CN111402150A - CT image metal artifact removing method and device - Google Patents
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Abstract
The invention provides a method and a device for removing metal artifacts of a CT image, wherein the method comprises the steps of firstly carrying out self-adaptive filtering on an original image to reduce the influence of noise; then separating out metal parts and other parts through a threshold value; carrying out forward projection on the original image, the metal image and other partial images to obtain sinograms of the original image, the metal image and the other partial images; obtaining a normalized sinogram by using the sinogram of the original image and sinograms of other partial images; erasing a metal sinogram area in the normalized sinogram, and performing linear interpolation supplement on the erased part; carrying out denormalization on the obtained interpolation image to obtain a prior sinogram; performing inverse filtering projection on the prior sinogram to obtain a preprocessed image; the preprocessing image and the original image can respectively obtain a high-frequency image and a low-frequency image through frequency splitting; and fusing the preprocessed low-frequency image with the high-frequency image of the original image and the high-frequency image of the preprocessed low-frequency image according to a certain weight to obtain a final image without the metal artifact.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for removing metal artifacts in CT images.
Background
The computed tomography technology is a technology capable of accurately imaging a cross-axis tomographic object, and can detect the tissue and organ structure inside a human body without damage, so that the computed tomography technology is widely applied to current clinical diagnosis. CT techniques play a tremendous role, for example, in the diagnosis and analysis of orthodontic dental measurements, lung diseases, brain diseases, and cardiovascular diseases, among others.
However, the patient under examination sometimes contains various metal implants, such as: surgical clips, dental fillings, heart pads, prostheses, etc., can create metal artifacts because the density of metal objects is much greater than that of human tissue structures. The metal artifacts are mainly caused by beam hardening, photon starvation, partial volume effect and scattering, and appear as streak artifacts and bright and dark bands, which seriously affect the quality of images and hinder accurate diagnosis. Therefore, how to remove the metal artifacts in the CT images and restore the real human tissue structure imaging as much as possible, improve the image quality and have important research significance in the field of medical diagnosis.
Metal artifact removal techniques have been studied for over a decade. The method of front projection, interpolation and inverse filtering projection has become a mainstream technology, and is effective and convenient. However, in use, although the interpolation-based method can remove the metal artifacts, the tissue structures around the metal and around the artifacts are seriously damaged, and a new image quality problem is introduced.
Disclosure of Invention
The present invention is directed to a method and apparatus for removing metal artifacts in CT images that overcomes, or at least partially solves, the above-mentioned problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
one aspect of the present invention provides a method for removing metal artifacts in CT images, comprising the following steps: s1, carrying out self-adaptive filtering on the original CT image to obtain an original filtering image; s2, performing threshold segmentation on the original filtering image, segmenting a metal image from the part meeting the metal threshold, and setting the value of the metal image as the original value of the metal image; segmenting a bone image from a part meeting a bone threshold value, setting the value of the bone image as a first preset value, segmenting a soft tissue image from a part meeting a soft tissue threshold value, setting the value of the soft tissue image as a second preset value, segmenting an air image from a part meeting an air threshold value, setting the value of the air image as a third preset value, and forming the bone image, the soft tissue image and the air image into a nonmetal image; s3, respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image; s4, comparing the sinogram of the original image with the sinogram of the non-metal image to obtain a normalized sinogram; s5, removing the valued part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain an interpolated sinogram; s6, multiplying the interpolated sinogram by the non-metal image sinogram to obtain an anti-normalized sinogram; s7, performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image; s8, performing frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image, and performing frequency splitting on the original filtered image to obtain a high-frequency original filtered image and a low-frequency original filtered image; s9, calculating a weight for image fusion according to the distance between a pixel in the original filtering image and a metal part, wherein the closer the distance, the larger the weight is, the farther the distance, the smaller the weight is; and S10, fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
The method for fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain the artifact-removed image comprises the following steps: calculating the value of each pixel point in the artifact image by using the following formula: removing the value of a certain pixel point in the artifact image, namely the value of the low-frequency preprocessed image, the weight, the value of the high-frequency original filtered image, and the value of the high-frequency preprocessed image, wherein the value of the certain pixel point is plus (1-weight); wherein, the weight value range is between 0 and 1.
Wherein the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu.
