Automatic identification and calibration method for scanning object center and CT system

Abstract

The present application discloses a method for automatically identifying and calibrating the center of a scanned object and a CT system. The method comprises: obtaining a CT tomographic image of the scanned object; identifying the center position of the scanned object in the CT tomographic image based on a set detection dimension; calculating the offset of the scanned object center in the CT system based on the reconstructed field of view and the center position of the scanned object; and adjusting the scanned object center to the rotation center of the CT system based on the obtained offset. The present application automatically identifies the center position of the scanned object in the CT tomographic image, combines the reconstructed field of view and the center position of the scanned object, calculates the offset of the scanned object in the CT system, and adjusts the position of the scanned object accordingly to ensure that its center is aligned with the rotation center of the CT system, thereby improving image quality, reducing image distortion or resolution loss caused by eccentricity, and reducing the technical requirements for the operator, thereby improving work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a method for automatically identifying and calibrating the center of a scanned object and a CT system. Background Art

[0002] The basic principle of X-ray computed tomography (CT) is to utilize the varying X-ray absorption properties of different materials to obtain data on the X-ray absorption coefficients of different tissues within the scanned object, then process and reconstruct an image using a computer. With the continuous advancement of medical technology, CT (computed tomography), as an efficient and accurate medical imaging diagnostic technology, has been widely used in various medical institutions.

[0003] Because CT equipment involves complex electronic, mechanical, and radiation technologies, its performance and stability are easily affected by a variety of factors, such as equipment aging, component wear, and environmental interference. Therefore, regular maintenance and calibration of CT equipment is crucial to ensuring its proper operation and accurate diagnosis. In this process, water phantoms serve as an important calibration tool and play an irreplaceable role.

[0004] During a CT scan of the human head, the center of rotation serves as the reference point around which the X-ray beam and detector scan. Positioning the head at the center of rotation ensures uniform X-ray penetration of tissue, minimizing image distortion and resolution loss caused by eccentricity, and providing clearer visualization of subtle brain structures (such as edema and hemorrhage). Children's scans may not always be able to secure their heads in the designated position as directed by the doctor. Therefore, it is crucial to automatically identify the center of the head and position the scanned head to the center of rotation of the CT gantry.

[0005] A water phantom is a circular container filled with water, with a uniform density. Due to its uniform density and physical properties similar to human tissue, and its shape similar to the human head, water phantom images have become an important reference for evaluating CT equipment performance and ensuring the accuracy of scan results. They can be used to assess CT machine performance parameters such as CT value accuracy, image noise, and slice thickness. By scanning a water phantom, a series of data related to equipment performance can be obtained, allowing necessary adjustments and optimizations to ensure the accuracy and reliability of scan results.

[0006] The placement of the water phantom is directly related to the accuracy and reliability of the calibration results. The placement of the head is also closely related to image quality. The current common method is for the operator to visually position the water phantom or head at the CT rotation center using the CT positioning laser light. This method has high requirements for the operator and relatively large errors. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and a CT system for automatically identifying and calibrating the center of a scanned object, so as to solve the problem in the prior art that when performing a CT scan, the operator needs to visually position the CT positioning laser light to place the scanned object at the CT rotation center, which is demanding and prone to errors.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A method for automatically identifying and calibrating the center of a scanned object comprises the following steps:

[0010] S1: Acquire a CT tomographic image of the scanned object;

[0011] S2: Based on the set detection dimension, identify the center position of the scanned object in the CT tomographic image;

[0012] S3: Calculate the offset of the center of the scan object in the CT system according to the reconstructed field of view and the center position of the scan object;

[0013] S4: According to the obtained offset, the center of the scan object is adjusted to the rotation center of the CT system.

[0014] Furthermore, based on the set detection dimension, the center position of the scanned object in the CT tomographic image is identified, specifically including:

[0015] S21: Represent the acquired CT tomographic image as a two-dimensional matrix I of size M×N, where M is the number of rows and N is the number of columns;

[0016] S22: Set four detection directions, and in each detection direction, obtain the row and column coordinates of the seed point, outer edge point, and inner edge point of the scan object in the two-dimensional matrix I;

[0017] S23: Calculate the row and column coordinates of the center position of the scanned object using the row and column coordinates of the seed point, outer edge point, and inner edge point acquired in each detection direction.

