CN111815521A - Cone beam CT metal artifact correction algorithm based on prior image - Google Patents

Cone beam CT metal artifact correction algorithm based on prior image Download PDF

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CN111815521A
CN111815521A CN202010446085.1A CN202010446085A CN111815521A CN 111815521 A CN111815521 A CN 111815521A CN 202010446085 A CN202010446085 A CN 202010446085A CN 111815521 A CN111815521 A CN 111815521A
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朱叶晨
刘仰川
高欣
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Nanjing Guoke Precision Medical Technology Co ltd
Suzhou Institute of Biomedical Engineering and Technology of CAS
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    • G06T7/136—Segmentation; Edge detection involving thresholding
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

The invention provides a cone beam CT metal artifact correction algorithm based on a priori image, which introduces bilateral filtering to preprocess an original reconstructed image, can remove noise and keep image edge information; then, a model is constructed by adopting projection data of the prior image and metal neighborhood projection data, interpolation restoration is carried out on a metal projection area, the metal artifact is eliminated, and meanwhile, the generation of a secondary artifact is inhibited, and the method comprises the following steps: firstly, preprocessing a reconstructed image containing metal artifacts such as bilateral filtering, metal threshold segmentation, tissue clustering and the like to obtain a metal image and a prior image; then carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data; then repairing the metal projection area to obtain repaired projection data; and finally, reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image.

Description

Cone beam CT metal artifact correction algorithm based on prior image
Technical Field
The invention belongs to the field of medical image processing, and relates to a cone beam CT metal artifact correction algorithm based on a priori image, which can be used for effectively inhibiting artifacts caused by metal implants in a reconstruction process aiming at a cone beam CT imaging system.
Background
Cone-beam CT (CBCT) is used in stomatology and orthopedics because of its advantages of small size, low radiation dose, high isotropic spatial resolution, etc., and is one of the most promising and practical imaging methods. However, metal implants (such as metal dentures and bone nails) carried in a patient can bring serious strip artifacts, namely metal artifacts, to a reconstructed image, and the diagnosis accuracy is greatly influenced. The Metal artifact correction (MAR) algorithm can reduce or even eliminate the streak artifacts and improve the quality of the reconstructed image in the image reconstruction process.
At present, MAR algorithms for cone beam CT are mainly classified into three types, namely interpolation, iteration and hybrid: the interpolation method comprises the steps of firstly segmenting a metal region from an original reconstructed image or projection data, then interpolating the metal region, and then fusing the reconstructed image and the metal image to realize metal artifact correction; the iterative method respectively reconstructs a metal projection region and a nonmetal projection region by adopting different parameters by means of the smoothing and artifact removing characteristics of an iterative reconstruction algorithm, and then fuses the two reconstructed images to realize metal artifact correction; the hybrid method combines two or more MAR methods (usually by combining interpolation and iteration methods) to improve the calibration performance.
Interpolation-based algorithms such as the mutual information and edge filtering based MAR algorithm, the reduced MAR algorithm based on the front projection data, etc. Firstly, such algorithms generally only use a metal neighborhood projection region to perform simple interpolation, so as to realize the restoration of projection data, and thus, a serious secondary artifact (a new artifact caused by improper correction) is easy to appear in a corrected image. Secondly, they do not consider the process of image preprocessing, so that the corrected image is prone to edge blurring.
And (4) iteration-based algorithms, such as algebraic iteration algorithm, maximum expectation algorithm and the like. Although the algorithms can effectively eliminate the metal artifacts, the algorithms have high complexity and hardware requirements and time-consuming and serious calculation processes, so that the clinical application limitation is large. The mixed method also has the problems while improving the artifact removing effect.
