A luggage CT reconstruction region determination method

Abstract

The present application relates to a kind of luggage CT reconstruction area determination method, belong to safety system detection, solve the problem of the waste of computer's computing resources caused by the large area of blank area in the CT reconstruction area in prior art.The method comprises the following steps: step 1: in the projection data used for reconstructing tomographic data M, the projection data between the rotation angle from s to s+m-1 is extracted, denoted as p1;Step 2: the projection data of p1 is preprocessed to obtain p2;Step 3: p2 is processed to determine the reconstruction area;Wherein, m is the angle number required for reconstructing a single tomographic;Wherein, s is the starting angle.The size of image reconstruction area is adaptively determined according to the actual size of the detection package.

Description

Technical Field

[0001] This invention relates to the field of security system detection technology, and in particular to a method for determining the CT reconstruction area of ​​luggage. Background Technology

[0002] In X-ray-based explosives inspection technology, X-ray computed tomography (CT) imaging technology is highly valued in the field of security inspection due to its unique advantages. X-ray CT security inspection technology obtains tomographic images of the scanned object by reconstructing CT data, and identifies dangerous items in the scanned object by analyzing the feature data in the tomographic images.

[0003] In security CT scanners, there is typically a detection channel. The size of this channel determines the maximum size of the package the system can detect. To accommodate the reconstruction of the largest package, the CT reconstructed image area must cover the area of ​​the largest detectable package. However, in actual inspections, the size of the package being inspected is usually much smaller than the maximum package size. This results in large blank areas within the system's reconstruction area, significantly wasting the computer's computing resources. Summary of the Invention

[0004] Based on the above analysis, the present invention aims to provide a method for determining the CT reconstruction area of ​​luggage, in order to solve the problem of large blank areas in the existing CT reconstruction area, which causes a waste of computer computing resources.

[0005] On one hand, embodiments of the present invention provide a method for determining the CT reconstruction region of luggage, including:

[0006] Step 1: Extract the projection data from the projection data used to reconstruct the fault data M, with the rotation angle between s and s+m-1, and denote it as p1;

[0007] Step 2: Preprocess the projection data of p1 to obtain p2;

[0008] Step 3: Process p2 to determine the reconstruction area;

[0009] Where m is the number of angles required to reconstruct a single fault;

[0010] Where s is the initial angle.

[0011] Furthermore, in step 2, the preprocessing includes dark field correction, bright field correction, and log correction;

[0012] Among them, dark field correction uses the method of subtracting dark field data from the acquired data to correct the CT detector image;

[0013] Brightness field correction employs a two-point correction method. Under the assumption that all CT detector pixels have a linear response, the coefficients used for correction are obtained by calculating the ratio between the set point value and the data after brightness field correction.

[0014] In this context, it is assumed that X-ray photons are monoenergetic and that the variation in X-ray intensity follows the Lambert-Beers law. Log correction is expressed as the line integral of the attenuation coefficient along the X-ray path by performing a logarithmic operation on the ratio of the incident ray intensity to the outgoing ray intensity, and is used as the projection measurement value required in CT reconstruction.

[0015] Furthermore, step 3 includes:

[0016] S311: Use the parallel bundle rearrangement algorithm to rearrange the data in p2 into a parallel bundle P. p ;

[0017] S312: Based on the relationship between the X-ray source and the CT detector at the initial angle s, from P p Obtain one or more angle data p where the ray direction is perpendicular to the height direction. i ;

[0018] S313: Based on angle data p i Determine the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstructed region;

[0019] S314: Based on a defined geometric relation, for p i The data in the image is processed to obtain the reconstructed height h1;

[0020] Where i is a positive integer, p i For p3, p4...p i , representing different angle data.

[0021] Furthermore, in step S311, the rearranged virtual detector projection data is represented as P. p (θ,t,b) satisfies:

[0022]

[0023] Where θ represents the rotation angle after rearrangement;

[0024] t is the distance within the row of parallel rays;

[0025] b is the row spacing;

[0026] R is the focal length;

[0027] P f This is the original data before rearrangement.