[0018] Furthermore, the four detection directions include two row directions and two column directions of the two-dimensional matrix I, the directions of the two row directions are opposite, and the directions of the two column directions are opposite.

[0019] Furthermore, in each detection direction, the row and column coordinates of the seed point, outer edge point, and inner edge point of the scanned object in the two-dimensional matrix I are obtained, specifically including:

[0020] S221: Set the intensity threshold T of seed point detection respectively seed , intensity threshold T for outer edge detection outer , the intensity threshold T for inner edge detection inner ;

[0021] S222: Detecting the first detection direction;

[0022] Seed point detection, from the first row to the M / 2 row of the two-dimensional matrix I, take the maximum pixel intensity value Y1=max(I(ir,1:N)) of each row and the intensity threshold T seed Compare, where ir is the row number, when Y1≥T is satisfied for the first time seed When , take the point as the seed point and record the row number R0 and column number R0 of the current seed point ind and terminate this step;

[0023] Outer edge detection, detect row by row from row R0 to row 1 of the two-dimensional matrix I, and ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T outer When I(ir, R0 is first met ind )≤T outer When , take the point as the outer edge point and record the row number R0 of the current outer edge point outer and terminate this step;

[0024] Inner edge detection, detect row by row from the R0th row to the M / 2th row of the two-dimensional matrix I, and convert R0 ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T seed When I(ir, R0 is first satisfied, ind )≤T seed And in R0 ind The absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R0 ind )-I(ir, R0 ind )]≤T inner When , take the point as the inner edge point and record the row number R0 of the current inner edge point inner and terminate this step;

[0025] S223: Detecting the second detection direction;

[0026] Seed point detection, from the Mth row to the M / 2th row of the two-dimensional matrix I, take the maximum pixel intensity value of each row Y2=max(I(ir,1:N)) and the intensity threshold T seed Compare, when Y2≥T is satisfied for the first time seed When , take this point as the seed point and record the row number R1 and column number R1 of the current seed point ind and terminate this step;

[0027] Outer edge detection, detect row by row from the R1th row to the Mth row of the two-dimensional matrix I, andind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T outer When I(ir, R1 is first satisfied, ind )≤T outer When , take the point as the outer edge point and record the row number R1 of the current outer edge point outer and terminate this step;

[0028] Inner edge detection, detect row by row from the R1th row to the M / 2th row of the two-dimensional matrix I, and convert R1 ind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T seed When I(ir, R1 is first satisfied, ind )≤T seed And in R1 ind The absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R1 ind )-I(ir,R1 ind )]≤T inner When , take the point as the inner edge point and record the row number R1 of the current inner edge point inner and terminate this step;

[0029] S224: Detecting the third detection direction;

[0030] Seed point detection, from the first column of the two-dimensional matrix I to the R0 ind Detect column by column, take the maximum pixel intensity value Y3=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, where ic is the column number, when Y3≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C0 and row number C0 ind and terminate this step;

[0031] Outer edge detection, detect from column C0 to column 1 of the two-dimensional matrix I, and ind The pixel intensity value I (C0 ind ,ic) and intensity threshold T outer When I (C0 ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C0 outer and terminate this step;

[0032] Inner edge detection, from the C0 column of the two-dimensional matrix I to the R0 column ind Check column by column and set C0 indThe pixel intensity value I (C0 ind ,ic) and intensity threshold T seed When I(C0 is first met, ind ,ic)≤T seed And in C0 ind The absolute value abs[I(C0 ind ,ic+1)-I(C0 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C0 inner and terminate this step;

[0033] S225: Detecting the fourth detection direction;

[0034] Seed point detection, from the Nth column of the two-dimensional matrix I to the N-R0th ind Detect column by column, take the maximum pixel intensity value Y4=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, when Y4≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C1 and row number C1 ind and terminate this step;

[0035] Outer edge detection, detect from the C1 column to the N column of the two-dimensional matrix I, and ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T outer When I (C1 ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C1 outer and terminate this step;

[0036] Inner edge detection, from the C1 column of the two-dimensional matrix I to the N-R0 column ind Check column by column and set C1 ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T seed When I (C1 ind ,ic)≤T seed And in C1 ind The absolute value abs[I(C1 ind ,ic+1)-I(C1 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C1 innerand terminate this step.

