CN110458913B - Method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT image - Google Patents
Method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT image Download PDFInfo
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Abstract
The inventionA method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT images is disclosed, which comprises the following steps: from the original scan data P 0 Reconstructing to generate an original image I 0 And a maximum FOV image I 1 (ii) a For the reconstructed maximum FOV image I 1 Performing multi-threshold segmentation to obtain n bone images I with different CT value ranges k (ii) a For n bone images I k Orthographic projection is carried out to obtain orthographic projection data P of each bone image k (ii) a Orthographic projection data P for each bone image k And a maximum FOV image I 1 Of the orthographic projection data
Fitting and weighting to obtain projection data P of the compensation image; reconstruction of a sclerosteosis-compensated image I of the maximum FOV from the final compensation term projection data P 2 (ii) a Sclerosteosis-compensated image I that will reconstruct the maximum FOV 2 Image conversion to original image I 0 Identical-sized bone-hardening compensation image I 3 (ii) a Compensating the bone sclerosis by image I 3 With the original image I 0 Adding to obtain the target image after the bone sclerosis correction. The method effectively corrects the bone sclerosis artifact, ensures the image quality and ensures the accuracy of clinical diagnosis.
Description
Technical Field
The invention relates to a method for correcting a bone hardening artifact in CT image reconstruction, in particular to a method for correcting a bone hardening artifact in CT image reconstruction by multi-threshold segmentation of a CT image. The invention belongs to the technical field of medical image processing.
Background
In the CT apparatus, a bulb (i.e., a light source) and a detector are the most important components, and the bulb emits X-rays (i.e., X-rays) all the way to cover a set scanning range; and scanning a set range by a detector, acquiring projection data of the patient, and finally reconstructing to obtain a patient tomogram with the required thickness.
In a CT machine imaging system, a CT image reconstruction algorithm is based on the assumption that X-rays emitted by a bulb tube are single-energy-spectrum light, and under the condition of the single energy spectrum, the ray intensity of the X-rays is attenuated along with the thickness of a scanned object according to the Beer law; however, in practical applications, the X-ray emitted from the bulb of the CT machine is a multi-energy spectrum light, so that the absorption coefficients of the same material for the X-photons with different energies are different, and the low-energy X-photons are more easily absorbed by most materials than the high-energy X-photons, therefore, after the X-ray passes through the human tissue, the proportion of the high-energy component in the X-ray increases, and as the thickness of the X-ray penetration increases, the peak value of the spectral distribution of the X-ray moves to the high-energy direction, so that the high-energy X-photons are more easily penetrated, i.e. the beam hardening phenomenon occurs. The difference between the actual CT reconstruction image and the ideal reconstruction result caused by the ray hardening can lead to cupping of the intensity in the reconstruction image, and the cupping artifact is very similar to the characteristics of a certain case, thus causing misdiagnosis of doctors!
In order to reduce the influence of the hardening artifact in the CT image reconstruction, the problem is usually solved by a combination of hardware and software. The addition of a flat filter to the hardware filters out the low energy X-ray spectrum to reduce the effects of radiation hardening. For a CT machine, clinical requirements cannot be met only by adding a flat filter, meanwhile, software is needed for correction, the human body scanning attenuation characteristic is simulated by using a water model, the difference between water model projection data and an ideal water model orthographic projection result is obtained through actual measurement, the fitting coefficient of hardening compensation is obtained, and the projection data of a scanned human body is fitted, corrected and hardened artifacts are corrected.
In addition, since the ray is attenuated to different degrees when passing through objects with different densities, the ray is more easily absorbed by the object with high density, and therefore, the ray is more easily hardened after passing through the object with high density in the CT scanning process. The density of human bone is relatively high, and hardening artifacts caused by scanning a part containing a large amount of bone are obvious. When scanning the head, there are generally two types of artifacts, one is a dark band between dense objects, caused by the difference between a ray through one object and a ray through multiple objects, with the shadow artifacts mainly distributed along the path linking these objects; another type of artifact is a degenerated bone-brain interface, where the image CT values of the soft tissue region are elevated, resulting in a blurred boundary. Such artifacts can affect the physician's confirmation of cerebral hemorrhage or edema related pathologies.
The software correction method for the bone hardening artifact generally comprises the steps of extracting a bone image from a target image to perform orthographic projection, fitting the orthographic projection data of the bone to obtain error projection data caused by the bone hardening, reconstructing by using the error data and adopting the same reconstruction parameters as the target image to obtain an error image caused by the bone hardening, and compensating the target image by using the error image to obtain a corrected image.
