CN118750019A - A dual-energy correction method and system based on bone sclerosis model - Google Patents

Abstract

The present invention discloses a dual-energy correction method and system based on a bone sclerosis model, comprising: adjusting the pulse frequency of X-rays in an acquisition device to irradiate the coordinate forms of soft tissue and bone tissue with X-rays respectively, so as to more comprehensively reflect the characteristics of soft tissue and bone tissue and improve the sampling accuracy of the image; designing a weight function through the value of bone mineral density, the mineral density of cortical bone, the average thickness of trabeculae and the average thickness of cortical bone, so as to make the reconstructed bone sclerosis model more accurate; iteratively adjusting the parameters in the reconstructed model through the artifact recognition result, realizing dynamic correction and improving the quality of the corrected image; comparing the corrected image with the actual bone image, and displaying the model in three dimensions in a virtual reality environment; generating a report based on the comparison result of the model of interest selected by the user, forming a personalized display result and improving the user experience.

Description

A dual-energy correction method and system based on bone sclerosis model

Technical Field

The present invention relates to the technical field of image and model processing, and in particular to a dual-energy correction method and system based on a bone sclerosis model.

Background Art

Since its discovery, X-ray imaging technology has played an important role in the field of medical imaging. Traditional X-ray imaging technology mainly relies on single-energy X-rays, which has certain limitations in distinguishing soft tissue from bone tissue. With the advancement of technology, dual-energy X-ray imaging technology has emerged. By acquiring images at different energy levels, it can better distinguish soft tissue from bone tissue, improve imaging contrast and diagnostic accuracy. However, although dual-energy X-ray imaging technology has improved image quality to a certain extent, there are still some problems in practical applications, such as the appearance of hardening effect artifacts, which will affect the accuracy of the image and thus affect the diagnostic results. Therefore, how to effectively correct these artifacts and improve image quality has become one of the current research hotspots.

In current research and literature, simple or complex image processing algorithms are usually used to correct hardening effect artifacts, but these methods are often highly dependent on bone density and structure, lack a deep understanding and modeling of bone hardening effects, resulting in unsatisfactory correction effects. There are also several deficiencies: First, single-frequency X-ray image sampling cannot fully reflect the complex characteristics of soft tissue and bone tissue, and it is difficult to effectively distinguish and correct artifacts caused by hardening effects; second, the traditional reconstruction weight adjustment method lacks comprehensive consideration of bone density and structural characteristics, resulting in insufficient accuracy of parameter adjustment during the correction process; third, the existing artifact recognition and correction methods are mostly static processing, lacking a dynamic iterative adjustment mechanism, resulting in unsatisfactory correction effects.

Summary of the invention

The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a dual-energy correction method based on a bone sclerosis model to solve the problems mentioned in the background technology.

In order to solve the above technical problems, the present invention provides the following technical solutions:

In a first aspect, the present invention provides a dual-energy correction method based on a bone sclerosis model, comprising:

Acquire X-ray images with multiple frequencies within the soft tissue imaging energy range and the bone tissue imaging energy range through multi-frequency sampling technology;

Using the density and structural characteristics of bones, the reconstruction weights in the image are adjusted to reconstruct the bone sclerosis model, and the artifacts caused by the sclerosis effect in the image are identified based on the reconstructed bone sclerosis model;

According to the identification results of the artifacts, the parameters in the reconstruction model are iteratively adjusted, and the corrected image is output, which is then compared with the actual bone image.

As a preferred solution of the dual-energy correction method based on the bone sclerosis model described in the present invention, wherein: X-ray images of multiple frequencies are acquired within the soft tissue imaging energy range and the bone tissue imaging energy range by multi-frequency sampling technology, including:

Determine the soft tissue imaging energy range and the bone tissue imaging energy range in the acquisition device;

The soft tissue and bone tissue to be imaged are represented in the acquisition device in the form of a number of coordinates;

The pulse frequency of X-rays in the acquisition device is adjusted to irradiate the coordinate forms of soft tissue and bone tissue with X-rays respectively, so as to obtain multiple groups of X-ray images of soft tissue imaging and multiple groups of X-ray images of bone tissue imaging.

