A method and system for correcting x-ray intensity fluctuations under ct tube voltage fluctuations

Abstract

This invention discloses a method and system for correcting X-ray intensity fluctuations under CT tube voltage fluctuations. The method includes: calculating the intensity ratio of X-rays after filtering under different tube voltage values ​​based on pre-scanning air data; establishing a mapping model of X-ray intensity ratio parameters based on the intensity ratio relationship; acquiring the feedback value of the tube voltage under the current exposure during patient scanning in real time; determining the corresponding X-ray intensity ratio parameter from the mapping model based on the tube voltage feedback value; correcting the incident original X-ray intensity of each channel based on the X-ray intensity ratio parameter; calculating the X-ray attenuation value of each channel passing through the scanned object, and reconstructing the image based on the attenuation value. During patient scanning, the corresponding ratio parameter can be dynamically retrieved, and the attenuation value can be calculated by the ratio of the corrected incident light intensity to the detected intensity. Combined with hardening effect correction and reconstruction algorithms, the accuracy and stability of image reconstruction are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of X-ray intensity calculation technology, and in particular to a method and system for correcting X-ray intensity fluctuations under CT tube voltage fluctuations. Background Technology

[0002] In computed tomography (CT) imaging, the calculation of X-ray attenuation as it passes through the scanned object is the core of image reconstruction, and the accuracy of the incident light intensity and the attenuated light intensity directly determines the image quality. Current techniques estimate the incident light intensity of each channel by using the X-ray intensity of a reference channel, assuming a fixed X-ray energy spectrum when the tube voltage is stable and a constant intensity ratio between each channel and the reference channel. However, in actual scanning, fluctuations in tube voltage alter the X-ray energy spectrum distribution, causing the intensity ratio between each channel and the reference channel to dynamically fluctuate with real-time changes in tube voltage. This fluctuation renders the fixed ratio relationship relied upon by traditional methods ineffective, especially when the sampling frequency of the high-voltage control device is high. Even small fluctuations in tube voltage can cause significant changes in the average X-ray energy, leading to errors in the calculation of incident light intensity for each channel, ultimately resulting in distorted attenuation values ​​and image artifacts. Therefore, how to correct the incident light intensity error caused by tube voltage fluctuations, eliminate the dynamic deviation in the ratio relationship when calculating attenuation values, and ultimately improve image quality through hardening correction and reconstruction algorithms is of high research value. Summary of the Invention

[0003] In view of this, the present invention proposes a method and system for correcting X-ray intensity fluctuations under CT tube voltage fluctuations, which can solve the problem of image accuracy degradation caused by the instability of the X-ray intensity ratio due to tube voltage fluctuations. The present invention provides the following technical solution:

[0004] A method for correcting X-ray intensity fluctuations due to CT tube voltage fluctuations, the method comprising:

[0005] Based on the pre-scanning conditions, scan the air data and calculate the intensity ratio of X-rays from each detector channel and the reference channel after filtering under different CT tube voltage values.

[0006] Based on the intensity ratio relationship, a mapping model is established, which includes the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each detection channel, as well as the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each reference channel.

[0007] During patient scanning, the feedback value of the tube voltage under the current exposure is obtained in real time;

[0008] The corresponding X-ray intensity ratio parameter is determined from the mapping model based on the feedback value of the tube voltage;

[0009] The incident raw X-ray intensity of each detector channel is corrected based on the X-ray intensity ratio parameter.

[0010] Calculate the X-ray attenuation value of each detector channel as it passes through the scanned object, and reconstruct the image based on the attenuation value.

[0011] Optionally, the step of calling pre-scan conditions to scan air data to model and calculate the intensity ratio of X-rays from each detector channel and the reference channel after filtering under different tube voltage values ​​includes:

[0012] During the pre-scanning phase, the pre-scanning conditions are invoked to scan the air data and record the tube voltage feedback value kVData and the X-ray intensity value AirDat0 of each detector channel for each exposure.

