CN114972115A - Image noise reduction method, device, equipment and medium - Google Patents
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Abstract
The invention provides an image denoising method, device, equipment and medium, relating to the technical field of medical image processing and comprising the following steps: acquiring projection data of water molds with different diameters under different scanning parameters; carrying out image reconstruction based on fixed reconstruction parameters, collecting noise, and establishing noise-current change curves corresponding to water models with different diameters; performing curve fitting to determine fitting parameters; adopting the difference reconstruction parameters to reconstruct the image so as to generate a correction coefficient set; acquiring an initial image, and calculating the equivalent water model diameter; calculating initial noise, and correcting the current initial noise to obtain target noise; the current initial image is subjected to noise reduction processing based on the current target noise to obtain a target image, and the problem that the filtering effect is poor due to unreasonable filtering parameters set in the noise reduction processing of the existing CT image is solved.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to an image noise reduction method, device, equipment and medium.
Background
CT (computed tomography) scanning is a medical imaging technique used in radiology for obtaining detailed images of the body in a non-invasive manner. The principle of the CT scanner is: x-ray attenuation of different tissues in the body is measured using a rotating X-ray tube and an array of detectors placed in a gantry, and then multiple X-ray measurements taken from different angles are processed on a computer using a reconstruction algorithm to generate tomographic (cross-sectional) images of the body.
With the rapid development of the related art of CT, how to ensure the image quality when using lower dose becomes an urgent problem to be solved. The image quality can be effectively improved and the diagnosis accuracy can be ensured by reducing the image noise on the premise of ensuring the relevant details of the image. In the prior art, noise reduction is mostly performed by filtering in an image domain, and the method needs to reasonably select a filtering mode and corresponding parameters, otherwise, the noise reduction effect is poor.
Disclosure of Invention
In order to overcome the technical defects, the invention aims to provide an image denoising method, device, equipment and medium, which solve the problem of poor filtering effect caused by unreasonable filtering parameters set in the denoising processing of the existing CT image.
The invention discloses an image noise reduction method, which is applied to a CT system and comprises the following steps:
acquiring projection data of water molds with different diameters under different scanning parameters;
carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise, and establishing noise-current change curves corresponding to water models with different diameters;
performing curve fitting on each noise-current change curve, and determining fitting parameters;
performing image reconstruction based on the projection data by using a difference reconstruction parameter to generate a correction coefficient set acting on the reconstruction parameter, wherein the correction coefficient set comprises correction coefficients corresponding to each sub-parameter in the reconstruction parameter;
acquiring target current and an initial image obtained by adopting target reconstruction parameters to reconstruct after scanning based on the target current, and calculating the equivalent water model diameter according to pixel points on the initial image;
matching a noise-current change curve and fitting parameters according to the equivalent water model diameter, and calculating initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water model diameter;
acquiring a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set, and correcting the initial noise to obtain a target noise;
and carrying out noise reduction processing on the initial image based on the target noise to obtain a target image.
Preferably, the curve fitting each noise-current change curve and determining fitting parameters includes:
for the noise-current curve corresponding to any diameter water model,
determining a fitting function, the fitting function determined as: y ═ α × x β Wherein y is noise; x is a current; both alpha and beta are fitting parameters;
and determining the fitting parameters according to the noise corresponding to the water model with the diameter under different currents.
Preferably, the image reconstruction using the difference reconstruction parameter again to generate a set of correction coefficients acting on the reconstruction parameter includes:
adjusting any sub-parameter in the fixed reconstruction parameters, and generating a first difference reconstruction parameter while keeping other sub-parameters unchanged;
reconstructing an image based on the first difference reconstruction parameter, and acquiring and obtaining noise;
obtaining a correction coefficient corresponding to the adjusted sub-parameter according to a ratio of the water model noise obtained by image reconstruction based on the first difference reconstruction parameter to the water model noise obtained by image reconstruction based on the fixed reconstruction parameter;
and restoring the adjusted sub-parameters, repeatedly acquiring any sub-parameter for adjustment until the correction coefficient corresponding to each sub-parameter is obtained, and generating a correction coefficient set acting on the reconstruction parameters.
