Scanning object center automatic identification and calibration method and CT system

Abstract

The invention discloses a scanning object center automatic identification and calibration method and a CT system. The scanning object center automatic identification and calibration method comprises the following steps: acquiring a CT cross-sectional image of a scanning object; based on the set detection dimension, identifying the central position of the scanned object in the CT tomographic image; calculating the offset of the center of the scanned object in the CT system according to the reconstructed view field and the center position of the scanned object; and adjusting the center of the scanned object to the rotation center of the CT system according to the obtained offset. The offset of the scanned object in the CT system is calculated by automatically identifying the central position of the scanned object in the CT tomographic image and combining the reconstructed visual field and the central position of the scanned object, the position of the scanned object is adjusted accordingly, it is ensured that the center of the scanned object is aligned with the rotation center of the CT system, the image quality is improved, and the image quality is improved. Image distortion or resolution reduction caused by eccentricity is reduced, the requirement for the technical level of operators is lowered, and the working efficiency is improved.

Description

Technical Field

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

[0002] X-ray computed tomography equipment, namely CT (Computed Tomography), its basic principle is to utilize the characteristics that different substances have different absorption capabilities for X-rays to obtain data on the X-ray absorption coefficients of internal tissues of different scanned objects, and use a computer to process and reconstruct images. With the continuous progress of medical technology, CT (computed tomography), as an efficient and accurate medical imaging diagnosis technology, has been widely used in various medical institutions.

[0003] Since CT equipment involves complex electronic, mechanical, and radiation technologies, its performance and stability are easily affected by various factors, such as equipment aging, component wear, environmental interference, etc. Therefore, regularly maintaining and calibrating CT equipment is the key to ensuring its normal operation and accurate diagnosis. In this process, a water phantom, as an important calibration tool, plays an irreplaceable role.

[0004] When performing a CT scan on the human head, the rotation center is the reference point around which the X-ray beam and the detector rotate. Placing the head at the rotation center can ensure that the X-ray penetrates the tissue at a uniform angle, reducing image distortion or resolution degradation caused by eccentricity, and making fine brain structures (such as edema, hemorrhage foci) more clearly presented. When scanning children, it is not always possible to fix the head at a specified position as required by the doctor. Therefore, how to automatically identify the center of the head and position the scanned head at the rotation center of the CT gantry is very important.

[0005] A water phantom is a cylindrical container filled with water, and its material density is uniform. With its uniform density characteristics and physical properties similar to those of human tissues, its shape is close to that of the human head. Therefore, the water phantom image becomes an important reference for evaluating the performance of CT equipment and ensuring the accuracy of scanning results, and can be used to evaluate the performance parameters of a CT machine, such as the accuracy of CT values, image noise, slice thickness, etc. By scanning the water phantom, a series of data related to the equipment performance can be obtained, and then necessary adjustments and optimizations can be made to the equipment to ensure the accuracy and reliability of its scanning results.

[0006] The placement position of the water phantom is directly related to the accuracy and reliability of the calibration result, and the placement position of the head is also closely related to the image quality. The currently commonly used method is for the operator to visually observe the CT positioning laser lamp to place the water phantom or the head at the CT rotation center. This method has the problems of high requirements for the operator and relatively large errors. Summary of the Invention

[0007] The object of the present invention is to provide a method for automatically identifying and calibrating the center of a scanning object and a CT system, so as to solve the problem in the prior art that during CT scanning, the operator needs to visually observe the CT positioning laser lamp to place the scanning object at the CT rotation center, which has high operation requirements and is prone to errors.

[0008] To solve the above technical problems, the technical solution adopted by the present invention is: A method for automatically identifying and calibrating the center of a scanning object includes the following steps: S1: Obtain the CT tomographic image of the scanning object; S2: Based on the set detection dimension, identify the center position of the scanning object in the CT tomographic image; S3: According to the reconstructed field of view and the center position of the scanning object, calculate the offset of the scanning object center in the CT system; S4: According to the obtained offset, adjust the scanning object center to the rotation center of the CT system.