[0013] The X-ray intensity value AirDat0 of the detector channel in the air data is preprocessed to obtain preprocessed data AirDat1;

[0014] Calculate the ratio of the X-ray intensity value of the reference channel to the pre-processed X-ray intensity value AirDat1 of each detector channel, which is data AirDat2;

[0015] The range of the tube voltage feedback value kVData is divided into multiple preset intervals, and the average intensity value MeanAirData of the ratio data AirDat2 in each interval is calculated.

[0016] The mean air data is used to calculate the ratio of X-ray intensity after filtration between each detector channel and the reference channel within the range of the corresponding tube voltage feedback value.

[0017] Optionally, the step of establishing a mapping model between the tube voltage value and the X-ray intensity ratio parameters of each detection channel and the reference channel based on the intensity ratio relationship includes:

[0018] The intensity ratio relationship is normalized to obtain the correction parameters corresponding to each detector channel.

[0019] Interpolate or average the correction parameters within the interval of each tube voltage feedback value to establish a piecewise mapping relationship between the tube voltage feedback value and the correction parameters; or perform polynomial fitting on the correspondence between the intensity ratio relationship and the tube voltage feedback value to obtain a mathematical function representing the mapping relationship between the tube voltage feedback value and the correction parameters.

[0020] Save the parameters of the segmented mapping relationship or fitting function and the corresponding range of tube voltage feedback values ​​as a calibration model file.

[0021] Optionally, determining the corresponding X-ray intensity scaling parameter from the mapping model based on the feedback value of the tube voltage includes:

[0022] The tube voltage feedback value obtained during patient scanning is compared with the tube voltage range pre-stored in the mapping model. If the tube voltage feedback value is less than the pre-stored minimum tube voltage value, the minimum tube voltage value is taken. If it is greater than the pre-stored maximum tube voltage value, the maximum tube voltage value is taken to determine the corrected real-time tube voltage feedback value.

[0023] The real-time X-ray intensity ratio parameter is obtained based on the real-time feedback value of the tube voltage and the mapping model.

[0024] Optionally, obtaining the real-time X-ray intensity ratio parameter based on the real-time feedback value of the tube voltage and the mapping model includes:

[0025] If the mapping model is a piecewise mapping relationship, then the pre-stored proportional parameter corresponding to the segment interval to which the tube voltage feedback value belongs is directly taken; or if the mapping model is a polynomial function, then the tube voltage feedback value is substituted into the mathematical function to calculate the corresponding X-ray intensity proportional parameter.

[0026] Optionally, calculating the X-ray attenuation value of each channel passing through the scanned object and reconstructing the image based on the attenuation value includes:

[0027] The X-ray attenuation value of each channel is calculated by combining the corrected incident original X-ray intensity with the attenuated X-ray intensity detected by the detector during patient scanning. The formula is as follows: Among them, I d The intensity of X-rays detected by the detector after attenuation. The corrected intensity of the original incident X-ray light;

[0028] For the attenuation value μ s，c X-ray hardening correction is performed to eliminate attenuation errors caused by the hardening effect when high-energy X-rays penetrate materials;

[0029] The corrected attenuation value μ s，c Reorganized into a projection data format to match the input requirements of image reconstruction algorithms;

[0030] A filtered back-projection algorithm or an iterative reconstruction algorithm is applied to the reconstructed attenuation value to reconstruct a CT image of the scanned object.

[0031] This invention further discloses a system for correcting X-ray intensity fluctuations due to CT tube voltage fluctuations, comprising:

[0032] The intensity ratio calculation module is used to scan air data according to pre-scanning conditions and calculate the intensity ratio of X-rays from each detector channel and the reference channel after filtering based on the air data under different CT tube voltage values.

[0033] The mapping model construction module is used to establish a mapping model based on the intensity ratio relationship. The mapping model includes the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each detection channel, as well as the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each reference channel.

