A CT image noise equalization and noise reduction method and system

Abstract

The present application discloses a CT image noise equalization and noise reduction method, which includes: obtaining an image I0; calculating a fluctuation distribution matrix M3 of each pixel point within the range of the image I0 relative to the average noise; scanning a patient to obtain an image I1, and indexing a first priori parameter according to a scanning condition to obtain a second priori parameter; interpolating a noise distribution priori parameter matrix M4 to obtain a noise distribution priori parameter matrix M of the image I1. ij ; Through the noise distribution prior parameter matrix M ij Calculate the noise reduction adjustment parameter μ ij ; Normalize image I1 to obtain image I2, and set the noise reduction adjustment parameter μ ij , iteratively denoise the image I2, and output the denoised image I3. Compared with the prior art, the prior parameter matrix of the patient's CT image can be obtained by obtaining the prior parameters of the noise distribution, and the image denoising adjustment parameters can be calculated to achieve noise equalization denoising of the patient's CT image, avoiding the problem of aggravating the imbalance between the center noise and the edge noise of the image after interpolation back projection in the traditional solution.

Description

Technical Field

[0001] The present application relates to the field of image denoising, and more particularly, to a CT image noise equalization denoising method and system. Background Art

[0002] Currently, the noise of CT images is an important factor in measuring image quality, and both the magnitude and distribution of the noise will affect the image quality. The main sources of CT image noise are quantum noise and electronic noise of projection data. Currently, medical CT mainly adopts a cone-beam structure, and in order to reduce the radiation of ineffective dose to patients, a pre-filter is usually added. The X-ray usually passes through the filter first, then through the scanned human body, and then reaches the detector unit of the X-ray detection. On the one hand, the X-ray received by the detector through the channel near the rotation center has a short filtered path, while the X-ray received by the detector in the outermost channels has a longer filtered path. Therefore, the noise fluctuation of the projection data received by the detector channels with a long filtered path is large. On the other hand, due to the CT cone-beam structure, the ray distribution near the central channel passing through the rotation center is denser after image reconstruction rearrangement, while the ray distribution in the channels closer to the edge of the scanning field of view is sparser. After interpolation backprojection, the imbalance between the central noise and the edge noise of the image is aggravated. Summary of the Invention

[0003] The main object of the present application is to provide a CT image noise equalization denoising method to achieve noise equalization denoising of CT images.

[0004] To achieve the above object, the present application provides a CT image noise equalization denoising method, the method comprising: scanning a target, reconstructing a full-scan image to obtain an image I0; calculating a fluctuation distribution matrix M3 of each pixel point within the range of the image I0 relative to the average noise, and saving it as a first prior parameter of the noise distribution; scanning a patient to obtain an image I1 before denoising, and indexing the first prior parameter according to the scanning conditions to obtain a second prior parameter, i.e., a noise distribution prior parameter matrix M4; performing an interpolation operation on the noise distribution prior parameter matrix M4 to obtain a noise distribution prior parameter matrix M of the image I1 ij ; calculating a denoising adjustment parameter μ through the noise distribution prior parameter matrix M ij ; normalizing the image I1 to obtain an image I2, setting the denoising adjustment parameter μ ij ; and performing iterative denoising processing on the image I2 according to the denoising adjustment parameter μ ij and outputting a denoised image I3. ij Further, the method for calculating the fluctuation distribution matrix M3 of each pixel point of the image I0 and saving it as the first prior parameter of the noise distribution includes:

[0005] ​

[0006] Calculate the standard deviation of the CT values in the central region of the image I0 to obtain the standard deviation result sd;

[0007] Calculate the average value mu of the CT values of the image I0;

[0008] Calculate the absolute value of the difference between the CT value of the image I0 and the average value mu to obtain the fluctuation distribution matrix M1 of the CT values of the image I0;

[0009] Perform an interpolation operation on the fluctuation distribution matrix M1 to obtain the fluctuation distribution matrix M2 of the CT values of the full scan field of view;

[0010] Calculate the fluctuation distribution matrix M3 through the fluctuation distribution matrix M2 and the standard deviation result sd: M3 = M2 / sd.