Preferably, the calculating an equivalent water model diameter according to the pixel points on the initial image includes:
calculating pixel sums of line pixels corresponding to each pixel in the Z direction in the initial image, wherein the line pixels are set as each pixel set in the X direction;
and determining the pixel sum as the equivalent water model circle area, and calculating according to a circle area calculation formula to obtain the equivalent water model diameter.
Preferably, the calculating of the initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water mode diameter further comprises:
when the noise-current change curve and the fitting parameters of which the water model diameters are consistent with the equivalent water model diameter are not obtained;
the initial noise is determined by interpolation according to the target current.
Preferably, obtaining a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set, and correcting the initial noise to obtain a target noise, includes:
acquiring correction coefficients matched with the sub-parameters in the target reconstruction parameters;
and performing product calculation on each correction coefficient corresponding to the target reconstruction parameter and the initial noise to obtain target noise.
Preferably, the sub-parameters of the reconstruction parameters include reconstruction kernel, layer thickness.
The present invention also provides an image noise reduction device, comprising:
the data acquisition module is used for acquiring projection data of water models with different diameters under different scanning acquisition numbers;
the fitting module is used for carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise and establishing a noise-current change curve corresponding to water models with different diameters; performing curve fitting on each noise-current change curve, and determining fitting parameters;
a coefficient determination module for performing image reconstruction based on the projection data using the difference reconstruction parameters to generate a correction coefficient set acting on the reconstruction parameters, wherein the correction coefficient set includes correction coefficients corresponding to respective sub-parameters in the reconstruction parameters;
the processing module is used for acquiring target current, reconstructing an acquired initial image by adopting target reconstruction parameters after scanning based on the target current, and calculating an equivalent water model diameter according to pixel points on the initial image;
the calculation module is used for matching a noise-current change curve and a fitting parameter according to the equivalent water model diameter and calculating initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water model diameter;
the correction module is used for acquiring a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set of the reconstruction parameter, and correcting the initial noise to acquire target noise;
and the execution module is used for carrying out noise reduction processing on the initial image based on the target noise to obtain a target image.
The present invention also provides a computer apparatus, comprising:
a memory for storing executable program code; and
a processor for calling said executable program code in said memory, the execution step including said image denoising method.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image noise reduction method.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of utilizing a plurality of water model projection data under different diameters under different scanning parameters to respectively reconstruct the projection data so as to obtain fitting parameters of noise relative to current change and correction coefficients acting on the reconstructed data, calculating equivalent water model diameters according to an initial image so as to obtain target noise matched with the initial image, and using the target noise as parameters of a filtering and denoising process, so that the problem that the filtering effect is poor due to unreasonable filtering parameters set in denoising processing of the existing CT image is solved, and better image quality is obtained after denoising.
Drawings
FIG. 1 is a flowchart illustrating a first embodiment of an image denoising method according to the present invention;
FIG. 2 is a diagram illustrating a noise-current variation curve according to a first embodiment of an image denoising method of the present invention;
FIG. 3 is a flowchart illustrating a method for generating a set of correction coefficients for reconstruction parameters according to a first embodiment of the image denoising method;
FIG. 4 is a flowchart illustrating a method for reducing noise of an image according to an embodiment of the present invention, wherein the method is used for calculating an equivalent water model diameter according to pixel points on an initial image;
FIG. 5 is a reference diagram of an initial image and a target image before and after denoising processing according to an embodiment of an image denoising method according to the present invention;
FIG. 6 is a block diagram of a second embodiment of an image denoising apparatus according to the present invention;
fig. 7 is a block schematic diagram of the apparatus of the present invention.