As a preferred solution of the dual-energy correction method based on the bone sclerosis model described in the present invention, wherein: using the density and structural characteristics of the bone, adjusting the reconstruction weight in the image, and reconstructing the bone sclerosis model, including:

The bone mineral density values and the mineral density of the cortical bone of the pixel points in the multiple sets of soft tissue imaging X-ray images and the multiple sets of bone tissue imaging X-ray images are calculated by the dual-energy X-ray absorption measurement formula;

The average thickness of trabecular bone was obtained through the trabecular bone segmentation algorithm;

The average thickness of the cortical bone is obtained through the cortical bone segmentation algorithm.

As a preferred embodiment of the dual-energy correction method based on the bone sclerosis model described in the present invention, it also includes:

A weighting function is designed by using the bone mineral density value, the mineral density of the cortical bone, the average thickness of the trabecular bone, and the average thickness of the cortical bone;

The bone sclerosis model is reconstructed according to the weight function.

As a preferred embodiment of the dual-energy correction method based on the bone sclerosis model of the present invention, the artifacts caused by the sclerosis effect in the image are identified according to the reconstructed bone sclerosis model, including:

The composite gradient operator is introduced to calculate the gradient caused by the image hardening effect;

Calculating the magnitude and direction of the gradient;

The calculated amplitude value is subjected to non-maximum suppression, and the high and low thresholds of the image are adjusted based on the calculated amplitude value;

Connect the edges of the adjusted image high and low thresholds to extract artifact features;

According to the matching results between the artifact features and the bone sclerosis model, the artifact area is marked.

As a preferred solution of the dual-energy correction method based on the bone sclerosis model described in the present invention, the following method is used: according to the recognition result of the artifact, the parameters in the reconstruction model are iteratively adjusted to output the corrected image, including:

Define an error function to calculate the difference between the artifact in the image and the expected artifact;

Using the difference, updating the parameters in the bone sclerosis model;

Calculate the gradient of the error function with respect to the parameters;

Correct the image according to the updated parameters;

Repeat the above steps until the error function reaches the maximum number of iterations of the model and output the corrected image.

As a preferred solution of the dual-energy correction method based on the bone sclerosis model described in the present invention, the corrected image is compared with the actual bone image, including:

Converting the corrected image into a three-dimensional model, displaying the three-dimensional model in a virtual reality environment, and generating a report based on the comparison results of interest selected by the user;

The report includes difference maps, analysis data and 3D model screenshots.

In a second aspect, the present invention provides a dual-energy correction system based on a bone sclerosis model, comprising:

A multi-frequency image acquisition module is configured to acquire X-ray images of multiple frequencies within a soft tissue imaging energy range and a bone tissue imaging energy range through a multi-frequency sampling technique;

The bone sclerosis model reconstruction and artifact recognition module is configured to adjust the reconstruction weight in the image using the density and structural characteristics of the bone, reconstruct the bone sclerosis model, and recognize artifacts caused by the sclerosis effect in the image based on the reconstructed bone sclerosis model;

The adaptive model parameter adjustment and visualization output module is configured to iteratively adjust the parameters in the reconstruction model according to the recognition result of the artifact, output the corrected image, and compare the corrected image with the actual bone image.

In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

Compared with the prior art, the beneficial effects of the invention are as follows: the present invention adjusts the pulse frequency of X-rays in the acquisition device to perform X-ray irradiation on the coordinate forms of soft tissue and bone tissue respectively, which can more comprehensively reflect the characteristics of soft tissue and bone tissue and improve the sampling accuracy of the image; a weight function is designed through the bone mineral density value, the mineral density of cortical bone, the average thickness of trabeculae and the average thickness of cortical bone, so that the reconstructed bone sclerosis model is more accurate; the parameters in the reconstructed model are iteratively adjusted through the artifact recognition results, dynamic correction is achieved, and the quality of the corrected image is improved; the corrected image is compared with the actual bone image, and the model is displayed in three dimensions in a virtual reality environment, and the comparison results of the model of interest selected by the user are generated and a report is formed to form a personalized display result, thereby improving the user experience.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

FIG1 is an overall flow chart of a dual-energy correction method based on a bone sclerosis model according to an embodiment of the present invention;

FIG2 is a diagram of an iterative adjustment process of a dual-energy correction method based on a bone sclerosis model according to an embodiment of the present invention;

FIG3 is a comparison diagram of experimental parameters of a dual-energy correction method based on a bone sclerosis model according to an embodiment of the present invention;