[0037] Furthermore, the row and column coordinates of the center position of the scanned object are calculated using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction, as follows:

[0038] Calculate the row coordinate Rcen of the center position of the scanned object;

[0039] Rcen=(R0+R1+R0 inner +R1 inner +R0 outer +R1 outer ) / 6;

[0040] Calculate the column coordinates Ccen of the center position of the scanned object;

[0041] Ccen=(C0+C1+C0 inner +C1 inner +C0 outer +C1 outer ) / 6.

[0042] Furthermore, the row and column coordinates of the center position of the scanned object are calculated using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction, and the following steps are also included:

[0043] Calculate the outer diameter Rad of the scanned object based on the row and column coordinates of the outer edge points and the inner edge points. outer and inner diameter Rad inner ;

[0044] Rad outer =((R1 outer -R0 outer )+(C1 outer -C0 outer )) / 4+1;

[0045] Rad inner =((R1 inner -R0 inner )+(C1 inner -C0 inner )) / 4-1;

[0046] The calculated outer diameter Rad of the scanned object outer and inner diameter Rad inner The actual outer diameter R of the scanned object outer and the actual inner diameter R inner Make comparisons;

[0047] If|Rad outer -R outer|≤R1 and |Radinner-Rinner|≤R2, then proceed to the next step;

[0048] Otherwise, return to the previous step;

[0049] Among them, R1 and R2 are the set difference thresholds.

[0050] Furthermore, the offset of the center of the scan object in the CT system is calculated based on the reconstructed field of view and the center position of the scan object, specifically including:

[0051] Calculate the horizontal offset distance D1 and vertical offset distance D2 of the center of the scan object relative to the rotation center of the CT system;

[0052] D1=Rcen / M×FOV;

[0053] D2=Ccen / N×FOV;

[0054] Among them, FOV is the field of view of the CT system.

[0055] Furthermore, a CT tomographic image of the scanned object is acquired by a filtered back projection algorithm.

[0056] The present application also provides a CT system that uses the above-mentioned automatic identification and calibration method for the center of a scanned object to perform automatic identification and calibration of the center of a scanned object.

[0057] The present application also provides an electronic terminal, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor implements the steps of the above-mentioned method for automatically identifying and calibrating the center of a scanning object when executing the computer program.

[0058] Due to the application of the above technical solution, the beneficial effects of this application compared with the prior art are:

[0059] This application automatically identifies the center position of the scanned object in the CT tomographic image, combines the reconstructed field of view and the center position of the scanned object, calculates the offset of the scanned object in the CT system, and adjusts the position of the scanned object accordingly to ensure that its center is aligned with the rotation center of the CT system. This improves image quality and reduces image distortion or resolution loss caused by eccentricity. This automatic calibration method not only greatly reduces the technical requirements for the operator but also improves work efficiency. It can ensure the reliability and accuracy of the scan results, especially when scanning children or patients who cannot cooperate independently. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 Flowchart of an algorithm for automatically identifying and calibrating the center of a scanned object according to an embodiment of the present invention;

[0062] Figure 2 FIG. 4 is a diagram illustrating a process of identifying the center position of a scanned object in a CT tomographic image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] In this application, terms such as "upper," "lower," "left," "right," "front," "back," "top," "bottom," "inner," "outer," "center," "vertical," "horizontal," "transverse," and "longitudinal" indicate positions or locations based on the positions or locations shown in the accompanying drawings. These terms are primarily intended to better describe the present invention and its embodiments and are not intended to limit the devices, elements, or components indicated to having a specific orientation, or to being constructed or operated in a specific orientation.