Reference numerals:
8-image noise reduction means; 81-a data acquisition module; 82-a fitting module; 83-coefficient determination module; 84-a processing module; 85-a calculation module; 86-a correction module; 87-an execution module; 9-a computer device; 91-a memory; 92-processor.
Detailed Description
The advantages of the invention are further illustrated in the following description of specific embodiments in conjunction with the accompanying drawings.
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
The first embodiment is as follows: the present embodiment discloses an image noise reduction method, referring to fig. 1 to 5, applied to a CT system, for reducing noise of an initial image obtained in the CT system, in this embodiment, the initial image is a CT image (a scout image) formed by a low-dose CT scan that is scanned under a target scanning parameter and is reconstructed based on the target reconstruction parameter, wherein the CT image contains more noise, in this scheme, projection data of a water phantom is used to estimate noise of the image, and the estimated noise is used as a filtering parameter to reduce noise to obtain a target image, so as to achieve a better noise reduction effect, specifically, the method includes the following steps:
s100: acquiring projection data of water molds with different diameters under different scanning parameters;
in this embodiment, the scan parameters include a key scan parameter and a current (tube current/scan current), the key scan parameter refers to a combination of scan parameters that may significantly affect image noise, the scan parameters that may significantly affect image noise include, but are not limited to, tube voltage, a filter, and the like, and the CT water phantom is a circular container filled with water. By way of example, a water model of 10/20/30cm in diameter is taken, and several projection data are obtained by scanning with different tube currents (e.g., 10mA apart from the minimum to maximum supported by the system) at different key scan parameters.
S200: carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise, and establishing noise-current change curves corresponding to water models with different diameters; performing curve fitting on each noise-current change curve, and determining fitting parameters;
it should be noted that the fixed reconstruction parameters refer to a preset set of reconstruction parameter combinations serving as a reference standard, including parameters that can significantly affect image noise, such as a reconstruction kernel, a layer thickness (e.g., a standard reconstruction kernel, a layer thickness of 10mm, etc.);
specifically, the performing curve fitting on each noise-current change curve and determining fitting parameters includes:
determining a fitting function for a noise-current change curve corresponding to a water model with any diameter, wherein the fitting function is determinedThe following are defined: y ═ α × x β Wherein y is noise; x is a current; both alpha and beta are fitting parameters; and determining the fitting parameters according to the noise corresponding to the water model with the diameter under different currents.
In the above step, referring to fig. 2, the noise-current change curve corresponding to the water model of each diameter changes in the form of an approximately exponential curve, and therefore, the function representation in the form of an exponential is adopted, that is, the fitting parameters α, β of each curve are calculated according to the plurality of projection data obtained in the above step. It should be noted that, in the present embodiment, the collected noise is measured according to the relevant standard, that is, the measurement regulation and control in the prior art are adopted, and will not be specifically described herein.
S300: performing image reconstruction based on the projection data by using a difference reconstruction parameter to generate a correction coefficient set acting on the reconstruction parameter, wherein the correction coefficient set comprises correction coefficients corresponding to sub-parameters in the reconstruction parameter;
in this embodiment, sub-parameters of all reconstruction parameters (including the fixed reconstruction parameter, the difference reconstruction parameter, and the target reconstruction parameter described below) include, but are not limited to, a reconstruction kernel, a layer thickness, and the like, and all of the sub-parameters are parameters affecting image noise.
Specifically, in the above steps, the image reconstruction is performed again by using the difference reconstruction parameter to generate a correction coefficient set acting on the reconstruction parameter, with reference to fig. 3, including:
s310: adjusting any sub-parameter in the fixed reconstruction parameters, and generating a first difference reconstruction parameter while keeping other sub-parameters unchanged;
for explanation, the above-mentioned difference reconstruction parameter refers to that one sub-parameter is adjusted compared with a fixed reconstruction parameter, and it should be noted that, if there is only one sub-parameter that is adjusted, for example, a reconstruction kernel is adjusted, data such as a layer thickness and the like are kept unchanged, so as to obtain a correction coefficient corresponding to the adjusted sub-parameter subsequently.