[0034] The data acquisition module is used to acquire the feedback value of the tube voltage under the current exposure in real time during the patient scanning process;

[0035] The proportional parameter determination module is used to determine the corresponding X-ray intensity proportional parameter from the mapping model based on the feedback value of the tube voltage;

[0036] The light intensity correction module is used to correct the incident raw X-ray light intensity of each detector channel based on the X-ray intensity ratio parameter.

[0037] The image reconstruction module is used to calculate the X-ray attenuation value of each detector channel passing through the scanned object, and to perform image reconstruction based on the attenuation value.

[0038] The present invention further discloses a computer-readable storage medium, characterized in that the storage medium stores a computer program, which, when executed by a processor, implements the above-described method.

[0039] The present invention further discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the above-described method when executing the program.

[0040] The present invention further discloses a computer program product, including a computer program, characterized in that the computer program implements the above-described method when executed by a processor.

[0041] According to the technical solution of this invention, by real-time monitoring of tube voltage fluctuations and dynamic correction through pre-scan modeling, the problems of unstable X-ray energy spectrum distribution and inaccurate intensity ratio between each detector channel and the reference channel caused by tube voltage changes in traditional CT imaging are effectively solved. By scanning air data during the pre-scan stage and statistically analyzing the intensity ratio between each channel and the reference channel within different tube voltage ranges, a mapping model between tube voltage and correction parameters is established. This allows for dynamic retrieval of the corresponding proportional parameters based on the real-time feedback of the tube voltage value during patient scanning, thereby accurately correcting the incident light intensity error caused by tube voltage fluctuations and avoiding the attenuation caused by the reliance on fixed proportional relationships in traditional methods. The error is calculated by dividing the incident light intensity by the ratio of the corrected incident light intensity to the detection intensity. Combined with hardening effect correction and reconstruction algorithms, the accuracy and stability of image reconstruction are significantly improved. Especially in scenarios where the sampling frequency of the high-voltage control device is high and the tube voltage fluctuation is large, the dynamic consistency of the incident light intensity calculation of each channel can be maintained through parameter adaptation by piecewise mapping or polynomial fitting. This eliminates the attenuation error caused by energy spectrum changes, reduces image artifacts, and makes the CT imaging results more realistic and reliable. At the same time, the fast recall of the pre-stored correction model and the closed-loop control with real-time feedback simplify the correction process and improve the system's adaptability to different tube voltage fluctuation ranges. Attached Figure Description

[0042] For illustrative and not limiting purposes, the present invention will now be described in conjunction with embodiments and accompanying drawings, wherein:

[0043] Figure 1 This is a flowchart illustrating the method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the structure of the X-ray intensity fluctuation correction system under CT tube voltage fluctuation in an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0047] It should be noted that, where there is no conflict, the embodiments and features of the embodiments in this application can be combined with each other. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0048] refer to Figure 1 This embodiment discloses a method for correcting X-ray intensity fluctuations due to CT tube voltage fluctuations, the method comprising the following steps:

[0049] S100: Scan air data according to pre-scan conditions to model the intensity ratio of X-rays from each detector channel to the reference channel after filtering, calculated based on the air data at different tube voltage values. In the pre-scan stage, the system first configures the pre-scan conditions and then initiates an air scan to eliminate the attenuation effect of the scanned object on X-rays. Specifically, at the start of each exposure during the scan, the tube voltage feedback value kVData output by the high-voltage control device is read in real time (e.g., the preset tube voltage for the current exposure is 120kV, and the actual tube voltage is 120.5kV), and the X-ray intensity value AirData0 detected by all detector channels in this exposure is recorded. During multi-cycle scanning, the above process is repeated, and all exposure data is collected through multiple exposures to ensure coverage of the tube voltage fluctuation range, for example, the recorded kVData range is 115.0kV to 125.0kV. After obtaining the exposure data, outliers in the X-ray intensity value AirData0 (such as discrete points caused by detector malfunctions) are interpolated or replaced, for example, by filling bad pixel data with the average value of adjacent channels. Simultaneously, air correction is performed to eliminate the influence of ambient noise, using the formula: AirData1 s，c =AirData0 s，c -AirBaCkground s，c Among them, AirBackground s，c Here, s represents the pre-calibrated air background noise value, s is the channel row number, and c is the channel sequence number. Further, the ratio AirDat2 of the X-ray intensity value of the reference channel to the X-ray intensity value AirDat1 of each detector channel is calculated for each exposure. That is, using the intensity of the reference channel as a benchmark, the ratio of each channel to the reference channel is calculated to eliminate the proportional change caused by tube current fluctuations. The calculation formula is: Among them, RefC sLet be the preprocessed intensity of the reference channel for the s-th exposure. For example, if the intensity of a reference channel in a certain exposure is counted as 1000, and AirData1 of channel 1 is counted as 1150, then AirData2 is approximately 1000 / 1150 ≈ 0.8696. Divide the maximum (e.g., 140.0kV) and minimum (e.g., 80.0kV) values ​​of all recorded kVData values ​​into intervals with a step size of 0.2kV. Statistically analyze all AirData2 data within each interval and calculate the average ratio for each channel. The calculation formula is: in, N is the set of all exposure indices within the k-th tube voltage range. k This represents the number of effective exposures within range k. For example, within the range of 120.0kV to 120.2kV, if the average AirData2 value of a certain channel is 0.92, then the average ratio of that channel within this range is recorded as 0.92. For each tube voltage range k, the filtered intensity ratio of channel c is defined as: Based on this, the ratio of all intervals k is Ratio k,c Save the calibration table according to the tube voltage range.

[0050] S200: Based on the aforementioned intensity ratio relationship, a mapping model is established. This model includes the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each detection channel, as well as the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each reference channel. After calculating the average ratio of each channel, for each tube voltage interval k and channel c, the correction parameter CorrectParamk.c is calculated using the following formula: According to the correction parameter CorrectParam k,c To establish the mapping model between the voltage value and the tube voltage value, choose one of the following two methods:

[0051] Polynomial fitting method: The center voltage value of interval k, such as the average voltage of the k-th interval, is used as the independent variable x, and the correction parameter CorrectParam is used as the independent variable x. k,c As the dependent variable y, a fitting operation is performed on all (x, y) data points in interval k of channel c, using a polynomial fit, for example: y = a²x 2 +a1x+a0, where a0, a1, and a2 are the coefficients obtained from the fitting.

[0052] Segmented mapping method: Directly map the center voltage value of each interval k and the corresponding correction parameter CorrectParam k Save the .c file as a discrete mapping pair.

[0053] Save the above mapping model parameters independently by channel.

[0054] S300: Real-time acquisition of the tube voltage feedback value under the current exposure during patient scanning.

[0055] During the patient scanning phase, the tube voltage feedback value for the current exposure is acquired in real time, ensuring precise synchronization with detector data. Specifically, the high-voltage generator of the CT equipment has a built-in voltage sensor that monitors the actual output value of the tube voltage in real time. The system communicates with the high-voltage control device via a digital interface to acquire the tube voltage feedback value for each exposure. At the start of each exposure, the system sends an exposure trigger signal to the high-voltage control device, triggering X-ray tube discharge; the high-voltage device immediately returns the actual value of the current tube voltage after discharge, ensuring strict correspondence with the X-ray intensity data acquired by the detector. Within each exposure cycle, the actual value of the current tube voltage is read through the interface. Based on the calibration table saved during the pre-scanning phase, a preset tube voltage range is extracted. The tube voltage feedback value acquired during patient scanning is compared with the pre-stored tube voltage range in the mapping model. If the tube voltage feedback value is less than the pre-stored minimum tube voltage value, the minimum tube voltage value is used; if it is greater than the pre-stored maximum tube voltage value, the maximum tube voltage value is used to determine the corrected real-time tube voltage feedback value. Finally, the real-time X-ray intensity ratio parameter is obtained based on the real-time tube voltage feedback value and the mapping model in step S200.