Disclosure of Invention
Aiming at the problems of secondary artifacts and edge blurring of an image corrected by the existing interpolation MAR algorithm, the patent provides a Prior-image-based cone-beam CT metal artifact correction algorithm (PIB-MAR). The algorithm introduces bilateral filtering to preprocess the original reconstructed image, can remove noise and keep the edge information of the image; and then, a model is constructed by adopting projection data (called as prior projection data) of a prior image and metal neighborhood projection data, and a metal projection area is subjected to interpolation repair, so that the generation of a secondary artifact is inhibited while a metal artifact is eliminated.
The invention achieves the above objects by the following technical solutions, and a cone beam CT metal artifact correction algorithm of the invention is characterized in that,
the method comprises the following steps: firstly, preprocessing a reconstructed image containing metal artifacts such as bilateral filtering, metal threshold segmentation, tissue clustering and the like to obtain a metal image and a prior image; then carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data; then repairing the metal projection area to obtain repaired projection data; and finally, reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image.
The specific technical details are as follows:
1) for a cone beam CT reconstructed image, a Bilateral Filter (BF) based on Gaussian distribution is adopted to smooth the image, so that noise suppression and edge protection are realized. The weight coefficient is composed of two parts: the gray value difference range between pixels is called as a pixel range domain filtering kernel function; the second is the Euclidean distance between pixels, which is called as a spatial domain filtering kernel function.
Setting f (X) as the reconstructed image containing metal artifact, normalizing the grey value, and bilateral filtering to obtain the output image fBF(X)：
Figure BDA0002508866860000021
Wherein X is (X)1,y1,z1) Denotes the center pixel, Y ═ x2,y2,z2) Representing the neighborhood pixels of X, Ω is the set of neighborhood pixels,
Figure BDA0002508866860000027
is a spatial domain kernel, ωσ2() is the domain core.
Figure BDA0002508866860000028
And
Figure BDA0002508866860000029
a single-peak gaussian function that is all non-negative, expressed as:
Figure BDA0002508866860000022
Figure BDA0002508866860000023
wherein σ1Is the distance standard deviation, σ, of a Gaussian function2Is the gray scale standard deviation of the gaussian function. The radial acting ranges of the spatial domain and the range domain filtering kernel functions are respectively controlled, and are non-negative selectable variable parameters. The two sizes directly determine the performance of the bilateral filter, and the weighted value of the pixel is adjusted by controlling the relative space and the gray scale change range between the pixels, thereby realizing the effect of filtering the image.
Bilateral filtering algorithm passes through control parameter sigma2To protect the image boundary information. If σ2The size of the composite material is larger,
Figure BDA0002508866860000024
the two-sided filtering is approximate to Gaussian filtering and is close to 1, and the highest noise suppression and the lowest edge protection are carried out on the image; on the contrary, if σ2The size of the composite material is small,
Figure BDA0002508866860000025
to be close to 0, bilateral filtering has low smoothing strength on the image, but mayAnd the image edge is better preserved. The practical experience shows that the Chinese herbal medicine is,
Figure BDA0002508866860000026
the value should be proportional to the noise intensity of the input image.
2) And obtaining a metal image by adopting metal threshold segmentation. In the reconstructed image, the CT values of different tissues are greatly different, for example, the CT value of air is-1000 HU, the CT value of fat is-120 to-90 HU, and the CT value of bone is 300-2000 HU, while the CT value of various metals is far greater than 2000HU, even ten thousands, therefore, the metals can be segmented from the reconstructed image by adopting a threshold method:
Figure BDA0002508866860000031
where T represents a threshold value, and the value can be determined by a histogram method, and can be set to 30% of the maximum pixel value. The region having a pixel value equal to or greater than T is a metal image, 1 is set in the binary image, and the pixel values of the other regions are set to 0.
3) And generating a prior image by adopting a similar tissue model. The generation process is as follows: filling the segmented metal region in the reconstructed image with a soft tissue CT value; clustering the filled reconstructed images by using a three-dimensional K-means algorithm, and clustering human tissues into air, fat, soft tissues and bones; assigning values to different clustered organizations to obtain a prior image, which is expressed as:
Figure BDA0002508866860000032
wherein omegaair、Ωfat、Ωsoft、ΩboneRespectively represent the clustering regions of air, fat, soft tissue and bone, omegametalRepresenting a metal partitioned area. The CT values of air, fat, soft tissue and bone can be set to-1000 HU, 0HU, 200HU and 750HU respectively to obtain a priori image fprior。
4) And carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data.