S320: reconstructing an image based on the first difference reconstruction parameter, and acquiring and obtaining noise;
in the above steps, the reconstruction of the image is the same as the method of reconstructing the image by using the fixed reconstruction parameters in the above step S200, and the difference is only that the specific reconstruction parameters are not the same.
S330: obtaining a correction coefficient corresponding to the adjusted sub-parameter according to a ratio of the water model noise obtained by image reconstruction based on the first difference reconstruction parameter to the water model noise obtained by image reconstruction based on the fixed reconstruction parameter;
as described in the above step, the two image reconstruction processes are the same, but the reconstruction parameters are not the same, so that the correction coefficients corresponding to the adjusted sub-parameters can be obtained according to the ratio of the two results, and it should be noted that, if the image reconstruction is performed with the projection data corresponding to the water phantom with a certain diameter, the projection data used in the two reconstruction processes (i.e., the projection data of the same scanning parameter for the water phantom with the same diameter) also need to be the same.
S340: and restoring the adjusted sub-parameters, repeatedly acquiring any one sub-parameter for adjustment until the correction coefficient corresponding to each sub-parameter is obtained, and generating a correction coefficient set acting on the reconstruction parameters.
As described above, in the present embodiment, it is emphasized that only one sub-parameter in the reconstruction parameters is adjusted each time, and then the correction coefficient corresponding to the sub-parameter is calculated, so that when another sub-parameter is adjusted, the originally adjusted sub-parameter still coincides with the corresponding sub-parameter in the fixed reconstruction parameters.
The image noise is corrected based on the reconstruction kernel, the layer thickness, and the like as described above. And (4) reconstructing the image by using the difference reconstruction parameters, and calculating the ratio of the image noise under the condition to the image noise when the fixed reconstruction parameters are used, so as to obtain a correction coefficient. The differential reconstruction parameter is a set of reconstruction parameter combinations, except one parameter, wherein the rest of reconstruction parameters are consistent with the fixed reconstruction parameters. By way of example and not limitation, if this parameter other than the fixed reconstruction parameter is a reconstruction kernel, the resulting ratio is referred to as a reconstruction kernel correction coefficient. In the same way, a series of correction coefficients such as layer thickness correction coefficient can be obtained.
S400: acquiring target current and an initial image obtained by adopting target reconstruction parameters to reconstruct after scanning based on the target current, and calculating the equivalent water model diameter according to pixel points on the initial image;
in the present embodiment, the initial image is a CT image with noise, and the noise in the initial image is calculated from the projection data of the equivalent water model in the above step with respect to the equivalent water model in the water model, and the matched water model noise in the initial image is obtained as the filter parameter, thereby improving the noise reduction effect.
Specifically, referring to fig. 4, the calculating the equivalent water model diameter according to the pixel point on the initial image includes:
s410: calculating pixel sums of line pixels corresponding to each pixel in the Z direction in the initial image, wherein the line pixels are set as each pixel set in the X direction;
in the above steps, each pixel point in the initial image, which is the same in the Z direction and different in the X direction (i.e., the horizontal direction), is taken as the same row, the sum of the pixel values of all the pixel points in each row is calculated, and the sum is taken as the area a (Z) of the equivalent circular water model corresponding to each pixel point in the Z direction.
S420: and determining the pixel sum as the equivalent water model circle area, and calculating according to a circle area calculation formula to obtain the equivalent water model diameter.
In the above steps, the area a (z) of the equivalent circular water model is obtained, and then the distribution of the equivalent water model diameter of the object along the z direction, that is, the distribution of the equivalent circular water model diameter of the object along the z direction can be estimated according to a (z), that is, the equivalent circular water model area a (z) is obtained
The calculation formula is obtained by conversion according to a circle area calculation formula.