[0011] Furthermore, through the noise distribution prior parameter matrix M ij Calculate the noise reduction adjustment parameter μ ij The calculation formula is:

[0012]

[0013] where μ ij is the noise reduction adjustment parameter, M ij is the noise distribution prior parameter matrix, L and μ0 are the constant parameters for noise reduction, a is the slope of the noise reduction with respect to the parameter change, and b is the intercept of the noise reduction with respect to the parameter change.

[0014] Furthermore, the method of normalizing the image I1 to obtain the image I2, setting the noise reduction adjustment parameter μ ij , and performing iterative noise reduction processing on the image I2 according to the noise reduction adjustment parameter μ ij and outputting the noise-reduced image I3 includes:

[0015] S1: Initialize the relevant parameters for iterative noise reduction. Specifically: u 0 = I2, n = 0, N tol = 10, where u 0 is the initial value of the image matrix, are the initial values of the intermediate parameters η x , η y , c x , c y introduced in the iterative calculation, n is the number of iterations, and N tol is the total number of iterations;

[0016] S2: Calculate the intermediate result of the image iteration. Specifically:

[0017]

[0018] , where f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, i is the row number of the image matrix, j is the column number of the image matrix, and u n is the result of the n-th iteration of the image; are the intermediate parameters η x , η y , c x , c y of the n-th calculation;

[0019] S3: Update the image iteration result. Specifically:

[0020] where u n+1 is the result of the (n + 1)-th iteration of the image, i is the row number of the image matrix, j is the column number of the image matrix, and f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, μ ij is the denoising adjustment parameter, L is the denoising constant parameter, and u 0 is the initial value of the input of the denoised image;

[0021] S4: Update the intermediate parameters. Specifically:

[0022]

[0023] where are the intermediate parameters η x , η y , c x , c y of the (n + 1)-th iteration, are the results of the n-th iteration of c x , c y , respectively, are the difference results of the (n + 1)-th result u n+1 of the image in the row and column directions, shrink is the activation function, and L is the denoising constant parameter;

[0024] S5: Update the iteration number. Specifically: n = n + 1, where n is the calculated value of the iteration number;

[0025] S6: Determine whether the iteration number n is greater than the set total iteration number N tol , if it is greater than the total iteration number, the iteration ends, and I3 = u n , otherwise, repeat the iterative steps of S2 to S5 above.

[0026] Further, after normalizing the image I1 to obtain the image I2 and setting the noise reduction adjustment parameter μ ij , according to the noise reduction adjustment parameter μ ij performing iterative noise reduction processing on the image I2 and outputting the denoised image I3, the method further includes:

[0027] Restoring the CT value of the output denoised image I3 to obtain the target image I4 with balanced noise distribution after denoising.

[0028] The present application further discloses a CT image noise equalization and noise reduction system, including:

[0029] An image reconstruction module for scanning a target, reconstructing a full scan image, and obtaining the image I0;

[0030] A prior parameter calculation module for calculating the fluctuation distribution matrix M3 of each pixel point within the range of the image I0 relative to the average noise and saving it as the first prior parameter of the noise distribution;

[0031] A parameter indexing module for scanning a patient to obtain the image I1 before noise reduction and indexing the first prior parameter according to the scanning conditions to obtain the second prior parameter, that is, the prior parameter matrix M4 of the noise distribution;

[0032] An adjustment parameter calculation module for performing an interpolation operation on the prior parameter matrix M4 of the noise distribution to obtain the prior parameter matrix M of the noise distribution of the image I1 ij ; and also for

[0033] calculating the noise reduction adjustment parameter μ ij through the prior parameter matrix M of the noise distribution ij ;

[0034] An iterative noise reduction processing module for normalizing the image I1 to obtain the image I2 and setting the noise reduction adjustment parameter μ ij , according to the noise reduction adjustment parameter μ ij performing iterative noise reduction processing on the image I2 and outputting the denoised image I3.