5) Utilizing the prior projection data and the metal neighborhood projection data to construct a model, repairing the metal projection area to obtain repaired projection data, wherein the projection repairing process is represented as:
Figure BDA0002508866860000033
where theta is the projection angle corresponding to the projection data,
Figure BDA0002508866860000034
is the original projection data and is the original projection data,
Figure BDA0002508866860000035
in order to be a priori the projection data,
Figure BDA0002508866860000036
for the repaired projection data, U ═ (U, v) is the projection data coordinate, mean (×) is the mean function, Δ is the 3 × 3 neighborhood of the metal boundary,
Figure BDA0002508866860000037
is a metal projection area.
6) And reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image.
Drawings
Fig. 1 is the main workflow of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
as shown in fig. 1, the method comprises the following steps: firstly, preprocessing a reconstructed image containing metal artifacts such as bilateral filtering, metal threshold segmentation, tissue clustering and the like to obtain a metal image and a prior image; then carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data; then repairing the metal projection area to obtain repaired projection data; and finally, reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image. The specific technical details are as follows:
1) for a cone beam CT reconstructed image, a Bilateral Filter (BF) based on Gaussian distribution is adopted to smooth the image, so that noise suppression and edge protection are realized. The weight coefficient is composed of two parts: the gray value difference range between pixels is called as a pixel range domain filtering kernel function; the second is the Euclidean distance between pixels, which is called as a spatial domain filtering kernel function.
Setting f (X) as the reconstructed image containing metal artifact, normalizing the grey value, and bilateral filtering to obtain the output image fBF(X)：
Figure BDA0002508866860000041
Wherein X is (X)1,y1,z1) Denotes the center pixel, Y ═ x2,y2,z2) Representing the neighborhood pixels of X, Ω is the set of neighborhood pixels,
Figure BDA0002508866860000042
is a kernel of a spatial domain, and is,
Figure BDA0002508866860000043
is a range domain kernel.
Figure BDA0002508866860000044
And
Figure BDA0002508866860000045
a single-peak gaussian function that is all non-negative, expressed as:
Figure BDA0002508866860000046
Figure BDA0002508866860000047
wherein σ1Is the distance standard deviation, σ, of a Gaussian function2Is the gray scale standard deviation of the gaussian function. The radial acting ranges of the spatial domain and the range domain filtering kernel functions are respectively controlled, and are non-negative selectable variable parameters. The two sizes directly determine the performance of the bilateral filter, and the weighted value of the pixel is adjusted by controlling the relative space and the gray scale change range between the pixels, thereby realizing the effect of filtering the image.
Bilateral filtering algorithm passes through control parameter sigma2To protect the image boundary information. If σ2The size of the composite material is larger,
Figure BDA0002508866860000048
the two-sided filtering is approximate to Gaussian filtering and is close to 1, and the highest noise suppression and the lowest edge protection are carried out on the image; on the contrary, if σ2The size of the composite material is small,
Figure BDA0002508866860000049
the bilateral filtering will be close to 0, and the smoothing intensity of the image is low, but the image edge can be better preserved. The practical experience shows that the Chinese herbal medicine is,
Figure BDA00025088668600000410
the value should be proportional to the noise intensity of the input image.
2) And obtaining a metal image by adopting metal threshold segmentation. In the reconstructed image, the CT values of different tissues are greatly different, for example, the CT value of air is-1000 HU, the CT value of fat is-120 to-90 HU, and the CT value of bone is 300-2000 HU, while the CT value of various metals is far greater than 2000HU, even ten thousands, therefore, the metals can be segmented from the reconstructed image by adopting a threshold method:
Figure BDA0002508866860000051
where T represents a threshold value, and the value can be determined by a histogram method, and can be set to 30% of the maximum pixel value. The region having a pixel value equal to or greater than T is a metal image, 1 is set in the binary image, and the pixel values of the other regions are set to 0.