Another aspect of the present invention provides a device for removing metal artifacts in CT images, including: the processing module is used for carrying out self-adaptive filtering on the original CT image to obtain an original filtering image; the segmentation module is used for carrying out threshold segmentation on the original filtering image, segmenting a metal image from a part meeting a metal threshold, and setting the value of the metal image as the original value of the metal image; segmenting a bone image from a part meeting a bone threshold value, setting the value of the bone image as a first preset value, segmenting a soft tissue image from a part meeting a soft tissue threshold value, setting the value of the soft tissue image as a second preset value, segmenting an air image from a part meeting an air threshold value, setting the value of the air image as a third preset value, and forming the bone image, the soft tissue image and the air image into a nonmetal image; the front projection module is used for respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image; the preprocessing module is used for comparing the sinogram of the original image with the sinogram of the nonmetal image to obtain a standardized sinogram; removing a valuable part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain an interpolated sinogram; multiplying the interpolated sinogram by the sinogram of the nonmetal image to obtain an anti-normalized sinogram; performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image; the splitting module is used for carrying out frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image and carrying out frequency splitting on the original filtered image to obtain a high-frequency original filtered image and a low-frequency original filtered image; the calculation module is used for calculating a weight for image fusion according to the distance between a pixel in the original filtering image and a metal part, wherein the closer the distance, the larger the weight is, and the farther the distance, the smaller the weight is; and the fusion module is used for fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
The fusion module fuses the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight in the following mode to obtain an artifact-removed image: the fusion module is specifically configured to calculate a value of each pixel point in the artifact image by using the following formula: removing the value of a certain pixel point in the artifact image, namely the value of the low-frequency preprocessed image, the weight, the value of the high-frequency original filtered image, and the value of the high-frequency preprocessed image, wherein the value of the certain pixel point is plus (1-weight); wherein, the weight value range is between 0 and 1.
Wherein the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu.
Therefore, according to the method and the device for removing the metal artifact of the CT image, provided by the invention, the image after the artifact is removed by using the traditional method, the artifact is already removed from the low-frequency part, and the high-frequency part is damaged, the high-frequency part of the preprocessed image obtained by processing by using the traditional method is fused with the high-frequency part of the original image, so that the artifact can be removed, the organizational structure of the image can be furthest reserved, the problems that the organizational structure of the original image is damaged and new image quality problems are introduced by processing by using the traditional method are solved, and the method and the device have higher practical value.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for removing metal artifacts in a CT image according to an embodiment of the present invention;
FIG. 2 is a flowchart of another method for removing metal artifacts in a CT image according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a CT image metal artifact removing device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example 1
Fig. 1 shows a flowchart of a CT image metal artifact removing method provided in embodiment 1 of the present invention, and referring to fig. 1, the CT image metal artifact removing method provided in the embodiment of the present invention includes the following steps:
and S1, carrying out self-adaptive filtering on the original CT image to obtain an original filtering image.
Specifically, a CT image containing metal artifacts is obtained as an original image, and adaptive filtering is performed on the original CT image f, so that the influence of noise on the image is reduced.
S2, performing threshold segmentation on the original filtering image, segmenting a metal image from the part meeting the metal threshold, and setting the value of the metal image as the original value of the metal image; and segmenting the part which meets the bone threshold value into a bone image, setting the value of the bone image as a first preset value, segmenting the part which meets the soft tissue threshold value into a soft tissue image, setting the value of the soft tissue image as a second preset value, segmenting the part which meets the air threshold value into an air image, setting the value of the air image as a third preset value, and forming the bone image, the soft tissue image and the air image into a nonmetal image.
Specifically, the original filtering image f obtained after the adaptive filtering processing is processedfiltPerforming threshold segmentation to segment metal image f at the part larger than the metal thresholdmetalKeeping the value of the part in the original filtering image; segmenting bone image according to bone and soft tissue threshold value and segmenting bone image according to soft tissueAnd an air threshold, separating soft tissue and air. The divided bone, soft tissue and air parts form a new non-metal part image fotherAnd abandoning the original value of the area in the original image, and uniformly filling the bone area with a bone value, the soft tissue area with a soft tissue value and the air area with an air value.
After the above processing, three images, the original filtering image f, can be obtainedfiltMetal image fmetalAnd a non-metal image fother。
As an optional implementation of the embodiment of the present invention, the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu. Specifically, in this example, a portion of 3000Hu or more, a portion of 2000Hu or more and less than 3000Hu, a portion of bone, a portion of 500Hu or more and less than 2000Hu, and a portion of air are considered. The value of the original metal is kept in the metal image, and in other position maps, the air can be set to-1000 Hu, the soft tissue can be set to 0Hu, and the bone can be set to 2000 Hu.
And S3, respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image.