[0035] Further, the iterative noise reduction processing module is also used to restore the CT value of the output denoised image I3 to obtain the target image I4 with balanced noise distribution after denoising.

[0036] The present application further discloses a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0037] The present application further discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above method is implemented.

[0038] The present application further discloses a computer program product, including a computer program which, when executed by a processor, implements the above method.

[0039] Compared with the prior art, a CT image noise equalization and noise reduction method and system provided by the present invention can obtain a prior parameter matrix of a patient's CT image through prior parameters of the obtained noise distribution, then perform an interpolation operation on the prior parameter matrix, and calculate a noise reduction adjustment parameter of the image based on the interpolated prior parameter matrix, thereby realizing noise equalization and noise reduction of the patient's CT image, and avoiding the problem that the traditional solution exacerbates the imbalance between the central noise and the edge noise of the image after interpolation backprojection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings constituting a part of this application are used to provide a further understanding of this application, making other features, objectives, and advantages of this application more obvious. The schematic embodiments and their descriptions of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0041] Figure 1 It is a schematic flow chart of the CT image noise equalization and noise reduction method in this embodiment;

[0042] Figure 2 It is a schematic structural diagram of the CT image noise equalization and noise reduction system in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0044] It should be noted that the terms "first", "second", etc. in the description of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily 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 this application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising 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.

[0045] 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 this application 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.

[0046] Moreover, in addition to being able to represent the 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 this application can be understood according to specific circumstances.

[0047] In addition, the meaning of the term "plurality" should be two or more.

[0048] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will detail this application with reference to the drawings and in combination with the embodiments.

[0049] Referring to Figure 1 , a CT image noise equalization and noise reduction method, the method includes the following steps:

[0050] S100: Scan the target, reconstruct the full scan image, and obtain image I0.

[0051] Among them, the target is a uniform phantom or air, and a full scan image with uniform noise, that is, I0, can be obtained during scanning. Exemplarily, the uniform phantom can be a water phantom, an LDPE phantom, etc. In application, the phantom should be large enough to cover the common scanning range as much as possible.

[0052] S200: Calculate the fluctuation distribution matrix M3 of each pixel point within the range of image I0 relative to the average noise, and save it as the first prior parameter of the noise distribution. Specifically, it includes:

[0053] S210: Calculate the standard deviation of the CT values in the central region of image I0 to obtain the standard deviation result sd.

[0054] As described above, the scanning target can be a phantom or air. Since the phantom can be in an irregular shape, there will be significant differences in different regions when calculating the CT values of image I0. Therefore, in this embodiment, if the scanning target is a phantom, when calculating the CT values, the effective region of image I0 is its central region, and the diameter of the central region is 40% of the minimum diameter of the phantom. Similarly, to ensure the uniformity of the data volume, when the scanning target is air, the effective region of image I0 is the central region with a diameter of 40% of the scanning region diameter.

[0055] S220: Calculate the average value mu of the CT values of image I0.

[0056] S230: Calculate the absolute value of the difference between the CT values of image I0 and the average value mu to obtain the CT value fluctuation distribution matrix M1 of image I0.

[0057] S240: Perform an interpolation operation on the fluctuation distribution matrix M1 to obtain the CT value fluctuation distribution matrix M2 of the full scan field of view.

[0058] S250: Calculate the fluctuation distribution matrix M3 through the fluctuation distribution matrix M2 and the standard deviation result sd: M3 = M2 / sd.

[0059] By performing an interpolation operation on the fluctuation distribution matrix M1, the integrity of the data can be improved. Together with a uniform scanning target, it is possible to reduce the random fluctuation of the CT value data, further ensuring the standardization of the fluctuation distribution matrix M3 as the first prior parameter.

[0060] After obtaining the first prior parameter of the noise distribution, the CT image of the patient can be further subjected to noise equalization and noise reduction. Specifically:

[0061] S300: Scan the patient to obtain the image I1 before noise reduction, and index the first prior parameter according to the scanning conditions to obtain the second prior parameter, that is, the noise distribution prior parameter matrix M4.

