to ray path A.

[0155] For example, for ray path H, similarity scores are calculated for multiple candidate ray paths, resulting in similarity scores of 0.75 for ray path I, 0.68 for ray path J, 0.40 for ray path K, 0.32 for ray path L, 0.28 for ray path M, and 0.25 for ray path N. If the preset threshold is 0.5, ray paths I and J can be selected as similar ray paths to ray path H. If the top 4 similarity scores (N=4) are taken, ray paths I, J, K, and L can be selected as similar ray paths to ray path H. If both the similarity scores are greater than the preset threshold of 0.5 and the top 4 similarity scores (N=4) are considered, ray paths I and J can be selected as similar ray paths to ray path H.

[0156] In the embodiments of this application, the range of ray paths used for superimposed projection data can be controlled by setting at least one of the following as the preset judgment conditions for ray path similarity: the similarity is greater than a preset threshold and the similarity is ranked in the top N positions. This is beneficial to improving the quality of the projection data used for superimposition. The two judgment rules can be used alone or in combination to adapt to different scanning objects, imaging accuracy and system computing power requirements, and can improve the flexibility of similar path screening.

[0157] According to some embodiments of this application, the superposition process includes: for a target ray path p m The function calculates the relationship between multiple ray paths and the target ray path p. m Similarity degree; select the top N similar ray paths with the highest similarity degree calculated by the function, where the j-th ray path p among the N similar ray paths jWith the target ray path p m The similarity is g(p) m ,p j ), where 1≤j≤N; obtain N projection values ​​corresponding to N similar ray paths, where the j-th ray path p in the N similar ray paths j The corresponding projection value is prj(p) j ); Assign weight coefficients to each of the N projected values, where the projected value prj(p j The corresponding weight coefficient is q(p) j The following formula is used to perform a weighted summation of the N projection values ​​to obtain the result relative to the target ray path p. m The corresponding target projection value prj(p) m ): .

[0158] For example, for the target ray path p m Calculate the relationship between multiple ray paths p1, p2, p3, p4 and the target ray path p. m The degree of similarity. Where g(p) m p1) = 0.90; g(p m p2) = 0.75; g(p m p3) = 0.60; g(p m p4) = 0.55. Select the top 3 similar ray paths based on their similarity, N = 3. Determine ray paths p1, p2, and p3 as the target ray path p. m For similar paths, the corresponding projection values ​​are: prj(p1)=88, prj(p2)=90, prj(p3)=85. The corresponding target projection value is prj(p... m =0.90×88+0.75×90+0.60×85=197.7.

[0159] In the embodiments of this application, by first calculating the similarity between multiple ray paths and the target ray path, selecting the top N most similar paths, and then weighting and summing the projection values ​​according to the weights corresponding to the similarity, projection data with high similarity can be superimposed, thereby suppressing random noise, improving the signal-to-noise ratio of the projection data, making the final target projection value more accurate, and improving the quality and detection accuracy of subsequent three-dimensional reconstruction images.

[0160] According to some embodiments of this application, the weighting coefficient q(p) j It can be calculated using the following formula: .

[0161] For example, the detector comprises multiple detector arms, each with a pixel size of 3*500, resulting in a total of 1500 detection units. At each sampling time, the middle column of the X-ray path to each arm (i.e., a 1*500 data scale) is selected as the target projection path for superposition. That is, at each sampling time, each arm has 1*500 target projection paths. For the target projection path p... m Five g(p) lines can be selected. m ,p j The path with the largest value (j=1,2,...,5) is taken as the path to be superimposed, and q(p) is used as the superposition path. m ,p j )=g(p m ,p j ) / As p j The projected values ​​are superimposed with weights.

[0162] molecule g(p) m ,p j ) represents the target projection path p m The similarity between the target path and the j-th path to be overlaid is calculated, with the denominator being the target projection path p. m The sum of similarity of all 5 paths to be superimposed can be normalized to make the sum of the weight coefficients of all paths to be superimposed equal to 1.