[0066] Furthermore, some of the above terms may be used to express other meanings besides indicating a position or location. For example, the term "on" may also be used to indicate a dependency or connection in certain circumstances. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0067] Furthermore, the terms "installed," "disposed," "provided with," "connected," "connected," and "socketed" should be interpreted broadly. For example, they can refer to fixed connections, removable connections, or integral structures; mechanical connections or electrical connections; direct connections or indirect connections through an intermediary; or internal communication between two devices, elements, or components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0068] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0069] See Figure 1 and Figure 2 An embodiment of the present application provides a method for automatically identifying and calibrating the center of a scanned object, comprising the following steps:

[0070] S1: Acquire a CT tomographic image of the scanned object.

[0071] S2: Based on the set detection dimension, identify the center position of the scanned object in the CT tomographic image.

[0072] S3: Calculate the offset of the center of the scan object in the CT system according to the reconstructed field of view and the center position of the scan object.

[0073] S4: According to the obtained offset, the center of the scan object is adjusted to the rotation center of the CT system.

[0074] By automatically identifying the center position of the scanned object in the CT tomographic image and combining the reconstructed field of view with the center position of the scanned object, the system calculates the offset of the scanned object. This offset is then used to adjust the scanned object's position to ensure alignment with the CT system's rotational center. This improves image quality and reduces image distortion or resolution loss caused by decentration. This automated calibration method significantly reduces the operator's skill level and improves work efficiency, ensuring the reliability and accuracy of scan results, particularly when scanning children or uncooperative patients.

[0075] In this embodiment, step S1 utilizes a filtered back-projection algorithm to acquire a CT slice image of the scanned object. Specifically, the following steps are performed: Projection data from the CT system is acquired at different angles; dark current, air, nonlinearity, and ray hardening corrections are performed on the projection data; the corrected projection data is subjected to Fourier transform, frequency domain filtering, and inverse Fourier transform; and the filtered projection data is back-projected along the original projection path into image space, uniformly distributing the value of each projection point onto its corresponding ray path, thereby obtaining a slice image. This method is conventional and will not be described in detail here.

[0076] Step S2: Based on the set detection dimension, identify the center position of the scanned object in the CT tomographic image. The specific steps are as follows:

[0077] S21: Represent the acquired CT tomographic image as a two-dimensional matrix I of size M×N, where M is the number of rows and N is the number of columns.

[0078] S22: Set four detection directions, and obtain the row and column coordinates of the seed point, outer edge point, and inner edge point of the scan object in the two-dimensional matrix I in each detection direction.

[0079] In this embodiment, the four detection directions include two row directions and two column directions of the two-dimensional matrix I, the two row directions are opposite to each other, and the two column directions are opposite to each other.

[0080] The specific steps are as follows:

[0081] S221: Set the intensity threshold T of seed point detection respectively seed , intensity threshold T for outer edge detection outer , the intensity threshold T for inner edge detection inner ;

[0082] S222: Detecting the first detection direction;

[0083] Seed point detection, from the first row to the M / 2 row of the two-dimensional matrix I, take the maximum pixel intensity value Y1=max(I(ir,1:N)) of each row and the intensity threshold T seed Compare, where ir is the row number, when Y1≥T is satisfied for the first time seed At this time, take this point as the seed point and record the row number R0 and column number R0 of the current seed point ind and terminate this step;

[0084] Outer edge detection, detect row by row from row R0 to row 1 of the two-dimensional matrix I, and ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T outer When I(ir, R0 is first satisfied,ind )≤T outer When , take the point as the outer edge point and record the row number R0 of the current outer edge point outer and terminate this step;

[0085] Inner edge detection, detect row by row from the R0th row to the M / 2th row of the two-dimensional matrix I, and convert R0 ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T seed When I(ir, R0 is first satisfied, ind )≤T seed And in R0 ind The absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R0 ind )-I(ir, R0 ind )]≤T inner When , take the point as the inner edge point and record the row number R0 of the current inner edge point inner and terminate this step;

[0086] S223: Detecting the second detection direction;