[0062] Among them, the scanning conditions refer to the information indicating the scanned part of the patient's body and the parameter settings of the scanning device. For example, relevant parameters such as tube voltage, tube current, and gantry rotation speed can accurately index the second prior parameter matching the first prior parameter.

[0063] S400: Perform an interpolation operation on the noise distribution prior parameter matrix M4 to obtain the noise distribution prior parameter matrix M of the image I1 ij 。

[0064] S500: Calculate the noise reduction adjustment parameter μ through the noise distribution prior parameter matrix M ij Calculate the noise reduction adjustment parameter μ ij ; where, the noise reduction adjustment parameter μ ij The calculation formula is:

[0065]

[0066] where, μ ij is the noise reduction adjustment parameter, M ij is the noise distribution prior parameter matrix, L and μ0 are the constant parameters for noise reduction, a is the slope of the noise reduction with respect to the parameter change, and b is the intercept of the noise reduction with respect to the parameter change.

[0067] S600: Normalize the image I1 to obtain the image I2, set the noise reduction adjustment parameter μ ij , and perform iterative noise reduction processing on the image I2 according to the noise reduction adjustment parameter μ ij , and output the denoised image I3; where, the method of iterative noise reduction processing includes:

[0068] S1: Initialize the relevant parameters for iterative noise reduction, specifically: u 0 = I2, n = 0, N tol = 10, where, u 0 is the initial value of the image matrix, are the initial values of the intermediate parameters η x , η y , c x , c y introduced in the iterative calculation respectively, n is the number of iterations, and N tol is the total number of iterations;

[0069] S2: Calculate the intermediate result of image iteration, specifically:

[0070]

[0071] , where, f n is the result of the nth calculation of the intermediate result f of the image noise reduction iterative calculation, i is the row number of the image matrix, j is the column number of the image matrix, and u n is the result of the nth iteration of the image, are the results of the nth calculation of the intermediate parameters η x , η y , c x , c y introduced in the iterative operation respectively;

[0072] S3: Update the image iteration result, specifically:

[0073] Among them, u n+1 is the result of the (n + 1)-th iteration of the image, i is the row number of the image matrix, j is the column number of the image matrix, and f n is the result of the n-th calculation of the intermediate result f in the iterative calculation of image noise reduction, and μ ij is the noise reduction adjustment parameter, L is the noise reduction constant parameter, and u 0 is the initial value of the input of the noise reduction image;

[0074] S4: Update the intermediate parameters. Specifically:

[0075]

[0076] Among them, are respectively the results of the (n + 1)-th iteration of the intermediate parameters η x , η y , c x , c y , are respectively the results of the n-th iteration of c x , c y , are respectively the differential results of the (n + 1)-th result u of the image in the row and column directions, shrink is the activation function, and L is the noise reduction constant parameter; n+1

[0077] S5: Update the number of iterations. Specifically: n = n + 1, where n is the calculated value of the number of iterations;

[0078] S6: Determine whether the number of iterations n is greater than the set total number of iterations N tol . If it is greater than the total number of iterations, the iteration ends, and I3 = u n . Otherwise, repeat the iterative steps of S2 to S5 above.

[0079] After obtaining the iterative noise reduction processing result and outputting the image I3, the CT image noise equalization and noise reduction method disclosed in this embodiment further includes:

[0080] S700: Restore the CT value of the output noise-reduced image I3 to obtain the target image I4 with a noise-equalized distribution after noise reduction.

[0081] ​In summary, the CT image noise equalization and denoising method provided by the present invention can obtain the prior parameter matrix of the patient's CT image through the prior parameters of the obtained noise distribution, and then perform an interpolation operation on the prior parameter matrix. Based on the interpolated prior parameter matrix, the denoising adjustment parameter of the image can be calculated, thereby realizing the noise equalization and denoising of the patient's CT image, and avoiding the problem that the traditional scheme exacerbates the imbalance between the central noise and the edge noise of the image after interpolation backprojection.