Specifically, the original filtered images f are respectivelyfiltMetal image fmetalAnd a non-metal image fotherPerforming front projection to obtain a sinogram s of an original imagefiltMetal image sinogram smetalAnd a sinogram s of a non-metal imageother. Specifically, in the example where a set of parallel rays is assumed to pass through all pixels of the image outside the image, and the distance between adjacent parallel rays is one pixel, along a ray, we can add the values of all the pixel points through which the ray passes together until the ray passes completely through the image, and no pixel points and shots are leftIn the projection process, most of rays cannot completely pass through pixel points, the example starts from the pixel points, two rays with the shortest distance to each pixel point are calculated, the value of each pixel point is weighted and calculated to the two rays according to the inverse proportion of the distance between the pixel point and the rays, such as the distance from the two rays L1 and L2 which are closest to the pixel point P to P is S1 and S2 respectively, and S1 is 3S 2, because the farther the distance is, the obtained value is smaller, the contribution of the P to the two rays L1 and L2 is 1/4E and 3/4E respectively, wherein E is the value of P, the example selects 360 degrees for projection, 360 projection density results can be obtained in 360 directions, and the projection density results are cascaded together, a 360-W sine graph can be obtained, W is the maximum projection angle in the ray direction, and the maximum projection angle in the projection angle is 0.5 degrees.
And S4, comparing the sinogram of the original image with the sinogram of the non-metal image to obtain a normalized sinogram.
In particular, the raw image sinogram s is comparedfiltSinogram s of non-metal imageotherTo obtain a normalized sinogram snorm. The present example directly employs the pixel division method.
And S5, removing the valuable part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain the interpolated sinogram.
In particular, the normalized sinogram s isnormRemove all metal image sinograms smetalThe part with value is subjected to linear interpolation to obtain an interpolated sinogram sinter. In the sinogram, each line is the result of the projection density at a certain angle, so that the missing metal sinogram part of each line is linearly interpolated.
And S6, multiplying the interpolated sinogram by the non-metal image sinogram to obtain an anti-normalized sinogram.
In particular, the interpolated sinogram s is interpolatedinterMultiplication of sinogram s of non-metal imageotherObtaining an anti-normalized sinogram sdenorm. The present example employs a pixel multiplication method.
And S7, performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image.
In particular, the denormalised sinogram s isdenormPerforming inverse filtering projection to obtain a preprocessed image fpre。
And S8, performing frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image, and performing frequency splitting on the original filtering image to obtain a high-frequency original filtering image and a low-frequency original filtering image.
In particular, the image f is preprocessedpreAnd the original filtered image f obtained in step S1filtRespectively, a high-frequency preprocessed image f can be obtained by frequency splittingHpreLow frequency pre-processing image fLpreAnd a high frequency raw filtered image fHfiltLow frequency original filtered image fLfilt。
S9, calculating a weight for image fusion according to the distance between the pixel in the original filtering image and the metal part, wherein the closer the distance, the higher the weight, the farther the distance, the smaller the weight.
Specifically, the metal image obtained in step S2 may be calculated to obtain a weight W for image fusion according to the distance between the pixel of the original filtered image and the metal partij. The closer the distance the greater the weight, the further the distance the lesser the weight.
And S10, fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
Specifically, the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original image are fused according to the weight obtained in the step S9, so as to obtain a final artifact-removed image.
As an optional implementation manner of the embodiment of the present invention, the fusing the low-frequency preprocessed image, the high-frequency preprocessed image, and the high-frequency original filtered image according to the weight to obtain the artifact-removed image includes: calculating the value of each pixel point in the artifact image by using the following formula: removing the value of a certain pixel point in the artifact image, namely the value of the low-frequency preprocessed image, the weight, the value of the high-frequency original filtered image, and the value of the high-frequency preprocessed image, wherein the value of the certain pixel point is plus (1-weight); wherein, the weight value range is between 0 and 1.
The specific fusion formula is as follows:
the value of a certain pixel point of the final image
WijThe value range is between 0 and 1. And (4) calculating each pixel respectively because the weight is different, and finally obtaining a final image after all the pixels are calculated.
Therefore, by using the method for removing the metal artifact of the CT image provided by the embodiment of the invention, the image after the artifact is removed by using the traditional method has the characteristics that the artifact is already removed in the low-frequency part and the high-frequency part is damaged, the high-frequency part of the preprocessed image obtained by processing by using the traditional method is fused with the high-frequency part of the original image, the artifact can be removed, the organizational structure of the image can be furthest kept, the problems that the organizational structure of the original image is damaged and a new image quality problem is introduced by processing by using the traditional method are solved, and the method has higher practical value.