[0056] For example, assuming the patient scanning phase involves the 500th exposure, after the exposure is triggered, the system sends a trigger signal to the high-voltage device; the high-voltage device returns a tube voltage feedback value of 122.1kV; the calibration table ranges [115.0kV, 125.0kV], and it is determined that the tube voltage feedback value is within the range; the tube voltage feedback value is passed to the calibration module for querying the correction parameters in the mapping model; the detector channel data and the tube voltage feedback value are marked as the same exposure event.

[0057] The technical solution in step S300 achieves real-time and accurate acquisition of tube voltage feedback values, solving the error problem caused by the reliance on preset tube voltage values ​​in traditional methods. This ensures strict synchronization between the real-time tube voltage feedback value and the patient's scan data, providing reliable input for subsequent dynamic correction and ultimately improving the uniformity and consistency of CT images.

[0058] S400: Determine the corresponding X-ray intensity ratio parameter from the mapping model based on the feedback value of the tube voltage.

[0059] Before the patient scanning phase begins, the system loads the calibration model file generated during the pre-scanning phase, including polynomial coefficients or piecewise interval parameters, and records the pre-stored tube voltage range for all channels. First, the tube voltage feedback value is determined: if the feedback value is less than the pre-stored minimum tube voltage value, the minimum tube voltage value is used; if it is greater than the pre-stored maximum tube voltage value, the maximum tube voltage value is used. The correction parameters for the intervals, i.e., the X-ray intensity ratio parameters, are determined based on the type of calibration model.

[0060] For segmented mapping, the first step is to perform interval positioning, that is, to calculate the interval index corresponding to the real-time tube voltage. The calculation formula is as follows: Wherein, kV_corrected is the effective value of the real-time tube voltage feedback after boundary processing, kVMin is the minimum tube voltage value recorded during the pre-scan phase, and interval_step is the tube voltage segmentation interval. The interval range is further determined based on the calculated interval index. The corresponding correction parameters are then read from the correction table according to the interval range.

[0061] For polynomial fitting, the correction parameters are first calculated using the pre-stored polynomial coefficients. The calculation formula is: CorrectParam c =a0+a1×kV_corrected+a2×kV_corrected 2 .

[0062] After calculating the correction parameters, they are bound to the currently exposed detector data to form the correction input.

[0063] S500: Corrects the incident raw X-ray intensity of each channel based on the aforementioned X-ray intensity ratio parameter. During the patient scanning phase, the correction parameter CorrectParam is obtained through real-time tube voltage feedback. c By combining the reference channel intensity and filter material parameters, the incident raw X-ray intensity of each channel is dynamically corrected. Specifically, bad pixel correction and air correction are performed on the patient scan data to obtain the preprocessed detection intensity I. d This refers to the attenuated X-ray intensity after scanning the patient. The preprocessed intensity RefC of the currently exposed reference channel is recorded. s The corrected incident light intensity of channel c is calculated based on the correction parameters. The calculation formula is: Where, μ filter L is the attenuation coefficient of the filter material at the current tube voltage. ref L is the path length through the filter material in the reference channel. s，c Let S be the path length of the filter material passing through the S-th row and the c-th channel.

[0064] S600: Calculate the X-ray attenuation value of each channel passing through the scanned object, and reconstruct the image based on the attenuation value.

[0065] The formula for calculating the X-ray attenuation value is as follows: After calculating the attenuation value, it is corrected. Where b0, b1, and b2 are pre-stored hardening correction coefficients. The corrected attenuation value μ... corrected，s，c The data is reconstructed into a projection matrix P(θ, r) based on the scanning angle and detector position, where θ is the scanning angle and r is the position index of the detector channel, while ensuring that the projection data format is consistent with the input requirements of the reconstruction algorithm.