[0028] Furthermore, in step S313, at pi At the given angle, the geometric relationship satisfies:

[0029] The rays emitted by multiple virtual ray sources are parallel to each other, aligned with the horizontal direction of the reconstructed area, and finally incident on the virtual detector.

[0030] Furthermore, step S314 includes:

[0031] S3141: Based on a defined geometric relation, for p i The data in the image is processed to obtain a binarized image N;

[0032] S3142: Accumulate the binarized image N along the direction of the ray to obtain a one-dimensional vector S;

[0033] S3143: In a one-dimensional vector S, record the last position that is less than the value t and whose preceding positions are all less than the value t as h1, and obtain the reconstructed height h1;

[0034] Where t is a positive integer, and its value range is: t < 5.

[0035] Furthermore, step S3141 includes:

[0036] S31411: Based on a defined geometric relationship, p i The data in the image is back-projected onto the reconstruction area according to the direction of the ray to obtain a two-dimensional image;

[0037] S31412: Perform mean filtering on the obtained two-dimensional image to eliminate the influence of noise and obtain image M;

[0038] S31413: Compare M with the numerical value b to obtain the binarized image N;

[0039] Where b is a positive number, representing system noise.

[0040] Furthermore, step 3 includes:

[0041] S321: Process the data at each angle to obtain the overlapping region U;

[0042] S322: The data obtained by comparing the overlapping region U with the numerical value l is used as the final reconstructed region;

[0043] Where, l = m – q;

[0044] m is the number of angles used to determine the overlapping region U;

[0045] q is a non-negative integer used to appropriately expand the reconstruction area, with a value range of 0–20 cm.

[0046] Furthermore, step S321 includes:

[0047] S3211: Compare the data of the first angle with the numerical value s, and convert it into binary data G;

[0048] S3212: For the binary data G, find the first non-zero position at both edges. The line connecting the corresponding detector unit and the X-ray source is the tangent line. Set the pixels in the reconstruction area between the tangent lines to 1 and set it to U.

[0049] S3213: Based on steps S3211 and S3212, process the data at the second angle, set the pixels between the tangent lines at this angle that are located in the reconstruction area to 1, and then add them to U;

[0050] S3214: Based on steps S3211-S3213, process the data at each angle to obtain the overlapping region U;

[0051] Where s represents background noise.

[0052] Furthermore, m represents the number of projection angles acquired when the CT detector rotates at an angle greater than 180°.

[0053] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0054] 1. In the projection data used to reconstruct tomographic data M, the present invention extracts and processes the projection data with rotation angles from s to s+m-1, and obtains the reconstruction height h1 based on the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstruction area. Thus, only the area where the object is located needs to be reconstructed. This enables the adaptive determination of the image reconstruction area size according to the actual size of the detected package, avoiding large blank areas in the system reconstruction area and thus avoiding the waste of computer computing resources.

[0055] 2. By processing data from different angles, the first non-zero position at both ends of the binarized data G is obtained, and the corresponding detector unit and the ray source position line, i.e., the tangent line, is obtained. After setting the pixels in the reconstruction area between the tangent lines at all angles to 1, they are added to U to obtain the final reconstruction area. In this way, the circumscribed convex polygon of an object is formed by multiple tangent rays, and the reconstruction area of ​​the object can be obtained. This realizes the adaptive determination of the image reconstruction area size according to the actual size of the detected package, avoiding large blank areas in the system reconstruction area, and thus avoiding the waste of computer computing resources.

[0056] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0057] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0058] Figure 1 This is a flowchart of the baggage CT reconstruction area determination method of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the geometric relationship between multiple virtual X-ray sources, X-rays, reconstruction regions, and virtual detectors at the p3 angle in this invention.

[0060] Figure 3 This is a schematic diagram showing the positional relationship between the CT radiation source, the reconstruction area, the scanned object, and the CT detector at a certain angle in this invention.

[0061] Figure 4 for Figure 3 A schematic diagram of the CT X-ray source and CT detector after being rotated at a certain angle;

[0062] Figure 5 This is a schematic diagram showing the overlapping area formed by tangent rays at two angles in this invention, which includes the scanned object.

