CN117765107A - Imaging quality optimization method and system for CT scanning of coronary artery disease - Google Patents
Imaging quality optimization method and system for CT scanning of coronary artery disease Download PDFInfo
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Abstract
The invention belongs to the CT field, and discloses an imaging quality optimization method and system for CT scanning of coronary artery diseases, wherein the method comprises the following steps: s1, CT scanning is carried out on a coronary artery imaging area of an imaging target, and CT projection data are obtained; s2, threshold division is carried out on CT projection data, and a divided image is obtained; s3, filtering the divided image, including: for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image; s31, calculating a neighborhood coefficient of the pixel point; s32, filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values; s4, reconstructing the filtered image to obtain a CT scanning image. The invention ensures the effect of quality optimization while improving the calculation efficiency of the CT imaging process including the quality optimization process.
Description
Technical Field
The invention relates to the CT field, in particular to an imaging quality optimization method and an imaging quality optimization system for CT scanning of coronary artery diseases.
Background
Coronary artery CT imaging is mainly used for examining diseases such as coronary heart disease. Compared with common CT imaging, coronary CT imaging requires that contrast medium is injected into an imaging target in the imaging process, and then CT scanning is carried out on the imaging target to obtain an imaging result.
In order to improve the quality of CT scan imaging, in the prior art, patent publication No. CN116071450a discloses a robust low dose CT imaging method, which only uses a single non-local mean filtering algorithm to perform filtering processing when filtering projection data. However, the non-local mean filtering algorithm has the disadvantage of higher time complexity, but in the projection data, the importance degrees of different areas are different, and not all areas have more complicated noise distribution, so that the filtering processing of the projection signal by adopting a single algorithm with higher time complexity results in longer time consumption in the process of optimizing the quality of CT scanning imaging, and influences the efficiency of CT imaging.
Disclosure of Invention
The invention aims to disclose an imaging quality optimization method and an imaging quality optimization system for CT scanning of coronary artery diseases, and solve the problem of how to shorten the time of a quality optimization process and further improve the efficiency of CT imaging when CT scanning imaging is carried out on coronary arteries.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, the present invention provides a method of optimizing imaging quality of a CT scan for coronary artery disease, comprising:
s1, CT scanning is carried out on a coronary artery imaging area of an imaging target, and CT projection data are obtained;
s2, threshold division is carried out on CT projection data, and a divided image is obtained;
s3, filtering the divided image, including:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
s31, calculating a neighborhood coefficient of the pixel point;
s32, filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values;
s4, reconstructing the filtered image to obtain a CT scanning image.
Optionally, S2 includes:
and carrying out threshold division on the CT projection data by adopting a preset first threshold value, and only reserving pixel points with gray values larger than the first threshold value in the CT projection data to obtain a division image.
Optionally, S2 includes:
a second threshold of the CT projection data is calculated using a preset threshold segmentation algorithm,
and carrying out threshold division on the CT projection data based on the second threshold value, and reserving only pixel points with gray values larger than the second threshold value in the CT projection data to obtain a division image.
Optionally, the preset threshold segmentation algorithm includes any one of a histogram-based threshold segmentation algorithm, a maximum inter-class variance segmentation algorithm, and a maximum entropy threshold segmentation algorithm.
Optionally, the calculation formula of the neighborhood coefficient is:
neicoef a neighborhood coefficients representing pixel a, neia representing a set of pixels in the partitioned image, numgray a Represents the number of pixels in the divided image, the gray value of which is the same as the pixel a, numsu represents the total number of pixels in the divided image, and gray b Grayneia representing gray value of pixel b ma Represents the maximum value, η, of the gray values of the pixel points in neia 1 、η 2 And eta 3 Respectively representing a gray level weight, a gray value fluctuation weight and a gray value weight set in advance.
Optionally, filtering the pixel point based on the neighborhood coefficient to obtain a filtered gray value, including:
if the neighborhood coefficient is larger than or equal to a preset neighborhood coefficient threshold value, adopting a first type filtering algorithm to filter the pixel points to obtain a filtered gray value;
if the neighborhood coefficient is smaller than the preset neighborhood coefficient threshold value, adopting a second type filtering algorithm to filter the pixel points to obtain a filtered gray value;
the operation time of the first type of filtering algorithm is longer than that of the second type of filtering algorithm.