Hereinafter, the method for removing a metal artifact of a CT image according to the embodiment of the present invention is generally described with reference to a flowchart of another method for removing a metal artifact of a CT image according to the embodiment of the present invention shown in fig. 2, and with reference to fig. 2, the method for removing a metal artifact of a CT image according to the embodiment of the present invention includes:
1. firstly, carrying out self-adaptive filtering on an original image to reduce the influence of noise;
2. then separating out metal parts and other parts through a threshold value;
3. carrying out forward projection on the original image, the metal image and other partial images to obtain sinograms of the original image, the metal image and the other partial images; obtaining a normalized sinogram by using the sinogram of the original image and sinograms of other partial images; erasing a metal sinogram area in the normalized sinogram, and performing linear interpolation supplement on the erased part; carrying out denormalization on the obtained interpolation image to obtain a prior sinogram; performing inverse filtering projection on the prior sinogram to obtain a preprocessed image;
4. the preprocessing image and the original image can respectively obtain a high-frequency image and a low-frequency image through frequency splitting;
5. calculating a fusion weight according to the distance from the metal part;
6. and fusing the preprocessed low-frequency image with the high-frequency image of the original image and the high-frequency image of the preprocessed low-frequency image according to a certain weight to obtain a final image without the metal artifact.
Therefore, the method for removing the metal artifact of the CT image can effectively remove the metal artifact, simultaneously reserve the structure of the original image and has higher practical value.
Fig. 3 shows a schematic structural diagram of a CT image metal artifact removing device provided in an embodiment of the present invention, in which the above method is applied to the CT image metal artifact removing device, the following only briefly describes the structure of the CT image metal artifact removing device, and please refer to the related description in the CT image metal artifact removing method for other things, referring to fig. 3, the CT image metal artifact removing device provided in embodiment 1 of the present invention includes:
the processing module is used for carrying out self-adaptive filtering on the original CT image to obtain an original filtering image;
the segmentation module is used for carrying out threshold segmentation on the original filtering image, segmenting a metal image from a part meeting a metal threshold, and setting the value of the metal image as the original value of the metal image; segmenting a bone image from a part meeting a bone threshold value, setting the value of the bone image as a first preset value, segmenting a soft tissue image from a part meeting a soft tissue threshold value, setting the value of the soft tissue image as a second preset value, segmenting an air image from a part meeting an air threshold value, setting the value of the air image as a third preset value, and forming the bone image, the soft tissue image and the air image into a nonmetal image;
the front projection module is used for respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image;
the preprocessing module is used for comparing the sinogram of the original image with the sinogram of the nonmetal image to obtain a standardized sinogram; removing a valuable part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain an interpolated sinogram; multiplying the interpolated sinogram by the sinogram of the nonmetal image to obtain an anti-normalized sinogram; performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image;
the splitting module is used for carrying out frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image and carrying out frequency splitting on the original filtered image to obtain a high-frequency original filtered image and a low-frequency original filtered image;
the calculation module is used for calculating a weight for image fusion according to the distance between a pixel in the original filtering image and a metal part, wherein the closer the distance, the larger the weight is, and the farther the distance, the smaller the weight is;
and the fusion module is used for fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
As an optional implementation manner of the embodiment of the present invention, the fusion module fuses the low-frequency preprocessed image, the high-frequency preprocessed image, and the high-frequency original filtered image according to the weight in the following manner, so as to obtain an artifact-removed image: the fusion module is specifically configured to calculate a value of each pixel point in the artifact image by using the following formula: removing the value of a certain pixel point in the artifact image, namely the value of the low-frequency preprocessed image, the weight, the value of the high-frequency original filtered image, and the value of the high-frequency preprocessed image, wherein the value of the certain pixel point is plus (1-weight); wherein, the weight value range is between 0 and 1.
As an optional implementation of the embodiment of the present invention, the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu.