[0063] Figure 6 This is a schematic diagram showing the positional relationship between the reconstruction area, the channel area, the determination line, and the conveyor belt line in this invention;

[0064] Figure 7 This is a schematic diagram of the luggage CT structure in this invention.

[0065] Figure label:

[0066] 1-CT X-ray source; 2-CT slip ring; 3-CT detector; 4-object; 5-conveyor belt; 6-conveyor belt motor; 7-motion control computer; 8-slip ring motor; 9-data processing computer; 10-reconstruction area; 11-virtual X-ray source; 12-ray; 13-virtual detector; 14-channel area; 15-definition line; 16-conveyor belt line; 17-ray I; 18-ray II; 19-ray III; 20-ray IV. Detailed Implementation

[0067] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0068] To address the above problems, this invention provides a method for determining the CT reconstruction region of luggage, comprising the following steps:

[0069] Step 1: Extract the projection data from the projection data used to reconstruct the fault data M, with the rotation angle between s and s+m-1, and denote it as p1;

[0070] Step 2: Preprocess the projection data of p1 to obtain p2;

[0071] Step 3: Process p2 to determine the reconstruction area;

[0072] Where m is the number of angles required to reconstruct a single fault;

[0073] Where s is the initial angle.

[0074] In step 2, the preprocessing includes dark field correction, bright field correction, and log correction.

[0075] Step 3 includes:

[0076] S311: Use the parallel bundle rearrangement algorithm to rearrange the data in p2 into a parallel bundle P. p ;

[0077] S312: Based on the relationship between the X-ray source and the CT detector at the initial angle s, from P p Obtain one or more angle data p where the ray direction is perpendicular to the height direction. i ;

[0078] S313: Based on angle data p i Determine the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstructed region;

[0079] S314: Based on a defined geometric relation, for p i The data in the image is processed to obtain the reconstructed height h1;

[0080] Where i is a positive integer, p i For p3, p4...p i , representing different angle data.

[0081] Compared with the prior art, the present invention extracts and processes the projection data with rotation angles from s to s+m-1 from the projection data used to reconstruct the tomographic data M, and obtains the reconstruction height h1 based on the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstruction area. This enables the adaptive determination of the image reconstruction area size according to the actual size of the detected package, avoiding large blank areas in the system reconstruction area and thus avoiding the waste of computer computing resources.

[0082] Specifically, in step 1, the security inspection CT system includes a CT detector 3, which is used to acquire reconstructed tomographic data M, where m is the number of projection angles acquired when the CT detector rotates at an angle greater than 180°.

[0083] Specifically, in step 2, the preprocessing includes dark field correction, bright field correction, and log correction.

[0084] Dark field correction uses the method of subtracting dark field data from the acquired data to correct CT detector images, in order to solve the inconsistency problem between different detection units caused by the difference in dark current response of multiple detection units in a dark field environment without radiation irradiation. Dark field correction usually uses the method of subtracting dark field data from the acquired data.

[0085] Bright field correction, also known as gain correction, employs a "two-point correction method." Under the assumption that all CT detector pixels have linear responses, the gain coefficient used for correction is obtained by calculating the ratio between the set point value and the data after gain correction. This method addresses the inconsistency between different detection units caused by differences in radiation distribution and inconsistencies between back-end electronic modules under radiation exposure.

[0086] Computed tomography (CT) reconstruction relies on X-ray beam measurements at different angles. Assuming that X-ray photons are monoenergetic and that the intensity of X-rays changes according to Lambert-Beers' law (i.e., the rays exhibit exponential decay), Log correction is expressed as the line integral of the attenuation coefficient along the X-ray path by performing a logarithmic operation on the ratio of the incident ray intensity to the outgoing ray intensity. This is used as the projection measurement value required in CT reconstruction.

[0087] Specifically, in step S311, the rearranged virtual detector projection data is represented as P. p (θ,t,b);

[0088] Among them, P p (θ,t,b) satisfies:

[0089]

[0090] Where θ represents the rotation angle after rearrangement;

[0091] t is the distance within the row of parallel rays;

[0092] b is the row spacing;

[0093] R is the focal length;

[0094] P f This is the original data before rearrangement.