S500: matching a noise-current change curve and fitting parameters according to the equivalent water model diameter, and calculating initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water model diameter;
in this embodiment, the noise-current change curve is matched according to the equivalent water model diameter, that is, whether a noise-current change curve corresponding to a water model having a diameter consistent with the equivalent water model diameter exists is found, and if so, the initial noise can be determined according to the target current directly according to the fitting parameter.
It should be noted that, a noise-current change curve completely consistent with the equivalent water mode diameter may not be obtained in the search process, where the calculating of the initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water mode diameter further includes: and when the noise-current change curve and the fitting parameters of which the water model diameter is consistent with the equivalent water model diameter are not obtained, determining the initial noise by interpolation according to the target current. Specifically, by way of example and not limitation, if the equivalent water mode diameter is between 20cm and 30cm, and the noise-current variation curve of the equivalent water mode diameter is between 20cm and 30cm can be obtained according to the above steps S100 to S300, at this time, the initial noise can be determined by interpolation, and the calculated z-position water mode diameter is assumed to be d z And satisfy d 1 <d z <d 2 。d 1 And d 2 The noise-current change curves of the water mould with the diameters of 20cm and 30cm are respectively, and the noise values corresponding to the current are respectively sigma 1 And σ 2 Then the initial noise σ z At σ 1 And σ 2 In the meantime.
S600: acquiring a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set, and correcting the initial noise to acquire target noise;
in the above steps, the initial noise estimated by the noise-current variation curve is corrected by using the correction coefficient, so as to obtain the target noise, where the correction is used to correct the influence of the adjustment of the target reconstruction parameter relative to the fixed reconstruction parameter on the generated noise, it should be noted that, since the target reconstruction parameter includes a plurality of sub-parameters and the correction coefficient set includes the correction coefficient for each sub-parameter, the target reconstruction parameter may be adjusted by a plurality of sub-parameters relative to the fixed reconstruction parameter, and therefore, the matching correction coefficient here should include a plurality of sub-parameters and may also be consistent with the number of sub-parameters.
Specifically, obtaining a matched correction coefficient from the correction coefficient set according to the target reconstruction parameter, and correcting the initial noise to obtain a target noise, includes:
s610: acquiring correction coefficients matched with the sub-parameters in the target reconstruction parameters;
specifically, in the above step, it should be noted that the correction system matched with each sub-parameter in the target reconstruction parameter may be all sub-parameters included in the target reconstruction parameter, or may be a sub-parameter adjusted by a relatively fixed reconstruction parameter in the target reconstruction parameter, and may be set according to a specific implementation scenario, and each sub-parameter may correspond to a plurality of correction coefficients, where the plurality of correction coefficients are sub-parameters adjusted by different amplitudes, and may be partially consistent, so that the corresponding correction coefficient may be obtained according to the corresponding matching of the sub-parameter in the target reconstruction parameter.
S620: and performing product calculation on each correction coefficient corresponding to the target reconstruction parameter and the initial noise to obtain target noise.
Specifically, the image estimation noise is corrected by using the correction coefficient to obtain a final estimated noise value, which may be represented as:
wherein σ z Is the initial noise; i is 1, 2 … …, n is the number of subparameters; f. of i The correction coefficients associated with the respective sub-parameters in the reconstruction parameters mentioned in the above step S300.
S700: and carrying out noise reduction processing on the initial image based on the target noise to obtain a target image.
In the above steps, the image is denoised by using the existing filtering denoising technology, wherein the noise sigma calculated in the above steps is used in the weight calculation in the filtering process z，final As a parameter. Obtaining the noise reduction image img after filtering corr I.e. the target image, the noise sigma z，final Based on the noise-current change curve fitting of the water model corresponding to the initial image and having the same diameterThe matching degree with the noise in the initial image is higher, so that the noise reduction effect is improved, and different from the parameters set in the prior art, referring to fig. 5, the left image in fig. 5 is the initial image of the lung image (without the noise reduction method in the present embodiment), and the right image in fig. 5 is the target image of the lung image (after noise reduction), in the present embodiment, the image noise is estimated by projection, and the image noise obtained by the calculation is used as one parameter of the filtering noise reduction algorithm, so that better image quality is obtained after noise reduction.