Optionally, the first type of filtering algorithm includes any one of a bilateral filtering algorithm, a wavelet transformation algorithm, a non-local mean value filtering algorithm, a mean shift filtering algorithm, a Kuwahara filtering algorithm and a kalman filtering algorithm;
the second type of filtering algorithm includes any one of a gaussian filtering algorithm, a median filtering algorithm, and a mean filtering algorithm.
In a second aspect, the invention provides an imaging quality optimization system for CT scanning of coronary artery disease, comprising a scanning module, a dividing module, a filtering module and a reconstruction module;
the scanning module is used for carrying out CT scanning on a coronary artery imaging area of an imaging target to obtain CT projection data;
the dividing module is used for threshold dividing the CT projection data to obtain divided images;
the filtering module is used for filtering the divided image, and comprises the following steps:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
calculating the neighborhood coefficient of the pixel point;
filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values;
the reconstruction module is used for reconstructing the filtered image to obtain a CT scanning image.
The beneficial effects are that:
compared with the prior art, in the process of optimizing projection data obtained by CT scanning of a coronary artery imaging region to improve the quality of a reconstructed image, instead of using only one filtering algorithm to carry out filtering processing on the projection data, the method and the device ensure the quality optimization effect while improving the calculation efficiency of the CT imaging process including the quality optimization process by calculating the neighborhood coefficient and selecting proper filtering algorithms for different pixel points based on the neighborhood coefficient to carry out filtering.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an imaging quality optimization method of CT scan for coronary artery disease according to the present invention.
Fig. 2 is a schematic diagram of projection data according to the present invention.
FIG. 3 is a schematic diagram of an imaging quality optimization system for CT scanning of coronary artery disease in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
in one embodiment as shown in fig. 1, the present invention provides a method for optimizing imaging quality of a CT scan for coronary artery disease, comprising: s1, CT scanning is carried out on a coronary artery imaging area of an imaging target, and CT projection data are obtained.
In some embodiments, the imaging target comprises a person suffering from coronary artery disease.
In other embodiments, the imaging target comprises a person not suffering from coronary artery disease.
In some embodiments, the coronary imaging region is the region of the upper body of the person up to the mandible, down to the left papillary line, down to the abdomen, pelvic cavity of any of the embodiments described above.
In other embodiments, the coronary imaging region is a region 1cm below the tracheal ridge to below the diaphragmatic surface of the heart.
In the present invention, CT projection data refers to an image obtained in one step prior to an image reconstruction step in the process of CT image generation, as shown in fig. 2. Fig. 2 is merely an illustrative schematic view, not CT projection data of a coronary artery.
S2, threshold division is carried out on CT projection data, and a divided image is obtained.
Specifically, the purpose of this step is mainly to reduce the number of pixels in the projection data involved in the filtering calculation process, so as to achieve the purpose of improving the speed of image optimization.
As can be seen from fig. 2, the projection data contains more black points, and the black points do not contain information of the coronary artery imaging region, so that the invention obtains a divided image by threshold division and deleting the pixel points, and can keep complete CT scanning information while accelerating the filtering speed.
Coronary artery CTA is an examination and diagnosis method for coronary heart disease patients, mainly by injecting contrast medium from shallow vein and then imaging through spiral CT coronary artery, thereby checking whether the coronary artery has stenosis degree, stenosis position and coronary calcification degree, and most of them are used for coronary heart disease examination. Coronary heart disease is a condition that coronary atherosclerotic plaque has stenosis, serious myocardial blood supply insufficiency can be even caused, and the coronary artery CTA can be effectively diagnosed.
Coronary CTA indication:
1) Diagnosing the diseases of the suspected coronary heart disease patients; 2) Checking the bridged blood vessel of the bridged patient; 3) Examination for coronary artery deformity; 4) Plaques of chronic occlusive lesions (CTOs) were evaluated.
Coronary CTA is not suitable for patients meeting any of the following conditions:
1) Patients with established coronary heart disease or highly suspected coronary heart disease; 2) Severe calcification of coronary arteries (Agatston calcification score > 400); 3) Evaluating the coronary stent lesion; 4) Contrast agent allergic persons; 5) Renal insufficiency; 6) Qualitative and quantitative coronary plaque.
The coronary arteries emanate from the left and right coronary sinus.