FIG. 4 is a comparison diagram of SSIM and MSE of a dual-energy correction method based on a bone sclerosis model according to an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

Example 1

1 is a first embodiment of the present invention, which provides a dual-energy correction method based on a bone sclerosis model, including:

S1. Acquire X-ray images of multiple frequencies within the soft tissue imaging energy range and the bone tissue imaging energy range by using multi-frequency sampling technology;

Further, determining the soft tissue imaging energy range and the bone tissue imaging energy range in the acquisition device;

Specifically, the soft tissue imaging energy range is assumed to be: _Es = [ _{Es_min} , _{Es_max} ]; the bone tissue imaging energy range is assumed to be: _Eb = [ _{Eb_min} , _{Eb_max} ];

Furthermore, the soft tissue and bone tissue to be imaged are represented in the acquisition device in the form of a number of coordinates;

Specifically, the coordinate format in the acquisition device is: (x _i , y _i , z _i );

It should be noted that the imaging energy range and coordinate representation determine the X-ray acquisition range and pulse frequency;

Furthermore, the pulse frequency of the X-ray in the acquisition device is adjusted to irradiate the coordinate forms of the soft tissue and the bone tissue with X-rays respectively, so as to obtain multiple groups of X-ray images of soft tissue imaging and multiple groups of X-ray images of bone tissue imaging;

Specifically, the pulse frequency received by the soft tissue is: f _s ; the pulse frequency received by the bone tissue is: f _b ;

Specifically, multiple groups of soft tissue imaging X-ray images and multiple groups of bone tissue imaging X-ray images are respectively expressed as:

I _s ={I _s1 ,I _s2 ,I _s3 ,…,I _sn }

I _b ={I _b1 ,I _b2 ,I _b3 ,…,I _bn }

It should be noted that by considering different pulse frequencies of soft tissue and bone tissue respectively, X-ray images of soft tissue imaging and bone tissue imaging at different frequencies are obtained, which realizes capturing the complex characteristics of soft tissue and bone tissue at different frequencies;

S2, using the density and structural characteristics of the bone, adjusting the reconstruction weight in the image, reconstructing the bone sclerosis model, and identifying artifacts caused by the sclerosis effect in the image based on the reconstructed bone sclerosis model;

Furthermore, the bone mineral density values of the pixels in the multiple sets of soft tissue imaging X-ray images and the multiple sets of bone tissue imaging X-ray images and the mineral density of the cortical bone are calculated by using the dual-energy X-ray absorption measurement formula;

Specifically, the dual-energy X-ray absorption measurement formula is expressed as:

Wherein, BMD is the bone mineral density value, CMD is the mineral density of cortical bone; f(E _s ,I _s ) function represents the contribution of soft tissue imaging image I _s to the bone mineral density value (BMD) within the soft tissue imaging energy range E _s ; g(E _b ,I _b ) function represents the contribution of bone tissue imaging image I _b to the bone mineral density value (BMD) within the bone tissue imaging energy range E _b ; h(E _s ,I _s ) function represents the contribution of soft tissue imaging image I _s to the cortical bone mineral density (CMD) within the soft tissue imaging energy range E _s ; k(E _b ,I _b ) function represents the contribution of bone tissue imaging image I _b to the cortical bone mineral density (CMD) within the bone tissue imaging energy range E _b ;

Furthermore, the average thickness of trabeculae is obtained through the trabeculae segmentation algorithm, and the formula is expressed as:

Where N is the total number of pixels of the average thickness of trabecular bone;

Furthermore, the average thickness of the cortical bone is obtained through the cortical bone segmentation algorithm, and the formula is expressed as:

Where M is the total number of pixels of the average thickness of cortical bone;

Specifically, the second-order derivative is used to capture the local curvature changes of the image, where the changes in curvature can reflect the structural characteristics of trabecular and cortical bones;

Furthermore, a weight function is designed by using the bone mineral density value, the mineral density of the cortical bone, the average thickness of the trabecular bone, and the average thickness of the cortical bone;

Specifically, the weight function is expressed as:

w＝αBMD+βCMD+γT _trab +δT _cort

Among them, α, β, γ and δ represent the weight coefficients of bone mineral density (BMD), cortical bone mineral density (CMD), average trabecular thickness T _trab and average cortical bone thickness T _cort in model reconstruction, respectively;