Example two: the embodiment provides an image noise reduction apparatus 8, referring to fig. 6, applied in a CT system, specifically including the following:
the data acquisition module 81 is used for acquiring projection data of water models with different diameters under different scanning acquisition numbers; carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise, and establishing noise-current change curves corresponding to water models with different diameters;
specifically, the reconstruction parameter refers to a preset set of reconstruction parameter combinations serving as a reference standard, including parameters that can significantly affect image noise, such as a reconstruction kernel, a layer thickness (e.g., a standard reconstruction kernel, a layer thickness of 10mm, etc.).
A fitting module 82, configured to perform curve fitting on each noise-current change curve and determine fitting parameters;
specifically, the noise-current change corresponding to the water modes of the respective diameters is expressed by a function in an exponential form.
A coefficient determining module 83, configured to perform image reconstruction based on the projection data by using the difference reconstruction parameter to generate a correction coefficient set acting on the reconstruction parameter, where the correction coefficient set includes correction coefficients corresponding to respective sub-parameters in the reconstruction parameter;
it should be noted that the differential reconstruction parameter refers to one of the sub-parameters being adjusted compared to the fixed reconstruction parameter, and it should be noted that there is one and only one sub-parameter being adjusted, such as adjusting the reconstruction kernel or the layer thickness.
The processing module 84 is configured to obtain a target current, reconstruct an obtained initial image based on the target current and target reconstruction parameters after scanning, and calculate an equivalent water model diameter according to pixel points on the initial image;
specifically, each pixel point in the initial image, which is the same in the Z direction and different in the X direction (i.e., the horizontal direction), is taken as the same row, the sum of pixel values of all pixel points in each row is calculated, the sum is taken as the area of the equivalent circular water model corresponding to each pixel point in the Z direction, and the equivalent water model diameter is obtained according to a circular area formula.
A calculating module 85, configured to match a noise-current change curve and fitting parameters according to the equivalent water model diameter, and calculate an initial noise according to a position of the target current in the noise-current change curve matched with the equivalent water model diameter;
a correcting module 86, configured to obtain a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set of the reconstruction parameter, and correct the initial noise to obtain a target noise;
and the execution module 87 is configured to perform noise reduction processing on the initial image based on the target noise to obtain a target image.
In the embodiment, the method is divided into two parts, one part comprises a data acquisition module 81, a fitting module 82 and a coefficient determination module 83 as a data preprocessing part, wherein the data acquisition module 81 is used for acquiring a plurality of water model projection data, the fitting module 82 and the coefficient determination module 83 respectively reconstruct the projection data, the fitting module 82 uses fixed reconstruction data to obtain fitting parameters of noise relative to current change, the coefficient determination module 83 uses difference reconstruction data for obtaining correction coefficients for the reconstruction data, the other part comprises a processing module 84, a calculation module 85, a correction module 86 and an execution module 87 as a processing part for a scout image, wherein the processing module 84 calculates equivalent water model diameters for obtaining matched fitting parameters by the calculation module 85 and calculating initial noise, the correction module 86 corrects the initial noise by using the correction coefficients obtained by the coefficient determination module 83, finally, the execution module 87 is adopted to perform noise reduction processing on the initial image based on the corrected initial noise (i.e. target noise).
In the embodiment, the noise of the image is estimated by combining the water model projection data, and the estimated noise is used as the filtering parameter to reduce the noise, so that the image quality after noise reduction is better, the method is different from the parameter set in the prior art, and the problem that the filtering effect is not good because the filtering parameter is unreasonable in the noise reduction processing of the existing CT image is solved.