The method of correcting by software has the following defects: when the eccentric image building or the reconstruction FOV is smaller than the coverage range of the actual scanning part, when the CT image is reconstructed, the reconstructed CT image only contains a part of bones in the actual scanning, then the fitting bone projection error data only contains a part of bone hardening errors, and then back projection is carried out to obtain the compensation information which is not real bone artifact, so that the bone hardening artifact is not removed completely or new artifact is introduced. In order to solve the problem, patent No. 201110455891, "a method for correcting bone hardening artifact in CT image reconstruction", uses a large FOV to reconstruct a target image, and corrects a bone artifact in the target image.
In fact, the conventional method for correcting bone hardening artifacts has another serious defect besides the above-mentioned defect, namely: the difference in the effect of different density bone heads on the hardening was not taken into account when fitting the bone artifact compensation term. The effect of the hardening artifact correction of different layers of different patients, different scanning parts and even the same scanning part of the same patient also has difference, if the difference is not considered, some hardening artifact which is not corrected still exists due to the deficiency of the hardening artifact correction of the layers, or some artifacts which are brought by the excessive correction of the layers, or some hardening artifact which is left due to the deficiency of the part correction, or some artifacts which are brought by the excessive correction of the parts, or some artifacts which are brought by the insufficient correction of the image of the patient on the same layer of the same part exist, or some artifacts which are brought by the excessive correction of the image of the patient.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention aims to provide a method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmented CT images. The method comprises the steps of segmenting a human skeleton image according to a plurality of threshold intervals, respectively projecting skeleton images in different CT value ranges, adding different weights to perform fitting compensation according to different influence degrees of different skeleton densities, and correcting bone hardening artifacts.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT images comprises the following steps:
s1: from the original scan data P 0 The reconstruction generates an original image I without sclerosteosis correction 0 ；
S2: from the original scan data P 0 Maximum scan range image I supported by reconstruction system 1 Maximum FOV image I for short 1 ，
S3: maximum FOV image I to be reconstructed 1 The skeleton is divided according to n different threshold value intervals to obtain n bone images I with different CT value ranges k The image extraction is as follows:
wherein: n is the total number of the skeleton segmentation intervals; k is the k-th threshold selected, k ∈ [1,n]A natural number; c lk ，C hk The upper limit and the lower limit of the kth threshold interval are respectively; x is the number of ij Is an original image I 0 CT values corresponding to the ith row and the j column; y is ij For the k-th bone image I extracted according to the k-th threshold value k CT value for i row and j column of (k =1 … n);
s4: respectively aligning the obtained n bone images I k (k =1 … n) and orthographic projection data P of each bone image is obtained k (k＝1…n)；
S5: for the reconstructed maximum FOV image I 1 Performing orthographic projection to obtain orthographic projection data
S6: orthographic projection data P for each bone image k And a maximum FOV image I 1 Of the orthographic projection data
Fitting and weighting to obtain projection data P of the compensation image;
s7: according to the final compensationReconstruction of a bone-hardening compensation image I of the maximum FOV from the projection data P 2 ；
S8: according to the selected image-creating condition, a bone-hardening compensation image I with the maximum FOV is reconstructed 2 Image conversion to original image I 0 Identical-sized bone-hardening compensation image I 3 ；
S9, compensating the bone hardening image I 3 With the original image I 0 Adding to obtain the target image after the bone sclerosis correction.
In a preferred embodiment of the present invention, the total number n of the skeleton segmentation intervals in step S3 ranges from 3 to 5.
In the preferred embodiment of the present invention, the step S6 is to orthographically project the data P for each bone image k And a maximum FOV image I 1 Of the orthographic projection data
The method for obtaining the projection data P of the compensation image by fitting and weighting comprises the following steps:
first, orthographic projection data P is applied to each bone image k (k =1 … n) and maximum FOV image I 1 Of the orthographic projection data
Performing quadratic polynomial fitting to obtain projection data P of bone artifact hardening compensation term fk (k =1 … n), the fitting equation is as follows:
wherein: k denotes the kth threshold, k ∈ [1,n]A natural number; p k Forward projection data for a k-th bone image; p fk Fitting results for the orthographic projection data of the kth bone image;
is a maximum FOV image I 1 Of the orthographic projection data a k ,b k ,c k Respectively as bone projection data quadratic coefficient, bone projection and maximum FOV image I 1 Projection product term coefficient and constant termCoefficient of a k ,b k ,c k The value range is between 10e-7 and 10 e-5;
then, the compensation term projection data P of each quadratic polynomial fitting is processed fk (k =1 … n) the weighted sum results in the projection data P of the compensated image, i.e.:
wherein: p is the projection data of the final compensation term, k represents the k threshold, k is the [1,n ]](ii) a n is the total number of the skeleton segmentation intervals; p fk Fitting results of quadratic terms for the orthographic projection data of the kth bone image; m is k As weighting coefficients of the fitting results, m k Equal to the proportion of bone to total bone in the k-th compartment.