[0066] Apply the Ram-Lak filter to the projected data P(θ, r) to perform a filtering operation: in, This is a Fourier transform. The filtered projection data... Backprojection into the reconstruction space generates a reconstructed CT image:

[0067] In summary, the proposed method for correcting X-ray intensity fluctuations caused by CT tube voltage fluctuations establishes the ratio of filtered intensity between each detector channel and the reference channel under different tube voltages using pre-scanned air data. Based on this, a mapping model (including piecewise mapping or polynomial fitting) between tube voltage and correction parameters is constructed. During patient scanning, the tube voltage feedback value is acquired in real time, and the corresponding correction parameters are dynamically determined to correct the incident light intensity of each channel. Combined with hardening effect correction and filtered back projection / iterative reconstruction algorithms, this method effectively solves the problem of attenuation value calculation errors caused by energy spectrum changes and inaccurate ratio relationships due to tube voltage fluctuations in traditional methods. It significantly improves image uniformity, signal-to-noise ratio, and reconstruction accuracy. At the same time, it supports multi-hardware adaptation and real-time processing, ensuring that even in scenarios with high sampling frequency or large fluctuation amplitude of high-voltage devices, it can still be quickly corrected through the pre-stored model. Ultimately, it achieves high-resolution, low-artifact CT image reconstruction, meeting the needs of routine and low-dose scanning.

[0068] refer to Figure 2 This embodiment further discloses a system for correcting X-ray intensity fluctuations under CT tube voltage fluctuations, comprising:

[0069] The intensity ratio calculation module 21 is used to call pre-scanning conditional air data to model and calculate the intensity ratio of X-rays between each detector channel and the reference channel after filtration under different tube voltage values. This includes: in the pre-scanning stage, calling pre-scanning conditional air data and recording the tube voltage feedback value kVData and the X-ray intensity value AirDat0 of each detector channel for each exposure; preprocessing the X-ray intensity value AirDat0 of the detector channels in the air data to obtain preprocessed data AirDat1; calculating the ratio data AirDat2 between the X-ray intensity value of the reference channel and the preprocessed X-ray intensity value AirDat1 of each detector channel for each exposure; dividing the range of the tube voltage feedback value kVData into multiple preset intervals, and calculating the average intensity value MeanAirData of the ratio data AirDat2 within each interval; calculating the intensity ratio of X-rays between each detector channel and the reference channel within the corresponding tube voltage feedback value interval based on the average intensity value MeanAirData, and saving the ratio relationship as a tube voltage fluctuation correction table according to the intervals for subsequent correction calls.

[0070] The mapping model construction module 22 is used to establish a mapping model between the tube voltage value and the X-ray intensity ratio parameters of each detector channel and the reference channel based on the intensity ratio relationship. This includes: normalizing the intensity ratio relationship to obtain correction parameters corresponding to each detector channel; interpolating or averaging the correction parameters within the interval of each tube voltage feedback value to establish a piecewise mapping relationship between the tube voltage feedback value and the correction parameters; or performing polynomial fitting on the correspondence between the intensity ratio relationship and the tube voltage feedback value to obtain a mathematical function representing the mapping relationship between the tube voltage feedback value and the correction parameters; and saving the parameters of the piecewise mapping relationship or the fitting function and the corresponding interval of the tube voltage feedback value as a calibration model file. The data acquisition module 23 is used to acquire the feedback value of the tube voltage under the current exposure during the patient scanning process in real time.