[0087] Seed point detection, from the Mth row to the M / 2th row of the two-dimensional matrix I, take the maximum pixel intensity value of each row Y2=max(I(ir,1:N)) and the intensity threshold T seed Compare, when Y2≥T is satisfied for the first time seed When , take this point as the seed point and record the row number R1 and column number R1 of the current seed point ind and terminate this step;

[0088] Outer edge detection, detect row by row from the R1th row to the Mth row of the two-dimensional matrix I, and ind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T outer When I(ir, R1 is first satisfied, ind )≤T outer When , take the point as the outer edge point and record the row number R1 of the current outer edge point outer and terminate this step;

[0089] Inner edge detection, detect row by row from the R1th row to the M / 2th row of the two-dimensional matrix I, and convert R1 ind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T seed When I(ir, R1 is first satisfied, ind )≤T seed And in R1 indThe absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R1 ind )-I(ir,R1 ind )]≤T inner When , take the point as the inner edge point and record the row number R1 of the current inner edge point inner and terminate this step;

[0090] S224: Detecting the third detection direction;

[0091] Seed point detection, from the first column of the two-dimensional matrix I to the R0 ind Detect column by column, take the maximum pixel intensity value Y3=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, where ic is the column number, when Y3≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C0 and row number C0 ind and terminate this step;

[0092] Outer edge detection, detect from column C0 to column 1 of the two-dimensional matrix I, and ind The pixel intensity value I (C0 ind ,ic) and intensity threshold T outer When I(C0 is first met, ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C0 outer and terminate this step;

[0093] Inner edge detection, from the C0 column of the two-dimensional matrix I to the R0 column ind Detect column by column and set C0 ind The pixel intensity value I (C0 ind ,ic) and intensity threshold T seed When I(C0 is first met, ind ,ic)≤T seed And in C0 ind The absolute value abs[I(C0 ind ,ic+1)-I(C0 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C0 inner and terminate this step;

[0094] S225: Detecting the fourth detection direction;

[0095] Seed point detection, from the Nth column of the two-dimensional matrix I to the N-R0th indDetect column by column, take the maximum pixel intensity value Y4=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, when Y4≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C1 and row number C1 ind and terminate this step;

[0096] Outer edge detection, detect from the C1 column to the N column of the two-dimensional matrix I, and ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T outer When I (C1 ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C1 outer and terminate this step;

[0097] Inner edge detection, from the C1 column of the two-dimensional matrix I to the N-R0 column ind Check column by column and set C1 ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T seed When I (C1 ind ,ic)≤T seed And in C1 ind The absolute value abs[I(C1 ind ,ic+1)-I(C1 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C1 inner and terminate this step.

[0098] S23: Calculate the row and column coordinates of the center position of the scanned object using the row and column coordinates of the seed point, outer edge point, and inner edge point acquired in each detection direction.

[0099] The specific steps are as follows:

[0100] Calculate the row coordinate Rcen of the center position of the scanned object;

[0101] Rcen=(R0+R1+R0 inner +R1 inner +R0 outer +R1 outer ) / 6;

[0102] Calculate the column coordinates Ccen of the center position of the scanned object;

[0103] Ccen=(C0+C1+C0 inner +C1 inner +C0 outer +C1 outer ) / 6.

[0104] Step S3: Calculating the offset of the center of the scan object in the CT system based on the reconstructed field of view and the center position of the scan object, specifically including:

[0105] Calculate the horizontal offset distance D1 and vertical offset distance D2 of the center of the scan object relative to the rotation center of the CT system;

[0106] D1=Rcen / M×FOV;

[0107] D2=Ccen / N×FOV;

[0108] Among them, FOV is the field of view of the CT system.

[0109] Step S4: According to the obtained offset, the center of the scan object is adjusted to the rotation center of the CT system. The specific process is: by controlling the movement of the scanning bed, the position of the scan object is adjusted so that its center is aligned with the rotation center of the CT system.