3) And generating a prior image by adopting a similar tissue model. The generation process is as follows: filling the segmented metal region in the reconstructed image with a soft tissue CT value; clustering the filled reconstructed images by using a three-dimensional K-means algorithm, and clustering human tissues into air, fat, soft tissues and bones; assigning values to different clustered organizations to obtain a prior image, which is expressed as:
Figure BDA0002508866860000052
wherein omegaair、Ωfat、Ωsoft、ΩboneRespectively represent the clustering regions of air, fat, soft tissue and bone, omegametalRepresenting a metal partitioned area. The CT values of air, fat, soft tissue and bone can be set to-1000 HU, 0HU, 200HU and 750HU respectively to obtain a priori image fprior。
4) And carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data.
5) Utilizing the prior projection data and the metal neighborhood projection data to construct a model, repairing the metal projection area to obtain repaired projection data, wherein the projection repairing process is represented as:
Figure BDA0002508866860000053
where theta is the projection angle corresponding to the projection data,
Figure BDA0002508866860000054
is the original projection data and is the original projection data,
Figure BDA0002508866860000055
in order to be a priori the projection data,
Figure BDA0002508866860000056
for the repaired projection data, U ═ (U, v) is the projection data coordinate, mean (×) is the mean function, Δ is the 3 × 3 neighborhood of the metal boundary,
Figure BDA0002508866860000057
is a metal projection area.
The invention provides a preprocessing flow of bilateral filtering and priori image generation and a projection repairing flow of a metal projection area. Compared with the prior art, the latter does not adopt the steps 1) and 3) for preprocessing, and does not adopt the model constructed in the step 5) for projection repair.
The key technology of the invention is as follows:
A) preprocessing by adopting the bilateral filter in the step 1), and reserving image edge information;
B) generating a prior image by adopting the class tissue model described in the step 3), and then carrying out forward projection to obtain prior projection data;
C) and (5) acquiring projection data of the repair projection by adopting the model constructed in the step 5), and finally acquiring a corrected image through image reconstruction and image fusion.
The invention has the advantages that:
A) the Euclidean distance and the gray difference among the pixels of the image are fully considered, bilateral filtering is introduced to preprocess the image, and the image has good noise suppression and edge protection capabilities;
B) the characteristic that the repaired metal projection area boundary has continuous properties is fully considered, the priori projection data and the metal neighborhood projection data are adopted to construct a model, and the metal projection area is subjected to interpolation repair, so that not only can the artifact caused by the metal implant in the original reconstructed image be effectively eliminated, but also the secondary artifact caused by improper correction can be inhibited.
Therefore, the algorithm has good artifact removal, noise resistance and boundary protection capability, thereby providing a new means for correcting metal artifacts.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A cone beam CT metal artifact correction algorithm is characterized in that: the method comprises the following steps:
step 1, preprocessing including bilateral filtering, metal threshold segmentation, tissue clustering and the like is carried out on a reconstructed image containing metal artifacts, so as to obtain a metal image and a prior image; then carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data;
step 2, repairing the metal projection area to obtain repaired projection data;
and 3, reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image.
2. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 1 comprises the following steps:
for a cone beam CT reconstructed image, a Bilateral Filter (BF) based on Gaussian distribution is adopted to smooth the image, so that noise suppression and edge protection are realized, and a weight coefficient of the BF is composed of two parts: the gray value difference range between pixels is called as a pixel range domain filtering kernel function; the Euclidean distance between pixels is called as a spatial domain filtering kernel function,
setting f (X) as the reconstructed image containing metal artifact, normalizing the grey value, and bilateral filtering to obtain the output image fBF(X)：
Figure FDA0002508866850000011
Wherein X is (X)1,y1,z1) Denotes the center pixel, Y ═ x2,y2,z2) Representing the neighborhood pixels of X, Ω is the set of neighborhood pixels,
Figure FDA0002508866850000012
is a kernel of a spatial domain, and is,
Figure FDA0002508866850000013
is a range domain kernel.