[0163] A ray p j Different target rays can be superimposed with different weights. For example, at the first sampling time, the ray p j Because it has a high similarity to the target projection path p1, it is selected as one of the top 5 similar paths to p1. At this time, the weight q1(p1, p) is calculated based on the similarity. j The value is set to 0.22, and this weight is used in the weighted summation of the target projection values ​​of p1. At another sampling time, the same ray p... j Since it also satisfies the same conditions as the adjacent target projection path p2, it is included in the set of paths to be superimposed by p2. At this time, the weight q2(p1, p2) is obtained. j The weight is set to 0.21, and this new weight is used in the calculation of the target projection value p2. The same ray path p... j It can participate in the superposition of multiple target projection data with different weights, making full use of the information of each effective ray without increasing additional sampling and hardware costs, thereby improving data utilization and imaging signal-to-noise ratio.

[0164] In the embodiments of this application, the top N paths with the highest similarity to the target projection path can be selected for superposition, and the weight coefficient of each path can be determined by using similarity normalization. This allows projection data that is more similar to the target path to occupy a higher proportion in the superposition, thereby improving the signal-to-noise ratio of the projection data. The weight calculation method is easy to implement and can improve the quality of projection data and computational efficiency.

[0165] According to some embodiments of this application, the system includes multiple detectors arranged at intervals along a first direction, each detector including a single row of detection units, each row of detection units including multiple detection units arranged along a third direction, the third direction being perpendicular to the first and second directions; or, the system includes multiple detectors arranged at intervals along the first direction, each detector including multiple rows of detection units, each row of detection units including multiple detection units arranged along a third direction, the third direction being perpendicular to the first and second directions; or, the system includes a single detector, the single detector being an area array detector.

[0166] A single-row detector unit can be a detector in which a single detector unit is arranged in one direction. A multi-row detector unit can be a detector in which multiple detector units are arranged in the same direction, for example, detector units are arranged in rows and columns to form a multi-row detector unit.

[0167] Figure 6A A schematic diagram of a first detector according to an embodiment of this application is shown.

[0168] like Figure 6A As shown, the system includes multiple detectors arranged at intervals along a first direction z. Each detector includes multiple single-row detection units 21, and each single-row detection unit 21 includes multiple detection units 20 arranged along a third direction y, which is perpendicular to the first direction z and the second direction x. Each single-row detection unit can form a detector arm. Multiple detector arms can be used to form a large-size detector.

[0169] For example, the system includes 120 detector arms, each with a pixel size of 1*500, resulting in 500 detection units. The pixel size is 3mm*3mm, meaning each detector arm can be 3mm wide and 1500mm high. The distance from the radiation source to the nearest arm is 3m, and the arms are arranged at equidistant angles of 2° intervals with the radiation source, totaling 60,000 detection units.

[0170] Figure 6B A schematic diagram of a second detector according to an embodiment of this application is shown.

[0171] like Figure 6BAs shown, the system includes multiple detectors arranged at intervals along a first direction z. Each detector includes multiple rows of detection units 22, and each row of detection units 22 includes multiple detection units 20 arranged along a third direction y, which is perpendicular to the first direction z and the second direction x. Each row of detection units 22 can contain multiple detection units 20 in the first direction z. Each row of detection units can form a detector arm. Multiple detector arms can be used to construct a large-size detector.

[0172] For example, the system includes 40 detector arms, each with a pixel size of 3*500, resulting in a total of 1500 detection units. The pixel size is 3mm*3mm, meaning each detector arm can be 9mm wide and 1500mm high. The distance from the X-ray source to the nearest arm is 3m, and the arms are arranged at equidistant angles of 2° intervals with the X-ray source, totaling 60,000 detection units.

[0173] When the system contains multiple detectors, the detection surfaces of different detectors can face the direction of the radiation source, and the detection surfaces of different detectors can have a certain angle between them, such as... Figure 4 As shown.

[0174] Figure 6C A schematic diagram of a third detector according to an embodiment of this application is shown.

[0175] like Figure 6C As shown, the system includes a single detector, which is a planar array detector. The planar array detector has multiple detection elements 20 in both the first direction z and the third direction y.