[0095] Specifically, in step S312, based on the relationship between the X-ray source and the CT detector at the initial angle s, it can be determined from p p To obtain single or multiple angle data in which the ray direction is perpendicular to the height direction, so as to obtain the reconstructed height h1 by processing the single or multiple angle data;

[0096] Among them, one or more of the angle data have a different relationship with the positions of data p3 and p4, or are close to the positions of p3 and p4.

[0097] For example, from p p We obtained two angle data, p3 and p4, where the ray direction is perpendicular to the height direction.

[0098] Specifically, in step S313, at p i At the given angle, the geometric relationship satisfies:

[0099] The rays emitted by multiple virtual ray sources are parallel to each other, aligned with the horizontal direction of the reconstructed area, and finally incident on the virtual detector.

[0100] For example, such as Figure 2 As shown, at angle p3, the rays emitted by multiple virtual ray sources 11 are parallel to each other, consistent with the horizontal direction of the reconstructed region 10, and finally incident on the virtual detector 13.

[0101] A similar geometric relationship exists at a position 180 degrees different from p3, that is, at angle p4, a similar geometric relationship exists.

[0102] Specifically, step S314 includes:

[0103] S3141: Based on a defined geometric relation, for p i The data in the image is processed to obtain a binarized image N;

[0104] S3142: Accumulate the binarized image N along the direction of the ray to obtain a one-dimensional vector S;

[0105] S3143: In a one-dimensional vector S, record the last position that is less than the value t and whose preceding positions are all less than the value t as h1, and obtain the reconstructed height h1;

[0106] Where t is a positive integer, and its value range is: t < 5.

[0107] Step S3141 includes:

[0108] S31411: Based on a defined geometric relationship, p i The data in the image is back-projected onto the reconstruction area according to the direction of the ray to obtain a two-dimensional image;

[0109] S31412: Perform mean filtering on the obtained two-dimensional image to eliminate the influence of noise and obtain image M;

[0110] S31413: Compare M with the numerical value b to obtain the binarized image N;

[0111] Here, b represents system noise. The image is converted into binary by numerical comparison to further eliminate the influence of system noise. It is generally a small positive number, and its specific value is related to the CT system.

[0112] In this process, after obtaining the reconstruction height h1, redundancy is added to the reconstruction height h1 to obtain the final reconstruction height h2, thereby improving the accuracy of the reconstruction results.

[0113] Wherein, the final reconstructed height h2 satisfies:

[0114] h2 = h1 + ss

[0115] Among them, ss is redundant and takes a value of 0-20cm.

[0116] Among them, such as Figure 7 As shown, the security inspection CT equipment includes: 1. CT X-ray source; 2. CT slip ring; 3. CT detector; 4. Object; 5. Conveyor belt; 6. Conveyor belt motor; 7. Motion control computer; 8. Slip ring motor; and 9. Data processing computer. The reconstructed height range is the area between the height of the conveyor belt and h2. Figure 3 As shown, the area represented by channel region 14, where the object is located, can be reconstructed using the above method, only the area between conveyor belt line 16 and the determination line 15 needs to be reconstructed. Therefore, during the actual inspection process, determining the reconstruction height determines the detection area, ensuring that the size of the package being inspected matches the package size. This avoids large blank areas within the system's reconstruction area, thus preventing a significant waste of computer computing resources.

[0117] Furthermore, step 3 above is not applicable in the following situations:

[0118] At a certain angle, the positional relationship between CT radiation source 1, reconstruction area 10, scanning object 4, and CT detector 3 is as follows: Figure 3As shown, ray 12 passes through object 4 via CT ray source 1 and is received by CT detector 3. At this time, ray I 17 and ray II 18 are tangent to the outer edge of object 4.

[0119] like Figure 4 As shown, similarly, after the CT radiation source 1 and the CT detector 3 rotate by a certain angle, radiation III19 and radiation IV20 are tangent to the outer edge of the object 4.

[0120] like Figure 5 As shown, the overlapping region 10 formed by the tangent rays at the two angles contains the scanned object 4.