Furthermore, the bone sclerosis model is reconstructed according to the weight function, which is expressed as:

in, is the model parameter, w _ij is the comprehensive weight of the i-th soft tissue image pixel and the j-th bone tissue image pixel;

Furthermore, a composite gradient operator is introduced to calculate the gradient caused by the image hardening effect, which is expressed as:

_Cx ＝ _Sx + _Px + _Rx + _Tx

_Cy ＝ _Sy + _Py + _Ry + _Ty

_Cz ＝ _Sz + _Pz + _Rz + _Tz

Wherein, S _x is the gradient component of the Sobel operator in the x direction, P _x is the gradient component of the Prewitt operator in the x direction, R _x is the gradient component of the Roberts operator in the x direction, and T _x is the gradient component of the Canny operator in the x direction; _Sy is the gradient component of the Sobel operator in the y direction, P _y is the gradient component of the Prewitt operator in the y direction, R _y is the gradient component of the Roberts operator in the y direction, and _Ty is the gradient component of the Canny operator in the y direction; S _z is the gradient component of the Sobel operator in the z direction, P _z is the gradient component of the Prewitt operator in the z direction, R _z is the gradient component of the Roberts operator in the z direction, and T _z is the gradient component of the Canny operator in the z direction;

Furthermore, the magnitude G and direction θ of the gradient are calculated to obtain:

Furthermore, the calculated amplitude value is subjected to non-maximum suppression, and the high and low thresholds of the image are adjusted based on the calculated amplitude value;

Specifically, non-maximum suppression is obtained:

Among them, ifG(x,y,z)is a local maximum alongθ(x,y,z) means if the gradient magnitude G(x,y,z) is a local maximum along the gradient direction θ(x,y,z);

Specifically, adjusting the high and low thresholds of the image is expressed as:

T _low = μ _G ―σ _G

Wherein, μ _G is the mean value of the gradient amplitude, σ _G is the standard deviation of the gradient amplitude;

Furthermore, the adjusted image high and low thresholds are edge-connected to extract artifact features, expressed as:

in, Indicates that if the gradient amplitude G′(x,y,z) after non-maximum suppression is greater than the high threshold or connected to a strong edge;

It should be noted that a strong edge refers to a pixel whose gradient amplitude is greater than a high threshold; 1 and -1 represent the retained value and the non-retained value, respectively;

Furthermore, the artifact area is marked according to the matching result between the artifact features and the bone sclerosis model;

Specifically, the extracted artifact features are matched with the bone sclerosis model, and the matching degree of each pixel point at the coordinate position is calculated:

Among them, eh(x,y,z) is the neighborhood of the position (x,y,z);

Specifically, the artifact area is marked as:

Among them, MatchingScore(x,y,z) represents the matching score between the artifact feature and the bone sclerosis model at the coordinate;

S3, iteratively adjusting the parameters in the reconstruction model according to the artifact recognition result, outputting a corrected image, and comparing the corrected image with the actual bone image;

Further, the iterative adjustment process is as follows;

S301, defining an error function, and calculating the difference between an artifact in the image and an expected artifact;

Specifically, the error function is defined as:

Among them, I _actual is the expected artifact;

S302, updating the parameters in the bone sclerosis model through the difference, the formula is expressed as:

Where η is the learning rate, is the gradient of the error function, Update parameters for the current time;

S303, calculate the gradient of the error function and the parameter, the formula is expressed as:

S304: Based on the updated parameters , correct the image and get:

Among them, I _corrected (x, y, z) is the corrected image;

S305, repeat S302 to S304 until the error function reaches the maximum number of iterations of the model, and output the corrected image;

Specifically, the error function Convergence or the error function reaches the maximum number of iterations of the model, where convergence requires the error function to reach the minimum value;

Specifically, if the model gradient is large, the maximum number of iterations is selected, and if the model gradient is small, convergence to the minimum value is selected;

It should be noted that by selecting iteration or convergence according to the gradient size, while achieving dynamic correction, the model's computing resources can be saved, preventing the model from overfitting the training data in high-gradient areas, reducing the risk of overfitting, and avoiding underfitting, thereby improving the generalization ability of the model;

Further, the corrected image is converted into a three-dimensional model, the three-dimensional model is displayed in a virtual reality environment, and a report is generated based on the comparison results of interest selected by the user;

Specifically, the virtual reality environment uses VR, and the comparison result is a three-dimensional stereogram display effect of the three-dimensional model and the actual skeleton;

The report includes difference diagrams, analysis data and 3D model screenshots.