[0009] Further, based on the set detection dimension, identifying the center position of the scanning object in the CT tomographic image specifically includes: S21: Represent the obtained 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 scanning object in the two-dimensional matrix I; S23: Use the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction to calculate the row and column coordinates of the center position of the scanning object.

[0010] Further, the four detection directions include two row directions and two column directions of the two-dimensional matrix I, and the two row directions are opposite to each other, and the two column directions are opposite to each other.

[0011] Further, in each detection direction, obtaining the row and column coordinates of the seed point, outer edge point, and inner edge point of the scanning object in the two-dimensional matrix I specifically includes: S221: Respectively set the intensity threshold T seed for seed point detection, the intensity threshold T outer for outer edge detection, and the intensity threshold T inner for inner edge detection; S222: Detect the first detection direction; For seed point detection, detect row by row from the first row to the M / 2 row of the two-dimensional matrix I, and take the maximum pixel intensity value Y1 = max(I(ir, 1:N)) of each row and the intensity threshold T seedCompare, where ir is the row number, when Y1≥T is satisfied for the first time seed When , 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; Outer edge detection, from the R0th row to the 1st row of the two-dimensional matrix I, ind The pixel intensity value I (ir, R0) of each row corresponding to the column ind ) and intensity threshold T outer When I(ir, R0 is first met, ind )≤T outer When , take this 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 intensity threshold T seed When I(ir, R0 is first met, ind )≤T seed And in R0 ind The absolute value abs[I(ir+1, R0 ind )-I(ir,R0 ind )]≤T inner When , take this 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 Y2=max(I(ir,1:N)) of each row 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; External edge detection, from the R1th row to the Mth row of the two-dimensional matrix I, R1 ind The pixel intensity value I (ir, R1) of each row corresponding to the column ind ) and intensity threshold T outer For comparison, when I(ir, R1 is first met ind )≤T outer When , take this 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 R1ind The pixel intensity value I (ir, R1) of each row corresponding to the column ind is compared with the intensity threshold T seed When it first satisfies I (ir, R1 ind ) ≤ T seed and the absolute value of the difference between the pixel intensity values of two adjacent rows in the R1 ind column, abs[I (ir + 1, R1 ind ) - I (ir, R1 ind )] ≤ T inner then this point is taken as the inner edge point, and the current inner edge point row number R1 is recorded inner and this step is terminated; S224: Detect in the third detection direction; Seed point detection, detect column by column from the 1st column to the R0 ind column of the two-dimensional matrix I, and take the maximum pixel intensity value Y3 = max(I(1:M, ic)) of each column and compare it with the intensity threshold T seed where ic is the column number. When it first satisfies Y3 ≥ T seed then this point is taken as the seed point, and the current seed point column number C0 and row number C0 are recorded ind and this step is terminated; Outer edge detection, detect column by column from the C0 column to the 1st column of the two-dimensional matrix I, and compare the pixel intensity value I(C0 ind , ic) of each column corresponding to the C0 ind row with the intensity threshold T outer When it first satisfies I(C0 ind , ic) ≤ T outer then this point is taken as the outer edge point, and the current outer edge point column number C0 is recorded outer and this step is terminated; Inner edge detection, detect column by column from the C0 column to the R0 ind column of the two-dimensional matrix I, and compare the pixel intensity value I(C0 ind , ic) of each column corresponding to the C0 ind row with the intensity threshold T seed When it first satisfies I(C0 ind , ic) ≤ T seed and the absolute value of the difference between the pixel intensity values of two adjacent columns in the C0 ind row, abs[I(C0 ind , ic + 1) - I(C0 ind , ic)] ≤ T inner then this point is taken as the inner edge point, and the current inner edge point column number C0 is recorded inner and this step is terminated; S225: Detect in the fourth detection direction; Seed point detection: starting from the Nth column of the two-dimensional matrix I, detect column by column towards the (N - R0)th column. ind Take the maximum pixel intensity value Y4 = max(I(1:M, ic)) of each column and compare it with the intensity threshold T. seed When Y4 ≥ T is first satisfied, seed take this point as the seed point, record the current seed point column number C1 and row number C1, ind and terminate this step. Outer edge detection: starting from the C1th column of the two-dimensional matrix I, detect column by column towards the Nth column. Compare the pixel intensity value I(C1, ic) of each column corresponding to the C1th row with the intensity threshold T. ind When I(C1, ic) ≤ T is first satisfied, ind take this point as the outer edge point, record the current outer edge point column number C1, outer and terminate this step. ind outer outer Inner edge detection: starting from the C1th column of the two-dimensional matrix I, detect column by column towards the (N - R0)th column. Compare the pixel intensity value I(C1, ic) of each column corresponding to the C1th row with the intensity threshold T. ind When I(C1, ic) ≤ T and the absolute value of the difference between the pixel intensity values of two adjacent columns in the C1th row, abs[I(C1, ic + 1) - I(C1, ic)] ≤ T are first satisfied, ind take this point as the inner edge point, record the current inner edge point column number C1, ind and terminate this step. seed ind seed ind ind ind inner inner