In the preferred embodiment of the present invention, the n bone images I with different CT value ranges obtained in step S3 k Before orthographic projection, 5 multiplied by 5 smoothing filtering processing is carried out to obtain a smoothed bone image I mk (k =1 … n), and then orthographic projection is performed to obtain orthographic projection data P of each bone image k (k＝1…n)。
Drawings
FIG. 1 is a flow chart of a method for correcting bone hardening artifacts in a multi-threshold segmented CT image according to the present invention;
FIG. 2 is a reconstructed raw image of a human head from raw scan data;
FIG. 3 is a maximum FOV image of a human head reconstructed from raw scan data;
FIG. 4 is a diagram of a skull segmentation with CT values ranging from 100 to 500;
FIG. 5 is a diagram of a skull segmentation with CT values ranging from 500 to 900;
FIG. 6 is a graph of a human skull segmentation with CT values greater than 900;
FIG. 7 is a reconstructed maximum FOV osteosclerosis compensated image of a human head;
FIG. 8 is a maximum FOV bone sclerosis compensated image of a human head under the same imaging conditions as the original image;
fig. 9 is a reconstructed human head image after correction of bone hardening artifacts.
Detailed Description
The structure and features of the present invention will be described in detail below with reference to the accompanying drawings and examples. It should be noted that various modifications can be made to the embodiments disclosed herein, and therefore, the embodiments disclosed in the specification should not be construed as limiting the present invention, but merely as exemplifications of embodiments thereof, which are intended to make the features of the present invention obvious.
The density of bones at different parts of a human body is different, and the CT value ranges of the imaged bones at different parts are different, so that the CT value range of the imaged bones of the same person is larger, and the CT values of the imaged bones are generally distributed from hundreds to thousands. In addition, the bone density of different persons and different parts of the same person is different, the influence of different bone densities on bone hardening is different, and if the single threshold value is used for dividing the bone hardening artifact in CT image reconstruction, the result is: sometimes the bone hardening artifact is under-corrected and sometimes over-corrected. The invention provides a method for correcting a bone hardening artifact in image reconstruction by multi-threshold segmentation CT images, which can more effectively correct the bone hardening artifact of the CT images on one hand; on the other hand, the influence of different bone densities of bones of different patients and different parts on the bone sclerosis correction effect is effectively overcome.
In order to achieve the object of the present invention, the present invention provides a method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT image, as shown in fig. 1, the method comprises the following steps:
s1: from the original scan data P 0 The reconstruction generates an original image I without sclerosteosis correction 0 。
Fig. 2 is an original image of a human head reconstructed from raw scan data.
S2: from the original scan data P 0 Maximum scan range image I supported by reconstruction system 1 (maximum FOV image I for short) 1 ) As shown in fig. 3.
The reconstructed view field of the image is the maximum scanning view field supported by a CT machine, the reconstructed convolution kernel is a standard convolution kernel selected clinically, the imaging center is the scanning center of the CT machine, and the imaging matrix is the maximum matrix supported by imaging software.
Reconstruction of the maximum FOV image I 1 The method aims to overcome the defect that an eccentrically constructed or reconstructed FOV image is smaller than the coverage range of an actual scanning part, and avoids the problem that the reconstructed image only contains a part of bones in actual scanning, further the fitting bone projection error data only contains a part of bone hardening errors, and then back projection is carried out to obtain bone artifact compensation information which is not real, so that the bone hardening artifacts are not completely removed or new artifacts are introduced.
S3: maximum FOV image I to be reconstructed 1 The skeleton is divided according to n different threshold value intervals to obtain n bone images I with different CT value ranges k The image extraction is as follows:
wherein: n is the total number of the skeleton segmentation intervals; k is the k-th threshold selected, k ∈ [1,n ]]A natural number; c lk ，C hk Respectively the upper limit and the lower limit of the kth threshold interval; x is the number of ij Is an original image I 0 CT values corresponding to the ith row and the j column; y is ij For the k-th bone image I extracted according to the k-th threshold value k CT value for i row and j column (k =1 … n).