Therefore, by using the device for removing the metal artifact of the CT image, provided by the embodiment of the invention, the image after the artifact is removed by using the traditional method, the artifact is already removed from the low-frequency part, and the high-frequency part is damaged.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. A CT image metal artifact removing method is characterized by comprising the following steps:
s1, carrying out self-adaptive filtering on the original CT image to obtain an original filtering image;
s2, performing threshold segmentation on the original filtering image, segmenting a metal image from a part meeting a metal threshold, and setting the value of the metal image as the original value of the metal image; segmenting a bone image from a part which meets a bone threshold value, setting the value of the bone image as a first preset value, segmenting a soft tissue image from a part which meets a soft tissue threshold value, setting the value of the soft tissue image as a second preset value, segmenting an air image from a part which meets an air threshold value, setting the value of the air image as a third preset value, and forming a non-metal image from the bone image, the soft tissue image and the air image;
s3, respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image;
s4, comparing the sinogram of the original image with the sinogram of the non-metal image to obtain a normalized sinogram;
s5, removing the valued part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain an interpolated sinogram;
s6, multiplying the interpolated sinogram by the non-metal image sinogram to obtain an anti-normalized sinogram;
s7, performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image;
s8, performing frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image, and performing frequency splitting on the original filtering image to obtain a high-frequency original filtering image and a low-frequency original filtering image;
s9, calculating a weight for image fusion according to the distance between the pixel in the original filtering image and the metal part, wherein the closer the distance, the larger the weight, the farther the distance, the smaller the weight;
and S10, fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
2. The method of claim 1, wherein fusing the low frequency pre-processed image, the high frequency pre-processed image, and the high frequency raw filtered image according to the weights to obtain an artifact-removed image comprises:
calculating the value of each pixel point in the artifact-removed image by using the following formula:
the value of a certain pixel point in the artifact-removed image is the value of the low-frequency preprocessed image + the weight + the value of the high-frequency original filtered image + (1-weight) the value of the high-frequency preprocessed image;
wherein the weight value range is between 0 and 1.
3. The method according to claim 1, wherein the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu.
4. A CT image metal artifact removal apparatus, comprising:
the processing module is used for carrying out self-adaptive filtering on the original CT image to obtain an original filtering image;
the segmentation module is used for carrying out threshold segmentation on the original filtering image, segmenting a metal image from a part meeting a metal threshold, and setting the value of the metal image as the original value of the metal image; segmenting a bone image from a part which meets a bone threshold value, setting the value of the bone image as a first preset value, segmenting a soft tissue image from a part which meets a soft tissue threshold value, setting the value of the soft tissue image as a second preset value, segmenting an air image from a part which meets an air threshold value, setting the value of the air image as a third preset value, and forming a non-metal image from the bone image, the soft tissue image and the air image;
the front projection module is used for respectively carrying out front projection on the original filtering image, the metal image and the nonmetal image to obtain a sinogram of the original image, a sinogram of the metal image and a sinogram of the nonmetal image;
the preprocessing module is used for comparing the sinogram of the original image with the sinogram of the non-metal image to obtain a normalized sinogram; removing a valuable part of the metal image sinogram from the normalized sinogram, and performing linear interpolation on the missing part to obtain an interpolated sinogram; multiplying the interpolated sinogram by a non-metal image sinogram to obtain an anti-normalized sinogram; performing inverse filtering projection on the denormalized sinogram to obtain a preprocessed image;
the splitting module is used for carrying out frequency splitting on the preprocessed image to obtain a high-frequency preprocessed image and a low-frequency preprocessed image, and carrying out frequency splitting on the original filtering image to obtain a high-frequency original filtering image and a low-frequency original filtering image;
the calculation module is used for calculating a weight for image fusion according to the distance between the pixel in the original filtering image and the metal part, wherein the closer the distance, the larger the weight is, the farther the distance, the smaller the weight is;
and the fusion module is used for fusing the low-frequency preprocessed image, the high-frequency preprocessed image and the high-frequency original filtering image according to the weight to obtain an artifact-removed image.
5. The apparatus of claim 4, wherein the fusion module fuses the low-frequency pre-processed image, the high-frequency pre-processed image and the high-frequency original filtered image according to the weights to obtain an artifact-removed image by:
the fusion module is specifically configured to calculate a value of each pixel point in the artifact-removed image by using the following formula: the value of a certain pixel point in the artifact-removed image is the value of the low-frequency preprocessed image + the weight + the value of the high-frequency original filtered image + (1-weight) the value of the high-frequency preprocessed image; wherein the weight value range is between 0 and 1.