[0071] The proportional parameter determination module 24 is used to determine the corresponding X-ray intensity proportional parameter from the mapping model based on the tube voltage feedback value, including: importing the tube voltage fluctuation correction table according to preset scanning conditions; comparing the tube voltage feedback value obtained when scanning the patient with the tube voltage range stored in the mapping model; if the tube voltage feedback value is less than the stored minimum tube voltage value, then taking the minimum tube voltage value; if it is greater than the stored maximum tube voltage value, then taking the maximum tube voltage value, to determine the corrected real-time tube voltage feedback value; obtaining the real-time X-ray intensity proportional parameter based on the real-time tube voltage feedback value and the mapping model, including: if the mapping model is a piecewise mapping relationship, then directly taking the stored proportional parameter corresponding to the piecewise interval to which the tube voltage feedback value belongs; or if the mapping model is a polynomial function, then substituting the tube voltage feedback value into the mathematical function to calculate the corresponding X-ray intensity proportional parameter;

[0072] The light intensity correction module 25 is used to correct the incident original X-ray light intensity of each channel based on the X-ray intensity ratio parameter.

[0073] Image reconstruction module 26 is used to calculate the X-ray attenuation value of each channel passing through the scanned object, and to perform image reconstruction based on the attenuation value, including: calculating the X-ray attenuation value of each channel by using the corrected incident original X-ray intensity and the attenuated X-ray intensity detected by the detector during patient scanning, using the following formula: Among them, I d The intensity of X-rays detected by the detector after attenuation. The corrected incident original X-ray intensity; the attenuation value μ s，c X-ray hardening correction is performed to eliminate attenuation errors caused by the hardening effect when high-energy X-rays penetrate the material; the corrected attenuation value μ is then used. s，c The data is reconstructed into a projection data format to match the input requirements of the image reconstruction algorithm. A filtered back-projection algorithm or an iterative reconstruction algorithm is then applied to the reconstructed projection data to reconstruct a CT image of the scanned object.

[0074] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3 As shown, the electronic device 50 includes: a processor 501, a memory 502, and a bus 503;

[0075] The processor 501 and the memory 502 communicate with each other via the bus 503; the processor 501 is used to call the program instructions in the memory 502 to execute the methods provided in the above-described embodiments.

[0076] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions that cause a computer to execute the methods provided in the above-described embodiments.

[0077] Those skilled in the art will understand that all or part of the steps of the above-described method implementation can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above-described method implementation. The aforementioned storage medium includes various storage media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0080] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for correcting X-ray intensity fluctuations due to CT tube voltage fluctuations, characterized in that, The method includes: Based on the pre-scanning conditions, scan the air data and calculate the intensity ratio of X-rays from each detector channel and the reference channel after filtering under different CT tube voltage values. Based on the intensity ratio relationship, a mapping model is established, which includes the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each detection channel, as well as the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each reference channel. During patient scanning, the feedback value of the tube voltage under the current exposure is obtained in real time; The corresponding X-ray intensity ratio parameter is determined from the mapping model based on the feedback value of the tube voltage; The incident raw X-ray intensity of each detector channel is corrected based on the X-ray intensity ratio parameter. Calculate the X-ray attenuation value of each detector channel as it passes through the scanned object, and reconstruct the image based on the attenuation value.

2. The method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations according to claim 1, characterized in that, The pre-scan conditional scan air data is invoked to model and calculate the intensity ratio of X-rays from each detector channel to the reference channel after filtration under different tube voltage values, including: During the pre-scanning phase, the pre-scanning conditions are invoked to scan the air data and record the tube voltage feedback value kVData and the X-ray intensity value AirDat0 of each detector channel for each exposure. The X-ray intensity value AirDat0 of the detector channel in the air data is preprocessed to obtain preprocessed data AirDat1; Calculate the ratio of the X-ray intensity value of the reference channel to the pre-processed X-ray intensity value AirDat1 of each detector channel, which is data AirDat2; The range of the tube voltage feedback value kVData is divided into multiple preset intervals, and the average intensity value MeanAirData of the ratio data AirDat2 in each interval is calculated. The mean air data is used to calculate the ratio of X-ray intensity after filtration between each detector channel and the reference channel within the range of the corresponding tube voltage feedback value.