[0110] In order to improve the accuracy of automatic recognition and calibration, in this embodiment, the row and column coordinates of the center position of the scanned object are calculated using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction, and the following steps are also included:

[0111] Calculate the outer diameter Rad of the scanned object based on the row and column coordinates of the outer edge points and the inner edge points. outer and inner diameter Rad inner ;

[0112] Rad outer =((R1 outer -R0 outer )+(C1 outer -C0 outer )) / 4+1;

[0113] Rad inner =((R1 inner -R0 inner )+(C1 inner -C0 inner )) / 4-1;

[0114] The calculated outer diameter Rad of the scanned object outer and inner diameter Rad inner The actual outer diameter R of the scanned object outer and the actual inner diameter R inner Make comparisons;

[0115] If|Rad outer -R outer |≤R1 and |Radinner-Rinner|≤R2, then proceed to the next step;

[0116] Otherwise, return to the previous step, i.e., perform step S22 again;

[0117] Among them, R1 and R2 are the set difference thresholds.

[0118] It should be noted that the values ​​of R1 and R2 can be 0mm to 10mm, specifically 0.5mm, 1mm, 2mm, 3mm, 4mm, 5mm, 6mm, 7mm, 8mm, 9mm, and 10mm. This application does not impose any specific restrictions on the value range of R1 and R2, and they can be adjusted according to needs. It should be understood that the smaller the values ​​of R1 and R2, the higher the automatic recognition accuracy required by the system.

[0119] At the same time, based on the same inventive concept as the above method, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned scanning object center automatic identification and calibration method.

[0120] It is worth noting that the aforementioned computer-readable storage medium can be understood as meaning that all or part of the steps of the aforementioned method can be implemented by hardware associated with the computer program. The computer program can be stored in a computer-readable storage medium, and when executed, the computer program performs the steps of the aforementioned method. The computer-readable storage medium can be any of random access memory, read-only memory, magnetic disk, or optical disk. This is prior art and will not be described in detail here.

[0121] In addition, based on the same inventive concept as the above method, the present application also provides an electronic terminal, which includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, the steps of the above-mentioned method for automatic identification and calibration of the center of the scanning object are implemented.

[0122] It is worth noting that the electronic terminal includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run the computer program so that the electronic terminal executes the various steps of the above-mentioned scanning object center automatic identification and calibration method.

[0123] As mentioned above, the memory may include random access memory, read-only memory, or non-volatile memory, such as at least one disk drive. The processor may be a general-purpose processor such as a central processing unit (CPU) or a network processor, or may be a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. These are prior art and will not be described in detail here.

[0124] In addition, based on the same inventive concept as the above method, the present application also provides a CT system that uses the above-mentioned automatic recognition and calibration method for the center of a scanned object to perform automatic recognition and calibration of the center of a scanned object.

[0125] The CT system includes a CT gantry and a scanning table. The CT gantry is equipped with an X-ray source and X-ray detector for scanning. The scanning table is used to carry the scanned object and is movable in the horizontal and vertical directions. The CT system further includes a memory, a processor, and a computer program stored in the memory and executable by the processor. The computer program is designed to implement a method for automatically identifying and calibrating the center of the scanned object under the control of the processor.

[0126] In this CT system, the processor, executing a computer program, automatically identifies and aligns the center of the scanned object through the positioning and movement of the scanning table, in conjunction with the X-ray source and detector. This process improves scanning accuracy and ensures the accuracy of imaging results, especially for complex or specialized scans. The memory's role in this process is to store relevant scanning data and calculation results for further processing and analysis by the processor.

[0127] Furthermore, the CT system's electronic terminals can connect to external devices or networks via communication interfaces, enabling real-time data transmission and remote control, improving the system's collaborative efficiency and intelligence. The overall system architecture aims to automate scanning operations, reduce human intervention, and thus enhance the accuracy and efficiency of CT scans.