The X-ray emitted by the CT machine ball tube passes through objects with different densities to be attenuated to different degrees, so that the change of energy spectrum distribution after the X-ray passes through the objects with different densities is different, namely the X-ray passes through bones with different densities to have different influences on ray hardening degrees, in order to more accurately fit the ray hardening influence caused by the fact that the ray passes through the bones, the invention adopts multi-threshold segmentation of the bones, the influence of the bones with different densities on the ray hardening is respectively fitted, the ray hardening influence is more accurately estimated, and the error caused by the different bone densities is reduced. The smaller the density interval division of the bone (namely, the larger the value of n), the more accurate the fitting result is, and meanwhile, in consideration of the problem of calculation amount, the value range of n is recommended to be 3-5.
FIG. 4 is a diagram showing the segmentation of human skull with CT values distributed between 100 and 500; FIG. 5 is a diagram showing the skull segmentation of human body with CT values distributed between 500 and 900; fig. 6 shows a human skull segmentation map with the CT value distribution greater than 900.
S4: respectively comparing the obtained n bone images I k (k =1 … n) and 5 × 5 smoothing filter processing is performed to obtain a smoothed bone image I mk (k＝1…n)。
The purpose of the smooth filtering is to prevent the fitted curve after the orthographic projection at the edge of the skeleton image from having abrupt points, and new artifacts are introduced when filtering and back-projecting are carried out to generate an error image.
S5: respectively to the smoothly filtered bone images I mk (k =1 … n) and orthographic projection data P of each bone image is obtained k (k =1 … n), the parameters selected for the orthographic projection of the bone image are consistent with the parameters during the actual CT scan.
S6: for the reconstructed maximum FOV image I 1 Performing orthographic projection to obtain orthographic projection data
S7: orthographic projection data P for each bone image k (k =1 … n) and maximum FOV image I 1 Of the orthographic projection data
Performing quadratic polynomial fitting to obtain projection data P of bone artifact hardening compensation term fk (k =1 … n), the fitting equation is as follows:
wherein: k denotes the kth threshold, k ∈ [1,n]A natural number; p k Forward projection data for a k-th bone image; p fk Fitting results for the orthographic projection data of the kth bone image;
is a maximum FOV image I 1 Of the orthographic projection data a k ,b k ,c k Respectively the bone projection data quadratic coefficient, the bone projection and the maximumLarge FOV image I 1 Projection product term coefficient and constant term coefficient, a k ,b k ,c k The value range is between 10e-7 and 10 e-5.
In the process of CT scanning, rays pass through the bone and the soft tissue at the same time, and the energy spectrum distribution of the rays is changed when the rays pass through the soft tissue, so that not only the influence of different bone densities but also the influence of the rays passing through the soft tissue are considered for more accurately estimating the bone hardening artifact, and therefore, the bone projection data after multi-threshold segmentation and the soft tissue projection data are adopted for fitting the hardening artifact compensation projection value for more accurately estimating the influence of the bone artifact.
S8: projection data P of compensation term fitting each quadratic multiple term fk (k =1 … n) the weighted summation results in the projection data P of the compensated image, i.e.:
wherein: p is the projection data of the final compensation term, k represents the k threshold, k is the [1,n ]](ii) a n is the total number of the skeleton segmentation intervals, and n is a natural number of 3-5; p is fk Fitting results of quadratic terms for the orthographic projection data of the kth bone image; m is k M is determined by using the proportion of bone to total bone in the k-th compartment as the weighting coefficient of the fitting result k The value of (a).
S9: reconstructing a scleroste-compensated image I of the maximum FOV from the final compensation term projection data P 2 As shown in fig. 7.
S10: according to the selected image-creating condition, a bone-hardening compensation image I with the maximum FOV is reconstructed 2 Image conversion to original image I 0 Bone sclerosis compensation image I with same size 3 As shown in fig. 8.
S11, compensating the bone hardening image I 3 With the original image I 0 The addition results in a target image after the bone-hardening correction, as shown in fig. 9.
The method comprises the steps of setting a plurality of segmented CT images with different threshold value intervals, taking into account that different patients and different scanning parts of the same patient have different bone densities and different bone densities have different influences on bone hardening, extracting a plurality of bone images with different CT value ranges, respectively carrying out orthographic projection, carrying out polynomial fitting, adding different weights to sum up to obtain a final fitting result, carrying out filtering back projection on the final fitting result of the orthographic projection to obtain a corrected image of a hardening artifact, and summing the corrected image of the hardening artifact and an original image to obtain a final corrected CT image.