[0121] Similarly, as more angles are added, more and more tangent rays form a circumscribed convex polygon of object 4, thereby obtaining the reconstructed region of the object. The specific method is shown below.

[0122] The methods for reconstructing the area include:

[0123] Step 1-2: Same as Step 1-2 above;

[0124] Step 3: Process the data at each angle to obtain the overlapping region U;

[0125] Specifically, including:

[0126] S31: Compare the data of the first angle with the numerical value s, and convert it into binary data G;

[0127] Where s represents background noise.

[0128] S32: For the binary data G, find the first non-zero position at both edges. The line connecting the corresponding detector unit and the X-ray source is the tangent line. Set the pixels in the reconstruction area between the tangent lines to 1 and set it to U.

[0129] S33: Based on steps S31 and S32, process the data at the second angle, set the pixels between the tangent lines at this angle that are located in the reconstruction area to 1, and then add them to U;

[0130] S34: Based on steps S31-S33, process the data at each angle to obtain the overlapping region U.

[0131] Step 4: Compare the overlapping region U with the value l and use the data as the final reconstructed region.

[0132] Where, l = m – q.

[0133] m is the number of angles used to determine the overlapping region U;

[0134] q is a non-negative integer used to appropriately expand the reconstruction area, with a value range of 0–20 cm.

[0135] Compared with the prior art, the present invention extracts and processes the projection data with rotation angles from s to s+m-1 from the projection data used to reconstruct the tomographic data M, and obtains the reconstruction height h1 based on the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstruction area. This enables the adaptive determination of the image reconstruction area size according to the actual size of the detected package, avoiding large blank areas in the system reconstruction area and thus avoiding the waste of computer computing resources.

[0136] By processing data from different angles, the first non-zero position at the two edges of the binarized data G is obtained, and then the corresponding detector unit and the position of the ray source are obtained, i.e., the tangent line. After setting the pixels in the reconstruction region between the tangent lines at all angles to 1, they are added to U to obtain the final reconstruction region. In this way, the circumscribed convex polygon of an object is formed by multiple tangent rays, and the reconstruction region of the object can be obtained. This realizes the adaptive determination of the image reconstruction region size according to the actual size of the detected package, avoiding large blank areas in the system reconstruction region, and thus avoiding the waste of computer computing resources.

[0137] Example 1:

[0138] A method for determining the CT reconstruction region of luggage, such as Figure 1 As shown, it includes:

[0139] Step 1: Extract the projection data from the rotation angle between s and s+m-1 from the projection data used to reconstruct the fault data M, and denote it as p1.

[0140] Where m is the number of projection angles acquired when the CT detector rotates at an angle greater than 180°.

[0141] Where s is the initial angle.

[0142] Among them, such as Figure 4 As shown, the security CT scanner includes a CT X-ray source 1, a CT slip ring 2, a CT detector 3, an object 4, a conveyor belt 5, a conveyor belt motor 6, a motion control computer 7, a slip ring motor 8, and a data processing computer 9. The object (luggage) 4 is placed on the conveyor belt 5 and, driven by the conveyor belt motor 6, moves at a constant speed with the conveyor belt 5 into the CT scanning area for scanning. The slip ring motor 8 controls the CT slip ring 2 to rotate at a constant speed. The CT X-ray source 1 emits an X-ray beam that penetrates the object 4. The CT detector 3 receives the attenuated signal transmitted through the object 4 and continuously transmits the received signal to the data processing computer 9. Thus, the data collected by the CT detector 3 forms the projection data of the tomographic data M.

[0143] The security CT system also includes a light barrier module, which sets the data acquisition time according to the trigger signal of the light barrier device to ensure the integrity of the acquired object data.

[0144] The light barrier device includes a light barrier transmitting module and a light barrier receiving module, which are respectively installed at both ends on one side of the security checkpoint entrance. The light barrier device controls the acquisition of detector data. When an object enters the security CT scanner, if the light barrier receiving module does not receive the pulse signal from the light barrier transmitting module, the light barrier device changes from an open state to an obstruction state. The light barrier is then triggered to generate a trigger signal for the object entering the security CT scanner and begins acquiring complete data of the object entering the security CT scanner. When the object leaves the security CT scanner, if the light barrier receiving module receives the pulse signal from the light barrier transmitting module again, the light barrier device changes from an obstruction state to an open state. The light barrier is then triggered to generate a trigger signal for the object leaving the security CT scanner and begins acquiring complete data of the object leaving the security CT scanner.