Example three:
to achieve the above object, the present invention further provides a computer device 9, and referring to fig. 7, the computer device may be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like executing a program. The computer device of the embodiment at least includes but is not limited to: a memory 91, a processor 92 communicatively connected to each other through a device bus. It should be noted that fig. 7 only shows a computer device with components, but it should be understood that not all of the shown components are required to be implemented, and more or fewer components may be implemented instead.
In this embodiment, the storage 91 may be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory 91 may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device. In this embodiment, the memory 91 is generally used for storing an operating device installed in a computer device and various types of application software, such as program codes, sample sets, and the like of an image noise reduction method according to an embodiment. Further, the memory 91 can also be used to temporarily store various types of data that have been output or are to be output.
Processor 92 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip in some embodiments. The processor 92 is typically used to control the overall operation of the computer device. In this embodiment, the processor 92 is configured to execute the program code stored in the memory 91 or process data, for example, execute an image noise reduction method according to an embodiment.
Example four:
to achieve the above objects, the present invention further provides a computer-readable storage device, which includes a plurality of storage media, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or D memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor 92, implements corresponding functions of the image noise reduction device of the second embodiment. The computer readable storage medium of this embodiment is used for storing data such as water-mode projection data, water-mode noise, a noise-current variation curve, fitting parameters, and correction coefficients, and when being executed by the processor 92, implements an image noise reduction method according to an embodiment and an image noise reduction apparatus according to a second embodiment.
It should be noted that the embodiments of the present invention have been described in terms of preferred embodiments, and not by way of limitation, and that those skilled in the art can make modifications and variations of the embodiments described above without departing from the spirit of the invention.
Claims (10)
1. An image noise reduction method is applied to a CT system, and comprises the following steps:
acquiring projection data of water molds with different diameters under different scanning parameters;
carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise, and establishing noise-current change curves corresponding to water models with different diameters;
performing curve fitting on each noise-current change curve, and determining fitting parameters;
performing image reconstruction based on the projection data by using a difference reconstruction parameter to generate a correction coefficient set acting on the reconstruction parameter, wherein the correction coefficient set comprises correction coefficients corresponding to each sub-parameter in the reconstruction parameter;
acquiring target current and an initial image obtained by adopting target reconstruction parameters to reconstruct after scanning based on the target current, and calculating the equivalent water model diameter according to pixel points on the initial image;
matching a noise-current change curve and fitting parameters according to the equivalent water model diameter, and calculating initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water model diameter;
acquiring a matched correction coefficient according to the target reconstruction parameter from the correction coefficient set, and correcting the initial noise to acquire target noise;
and carrying out noise reduction processing on the initial image based on the target noise to obtain a target image.
2. The noise reduction method according to claim 1, wherein the curve-fitting each noise-current variation curve and determining fitting parameters comprises:
for the noise-current curve corresponding to any diameter water model,
determining a fitting function, the fitting function determined as: y ═ α × x β Wherein y is noise; x is a current; both alpha and beta are fitting parameters;
and determining the fitting parameters according to the noise corresponding to the water model with the diameter under different currents.
3. The noise reduction method according to claim 1, wherein the reconstructing the image again using the difference reconstruction parameter to generate a set of correction coefficients for the reconstruction parameter comprises:
adjusting any sub-parameter in the fixed reconstruction parameters, and generating a first difference reconstruction parameter while keeping other sub-parameters unchanged;
reconstructing an image based on the first difference reconstruction parameter, and acquiring and obtaining noise;
obtaining a correction coefficient corresponding to the adjusted sub-parameter according to a ratio of the water model noise obtained by image reconstruction based on the first difference reconstruction parameter to the water model noise obtained by image reconstruction based on the fixed reconstruction parameter; and restoring the adjusted sub-parameters, repeatedly acquiring any sub-parameter for adjustment until the correction coefficient corresponding to each sub-parameter is obtained, and generating a correction coefficient set acting on the reconstruction parameters.