[0176] For example, the system may include a single area array detector with a pixel size of 120*500, totaling 60,000 detection units.

[0177] Figure 6D The illustration shows a side view of a linear CT imaging system according to an embodiment of this application.

[0178] like Figure 6D As shown, the detection units of a single-row detector, a multi-row detector, or an array detector can be arranged along the third direction y, and the beam of the X-ray source can be distributed in the third direction y to irradiate the scanned object 3.

[0179] In the embodiments of this application, the detector can adopt a structure of multiple single-row detection units, multiple multi-row detection units, or a single area array detector, which can be flexibly arranged to adapt to different scanning size detection scenarios. The superposition of projection data from different detection units can improve detection accuracy and enhance the flexibility of system hardware configuration.

[0180] According to some embodiments of this application, the system includes a single radiation source, which is disposed opposite to the intermediate row of detection units in a first direction.

[0181] The middle row of detector units can be a row of detector units located at the geometric center of the detector. The beam exit center of the X-ray source and the center of the middle row of detector units can be located on the same central ray, forming a symmetrical and centrally located layout.

[0182] If the system includes a single detector, and the single detector is an array detector, the single radiation source can be positioned opposite to the detection unit in the middle of the array detector.

[0183] If the system includes multiple detectors, each detector is equidistant along a first direction or distributed relative to the radiation source at the same circumferential angle. A single radiation source can be positioned opposite the middle row of detector units located at the center of the multiple detectors.

[0184] like Figure 2 As shown, the system includes a single X-ray source 1 and a single array detector. In the first direction z, the single X-ray source 1 and the middle row of detector units of the detector 2 are arranged opposite each other.

[0185] In the embodiments of this application, a single X-ray source is used and positioned opposite the middle row of detection units in the first direction. This simplifies the system hardware structure and installation and calibration process, facilitates X-ray path calculation, similar X-ray path screening, and projection data overlay, which helps improve data processing accuracy and speed, reduces system cost and complexity, and improves overall detection reliability.

[0186] According to some embodiments of this application, the system may include multiple radiation sources, and the data processing device is configured to separately superimpose at least two projection data generated by at least two detection units for each radiation source's radiation beam.

[0187] The system can deploy two or more X-ray sources, each capable of independently emitting a beam to irradiate the scanned object from multiple angles. The data processing device can independently perform similar path filtering, weight calculation, and projection data superposition based on the projection data of a single X-ray source and its corresponding detection unit. Projection data from different X-ray sources can be superimposed without mixing.

[0188] Figure 7A A schematic diagram of a multi-ray source linear CT imaging system according to an embodiment of this application is shown. Figure 7B A schematic diagram of another multi-source linear CT imaging system according to an embodiment of this application is shown.

[0189] like Figure 7AAs shown, the system includes a first radiation source 101, a second radiation source 102, and a third radiation source 103, which are arranged along the third direction y. Each of the three radiation sources corresponds to a detector 2. The detection surface of detector 2 can be in the yz plane.

[0190] The first radiation source 101, the second radiation source 102, and the third radiation source 103 emit radiation that passes through the scanned object 3 and is received by the detector 2, generating a first set of projection data, a second set of projection data, and a third set of projection data, respectively. At the first moment, the data processing device 5 processes the first set of projection data separately, filtering for similar radiation paths; calculates the similarity degree and weight, and performs a weighted summation to obtain the first target projection data. At the second moment, the data processing device 5 processes the second set of projection data separately, filtering for similar radiation paths; calculates the similarity degree and weight, and performs a weighted summation to obtain the second target projection data. At the third moment, the data processing device 5 processes the second set of projection data separately, filtering for similar radiation paths; calculates the similarity degree and weight, and performs a weighted summation to obtain the third target projection data. By superimposing the projection data from different radiation sources, multi-directional and multi-angle radiation irradiation of the scanned object can be achieved.