[0145] Among them, "objects entering and leaving the security CT scanner" refers to objects entering and leaving the scanning area of ​​the security CT scanner detector.

[0146] In security CT, there is a certain interval between the light barrier device and the detector. The time for data acquisition is set according to the trigger signal of the light barrier device to ensure the integrity of the acquired object data. During the period from when the light barrier is triggered by an object entering it to when the object leaves it, the detector continuously acquires data to obtain the projection data of the tomographic data M.

[0147] Step 2: Preprocess the projection data of p1 to obtain p2;

[0148] Specifically, preprocessing includes dark field correction, bright field correction, and log correction.

[0149] Dark field correction uses the method of subtracting dark field data from the acquired data to correct the detector image, in order to solve the inconsistency problem between different detector units caused by the difference in the dark current response of the multiple detector units in the dark field environment without radiation. Dark field correction usually uses the method of subtracting dark field data from the acquired data.

[0150] Bright field correction, also known as gain correction, employs a "two-point correction method." Under the assumption that all detector pixels have linear responses, the gain coefficient used for correction is obtained by calculating the ratio between the set point value and the data after gain correction. This method aims to address the inconsistency between different detection units caused by differences in radiation distribution and inconsistencies between back-end electronic modules under radiation irradiation.

[0151] Computed tomography (CT) reconstruction relies on X-ray beam measurements at different angles. Assuming that X-ray photons are monoenergetic and that the intensity of X-rays changes according to Lambert-Beers' law (i.e., the rays exhibit exponential decay), Log correction is expressed as the line integral of the attenuation coefficient along the X-ray path by performing a logarithmic operation on the ratio of the incident ray intensity to the outgoing ray intensity. This is used as the projection measurement value required in CT reconstruction.

[0152] Step 3: Use the parallel bundle rearrangement algorithm to rearrange the data in p2 into a parallel bundle P. p The rearranged virtual detector projection data is represented as P. p (θ,t,b);

[0153] Among them, P p (θ,t,b) satisfies:

[0154]

[0155] Where θ represents the rotation angle after rearrangement;

[0156] t is the distance within the row of parallel rays;

[0157] b is the row spacing;

[0158] R is the focal length;

[0159] P f This is the original data before rearrangement.

[0160] Step 4: Based on the relationship between the X-ray source and the CT detector at the initial angle s, from P p Two angle data p3 and p4 were obtained where the ray direction is perpendicular to the height direction;

[0161] Step 5: Based on two angle data p3 and p4, determine the geometric relationship between the rays emitted by the multiple virtual ray sources and the reconstructed region;

[0162] Among them, such as Figure 2 As shown, at angle p3, after being rearranged into parallel beams, the rays emitted by the multiple virtual ray sources 11 are parallel to each other, consistent with the horizontal direction of the reconstructed region 10, and finally incident on the virtual detector 13.

[0163] A similar geometric relationship exists at a position 180 degrees different from p3, that is, at angle p4, a similar geometric relationship exists.

[0164] Step 6: Based on the determined geometric relationships, process the data in p3 and p4 to obtain the binarized image N;

[0165] Specifically, including:

[0166] S61: Based on the determined geometric relationship, the data in p3 and p4 are back-projected into the reconstruction region 10 according to the direction of ray 12 to obtain a two-dimensional image;

[0167] The back projection process is the process of smearing the collected data back along the direction of the light rays, that is, the pixel values ​​of the positions through which ray 12 passes are all data collected by the detector corresponding to ray 12.

[0168] S62: Perform mean filtering on the obtained two-dimensional image to eliminate the influence of noise and obtain image M.

[0169] S63: Compare M with the numerical value b to obtain the binarized image N.

[0170] Here, b represents system noise. The image is converted into binary by numerical comparison to further eliminate the influence of system noise. It is generally a small positive number, and its specific value is related to the CT system.