[0012] Furthermore, using the row and column coordinates of the seed points, outer edge points, and inner edge points obtained in each detection direction, calculate the row and column coordinates of the center position of the scanned object 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 coordinate Ccen of the center position of the scanned object; Ccen = (C0 + C1 + C0 inner + C1 inner + C0 outer + C1outer ) / 6.

[0013] Further, using the row and column coordinates of the seed points, outer edge points, and inner edge points obtained in each detection direction, calculating the row and column coordinates of the center position of the scanned object further includes the following steps: According to the obtained row and column coordinates of the outer edge points and inner edge points, calculate the outer diameter Rad 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; Compare the calculated outer diameter Rad outer and inner diameter Rad inner of the scanned object with the actual outer diameter R outer and actual inner diameter R inner of the scanned object; If |Rad outer - R outer | ≤ R1 and |Radinner - Rinner| ≤ R2, then proceed to the next step; Otherwise, return to the previous step; wherein, R1 and R2 are set difference thresholds.

[0014] Further, according to the reconstructed field of view and the center position of the scanned object, calculate the offset of the center of the scanned object in the CT system, specifically including: Calculate the horizontal offset distance D1 and vertical offset distance D2 of the center of the scanned object relative to the rotation center of the CT system; D1 = Rcen / M × FOV; D2 = Ccen / N × FOV; wherein, FOV is the field of view range of the CT system.

[0015] Further, obtain the CT tomographic image of the scanned object through the filtered back projection algorithm.

[0016] This application also provides a CT system, which performs automatic identification and calibration of the center of the scanned object by using the automatic identification and calibration method of the center of the scanned object as described above.

[0017] The present application also provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned automatic recognition and calibration method for the center of the scanned object are implemented.

[0018] Due to the application of the above technical solution, the beneficial effects of the present application compared with the prior art are as follows: The present application automatically recognizes 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 the image quality and reducing image distortion or resolution degradation caused by eccentricity. This automatic calibration method not only greatly reduces the requirements for the technical level of operators, but also improves work efficiency. Especially in the scanning of children or patients who cannot cooperate independently, it can ensure the reliability and accuracy of the scanning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is the algorithm flowchart of the automatic recognition and calibration method for the center of the scanned object in the embodiment of the present invention; Figure 2 It is the process diagram for recognizing the center position of the scanned object in the CT tomographic image in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0022] It should be noted that in the description and claims of this application and the above-mentioned drawings, the terms "first", "second", etc. are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so as to implement the embodiments of the present application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] In this application, the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present invention and its embodiments, and are not used to limit that the indicated devices, elements or components must have a specific orientation, or be constructed and operated in a specific orientation.

[0024] Moreover, in addition to being able to represent an orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in the present invention can be understood according to specific circumstances.

[0025] In addition, the terms "install", "set", "provided with", "connect", "connected", "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or there is internal communication between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] It should be noted that, without conflict, the embodiments and features in the embodiments of this application can be combined with each other. The following will refer to the drawings and combine with embodiments to detail this application.