Under normal conditions (non-variance), the left coronary sinus generally emits a left trunk, which in turn branches into a left anterior descending branch and a left circumflex branch;
the main branches of the left anterior descending branch are the first diagonal branch, the second diagonal branch, and so on; the main branch of the left round branch is a blunt round branch; sometimes the left trunk also gives off the middle branch.
The right coronary sinus gives off the right coronary artery, whose main branches are the right posterior descending branch and the left ventricular descending branch.
Interpretation process of CT image:
1) Looking at the ostia of the coronary arteries
In normal humans, the left coronary artery is derived from the left coronary sinus, while the right coronary artery is derived from the right coronary sinus. However, there are many patients with anatomical variations that can be classified according to whether they affect myocardial perfusion:
(1) coronary artery malformations affecting myocardial perfusion and presenting a potential risk:
the left coronary artery of a coronary fistula originates in the pulmonary artery; congenital coronary artery stenosis or absence; the left coronary artery originates in the right coronary sinus; a single coronary artery.
(2) Coronary artery malformations that do not affect myocardial perfusion:
the left circumflex branch of the right coronary sinus is opened; the anterior descending branch is open in the right coronary sinus. The right coronary artery originates from the left sinus.
2) Examining the extent and nature of arteriosclerosis in patients
(1) Severity of
Mild stenosis: coronary atherosclerosis is diagnosed if the stenosis degree of any coronary artery is 30% -50%;
moderately narrow: any coronary artery stenosis, between 50% and 70%, is diagnosed as coronary heart disease;
severe stenosis: any coronary artery with a stenosis degree exceeding 70% is known as severe stenosis or occlusion, and usually results in an acute cardiovascular event and severe death.
(2) Patch attributes
Non-calcified plaque: is an unstable soft plaque, has gray low-density areas on the coronary artery CT, is thin in fiber cap, and has the risk of acute thrombosis caused by rupture;
calcified plaque: the soft plaque becomes progressively stiffer with a thicker fibrous cap. Coronary CT images show white and bright spots, but due to their high density and poor transparency, interpretation of the results may be affected. Coronary angiography generally requires a further determination of the extent of lumen stenosis, which is relatively stable in nature;
note that: the degree of calcification can be quantified by coronary calcification scoring, typically using Agaston scoring, calculated by multiplying the calcification density score by the calcification area.
130-199 is 1 minute, 200-299 is 2 minutes, 300-399 is 3 minutes, and more than 400 is 4 minutes.
The risk of cardiovascular events increases suddenly when the score reaches 100 and increases sharply when the score reaches 400, indicating that the 5-year incidence of cardiovascular events exceeds 10%.
Mixing plaques: the plaque has both non-calcified and calcified plaque, and the thickness of the fibrous cap needs to be determined to infer the plaque stability. Coronary CT displays an off-white cross image.
Post-stent lesions: the interpretation effect is similar to calcified plaque, with poor transparency. The imaging effect is better when there is no restenosis. Coronary probes are not ideal in effect when restenosis occurs, and often require further clarification by coronary angiography.
In some embodiments, S2 comprises:
and carrying out threshold division on the CT projection data by adopting a preset first threshold value, and only reserving pixel points with gray values larger than the first threshold value in the CT projection data to obtain a division image.
In the above embodiment, the first threshold is set mainly by experience of a doctor.
Further, the first threshold may be 5.
In other embodiments, S2 comprises:
a second threshold of the CT projection data is calculated using a preset threshold segmentation algorithm,
and carrying out threshold division on the CT projection data based on the second threshold value, and reserving only pixel points with gray values larger than the second threshold value in the CT projection data to obtain a division image.
In the above embodiment, the value of the second threshold is not set in advance any more, if the value is adaptively generated based on the CT projection data, the dependency of the present invention on the experience of the doctor is lower, and the difficulty of using the present invention is reduced.
In some embodiments, calculating the second threshold of the CT projection data using a preset threshold segmentation algorithm comprises:
calculating CT projection data by adopting a preset threshold segmentation algorithm to obtain a segmentation threshold imgthr;
first calculation:
calculating a first variation interval;
calculating imgthr based on the first variation interval to obtain a first intermediate threshold;
calculating a first scale variation amplitude between a first intermediate threshold and imgthr based on the CT projection data;
judging whether the first ratio change amplitude is smaller than a set change amplitude threshold value, if so, taking the value of imgthr as the value of a second threshold value, and if not, entering the next calculation;
calculation n:
calculating an nth variation interval;
calculating an n-1 intermediate threshold value based on the n-th change interval to obtain an n-th intermediate threshold value;
calculating an nth proportional variation amplitude between an nth intermediate threshold and an nth-1 th intermediate threshold based on the CT projection data;
and judging whether the n-th proportional change amplitude is smaller than a set change amplitude threshold value, if so, taking the value of the n-1-th intermediate threshold value as the value of the second threshold value, and if not, entering the next calculation.