[0171] Step 7: Accumulate the binarized image N along the direction of ray 12 to obtain a one-dimensional vector S;

[0172] Step 8: In the one-dimensional vector S, record the last position that is less than the value t and whose preceding positions are all less than the value t as h1, and obtain the reconstructed height h1;

[0173] Where t is a positive integer, and its value range is: t < 5.

[0174] Step 9: Add redundancy to height h1 to obtain the final reconstructed height h2.

[0175] Where the reconstruction height h2 satisfies:

[0176] h2 = h1 + ss

[0177] Where ss is redundant and takes a value of 10cm.

[0178] In the context of security CT scanners, the reconstructed height range corresponds to the area between the height of the conveyor belt and h2. For example... Figure 3 As shown, the area represented by channel region 14, where the object is located, can be reconstructed using the above method, only the area between conveyor belt line 16 and the determination line 15 needs to be reconstructed. Therefore, during the actual inspection process, determining the reconstruction height determines the detection area, ensuring that the size of the package being inspected matches the package size. This avoids large blank areas within the system's reconstruction area, thus preventing a significant waste of computer computing resources.

[0179] Specifically, the process of reconstructing three-dimensional data of security inspection CT images is as follows: First, the object 4 is placed on the conveyor belt 5. Driven by the conveyor belt motor 6, it moves at a constant speed with the conveyor belt 5 and enters the CT scanning area for scanning. The slip ring motor 8 controls the CT slip ring 2 to rotate at a constant speed. The CT X-ray source 1 emits an X-ray beam that penetrates the object 4. The CT detector 3 receives the attenuated signal that has passed through the object 4 and continuously transmits the received signal to the data processing computer 9.

[0180] The data processing computer 9 calculates the reconstruction area in the fault data based on the collected data, uses the reconstruction algorithm to reconstruct the determined reconstruction area, and finally displays the three-dimensional data of all faults on the screen to realize image reconstruction.

[0181] Example 2

[0182] A method for determining the CT reconstruction region of luggage, which differs from Example 1 in that:

[0183] Step 3: Process the data at each angle to obtain the overlapping region U;

[0184] Specifically, including:

[0185] S31: Compare the data of the first angle with the numerical value s, and convert it into binary data G;

[0186] Where s represents background noise.

[0187] S32: For the binary data G, find the first non-zero position at both edges. The line connecting the corresponding detector unit and the X-ray source is the tangent line. Set the pixels in the reconstruction area between the tangent lines to 1 and set it to U.

[0188] S33: Based on steps S31 and S32, process the data at the second angle, set the pixels between the tangent lines at this angle that are located in the reconstruction area to 1, and then add them to U;

[0189] S34: Based on steps S31-S33, process the data at each angle to obtain the overlapping region U.

[0190] Step 4: Compare the overlapping region U with the value l and use the data as the final reconstructed region.

[0191] Where, l = m – q.

[0192] m is the number of angles used to determine the overlapping region U;

[0193] q is a non-negative integer used to appropriately expand the reconstruction area, with a value range of 0–20 cm.

[0194] This method obtains the reconstructed region by processing data from different angles. The applicable scenarios for this method are as follows:

[0195] At a certain angle, the positional relationship between CT radiation source 1, reconstruction area 10, scanning object 4, and CT detector 3 is as follows: Figure 3 As shown, ray 12 passes through object 4 via CT ray source 1 and is received by CT detector 3. At this time, ray I 17 and ray II 18 are tangent to the outer edge of object 4.

[0196] like Figure 4 As shown, similarly, after the CT radiation source 1 and the CT detector 3 are rotated by a certain angle, radiation III19 and radiation IV20 are tangent to the outer edge of the object 4.

[0197] like Figure 5 As shown, the overlapping region 10 formed by the tangent rays at the two angles contains the scanned object 4.

[0198] Similarly, as more angles are added, more and more tangent rays form a circumscribed convex polygon of object 4, thereby obtaining the reconstructed region of the object. The specific method is as shown above.