[0027] Please refer to Figure 1 and Figure 2 , an embodiment of the present application provides an automatic recognition and calibration method for the center of a scanning object, including the following steps: S1: Obtain the CT tomographic image of the scanning object.

[0028] S2: Identify the central position of the scanned object in the CT tomographic image based on the set detection dimensions.

[0029] S3: Calculate the offset of the center of the scanned object in the CT system according to the reconstruction field of view and the central position of the scanned object.

[0030] S4: Adjust the center of the scanned object to the rotation center of the CT system according to the obtained offset.

[0031] By automatically identifying the central position of the scanned object in the CT tomographic image, combining the reconstruction field of view and the central position of the scanned object, calculating the offset of the scanned object in the CT system, and adjusting the position of the scanned object accordingly, the center of the scanned object is ensured to be aligned with the rotation center of the CT system. This improves the image quality and reduces image distortion or resolution degradation caused by eccentricity. This automatic calibration method not only greatly reduces the requirements for the technical level of operators but also improves work efficiency. Especially in the scanning of children or patients who cannot cooperate independently, it can ensure the reliability and accuracy of the scanning results.

[0032] In this embodiment, step S1 uses the filtered back-projection algorithm to obtain the CT tomographic image of the scanned object, specifically as follows: Obtain the projection data of the CT system at different angles; perform dark current correction, air correction, non-linear correction, and ray hardening correction on the projection data; perform Fourier transform, frequency domain filtering, and inverse Fourier transform on the corrected projection data; back-project the filtered projection data along the original projection path to the image space, that is, evenly distribute the value of each projection point to its corresponding ray path, thereby obtaining the tomographic image. This method is a conventional technique, so it will not be described in detail here.

[0033] Step S2: Identify the central position of the scanned object in the CT tomographic image based on the set detection dimensions. The specific steps are as follows: S21: Represent the obtained 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.

[0034] S22: Set four detection directions. In each detection direction, obtain 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.

[0035] 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.