The invention has the advantages that: the method can effectively correct the bone sclerosis artifacts, overcomes the influence of the bone density difference of different patients and different scanning parts on the bone sclerosis correction, ensures the image quality and ensures the accuracy of clinical diagnosis.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. A method for correcting bone hardening artifacts in image reconstruction by multi-threshold segmentation CT images is characterized in that: it comprises the following steps:
s1: from the original scan data P 0 The reconstruction generates an original image I without sclerosteosis correction 0 ；
S2: from the original scan data P 0 Maximum scan range image I supported by reconstruction system 1 Maximum FOV image I for short 1 ，
S3: maximum FOV image I to be reconstructed 1 The skeleton is divided according to n different threshold value intervals to obtain n bone images I with different CT value ranges k The image extraction is as follows:
wherein: n is the total number of the skeleton segmentation intervals; k is the k-th threshold selected, k ∈ [1,n]A natural number; c lk ，C hk The upper limit and the lower limit of the kth threshold interval are respectively; x is the number of ij Is an original image I 0 CT values corresponding to the ith row and the j column; y is ij For the k bone image I extracted according to the k threshold value k CT value for i row and j column of (k =1 … n);
s4: respectively aligning the obtained n bone images I k (k =1 … n) and orthographic projection data P of each bone image is obtained k (k＝1…n)；
S5: for the reconstructed maximum FOV image I 1 Performing orthographic projection to obtain orthographic projection data
S6: orthographic projection data P for each bone image k And a maximum FOV image I 1 Of the orthographic projection data
Fitting and weighting to obtain projection data P of the compensation image;
s7: reconstruction of a sclerosteosis-compensated image I of the maximum FOV from the final compensation term projection data P 2 ；
S8: according to the selected image-creating condition, a bone-hardening compensation image I with the maximum FOV is reconstructed 2 Image conversion to original image I 0 Identical-sized bone-hardening compensation image I 3 ；
S9, compensating the bone sclerosis image I 3 With the original image I 0 Adding to obtain the target image after the bone sclerosis correction.
2. The method of claim 1, wherein the multi-threshold segmentation CT image is used for correcting bone hardening artifacts in image reconstruction, and the method comprises the following steps: the value range of the total number n of the skeleton segmentation intervals in the step S3 is 3-5.
3. The method for correcting bone hardening artifacts in image reconstruction by using multi-threshold segmentation CT image as claimed in claim 1, wherein: the step S6 is to orthographically project data P of each bone image k And a maximum FOV image I 1 Forward projection data P I1 The method for obtaining the projection data P of the compensation image by fitting and weighting comprises the following steps:
first, orthographic projection data P is applied to each bone image k (k =1 … n) and maximum FOV image I 1 Forward projection data P I1 Performing quadratic polynomial fitting to obtain projection data P of bone artifact hardening compensation term fk (k =1 … n), the fitting equation is as follows:
wherein: k denotes the kth threshold, k ∈ [1,n]A natural number; p k Forward projection data for a k-th bone image; p fk Fitting results for the orthographic projection data of the kth bone image;
is a maximum FOV image I 1 Of the orthographic projection data a k ,b k ,c k Respectively as bone projection data quadratic coefficient, bone projection and maximum FOV image I 1 Projection product term coefficient and constant term coefficient, a k ,b k ,c k The value range is between 10e-7 and 10 e-5;
then, the compensation term projection data P of each quadratic polynomial fitting is calculated fk (k =1 … n) the weighted summation results in the projection data P of the compensated image, i.e.:
wherein: p is the projection data of the final compensation term, k represents the k threshold, k is the [1,n ]](ii) a n is the total number of the skeleton segmentation intervals; p fk Fitting results of quadratic terms for the orthographic projection data of the kth bone image; m is a unit of k Weighting coefficients for the fitting results，m k Equal to the proportion of bone to total bone in the k-th compartment.
4. The method of claim 3, wherein the multi-threshold segmentation CT image is used for correcting bone hardening artifacts in image reconstruction, and the method comprises the following steps: the n bone images I with different CT value ranges obtained in the step S3 k Before orthographic projection, 5 multiplied by 5 smoothing filtering processing is carried out to obtain a smoothed bone image I mk (k =1 … n), and then orthographic projection is performed to obtain orthographic projection data P of each bone image k (k＝1…n)。
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