[0082] Reference Figure 2 , this embodiment further discloses a CT image noise equalization and denoising system, including:

[0083] An image reconstruction module 21, configured to scan a target, reconstruct a full-scan image, and obtain an image I0;

[0084] A prior parameter calculation module 22, configured to calculate a fluctuation distribution matrix M3 of each pixel point in the image I0 with respect to the average noise, and save it as the first prior parameter of the noise distribution. This embodiment discloses a calculation method for the fluctuation distribution matrix M3: calculate the standard deviation of the CT values in the central region of the image I0 to obtain a standard deviation result sd;

[0085] Calculate the average value mu of the CT values of the image I0;

[0086] Calculate the absolute value of the difference between the CT value of the image I0 and the average value mu to obtain a fluctuation distribution matrix M1 of the CT values of the image I0;

[0087] Perform an interpolation operation on the fluctuation distribution matrix M1 to obtain a fluctuation distribution matrix M2 of the CT values of the full scan field of view;

[0088] Calculate the fluctuation distribution matrix M3 through the fluctuation distribution matrix M2 and the standard deviation result sd: M3 = M2 / sd.

[0089] A parameter indexing module 23, configured to scan a patient to obtain an image I1 before denoising, and index the first prior parameter according to the scanning conditions to obtain a second prior parameter, that is, a noise distribution prior parameter matrix M4;

[0090] An adjustment parameter calculation module 24, configured to perform an interpolation operation on the noise distribution prior parameter matrix M4 to obtain the noise distribution prior parameter matrix M of the image I1 ij ; It is also used for

[0091] Calculate the denoising adjustment parameter μ through the noise distribution prior parameter matrix M ij , where the calculation formula for the denoising adjustment parameter μ ij is: ij

[0092]

[0093] Among them, μ ij is the noise reduction adjustment parameter, M ij is the prior parameter matrix of the noise distribution, L and μ0 are the constant parameters for noise reduction, a is the slope of the noise reduction varying with the parameter, and b is the intercept of the noise reduction varying with the parameter.

[0094] The iterative noise reduction processing module 25 is used to normalize the image I1 to obtain the image I2, set the noise reduction adjustment parameter μ ij , and perform iterative noise reduction processing on the image I2 according to the noise reduction adjustment parameter μ ij , and output the noise-reduced image I3; it is also used for

[0095] restoring the CT value of the output noise-reduced image I3 to obtain the target image I4 with a noise-equalized distribution after noise reduction.

[0096] Among them, the method of iterative noise reduction processing includes:

[0097] S1: Initialize the relevant parameters for iterative noise reduction. Specifically: u 0 = I2, n = 0, N tol = 10, where u 0 is the initial value of the image matrix, are respectively the initial values of the intermediate parameters η x , η y , c x , c y introduced in the iterative calculation, n is the number of iterations, and N tol is the total number of iterations;

[0098] S2: Calculate the intermediate result of the image iteration. Specifically:

[0099]

[0100] , where f n is the result of the nth calculation of the intermediate result f of the image noise reduction iteration calculation, i is the row number of the image matrix, j is the column number of the image matrix, u n is the result of the nth iteration of the image, are respectively the results of the nth calculation of the intermediate parameters η x , η y , c x , c y in the iterative operation;

[0101] S3: Update the image iteration result. Specifically:

[0102] Among them, u n+1 is the result of the (n + 1)-th iteration of the image, i is the row number of the image matrix, j is the column number of the image matrix, and f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, μ ij is the denoising adjustment parameter, L is the denoising constant parameter, and u 0 is the initial value input for the denoised image;

[0103] S4: Update the intermediate parameters. Specifically:

[0104]

[0105] Among them, are respectively the results of the (n + 1)-th iteration of the intermediate parameters η x and η y and c x and c y in the iterative operation, are respectively the results of the n-th iteration of c x and c y in the iterative operation, are respectively the differential results of the (n + 1)-th result u n+1 of the image in the row and column directions. shrink is the activation function, and L is the denoising constant parameter;

[0106] S5: Update the number of iterations. Specifically: n = n + 1, where n is the calculated value of the number of iterations;

[0107] S6: Determine whether the number of iterations n is greater than the set total number of iterations N tol , if it is greater than the total number of iterations, the iteration ends, and I3 = u n , otherwise, repeat the iterative steps of S2 to S5 above.