[0036] The specific steps are as follows: S221: Set the intensity threshold T for seed point detection seed 、the intensity threshold T for outer edge detection outer 、the intensity threshold T for inner edge detectioninner ; S222: Detect in the first detection direction; Seed point detection: Detect row by row from the first row to the M / 2-th row of the two-dimensional matrix I, and take the maximum pixel intensity value Y1 = max(I(ir, 1:N)) of each row and compare it with the intensity threshold T seed where ir is the row number. When Y1 ≥ T is first satisfied seed at that time, take this point as the seed point, record the current seed point row number R0 and column number R0 ind and terminate this step; Outer edge detection: Detect row by row from the R0-th row to the first row of the two-dimensional matrix I, and compare the pixel intensity value I(ir, R0 ind of each row corresponding to the R0-th column ind with the intensity threshold T outer When I(ir, R0 ind ≤ T is first satisfied outer at that time, take this point as the outer edge point, record the current outer edge point row number R0 outer and terminate this step; Inner edge detection: Detect row by row from the R0-th row to the M / 2-th row of the two-dimensional matrix I, and compare the pixel intensity value I(ir, R0 ind of each row corresponding to the R0-th column ind with the intensity threshold T seed When I(ir, R0 ind ≤ T is first satisfied seed and the absolute value of the difference between the pixel intensity values of two adjacent rows in the R0-th column abs[I(ir + 1, R0 ind - I(ir, R0 ind )] ≤ T ind at that time, take this point as the inner edge point, record the current inner edge point row number R0 inner and terminate this step; inner and terminate this step; S223: Detect in the second detection direction; Seed point detection: Detect row by row from the M-th row to the M / 2-th row of the two-dimensional matrix I, and take the maximum pixel intensity value Y2 = max(I(ir, 1:N)) of each row and compare it with the intensity threshold T seed When Y2 ≥ T is first satisfied seed at that time, take this point as the seed point, record the current seed point row number R1 and column number R1 ind and terminate this step; Outer edge detection: Detect row by row from the R1-th row to the M-th row of the two-dimensional matrix I, and compare the pixel intensity value I(ir, R1 ind of each row corresponding to the R1-th column ind with the intensity threshold T outerFor comparison, when I(ir, R1 is first met ind )≤T outer When , take this 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 I (ir, R1) of each row corresponding to the column ind ) and intensity threshold T seed For comparison, when I(ir, R1 is first met ind )≤T seed And in R1 ind The absolute value abs[I(ir+1, R1 ind )-I(ir,R1 ind )]≤T inner When , take this 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 R0th 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; External edge detection, from the C0th column to the 1st column of the two-dimensional matrix I, 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 C0th column of the two-dimensional matrix I to the R0th ind Check row by row 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(C0ind , I(c + 1) - I(C0 ind , ic)] ≤ T inner When this condition is met, take this point as the inner edge point and record the current inner edge point column number C0 inner And terminate this step; S225: Detect in the fourth detection direction; Seed point detection: Detect column by column from the Nth column to the (N - R0)th column of the two-dimensional matrix I. Take the maximum pixel intensity value Y4 = max(I(1:M, ic)) of each column and compare it with the intensity threshold T ind When it first satisfies Y4 ≥ T seed Take this point as the seed point and record the current seed point column number C1 and row number C1 seed And terminate this step; ind Outer edge detection: Detect column by column from the C1th column to the Nth column of the two-dimensional matrix I. Compare the pixel intensity value I(C1 , ic) of each column corresponding to the C1th row with the intensity threshold T ind When it first satisfies I(C1 ind , ic) ≤ T outer Take this point as the outer edge point and record the current outer edge point column number C1 ind And terminate this step; outer Inner edge detection: Detect column by column from the C1th column to the (N - R0)th column of the two-dimensional matrix I. Compare the pixel intensity value I(C1 outer , ic) of each column corresponding to the C1th row with the intensity threshold T When it first satisfies I(C1 ind , ic) ≤ T ind And the absolute value of the difference between the pixel intensity values of two adjacent columns in the C1th row abs[I(C1 ind , ic + 1) - I(C1 seed , ic)] ≤ T ind Take this point as the inner edge point and record the current inner edge point column number C1 seed And terminate this step. ind ind ind , ic + 1) - I(C1 inner , ic)] ≤ T inner When this condition is met, take this point as the inner edge point and record the current inner edge point column number C1 inner And terminate this step.

[0037] S23: Calculate the row and column coordinates of the center position of the scanned object using the row and column coordinates of the seed points, outer edge points, and inner edge points obtained in each detection direction.

[0038] The specific steps are as follows: Calculate the row coordinate Rcen of the center position of the scanned object; Rcen = (R0 + R1 + R0 inner + R1inner +R0 outer +R1 outer ) / 6; Calculate the column coordinate Ccen of the center position of the scanned object; Ccen = (C0 + C1 + C0 inner + C1 inner + C0 outer + C1 outer ) / 6.

[0039] Step S3: Calculate the offset of the center of the scanned object in the CT system according to the reconstructed field of view and the center position of the scanned object, specifically including: Calculate the horizontal offset distance D1 and the vertical offset distance D2 of the center of the scanned object relative to the rotation center of the CT system; D1 = Rcen / M × FOV; D2 = Ccen / N × FOV; where FOV is the field of view range of the CT system.

[0040] Step S4: Adjust the center of the scanned object to the rotation center of the CT system according to the obtained offset. The specific process is: By controlling the movement of the scanning bed, adjust the position of the scanned object so that its center is aligned with the rotation center of the CT system.

[0041] To improve the accuracy of automatic recognition and calibration, in this embodiment, using the row and column coordinates of the seed points, outer edge points, and inner edge points obtained in each detection direction, calculate the row and column coordinates of the center position of the scanned object, and further include the following steps: Calculate the outer diameter Rad outer and the 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; Compare the calculated outer diameter Rad outer and the inner diameter Rad inner of the scanned object with the actual outer diameter R outer and the actual inner diameter R inner of the scanned object; If |Radouter -R outer If |≤R1 and |Radinner - Rinner|≤R2, then proceed to the next step; Otherwise, return to the previous step, that is, repeat step S22; wherein, R1 and R2 are set difference thresholds.