[0191] like Figure 7B As shown, the system includes a first radiation source 101 and a second radiation source 102, which are arranged along a first direction z. The first radiation source 101 corresponds to a first detector 201, and the second radiation source 102 corresponds to a second detector 202. The detection surface of the first detector 201 can be in a horizontal plane, and the detection surface of the second detector 202 can be in a vertical plane. The scanning areas of the first radiation source 101 and the second radiation source 102 can be in the same space. For example, at a certain moment, the scanned object 3 is simultaneously irradiated by the first radiation source 101 and the second radiation source 102. The scanning areas of the first radiation source 101 and the second radiation source 102 can also be continuous spaces. For example, at a certain moment, the scanned object 3 is irradiated by the first radiation source 101, and at another moment, the scanned object 3 is irradiated by the second radiation source 102.

[0192] The first X-ray source 101 emits rays that pass through the scanned object 3 and are received by the first detector 201, generating a first set of projection data. The second X-ray source 102 emits rays that pass through the scanned object 3 and are received by the corresponding second detector 202, generating a second set of projection data. The data processing device 5 processes the first set of projection data separately, filtering for similar ray paths; it calculates the similarity degree and weight, and performs a weighted summation to obtain the first target projection data. The data processing device 5 processes the second set of projection data separately, filtering for similar ray paths; it calculates the similarity degree and weight, and performs a weighted summation to obtain the second target projection data.

[0193] The embodiments of this application can use multiple X-ray sources and perform separate superposition processing on the projection data corresponding to each X-ray source. This can achieve multi-directional and multi-angle X-ray irradiation through multiple X-ray sources, enrich the perspective and information content of the projection data, and improve the imaging integrity and defect detection capabilities.

[0194] Based on the aforementioned linear CT imaging system, embodiments of this application also provide a scanning imaging method. The following will be combined with... Figure 8 This scanning imaging method is described in detail.

[0195] Figure 8 A flowchart illustrating a scanning imaging method according to an embodiment of this application is shown schematically.

[0196] like Figure 8 As shown, the scanning imaging method of this embodiment includes steps S810 to S830.

[0197] In step S810, the scanning object is controlled to move along the linear scanning channel.

[0198] In step S820, as the object moves along the linear scanning channel, the scanning component is controlled to scan the object to obtain multiple projection data generated by multiple detection units.

[0199] In step S830, at least two projection data generated by at least two detection units are superimposed.

[0200] In the method provided in this application embodiment, the scanning object is controlled to move along a straight scanning channel, and during the movement, a scanning component is used to continuously scan to obtain multiple sets of projection data corresponding to multiple detection units. Then, the projection data generated by at least two detection units are superimposed. Since continuous motion scanning can obtain sufficient projection data covering different positions and angles of the scanning object, superimposing at least two projection data can effectively suppress random noise, improve the signal-to-noise ratio, and improve the accuracy of projection data and imaging quality.

[0201] It should be noted that the numbering of each step in the above method is not a restriction on the order of the method. In the absence of conflict, the steps in the method can be executed in parallel or in a different order than that described in this application.

[0202] Figure 9 A block diagram of an electronic device suitable for implementing the methods described above, according to an embodiment of this application, is illustrated schematically. Figure 9The illustrated electronic device is merely an example and should not be construed as limiting the functionality or scope of use of the embodiments of this application. For example, either the data processing device 5 or the imaging device 6 can be implemented as the described electronic device. In the embodiments of this application, the data processing device 5 and the imaging device 6 can be implemented separately; for example, the data processing device 5 is implemented as... Figure 9 The imaging device 6 shown in the electronic device can be implemented as a display device with a display screen. In other embodiments, the data processing device 5 and the imaging device 6 can be implemented as an integrated device, that is, they can be unified into a single electronic device. For example, the data processing device 5 and the imaging device 6 can be implemented as... Figure 9 The electronic device shown is in the form of a display screen. The embodiments of this application do not impose particular limitations on the specific implementation of the data processing device 5 and the imaging device 6.

[0203] like Figure 9 As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory 902 or a program loaded from a storage portion 908 into a random access memory 903. The processor 901 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.