Furthermore, this embodiment also provides a dual-energy correction system based on a bone sclerosis model, comprising:

A multi-frequency image acquisition module is configured to acquire X-ray images of multiple frequencies within a soft tissue imaging energy range and a bone tissue imaging energy range through a multi-frequency sampling technique;

The bone sclerosis model reconstruction and artifact recognition module is configured to adjust the reconstruction weight in the image using the density and structural characteristics of the bone, reconstruct the bone sclerosis model, and recognize artifacts caused by the sclerosis effect in the image based on the reconstructed bone sclerosis model;

The adaptive model parameter adjustment and visualization output module is configured to iteratively adjust the parameters in the reconstruction model according to the recognition results of the artifacts, output the corrected image, and compare the corrected image with the actual bone image.

This embodiment also provides a computer device, which is applicable to the dual-energy correction method based on the bone sclerosis model, and includes:

Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the dual-energy correction method based on the bone sclerosis model proposed in the above embodiment.

The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the dual-energy correction method based on the bone sclerosis model proposed in the above embodiment is implemented.

The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

Example 2

3 and 4 , which are the second embodiment of the present invention, the embodiment provides a dual-energy correction method based on a bone sclerosis model, including: further verifying the beneficial effects involved in the present invention by means of simulation experiments;

A GE Discovery CT750 HD dual-energy CT scanner was used in the experiment, and its parameters were configured as follows: soft tissue imaging energy range: 40-80 k (eV), bone tissue imaging energy range: 80-120 k (eV), X-ray pulse frequency: 1 k per second (Hz) in the initial state;

Four human bone images were selected, from different age groups, namely, 20, 30, 50 and 70 years old; four bone models were randomly collected, simulating bones of different densities and structures; the experiment was conducted at a temperature of 22±2°C and a humidity of 50±5°C; after the calculation and implementation of the scheme of the present invention, the corrected images were obtained and compared with the actual bone images, and the data results in two groups of comparison images were extracted, as shown in Tables 1 and 2;

Table 1 Results of the comparison group (actual image) and the test group (after correction)

From Table 1, it can be seen that the BMD value of the test group is lower than that of the control group, which indicates that after removing the hardening effect artifact, the method of the present invention can more accurately reflect the density of the actual bone mineral, which is reduced by about 50mg/ ^cm3 , and in the correction process, it can effectively reduce the error caused by the hardening effect, improve the authenticity of the image, and the CMD value is reduced by about 45mg/ ^cm3 and 50mg/ ^cm3 respectively; although the data of the average thickness of trabecular bone and cortical bone are relatively close, combined with the hardening effect artifact, it can be seen that the artifact is reduced from 15% and 14% to 5% and 4% respectively, which has a significant effect; secondly, in terms of image clarity, it can be seen that the image clarity after correction significantly improves the image quality;

In order to further quantify the correction effect, the structural similarity index (SSIM) and mean square error (MSE) are used to evaluate the image quality. The specific formula is as follows:

Among them, μ represents the mean value and σ represents the standard deviation;

Where H is the total number of all pixels;

The results are shown in Table 2;

Table 2

parameter Comparison group 1 Experimental Group 1 Comparison group 2 Experimental Group 2 SSIM 0.85 0.95 0.87 0.96 MSE 0.012 0.004 0.011 0.003

From Table 2, we can see that the SSIM value of the test group is significantly higher than that of the control group, reaching 0.95 and 0.96 respectively, while the control group is only 0.85 and 0.87; this indicates that the corrected image is more similar to the actual image in structure, reflecting a higher image quality; and the MSE value of the test group is significantly lower than that of the control group, which is 0.004 and 0.003 respectively, while the control group is 0.012 and 0.011; this indicates that the difference between the corrected image and the actual image is reduced, further verifying the effectiveness of the correction method.

Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims (10)

1. A dual-energy correction method based on a bone sclerosis model, characterized by comprising: Acquire X-ray images with multiple frequencies within the soft tissue imaging energy range and the bone tissue imaging energy range through multi-frequency sampling technology; Using the density and structural characteristics of bones, the reconstruction weights in the image are adjusted to reconstruct the bone sclerosis model, and the artifacts caused by the sclerosis effect in the image are identified based on the reconstructed bone sclerosis model; According to the identification results of the artifacts, the parameters in the reconstruction model are iteratively adjusted, and the corrected image is output, which is then compared with the actual bone image. 2. The dual-energy correction method based on the bone sclerosis model according to claim 1, characterized in that X-ray images of multiple frequencies are acquired within the soft tissue imaging energy range and the bone tissue imaging energy range by using a multi-frequency sampling technique, comprising: Determine the soft tissue imaging energy range and the bone tissue imaging energy range in the acquisition device; The soft tissue and bone tissue to be imaged are represented in the acquisition device in the form of a number of coordinates; The pulse frequency of the X-ray in the acquisition device is adjusted to irradiate the coordinate forms of the soft tissue and the bone tissue with X-rays respectively, so as to obtain multiple groups of X-ray images of soft tissue imaging and multiple groups of X-ray images of bone tissue imaging. 3. The dual-energy correction method based on the bone sclerosis model according to claim 2, characterized in that the bone sclerosis model is reconstructed by adjusting the reconstruction weights in the image using the density and structural characteristics of the bone, comprising: The bone mineral density values and the mineral density of the cortical bone of the pixel points in the multiple sets of soft tissue imaging X-ray images and the multiple sets of bone tissue imaging X-ray images are calculated by the dual-energy X-ray absorption measurement formula; The average thickness of trabecular bone was obtained through the trabecular bone segmentation algorithm; The average thickness of the cortical bone is obtained through the cortical bone segmentation algorithm. 4. The dual-energy correction method based on the bone sclerosis model according to claim 3, characterized in that it also includes: A weighting function is designed by using the bone mineral density value, the mineral density of the cortical bone, the average thickness of the trabecular bone, and the average thickness of the cortical bone; The bone sclerosis model is reconstructed according to the weight function. 5. The dual-energy correction method based on bone sclerosis model according to claim 3 or 4, characterized in that the artifacts caused by the sclerosis effect in the image are identified according to the reconstructed bone sclerosis model, comprising: The composite gradient operator is introduced to calculate the gradient caused by the image hardening effect; Calculating the magnitude and direction of the gradient; The calculated amplitude value is subjected to non-maximum suppression, and the high and low thresholds of the image are adjusted based on the calculated amplitude value; Connect the edges of the adjusted image high and low thresholds to extract artifact features; According to the matching results between the artifact features and the bone sclerosis model, the artifact area is marked. 6. The dual-energy correction method based on the bone sclerosis model according to claim 5, characterized in that, according to the recognition result of the artifact, the parameters in the reconstruction model are iteratively adjusted to output the corrected image, comprising: Define an error function to calculate the difference between the artifact in the image and the expected artifact; Using the difference, updating the parameters in the bone sclerosis model; Calculate the gradient of the error function with respect to the parameters; Correct the image according to the updated parameters; Repeat the above steps until the error function reaches the maximum number of iterations of the model and output the corrected image. 7. The dual-energy correction method based on the bone sclerosis model according to claim 6, characterized in that the corrected image is compared with the actual bone image, comprising: Converting the corrected image into a three-dimensional model, displaying the three-dimensional model in a virtual reality environment, and generating a report based on the comparison results of interest selected by the user; The report includes difference maps, analysis data and 3D model screenshots. 8. A dual-energy correction system based on a bone sclerosis model, based on the dual-energy correction method based on a bone sclerosis model according to any one of claims 1 to 7, characterized in that it comprises: A multi-frequency image acquisition module is configured to acquire X-ray images of multiple frequencies within a soft tissue imaging energy range and a bone tissue imaging energy range through a multi-frequency sampling technique; The bone sclerosis model reconstruction and artifact recognition module is configured to adjust the reconstruction weight in the image using the density and structural characteristics of the bone, reconstruct the bone sclerosis model, and recognize artifacts caused by the sclerosis effect in the image based on the reconstructed bone sclerosis model; The adaptive model parameter adjustment and visualization output module is configured to iteratively adjust the parameters in the reconstruction model according to the recognition results of the artifacts, output the corrected image, and compare the corrected image with the actual bone image. 9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 1 to 7 when executing the computer program. 10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any one of the methods of claims 1 to 7 when executed by a processor.