[0042] It should be noted that the values of R1 and R2 can be from 0mm to 10mm. Specifically, they can be 0.5mm, 1mm, 2mm, 3mm, 4mm, 5mm, 6mm, 7mm, 8mm, 9mm, 10mm. The present application does not specifically limit the value ranges of R1 and R2, and they can be adjusted according to requirements. It should be understood that the smaller the values of R1 and R2, the higher the automatic recognition accuracy required by the system.

[0043] Meanwhile, based on the same inventive concept as the above method, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the above method for automatically recognizing and calibrating the center of a scanned object.

[0044] It should be noted that the above computer-readable storage medium can be understood as follows: All or part of the steps of implementing the above method can be completed by hardware related to the computer program. The computer program can be stored in the computer-readable storage medium. When the computer program is executed, it executes the steps including the above various methods. The computer-readable storage medium is any one of a random access memory, a read-only memory, a magnetic disk, or an optical disc. This is prior art and will not be elaborated here.

[0045] In addition, based on the same inventive concept as the above method, the present application also provides an electronic terminal. The electronic terminal 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, it implements the steps of the above method for automatically recognizing and calibrating the center of a scanned object.

[0046] It should be noted 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 the computer program, 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 each step of the above method for automatically recognizing and calibrating the center of a scanned object.

[0047] As described above, the memory may include random access memory, may also include read-only memory, and may further include non-volatile memory, such as at least one disk memory. The processor may be a general-purpose processor such as a central processing unit or a network processor, or may also be a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. This is prior art and will not be elaborated here.

[0048] In addition, based on the same inventive concept as the above method, the present application also provides a CT system, which performs automatic recognition and calibration of the center of the scanned object by using the automatic recognition and calibration method of the center of the scanned object as described above.

[0049] The CT system includes a CT gantry and a scanning bed. An X-ray source and an X-ray detector for scanning are installed on the CT gantry. The scanning bed is used to carry the scanned object and can move in the horizontal or vertical direction. The CT system further includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is designed to implement the method for automatic recognition and calibration of the center of the scanned object under the control of the processor.

[0050] In this CT system, when the processor executes the computer program, it automatically recognizes the center position of the scanned object and performs calibration by positioning and moving the scanning bed in cooperation with the X-ray source and the X-ray detector. This process can improve the scanning accuracy and ensure the accuracy of the imaging results, especially in the case of complex or special scanning requirements. The role of the memory in this process is to store the relevant data and calculation results during the scanning process for further processing and analysis by the processor.

[0051] In addition, the electronic terminal of the CT system may be connected to external devices or networks through a communication interface to achieve real-time data transmission and remote control, improving the collaborative work efficiency and intelligent level of the system. The architecture of the entire system is designed to improve the automation degree of the scanning operation and reduce human intervention, thereby improving the accuracy and efficiency of CT scanning.

[0052] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic recognition and calibration method for the center of a scanned object, characterized in that, It includes the following steps: S1: Obtain the CT tomographic image of the scanning object; S2: Based on the set detection dimension, identify the central position of the scanning object in the CT tomographic image; S3: According to the reconstruction field of view and the central position of the scanning object, calculate the offset of the center of the scanning object in the CT system; S4: According to the obtained offset, adjust the center of the scanning object to the rotation center of the CT system.

2. The automatic recognition and calibration method for the center of a scanned object according to claim 1, wherein Based on the set detection dimension, identifying the central position of the scanning object in the CT tomographic image specifically includes: S21: Represent the obtained 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. In each detection direction, obtain the row and column coordinates of the seed point, outer edge point, and inner edge point of the scanning object in the two-dimensional matrix I; S23: Use the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction to calculate the row and column coordinates of the central position of the scanning object.

3. The automatic recognition and calibration method for the center of a scanned object according to claim 2, characterized in that 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.