[0128] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically identifying and calibrating the center of a scanned object, characterized in that: The steps include: S1: Acquire a CT tomographic image of the scanned object; S2: Based on the set detection dimension, identify the center position of the scanned object in the CT tomographic image; S3: Calculate the offset of the center of the scan object in the CT system according to the reconstructed field of view and the center position of the scan object; S4: Adjust the center of the scanned object to the rotation center of the CT system according to the obtained offset; Based on the set detection dimensions, the center position of the scanned object in the CT tomographic image is identified, including: S21: Represent the acquired CT tomographic image as a two-dimensional matrix I of size M×N, where M is the number of rows and N is the number of columns; S22: Set four detection directions, and in each detection direction, obtain the row and column coordinates of the seed point, outer edge point, and inner edge point of the scan object in the two-dimensional matrix I; S23: Calculate the row and column coordinates of the center position of the scanned object using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction; The row and column coordinates of the center position of the scanned object are calculated using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction, as follows: Calculate the row coordinate Rcen of the center position of the scanned object; Rcen=(R0+R1+R0 inner +R1 inner +R0 outer +R1 outer ) / 6; Calculate the column coordinates Ccen of the center position of the scanned object; Ccen=(C0+C1+C0 inner +C1 inner +C0outer+C1outer) / 6; Where R0 represents the row number of the seed point in the first detection direction, R1 represents the row number of the seed point in the second detection direction; R0inner represents the row number of the inner edge detected in the first detection direction, R1inner represents the row number of the inner edge detected in the second detection direction, R0outer represents the row number of the outer edge detected in the first detection direction, and R1outer represents the row number of the outer edge detected in the second detection direction; C0 represents the seed point column number in the third detection direction, C1 represents the seed point column number in the fourth detection direction, C0inner represents the inner edge column number detected in the third detection direction, C1inner represents the inner edge column number detected in the fourth detection direction, C0outer represents the outer edge column number detected in the third detection direction, and C1outer represents the outer edge column number detected in the fourth detection direction; Calculating the row and column coordinates of the center position of the scanned object using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction also includes the following steps: Calculate the outer diameter Rad of the scanned object based on the row and column coordinates of the outer edge points and the inner edge points. outer and inner diameter Rad inner ; Rad outer =((R1 outer -R0 outer )+(C1 outer -C0 outer )) / 4+1; Rad inner =((R1 inner -R0 inner )+(C1 inner -C0 inner )) / 4-1; The calculated outer diameter Rad of the scanned object outer and inner diameter Rad inner The actual outer diameter R of the scanned object outer and the actual inner diameter R inner Make comparisons; If|Rad outer -R outer |≤Q1 and |Radinner-Rinner|≤R2, then proceed to the next step; Otherwise, return to the previous step; Among them, Q1 and R2 are the set difference thresholds; Based on the reconstructed field of view and the center position of the scanned object, the offset of the center of the scanned object in the CT system is calculated, specifically including: Calculate the horizontal offset distance D1 and vertical offset distance D2 of the center of the scan object relative to the rotation center of the CT system; D1=Rcen / M×FOV; D2=Ccen / N×FOV; Among them, FOV is the field of view of the CT system.

2. The method for automatically identifying and calibrating the center of a scanned object according to claim 1, wherein: The four detection directions include two row directions and two column directions of the two-dimensional matrix I, the two row directions are in opposite directions, and the two column directions are in opposite directions.