3. The method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations according to claim 2, characterized in that, The mapping model established based on the intensity ratio relationship between the tube voltage value and the X-ray intensity ratio parameters of each detection channel and the reference channel includes: The intensity ratio relationship is normalized to obtain the correction parameters corresponding to each detector channel. For each interval of the tube voltage feedback value, the correction parameter is interpolated or averaged to establish a piecewise mapping relationship between the tube voltage feedback value and the correction parameter; or A polynomial fit is performed on the correspondence between the intensity ratio and the tube voltage feedback value to obtain a mathematical function representing the mapping relationship between the tube voltage feedback value and the correction parameter; Save the parameters of the segmented mapping relationship or fitting function and the corresponding range of tube voltage feedback values ​​as a calibration model file.

4. The method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations according to claim 3, characterized in that, The process of determining the corresponding X-ray intensity ratio parameter from the mapping model based on the feedback value of the tube voltage includes: The tube voltage feedback value obtained during patient scanning is compared with the tube voltage range pre-stored in the mapping model. If the tube voltage feedback value is less than the pre-stored minimum tube voltage value, the minimum tube voltage value is taken. If it is greater than the pre-stored maximum tube voltage value, the maximum tube voltage value is taken to determine the corrected real-time tube voltage feedback value. The real-time X-ray intensity ratio parameter is obtained based on the real-time feedback value of the tube voltage and the mapping model.

5. The method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations according to claim 4, characterized in that, The real-time X-ray intensity ratio parameter obtained based on the real-time feedback value of the tube voltage and the mapping model includes: If the mapping model is a piecewise mapping relationship, then based on the piecewise interval to which the tube voltage feedback value belongs, the pre-stored proportional parameter corresponding to that interval is directly taken; or If the mapping model is a polynomial function, then the tube voltage feedback value is substituted into the mathematical function to calculate the corresponding X-ray intensity ratio parameter.

6. The method for correcting X-ray intensity fluctuations under CT tube voltage fluctuations according to claim 1, characterized in that, The calculation of the X-ray attenuation value of each channel passing through the scanned object, and the image reconstruction based on the attenuation value, includes: The X-ray attenuation value of each channel is calculated by combining the corrected incident original X-ray intensity with the attenuated X-ray intensity detected by the detector during patient scanning. The formula is as follows: Among them, I d The intensity of X-rays detected by the detector after attenuation. The corrected intensity of the original incident X-ray light; For the attenuation value μ s,c X-ray hardening correction is performed to eliminate attenuation errors caused by the hardening effect when high-energy X-rays penetrate materials; The corrected attenuation value μ s,c Reorganized into a projection data format to match the input requirements of image reconstruction algorithms; A filtered back-projection algorithm or an iterative reconstruction algorithm is applied to the reconstructed attenuation value to reconstruct a CT image of the scanned object.

7. A system for correcting X-ray intensity fluctuations due to CT tube voltage fluctuations, characterized in that, include: The intensity ratio calculation module is used to scan air data according to pre-scanning conditions and calculate the intensity ratio of X-rays from each detector channel and the reference channel after filtering based on the air data under different CT tube voltage values. The mapping model construction module is used to establish a mapping model based on the intensity ratio relationship. The mapping model includes the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each detection channel, as well as the mapping relationship between the CT tube voltage value and the X-ray intensity ratio parameter of each reference channel. The data acquisition module is used to acquire the feedback value of the tube voltage under the current exposure in real time during the patient scanning process; The proportional parameter determination module is used to determine the corresponding X-ray intensity proportional parameter from the mapping model based on the feedback value of the tube voltage; The light intensity correction module is used to correct the incident raw X-ray light intensity of each detector channel based on the X-ray intensity ratio parameter. The image reconstruction module is used to calculate the X-ray attenuation value of each detector channel passing through the scanned object, and to perform image reconstruction based on the attenuation value.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

Images