4. The automatic recognition and calibration method for the center of a scanned object according to claim 3, characterized in that In each detection direction, obtaining the row and column coordinates of the seed point, outer edge point, and inner edge point of the scanning object in the two-dimensional matrix I specifically includes: S221: Set the intensity threshold T for seed point detection respectively seed , the intensity threshold T for outer edge detection outer , and the intensity threshold T for inner edge detection inner ; S222: Detect the first detection direction; Seed point detection: Starting from the first row of the two-dimensional matrix I, detect row by row up to the M / 2-th row. Take the maximum pixel intensity value Y1 = max(I(ir, 1:N)) of each row and compare it with the intensity threshold T, where ir is the row number. When Y1 ≥ T is first satisfied, take this point as the seed point, record the current seed point row number R0 and column number C0, and terminate this step. seed Compare, where ir is the row number. When Y1 ≥ T is first satisfied seed take this point as the seed point, record the current seed point row number R0 and column number C0 ind and terminate this step; Outer edge detection, detecting row by row from the R0-th row to the 1st row of the two-dimensional matrix I, and taking R0 ind The pixel intensity value I(ir, R0 ind ) of each row corresponding to the R0 outer column is compared with the intensity threshold T ind . When it first satisfies I(ir, R0 outer ) ≤ T outer , take this point as the outer edge point, record the current outer edge point row number R0 outer and terminate this step; Inner edge detection is performed row by row from the R0-th row to the M / 2-th row of the two-dimensional matrix I, where R0 ind The pixel intensity value I(ir, R0) of each row corresponding to the R0 ind column is compared with the intensity threshold T seed When it first satisfies I(ir, R0 ind ) ≤ T seed and the absolute value of the difference between the pixel intensity values of two adjacent rows in the R0 ind column, abs[I(ir + 1, R0 ind ) - I(ir, R0 ind )] ≤ T inner then this point is taken as the inner edge point, and the current inner edge point row number R0 is recorded inner and this step is terminated; S223: Detect the second detection direction; Seed point detection: starting from the M-th row of the two-dimensional matrix I, detect row by row towards the M / 2-th row, and take the maximum pixel intensity value Y2 = max(I(ir, 1:N)) of each row and compare it with the intensity threshold T seed When Y2 ≥ T is first satisfied seed take this point as the seed point, record the current seed point row number R1 and column number R1 ind and terminate this step; Outer edge detection is performed row by row from the R1-th row to the M-th row of the two-dimensional matrix I, and the pixel intensity value I(ir, R1 ind ) of each row corresponding to the R1 ind column is compared with the intensity threshold T outer . When I(ir, R1 ind ) ≤ T outer is first satisfied, this point is taken as the outer edge point, and the row number R1 of the current outer edge point is recorded outer and this step is terminated; Inner edge detection is performed row by row from the R1-th row to the M / 2-th row of the two-dimensional matrix I. The pixel intensity value I(ir, R1 ind ) of each row corresponding to the R1 ind column is compared with the intensity threshold T seed . When it first satisfies I(ir, R1 ind ) ≤ T seed and the absolute value of the difference between the pixel intensity values of two adjacent rows in the R1 ind column, abs[I(ir + 1, R1 ind ) - I(ir, R1 ind )] ≤ T inner , take this point as the inner edge point, record the current inner edge point row number R1 inner and terminate this step; S224: Detect the third detection direction; Seed point detection, column-by-column detection from the first column to the R0th column of the two-dimensional matrix I ind Take the maximum pixel intensity value of each column Y3 = max(I(1:M, ic)) and compare it with the intensity threshold T, where ic is the column number. When Y3 ≥ T is satisfied for the first time seed Take this point as the seed point, record the current seed point column number C0 and row number C0 seed And terminate this step; ind ​ Outer edge detection, detecting column by column from the C0-th column to the 1st column of the two-dimensional matrix I, and taking the C0 ind -th row. Compare the pixel intensity value I(C0 ind , ic) of each column with the intensity threshold T outer . When I(C0 ind , ic) ≤ T outer is first satisfied, take this point as the outer edge point, record the column number C0 of the current outer edge point outer and terminate this step; Inner edge detection, starting from the C0-th column to the R0-th ind column of the two-dimensional matrix I, detecting column by column, and comparing the pixel intensity value I(C0 ind , ic) of each column corresponding to the C0 ind -th row with the intensity threshold T seed . When it first satisfies I(C0 ind , ic) ≤ T seed and the absolute value of the difference between the pixel intensity values of two adjacent columns in the C0 ind -th row, abs[I(C0 ind , ic + 1) - I(C0 ind , ic)] ≤ T inner , take this point as the inner edge point, record the current inner edge point column number C0 inner and terminate this step; S225: Detect the fourth detection direction; Seed point detection, column-by-column detection from the Nth column to the (N - R0)th column of the two-dimensional matrix I, taking the maximum pixel intensity value Y4 = max(I(1:M, ic)) of each column and comparing it with the intensity threshold T ind When, for the first time, Y4 ≥ T is satisfied, take this point as the seed point, record the current seed point column number C1 and row number C1 seed and terminate this step; seed ind ​ Outer edge detection is performed column by column from the C1-th column to the N-th column of the two-dimensional matrix I. The pixel intensity value I(C1 ind , ic) of each column corresponding to the C1 ind -th row is compared with the intensity threshold T outer . When I(C1 ind , ic) ≤ T outer is first satisfied, this point is taken as the outer edge point, and the column number C1 outer of the current outer edge point is recorded and this step is terminated; Inner edge detection, column-by-column detection from the C1-th column to the (N - R0)-th column of the two-dimensional matrix I, and compare the pixel intensity value I(C1, ic) of each column corresponding to the C1-th row with the intensity threshold T ind When the following conditions are first satisfied: I(C1, ic) ≤ T ind and the absolute value of the difference between the pixel intensity values of two adjacent columns in the C1-th row, abs[I(C1, ic + 1) - I(C1, ic)] ≤ T ind take this point as the inner edge point, record the current inner edge point column number C1 seed and terminate this step. ind seed ind ind ind inner inner ​​​​​​​ 5. The automatic recognition and calibration method for the center of a scanned object according to claim 4, wherein Using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction to calculate the row and column coordinates of the central position of the scanning object is specifically as follows: Calculate the row coordinate Rcen of the central position of the scanning object; Rcen = (R0 + R1 + R0 inner + R1 inner + R0 outer + R1 outer ) / 6; Calculate the column coordinate Ccen of the central position of the scanning object; Ccen = (C0 + C1 + C0 inner + C1 inner + C0 outer + C1 outer ) / 6.