[0108] Reference Figure 2 , this embodiment further discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the above CT image noise equalization denoising method.

[0109] This embodiment further discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the above CT image noise equalization denoising method.

[0110] This embodiment further discloses a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above CT image noise equalization denoising method.

[0111] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various storage media such as ROM, RAM, magnetic disk, or optical disk that can store program codes.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0113] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0114] The specific manners of the execution operations of the above units in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0115] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A CT image noise equalization and noise reduction method, characterized in that The method includes: Scanning a target, reconstructing a full-scan image, and obtaining image I0, where the target is a uniform phantom or air; Calculating a fluctuation distribution matrix M3 of each pixel point within the range of image I0 relative to the average noise, and saving it as the first prior parameter of the noise distribution; Scanning a patient to obtain an image I1 before noise reduction, and indexing the first prior parameter according to the scanning conditions to obtain a second prior parameter, i.e., a noise distribution prior parameter matrix M4; Interpolate the prior parameter matrix M4 of the noise distribution to obtain the prior parameter matrix M of the noise distribution of the image I1 ij ; Through the prior parameter matrix M of the noise distribution ij Calculate the noise reduction adjustment parameter μ ij , and the calculation formula is: where μ ij is the noise reduction adjustment parameter, M ij is the prior parameter matrix of the noise distribution, L and μ0 are the constant parameters for noise reduction, a is the slope of the noise reduction varying with the parameter, and b is the intercept of the noise reduction varying with the parameter; Normalize the image I1 to obtain the image I2, and set the noise reduction adjustment parameter μ ij , and perform iterative noise reduction processing on the image I2 according to the noise reduction adjustment parameter μ ij and output the denoised image I3, including: S1: Initialize the relevant parameters of iterative noise reduction, specifically: n = 0, N tol = 10, where u 0 is the initial value of the image matrix, are the intermediate parameters η x , η y , c x , c y initial values, n is the number of iterations, N tol is the total number of iterations; S2: Calculating the intermediate result of image iteration, specifically: ， where f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, i is the row number of the image matrix, j is the column number of the image matrix, and u n is the result of the n-th iteration of the image, are the results of the n-th calculations of the intermediate parameters η x and η y and c x and c y respectively; S3: Updating the image iteration result, specifically: Among them, u n+1 is the result of the (n + 1)-th iteration of the image, i is the row number of the image matrix, j is the column number of the image matrix, and f n is the result of the n-th calculation of the intermediate result f of the iterative calculation of image noise reduction, μ ij is the noise reduction adjustment parameter, L is the noise reduction constant parameter, and u 0 is the initial value input for the noise reduction image; S4: Updating the intermediate parameter, specifically: Among them, are respectively the results of the (n + 1)-th iteration of the intermediate parameters η x , η y , c x , c y ; the results of the (n + 1)-th iteration of are respectively the results of the n-th iteration of c x , c y ; the results of the (n + 1)-th iteration of the image are respectively the differential results of u n+1 in the row and column directions, shrink is the activation function, and L is the noise reduction constant parameter;​ S5: Updating the iteration number, specifically: n = n + 1, where n is the calculated value of the iteration number; S6: Determine whether the number of iterations n is greater than the set total number of iterations N tol , if it is greater than the total number of iterations, the iteration ends, and I3 = u n , otherwise, repeat the iterative steps of S2 to S5 above.

2. The CT image noise equalization and noise reduction method according to claim 1, characterized in that, The method for calculating the fluctuation distribution matrix M3 of each pixel point of image I0 and saving it as the first prior parameter of the noise distribution includes: Calculating the standard deviation of the CT values in the central region of image I0 to obtain a standard deviation result sd; Calculating the average value mu of the CT values of image I0; Calculating the absolute value of the difference between the CT value of image I0 and the average value mu to obtain a fluctuation distribution matrix M1 of the CT values of image I0; Performing an interpolation operation on the fluctuation distribution matrix M1 to obtain a fluctuation distribution matrix M2 of the CT values of the full scan field of view; Calculating the fluctuation distribution matrix M3 through the fluctuation distribution matrix M2 and the standard deviation result sd: M3 = M2 / sd.