3. The method for automatically identifying and calibrating the center of a scanning object according to claim 2, wherein: In each detection direction, the row and column coordinates of the seed point, outer edge point, and inner edge point of the scanned object in the two-dimensional matrix I are obtained, specifically including: S221: Set the intensity threshold T of seed point detection respectively seed , intensity threshold T for outer edge detection outer , the intensity threshold T for inner edge detection inner ; S222: Detecting the first detection direction; Seed point detection, from the first row to the M / 2 row of the two-dimensional matrix I, take the maximum pixel intensity value Y1=max(I(ir,1:N)) of each row and the intensity threshold T seed Compare, where ir is the row number, when Y1≥T is satisfied for the first time seed When , take the point as the seed point and record the row number R0 and column number R0 of the current seed point ind and terminate this step; Outer edge detection, detect row by row from row R0 to row 1 of the two-dimensional matrix I, and ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T outer When I(ir, R0 is first satisfied, ind )≤T outer When , take the point as the outer edge point and record the row number R0 of the current outer edge point outer and terminate this step; Inner edge detection, detect row by row from the R0th row to the M / 2th row of the two-dimensional matrix I, and convert R0 ind The pixel intensity value I(ir, R0) of each row corresponding to the column ind ) and the intensity threshold T seed When I(ir, R0 is first satisfied, ind )≤T seed And in R0 ind The absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R0 ind )-I(ir, R0 ind )]≤T inner When , take the point as the inner edge point and record the row number R0 of the current inner edge point inner and terminate this step; S223: Detecting the second detection direction; Seed point detection, from the Mth row to the M / 2th row of the two-dimensional matrix I, take the maximum pixel intensity value of each row Y2=max(I(ir,1:N)) and the intensity threshold T seed Compare, when Y2≥T is satisfied for the first time seed When , take this point as the seed point and record the row number R1 and column number R1 of the current seed point ind and terminate this step; Outer edge detection, detect row by row from the R1th row to the Mth row of the two-dimensional matrix I, and ind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T outer When I(ir, R1 is first satisfied, ind )≤T outer When , take the point as the outer edge point and record the row number R1 of the current outer edge point outer and terminate this step; Inner edge detection, detect row by row from the R1th row to the M / 2th row of the two-dimensional matrix I, and convert R1 ind The pixel intensity value of each row corresponding to the column I (ir, R1 ind ) and the intensity threshold T seed When I(ir, R1 is first satisfied, ind )≤T seed And in R1 ind The absolute value of the difference between the pixel intensity values ​​of two adjacent rows of the column is abs[I(ir+1, R1 ind )-I(ir,R1 ind )]≤T inner When , take the point as the inner edge point and record the row number R1 of the current inner edge point inner and terminate this step; S224: Detecting the third detection direction; Seed point detection, from the first column of the two-dimensional matrix I to the R0 ind Detect column by column, take the maximum pixel intensity value Y3=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, where ic is the column number, when Y3≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C0 and row number C0 ind and terminate this step; Outer edge detection, detect from column C0 to column 1 of the two-dimensional matrix I, and ind The pixel intensity value I (C0 ind ,ic) and intensity threshold T outer When I (C0 ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C0 outer and terminate this step; Inner edge detection, from the C0 column of the two-dimensional matrix I to the R0 column ind Check column by column and set C0 ind The pixel intensity value I (C0 ind ,ic) and intensity threshold T seed When I (C0 ind ,ic)≤T seed And in C0 ind The absolute value abs[I(C0 ind ,ic+1)-I(C0 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C0 inner and terminate this step; S225: Detecting the fourth detection direction; Seed point detection, from the Nth column of the two-dimensional matrix I to the N-R0th ind Detect column by column, take the maximum pixel intensity value Y4=max(I(1:M,ic)) of each column and the intensity threshold T seed Compare, when Y4≥T is satisfied for the first time seed When , take this point as the seed point and record the current seed point column number C1 and row number C1 ind and terminate this step; Outer edge detection, detect from the C1 column to the N column of the two-dimensional matrix I, and ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T outer When I (C1 ind ,ic)≤T outer When , take this point as the outer edge point and record the current outer edge point column number C1 outer and terminate this step; Inner edge detection, from the C1 column of the two-dimensional matrix I to the N-R0 column ind Check column by column and set C1 ind The pixel intensity value I (C1 ind ,ic) and intensity threshold T seed When I (C1 ind ,ic)≤T seed And in C1 ind The absolute value abs[I(C1 ind ,ic+1)-I(C1 ind ,ic)]≤T inner When , take this point as the inner edge point and record the current inner edge point column number C1 inner and terminate this step.

4. The method for automatically identifying and calibrating the center of a scanned object according to claim 1, wherein: The CT tomographic image of the scanned object is obtained through the filtered back projection algorithm.

5. A CT system, characterized in that: Automatic recognition and calibration of the scanning object center is performed using the scanning object center automatic recognition and calibration method as described in any one of claims 1 to 4.

6. An electronic terminal, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, the steps of the method for automatically identifying and calibrating the center of a scanned object according to any one of claims 1 to 4 are implemented.

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