4. The method for reducing noise according to claim 1, wherein the calculating an equivalent water-mode diameter according to the pixel points on the initial image comprises:
calculating pixel sums of line pixels corresponding to each pixel in the Z direction in the initial image, wherein the line pixels are set as each pixel set in the X direction;
and determining the pixel sum as the equivalent water model circle area, and calculating according to a circle area calculation formula to obtain the equivalent water model diameter.
5. The noise reduction method according to claim 1, wherein calculating an initial noise from a position of the target current in a noise-current variation curve matched to the equivalent water mode diameter further comprises:
when the noise-current change curve and the fitting parameters of which the water model diameter is consistent with the equivalent water model diameter are not obtained;
the initial noise is determined by interpolation according to the target current.
6. The noise reduction method according to claim 1, wherein obtaining a matched correction coefficient from the correction coefficient set according to the target reconstruction parameter, and correcting the initial noise to obtain a target noise comprises:
acquiring correction coefficients matched with all sub-parameters in the target reconstruction parameters;
and performing product calculation on each correction coefficient corresponding to the target reconstruction parameter and the initial noise to obtain target noise.
7. The noise reduction method according to claim 1, comprising:
sub-parameters of the reconstruction parameters include reconstruction kernel, layer thickness.
8. An image noise reduction apparatus, comprising:
the data acquisition module is used for acquiring projection data of water models with different diameters under different scanning acquisition numbers;
the fitting module is used for carrying out image reconstruction on each projection data based on fixed reconstruction parameters, acquiring noise and establishing a noise-current change curve corresponding to water models with different diameters; performing curve fitting on each noise-current change curve, and determining fitting parameters;
a coefficient determination module, configured to perform image reconstruction based on the projection data using the difference reconstruction parameter to generate a correction coefficient set acting on a reconstruction parameter, where the correction coefficient set includes correction coefficients corresponding to respective sub-parameters in the reconstruction parameter;
the processing module is used for acquiring target current, reconstructing an acquired initial image by adopting target reconstruction parameters after scanning based on the target current, and calculating equivalent water model diameter according to pixel points on the initial image;
the calculation module is used for matching a noise-current change curve and a fitting parameter according to the equivalent water model diameter and calculating initial noise according to the position of the target current in the noise-current change curve matched with the equivalent water model diameter;
the correction module is used for acquiring a matched correction coefficient from the correction coefficient set according to the target reconstruction parameter, correcting the initial noise and acquiring a target noise;
and the execution module is used for carrying out noise reduction processing on the initial image based on the target noise to obtain a target image.
9. A computer device, characterized in that the computer device comprises:
a memory for storing executable program code; and
a processor for invoking said executable program code in said memory, the executing step comprising the image denoising method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that:
the computer program when executed by a processor implements the steps of the image denoising method of any one of claims 1 through 7.
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| CN115294232A (en) * | 2022-09-30 | 2022-11-04 | 浙江太美医疗科技股份有限公司 | Identification method and device of reconstruction algorithm, electronic equipment and storage medium |
| CN117974833A (en) * | 2024-04-01 | 2024-05-03 | 腾讯科技（深圳）有限公司 | Image generation method, apparatus, device, readable storage medium, and program product |
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| CN115294232A (en) * | 2022-09-30 | 2022-11-04 | 浙江太美医疗科技股份有限公司 | Identification method and device of reconstruction algorithm, electronic equipment and storage medium |
| CN117974833A (en) * | 2024-04-01 | 2024-05-03 | 腾讯科技（深圳）有限公司 | Image generation method, apparatus, device, readable storage medium, and program product |
| CN117974833B (en) * | 2024-04-01 | 2024-05-31 | 腾讯科技（深圳）有限公司 | Image generation method, apparatus, device, readable storage medium, and program product |
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