[0204] Random access memory 903 stores various programs and data required for the operation of electronic device 900. Processor 901, read-only memory 902, and random access memory 903 are interconnected via bus 904. Processor 901 executes various steps of the method flow according to embodiments of this application by executing programs stored in read-only memory 902 and / or random access memory 903. It should be noted that the programs may also be stored in one or more memories other than read-only memory 902 and random access memory 903. Processor 901 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0205] According to embodiments of this application, the electronic device 900 may further include an input / output interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card, such as a local area network card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0206] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0207] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 902 described above, and / or random access memory 903, and / or one or more memories other than read-only memory 902 and random access memory 903.

[0208] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0209] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0210] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by processor 901, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0211] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0212] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A linear CT imaging system, characterized in that, The system includes: A linear scanning channel, wherein the linear scanning channel is used for the object to be scanned to pass through, and the linear scanning channel extends along a first direction; A scanning assembly includes a radiation source and a detector, the radiation source and the detector being disposed on opposite sides of the scanning channel along a second direction. The radiation source is configured to emit a radiation beam, and the detector includes multiple detection units, each configured to: detect the radiation beam emitted by the radiation source and passing through the scanned object, and generate projection data based on the detected radiation beam. The scanning component and the scanning object move in a relative linear motion. The system also includes a data processing device electrically connected to the detector, the data processing device being configured to superimpose at least two projection data generated by at least two of the detector units.

2. The system according to claim 1, characterized in that, The detector includes multiple rows of detection units arranged along a first direction, and the data processing device is configured to superimpose at least two projection data generated by at least two detection units located in different rows.

3. The system according to claim 1 or 2, characterized in that, The data processing device is configured to superimpose at least two projection data generated by at least two detection units collected at different times.

4. The system according to claim 2, characterized in that, The multi-row detection unit includes a first row of detection units and a second row of detection units. In the relative linear motion direction, the first row of detection units is located upstream of the second row of detection units. The data processing device is configured to superimpose first projection data collected by at least one detection unit in the first row of detection units at a first moment and second projection data collected by at least one detection unit in the second row of detection units at a second moment, wherein the first moment is earlier than the second moment.

5. The system according to any one of claims 1-4, characterized in that, The overlay process includes: assigning weight coefficients to the at least two projection data to be overlaid; and performing a weighted summation on the at least two projection data according to the weight coefficients.

6. The system according to claim 5, characterized in that, The weight coefficients assigned to the at least two projection data to be superimposed are all the same; or, at least two of the weight coefficients assigned to the at least two projection data to be superimposed are different; or, any two of the weight coefficients assigned to the at least two projection data to be superimposed are different.

7. The system according to any one of claims 1-6, characterized in that, The plurality of detection units are respectively configured to: detect the ray beams emitted by the ray source and passing through the scanned object along each ray path, and generate projection data based on the detected ray beams; The data processing device is configured to superimpose at least two projection data generated by at least two of the detection units and having similar ray paths. The similar ray paths include multiple ray paths whose similarity to each other meets a preset condition.

8. The system according to claim 7, characterized in that, The similarity of the ray paths is characterized by at least one of the following: Geometric overlap of multiple ray paths; The degree of difference between the projection values ​​of multiple projection data corresponding to multiple ray paths.

9. The system according to claim 7 or 8, characterized in that, The system also includes an imaging device configured to generate a three-dimensional reconstructed image based on target projection data generated from multiple target projection paths.

10. The system according to claim 9, characterized in that, Each of the multiple target projection paths corresponds to multiple similar ray paths; The data processing device is configured to: for each target projection path, superimpose multiple projection data having multiple similar ray paths corresponding to the target projection path to generate target projection data corresponding to the target projection path.

11. The system according to claim 10, characterized in that, At least one of the target projection paths is one of a plurality of similar ray paths corresponding to the target projection path.

12. The system according to claim 11, characterized in that, At least one of the target projection paths is a ray path determined based on the geometric relationship between the scanning components, the linear scanning channel, and the scanned object in the system.

13. The system according to any one of claims 7-12, characterized in that, The data processing device is configured to: in response to the absence of prior information about the scanned object, characterize the similar ray path using a three-dimensional vector corresponding to the straight path from the ray source to the detection unit.