[0199] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0200] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the CT reconstruction region of luggage, characterized in that, include: Step 1: Extract the projection data from the projection data used to reconstruct the fault data M, with the rotation angle between s and s+m-1, and denote it as p1; Step 2: Preprocess the projection data of p1 to obtain p2; Step 3: Process p2 to determine the reconstruction area; Where m is the number of angles required to reconstruct a single fault; Where s is the initial angle; Step 3 includes: S311: Use the parallel bundle rearrangement algorithm to rearrange the data in p2 into parallel bundles. ; S312: Based on the relationship between the X-ray source and the CT detector at the initial angle s, from... P p Obtain one or more angle data p where the ray direction is perpendicular to the height direction. i ; S313: Based on angle data p i Determine the geometric relationship between the rays emitted by multiple virtual ray sources and the reconstructed region; S314: Based on a defined geometric relation, for p i The data in the image is processed to obtain the reconstructed height h1; Where i is a positive integer, p i For p3, p4...p i , representing different angle data; Step 3 also includes: S321: Process the data at each angle to obtain the overlapping region U; S322: The data obtained by comparing the overlapping region U with the numerical value l is used as the final reconstructed region; Where l = m–q; m is the number of angles used to determine the overlapping region U; q is a non-negative integer used to appropriately expand the reconstruction area, with a value range of 0–20cm.

2. The method according to claim 1, characterized in that: In step 2, the preprocessing includes dark field correction, bright field correction, and log correction; Among them, dark field correction uses the method of subtracting dark field data from the acquired data to correct the CT detector image; Brightness field correction employs a two-point correction method. Under the assumption that all CT detector pixels have a linear response, the coefficients used for correction are obtained by calculating the ratio between the set point value and the data after brightness field correction. In this context, it is assumed that X-ray photons are monoenergetic and that the variation in X-ray intensity follows the Lambert-Beers law. Log correction is expressed as the line integral of the attenuation coefficient along the X-ray path by performing a logarithmic operation on the ratio of the incident ray intensity to the outgoing ray intensity, and is used as the projection measurement value required in CT reconstruction.

3. The method according to claim 1, characterized in that, In step S311, the rearranged virtual detector projection data is represented as follows: satisfy: in, Indicates the rotation angle after rearrangement; The distance within the row of parallel rays; b is the row spacing; Focal length; This is the original data before rearrangement.

4. The method according to claim 1, characterized in that: In step S313, at p i At the given angle, the geometric relationship satisfies: The rays emitted by multiple virtual ray sources are parallel to each other, aligned with the horizontal direction of the reconstructed area, and finally incident on the virtual detector.

5. The method according to claim 1, characterized in that, Step S314 includes: S3141: Based on a defined geometric relation, for p i The data in the image is processed to obtain a binarized image N; S3142: Accumulate the binarized image N along the direction of the ray to obtain a one-dimensional vector S; S3143: In a one-dimensional vector S, record the last position that is less than the value t and whose preceding positions are all less than the value t as h1, and obtain the reconstructed height h1; Where t is a positive integer, and its value range is: t < 5.

6. The method according to claim 5, characterized in that, Step S3141 includes: S31411: Based on a defined geometric relationship, p i The data in the image is back-projected onto the reconstruction area according to the direction of the ray to obtain a two-dimensional image; S31412: Perform mean filtering on the obtained two-dimensional image to eliminate the influence of noise and obtain image M; S31413: Compare M with the numerical value b to obtain the binarized image N; Where b is a positive number, representing system noise.

7. The method according to claim 1, characterized in that, Step S321 includes: S3211: Compare the data of the first angle with the numerical value s, and convert it into binary data G; S3212: For the binary data G, find the first non-zero position at both edges. The line connecting the corresponding detector unit and the X-ray source is the tangent line. Set the pixels in the reconstruction area between the tangent lines to 1 and set it to U. S3213: Based on steps S3211 and S3212, process the data at the second angle, set the pixels between the tangent lines at this angle that are located in the reconstruction area to 1, and then add them to U; S3214: Based on steps S3211-S3213, process the data at each angle to obtain the overlapping region U; Where s represents background noise.

8. The method according to claim 1, characterized in that: The value of m is the number of projection angles acquired when the CT detector rotates at an angle greater than 180°.

Images