3. The CT image noise equalization and noise reduction method according to claim 1, characterized in that, After normalizing the image I1 to obtain the image I2 and setting the noise reduction adjustment parameter μ ij , according to the noise reduction adjustment parameter μ i j, after performing iterative noise reduction processing on the image I2 and outputting the denoised image I3, the method further includes: Restoring the CT values of the output denoised image I3 to obtain a target image I4 with a noise-equalized distribution after denoising.

4. A CT image noise equalization and noise reduction system, characterized in that, It includes: An image reconstruction module for scanning a target, reconstructing a full-scan image, and obtaining image I0, where the target is a uniform phantom or air; A prior parameter calculation module for calculating a fluctuation distribution matrix M3 of each pixel point within the range of image I0 relative to the average noise, and saving it as the first prior parameter of the noise distribution; A parameter indexing module for scanning a patient to obtain an image I1 before noise reduction, and indexing the first prior parameter according to the scanning conditions to obtain a second prior parameter, i.e., a noise distribution prior parameter matrix M4; An adjustment parameter calculation module, configured to perform an interpolation operation on the prior parameter matrix M4 of the noise distribution to obtain the prior parameter matrix M of the noise distribution of the image I1 ij ; and is further configured to Via the prior parameter matrix M of the noise distribution ij Calculate the noise reduction adjustment parameter μ ij , and the calculation formula is: Among them, μ ij is the noise reduction adjustment parameter, M ij is the prior parameter matrix of the noise distribution, L and μ0 are the constant parameters for noise reduction, a is the slope of the noise reduction varying with the parameter, and b is the intercept of the noise reduction varying with the parameter; An iterative noise reduction processing module is used to normalize the image I1 to obtain an image I2, and set the noise reduction adjustment parameter μ ij , and perform iterative noise reduction processing on the image I2 according to the noise reduction adjustment parameter μ ij and output the denoised image I3, including: S1: Initialize the relevant parameters of iterative noise reduction. Specifically: n = 0, N tol = 10, where u 0 is the initial value of the image matrix, are the intermediate parameters η x and η y , c x , c y are the initial values, n is the number of iterations, and N tol is the total number of iterations; S2: Calculating the intermediate result of image iteration, specifically: ， where f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, i is the row number of the image matrix, j is the column number of the image matrix, and u n is the result of the n-th iteration of the image, are the results of the n-th calculations of the intermediate parameters η x and η y and c x and c y respectively; S3: Updating the image iteration result, specifically: Among them, u n+1 is the result of the (n + 1)-th iteration of the image, i is the row number of the image matrix, j is the column number of the image matrix, and f n is the result of the n-th calculation of the intermediate result f of the image denoising iterative calculation, μ ij is the denoising adjustment parameter, L is the denoising constant parameter, and u 0 is the initial value of the input of the denoised image; S4: Updating the intermediate parameter, specifically: Among them, are the results of the (n + 1)-th iteration of the intermediate parameters η x , η y , c x , c y respectively, are respectively the results of the n-th iteration of c x , c y respectively, are respectively the differential results of the (n + 1)-th result u n+1 of the image in the row and column directions, shrink is the activation function, and L is the noise reduction constant parameter; S5: Updating the iteration number, specifically: n = n + 1, where n is the calculated value of the iteration number; S6: Determine whether the iteration number n is greater than the set total iteration number N tol , if it is greater than the total iteration number, the iteration ends, and I3 = u n , otherwise, repeat the iteration steps of S2 to S5 above.

5. The CT image noise equalization and noise reduction system according to claim 4, wherein, The iterative denoising processing module is further configured to restore the CT values of the output denoised image I3 to obtain a target image I4 with a noise-equalized distribution after denoising.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 3 above is implemented.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method described in any one of claims 1 to 3 above is implemented.

8. A computer program product, including a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 3 is implemented.

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