14. The system according to any one of claims 7-12, characterized in that, The data processing device is configured to: in response to prior information about the existence of the scanned object, represent the similar ray path using a three-dimensional vector corresponding to the straight path from the position where the ray beam enters the scanned object to the position where the ray beam leaves the scanned object.

15. The system according to any one of claims 7-14, characterized in that, The relative linear motion velocity of the scanned object is v, the sampling frequency of the detector is f, and the minimum interval between two adjacent target projection paths on the central axis of the linear scanning channel is d. The velocity v, the frequency f, and the interval d satisfy the following relationship: v / f <d。 16. The system according to any one of claims 7-15, characterized in that, The similarity of the ray paths is characterized by the following function: g(p1,p2)=w1cosθ+w2e -as , Where p1 and p2 represent two ray paths respectively, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the interval distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the interval distance, and w1 and w2 are weighting coefficients.

17. The system according to any one of claims 7-15, characterized in that, The similarity of the ray paths is characterized by the following function: g(p1,p2)=(w1cosθ+w2e -as )*(1 / (|prj(p1)–prj(p2)|+b)), Where p1 and p2 represent two ray paths respectively, g(p1,p2) represents the similarity between the two ray paths p1 and p2, θ represents the angle between the two ray paths p1 and p2, s represents the interval distance between the two ray paths p1 and p2 on the central axis of the linear scanning channel, parameter a is used to control the sensitivity of the interval distance, w1 and w2 are weighting coefficients; prj(p1) represents the projection value of the projection data corresponding to ray path p1, prj(p2) represents the projection value of the projection data corresponding to ray path p2, parameter b is a weighting factor, b>0.

18. The system according to any one of claims 7-17, characterized in that, The similarity of the ray paths meets the preset conditions including at least one of the following: The similarity calculated by the function is greater than a preset threshold; The similarity calculated by the function is ranked in the top N positions, where N is the preset number of similar ray paths.

19. The system according to any one of claims 7-18, characterized in that, The overlay process includes: For a target ray path p m The function calculates the relationship between multiple ray paths and the target ray path p. m Similarity; Select the N most similar ray paths ranked Nth by similarity calculated by the function, where the j-th ray path p among the N most similar ray paths j With the target ray path p m The similarity is g(p) m ,p j ), where 1≤j≤N; Obtain N projection values ​​corresponding to the N similar ray paths, where the j-th ray path p among the N similar ray paths j The corresponding projection value is prj(p) j ); Each of the N projected values ​​is assigned a weight coefficient, wherein the projected value prj(p j The corresponding weight coefficient is q(p) j ); The N projection values ​​are weighted and summed using the following formula to obtain the result relative to the target ray path p. m The corresponding target projection value prj(p) m ): 。 20. The system according to claim 19, characterized in that, The weighting coefficient q(pj) is calculated using the following formula: 。 21. The system according to any one of claims 1-20, characterized in that, The system includes multiple detectors arranged at intervals along a first direction. Each detector includes a single row of detection units, and each row of detection units includes multiple detection units arranged along a third direction, which is perpendicular to both the first and second directions; or... The system includes multiple detectors arranged at intervals along a first direction. Each detector includes multiple rows of detection units, and each row of detection units includes multiple detection units arranged along a third direction, which is perpendicular to both the first and second directions; or... The system includes a single detector, which is an area array detector.

22. The system according to any one of claims 1-21, characterized in that, The system includes a single radiation source, which is positioned opposite to the intermediate row of detection units in a first direction.

23. The system according to any one of claims 1-21, characterized in that, The system includes multiple radiation sources, and the data processing device is configured to individually superimpose at least two projection data generated by at least two detection units for each radiation source's radiation beam.

24. A scanning imaging method using the system according to any one of claims 1-23, characterized in that, The method includes: Control the movement of the scanned object along the linear scanning channel; During the movement of the scanned object along the straight scanning channel, the scanning component is controlled to scan the scanned object to obtain multiple projection data generated by multiple detection units; The projection data generated by at least two of the detection units are superimposed.

Images