6. The automatic recognition and calibration method for the center of a scanned object according to claim 5, characterized in that, Using the row and column coordinates of the seed point, outer edge point, and inner edge point obtained in each detection direction to calculate the row and column coordinates of the central position of the scanning object further includes the following steps: Calculate the outer diameter Rad outer and the 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; Compare the calculated outer diameter Rad outer and inner diameter Rad inner of the scanned object with the actual outer diameter R outer and actual inner diameter R inner of the scanned object; If |Rad outer -R outer | ≤ R1 and |Radinner - Rinner| ≤ R2, then proceed to the next step; Otherwise, return to the previous step; where R1 and R2 are set difference thresholds.

7. The automatic recognition and calibration method for the center of a scanned object according to claim 5 or 6, characterized in that According to the reconstruction field of view and the central position of the scanning object, calculating the offset of the center of the scanning object in the CT system specifically includes: Calculate the horizontal offset distance D1 and vertical offset distance D2 of the center of the scanning object relative to the rotation center of the CT system; D1 = Rcen / M×FOV; D2 = Ccen / N×FOV; where FOV is the field of view range of the CT system.

8. The automatic recognition and calibration method for the center of a scanned object according to claim 1, characterized in that, Obtain the CT tomographic image of the scanning object through the filtered back-projection algorithm.

9. A CT system, characterized in that, Adopt the automatic identification and calibration method for the center of the scanning object as described in any one of claims 1 to 8 for automatic identification and calibration of the center of the scanning object.

10. An electronic terminal, characterized in that, It 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, it implements the steps of the automatic identification and calibration method for the center of the scanning object as described in any one of claims 1 to 8.

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