6. The apparatus of claim 4, wherein the metal threshold is a value of 3000Hu or more; the bone threshold value is a value which is greater than or equal to 2000Hu and smaller than 3000Hu, the soft tissue threshold value is a value which is greater than or equal to-500 Hu and smaller than 2000Hu, the air threshold value is a value which is smaller than-500 Hu, the first preset value is 2000Hu, the second preset value is 0Hu, and the third preset value is-1000 Hu.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563940A (en) * | 2020-07-15 | 2020-08-21 | 南京安科医疗科技有限公司 | Method for removing splicing artifacts in stepping axis scanning CT reconstruction and electronic medium |
| CN112767265A (en) * | 2021-01-11 | 2021-05-07 | 明峰医疗***股份有限公司 | Method, system and computer readable storage medium for correcting segmented image of removing metal artifact of CT image |
| CN114332270A (en) * | 2021-12-02 | 2022-04-12 | 赛诺威盛科技(北京)股份有限公司 | CT image metal artifact removing method and device for minimally invasive interventional surgery |
| CN114417721A (en) * | 2022-01-19 | 2022-04-29 | 北京万旺科技有限公司 | Event sequence prediction model construction method, prediction method, device and terminal |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050123089A1 (en) * | 2003-12-09 | 2005-06-09 | Man Bruno D. | Method and apparatus for reduction of artifacts in computed tomography images |
| US20090074278A1 (en) * | 2004-10-12 | 2009-03-19 | Universite Laval | Method and apparatus for metal artifact reduction in computed tomography |
| CN103190928A (en) * | 2011-08-10 | 2013-07-10 | 西门子公司 | Method, computing unit, CT system and C-arm system for reducing metal artifacts |
| CN105701778A (en) * | 2016-01-11 | 2016-06-22 | 赛诺威盛科技（北京）有限公司 | Method of removing metal artifact from CT image |
| CN106886982A (en) * | 2017-02-20 | 2017-06-23 | 江苏美伦影像***有限公司 | CBCT image annular artifact minimizing technologies |
| CN107530040A (en) * | 2015-04-01 | 2018-01-02 | 株式会社日立制作所 | X ray CT device, restructing operation device and X ray CT image generating method |
-
2020
- 2020-03-09 CN CN202010155674.4A patent/CN111402150B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050123089A1 (en) * | 2003-12-09 | 2005-06-09 | Man Bruno D. | Method and apparatus for reduction of artifacts in computed tomography images |
| US20090074278A1 (en) * | 2004-10-12 | 2009-03-19 | Universite Laval | Method and apparatus for metal artifact reduction in computed tomography |
| CN103190928A (en) * | 2011-08-10 | 2013-07-10 | 西门子公司 | Method, computing unit, CT system and C-arm system for reducing metal artifacts |
| CN107530040A (en) * | 2015-04-01 | 2018-01-02 | 株式会社日立制作所 | X ray CT device, restructing operation device and X ray CT image generating method |
| CN105701778A (en) * | 2016-01-11 | 2016-06-22 | 赛诺威盛科技（北京）有限公司 | Method of removing metal artifact from CT image |
| CN106886982A (en) * | 2017-02-20 | 2017-06-23 | 江苏美伦影像***有限公司 | CBCT image annular artifact minimizing technologies |
Non-Patent Citations (3)
| Title |
|---|
| YONGBIN ZHANG ETC.: "Reducing metal artifacts in cone-beam CT images by preprocessing projection data", INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY*BIOLOGY*PHYSICS * |
| 余晓锷;李婵娟;: "基于插值方法去除CT图像中的金属伪影", 中国组织工程研究与临床康复 * |
| 谷建伟;张丽;陈志强;邢宇翔;: "基于投影的CT图像金属伪影非线性权重校正", 清华大学学报(自然科学版) * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563940A (en) * | 2020-07-15 | 2020-08-21 | 南京安科医疗科技有限公司 | Method for removing splicing artifacts in stepping axis scanning CT reconstruction and electronic medium |
| CN111563940B (en) * | 2020-07-15 | 2020-10-30 | 南京安科医疗科技有限公司 | Method for removing splicing artifacts in stepping axis scanning CT reconstruction and electronic medium |
| CN112767265A (en) * | 2021-01-11 | 2021-05-07 | 明峰医疗***股份有限公司 | Method, system and computer readable storage medium for correcting segmented image of removing metal artifact of CT image |
| CN114332270A (en) * | 2021-12-02 | 2022-04-12 | 赛诺威盛科技(北京)股份有限公司 | CT image metal artifact removing method and device for minimally invasive interventional surgery |
| CN114417721A (en) * | 2022-01-19 | 2022-04-29 | 北京万旺科技有限公司 | Event sequence prediction model construction method, prediction method, device and terminal |
| CN114417721B (en) * | 2022-01-19 | 2023-03-03 | 北京万旺科技有限公司 | Event sequence prediction model construction method, prediction method, device and terminal |
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