3. The cone beam CT metal artifact correction algorithm of claim 2, wherein:
Figure FDA0002508866850000014
and
Figure FDA0002508866850000015
a single-peak gaussian function that is all non-negative, expressed as:
Figure FDA0002508866850000016
Figure FDA0002508866850000021
wherein σ1Is the distance standard deviation, σ, of a Gaussian function2Is the gray standard deviation of Gaussian function, which respectively controls the radial action range of the filtering kernel function of the space domain and the range domain, and both are non-negative selectable variable parameters, the size of the two parameters directly determines the performance of the bilateral filter, and the weighted value of the pixel is adjusted by controlling the relative space and the gray variation range among the pixels, thereby realizing the effect of filtering the image,
bilateral filtering algorithm passes through control parameter sigma2To protect image boundary information if sigma2The size of the composite material is larger,
Figure FDA0002508866850000022
the two-sided filtering is approximate to Gaussian filtering and is close to 1, and the highest noise suppression and the lowest edge protection are carried out on the image; on the contrary, if σ2The size of the composite material is small,
Figure FDA0002508866850000023
the bilateral filtering will be close to 0, and the smoothing intensity of the image is low, but the image edge can be better preserved.
4. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 2 comprises the following steps:
obtaining a metal image by metal threshold segmentation, specifically comprising the following steps of segmenting metal from a reconstructed image by a threshold method:
Figure FDA0002508866850000024
where T represents a threshold value, and the value can be determined by a histogram method, and can be set to 30% of the maximum pixel value. The region having a pixel value equal to or greater than T is a metal image, 1 is set in the binary image, and the pixel values of the other regions are set to 0.
5. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 3 comprises the following steps:
generating a prior image by adopting a class tissue model, wherein the generation process comprises the following steps: filling the segmented metal region in the reconstructed image with a soft tissue CT value; clustering the filled reconstructed images by using a three-dimensional K-means algorithm, and clustering human tissues into air, fat, soft tissues and bones; assigning values to different clustered organizations to obtain a prior image, which is expressed as:
Figure FDA0002508866850000031
wherein omegaair、Ωfat、Ωsoft、ΩboneRespectively represent the clustering regions of air, fat, soft tissue and bone, omegametalRepresenting a metal partitioned area. The CT values of air, fat, soft tissue and bone can be set to-1000 HU, 0HU, 200HU and 750HU respectively to obtain a priori image fprior。
6. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 3 comprises the following steps:
and carrying out forward projection on the metal image and the prior image to obtain a metal projection area and prior projection data.
7. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 3 comprises the following steps:
utilizing the prior projection data and the metal neighborhood projection data to construct a model, repairing the metal projection area to obtain repaired projection data, wherein the projection repairing process is represented as:
Figure FDA0002508866850000032
where theta is the projection angle corresponding to the projection data,
Figure FDA0002508866850000033
is the original projection data and is the original projection data,
Figure FDA0002508866850000034
in order to be a priori the projection data,
Figure FDA0002508866850000035
for the repaired projection data, U ═ (U, v) is the projection data coordinate, mean (×) is the mean function, Δ is the 3 × 3 neighborhood of the metal boundary,
Figure FDA0002508866850000036
is a metal projection area.
8. The cone beam CT metal artifact correction algorithm of claim 1, wherein: the step 3 comprises the following steps:
and reconstructing the repaired projection data by using an analytic reconstruction algorithm to obtain an intermediate reconstructed image, and fusing the intermediate reconstructed image with the metal image segmented in the first step to obtain a final corrected image.
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