In the above embodiment, after the segmentation threshold is calculated, the present invention does not directly use the segmentation threshold as the value of the second threshold, mainly because the segmentation threshold calculated by the image segmentation algorithm is used to maximize the difference between the pixel points at both ends of the threshold. In contrast, since the present invention is to adaptively delete the pixels not including the information of the coronary artery region, the present invention needs to make the number of pixels at both ends of the division threshold hardly change when the division threshold fluctuates around the second threshold. Thus, the present invention determines the value of the second threshold based on the segmentation threshold by way of multiple calculations. Thus, the pixel point containing the information of the coronary artery region can be accurately retained.
In some embodiments, the amplitude of change threshold is
In some embodiments, the first variation interval is a predetermined value, such as 20.
In some embodiments, calculating imgthr based on the first variation interval results in a first intermediate threshold, including subtracting the value of imgthr from the value of the first variation interval results in the first intermediate threshold.
In some embodiments, calculating a first scale change magnitude between a first intermediate threshold and imgthr based on the CT projection data includes:
acquiring the number N of pixel points with gray values smaller than a first intermediate threshold in CT projection data 1 ；
Acquiring the number N of pixel points with gray values smaller than imgthr in CT projection data 0 ；
The calculation formula of the first proportional change amplitude is as follows:
scachgtmp 1 represents the first proportional change amplitude, N int Representing the total number of pixels in the CT projection data.
In some embodiments, the acquiring of the nth variation interval includes:
if n is 2, the value of the nth variation interval is the same as the value of the first variation interval;
if n is greater than 2, the calculation function of the nth variation interval is:
itrchg n represents the nth interval of variation, ithchg 1 Represents the first change interval, w represents the set interval weight, bstval represents the preset positive integer, scachgtmp n-1 And scachgtmp n-2 Respectively representing the n-1 th proportional change amplitude and the n-2 nd proportional change amplitude, max representing a larger value acquisition function, max (scachgtmp n-1 ,scachgtmp n-2 ) Has a value of scachgtmp n-1 And scachgtmp n-2 The larger of the medium values.
In the present invention, the change interval of the numerical change of the intermediate threshold is not fixed, but is determined with the amount of change between the adjacent two proportional change magnitudes and the numerical value of the number of the intermediate threshold. The invention has the advantages that the value of the intermediate threshold value is not required to be reduced at equal intervals, the second threshold value can be obtained more quickly, the change interval of the intermediate threshold value is enabled to be smaller and smaller along with the approaching of the final second threshold value, in addition, if the change amount between the proportion change amplitudes is smaller, the change interval is also reduced along with the approaching of the final second threshold value, so that the aim of approaching the final second threshold value more accurately is fulfilled, and when the change amount between the proportion change amplitudes is larger, the invention also enables the change interval to be maintained at a larger value, and the final required result is approximated more quickly.
In some embodiments, the predetermined positive integer is a product of a number of rows and a number of columns of the CT projection data.
In some embodiments, w has a value of 0.5.w is a number between 0 and 1.
In some embodiments, calculating the n-1 th intermediate threshold based on the n-th variation interval, results in the n-th intermediate threshold, comprising:
subtracting the value of the n-1 th intermediate threshold value from the value of the n-th variation interval to obtain the n-th intermediate threshold value.
In some embodiments, calculating an nth magnitude of proportional change between an nth intermediate threshold and an n-1 th intermediate threshold based on the CT projection data includes:
acquiring the number N of pixel points with gray values smaller than an nth intermediate threshold in CT projection data n ；
Acquiring the number N of pixel points with gray values smaller than the N-1 th intermediate threshold in CT projection data n-1 ；
The calculation formula of the n-th proportional change amplitude is as follows:
scachgtmp n represents the N-th proportional change amplitude, N int Representing the total number of pixels in the CT projection data.
In some embodiments, the preset threshold segmentation algorithm includes any one of a histogram-based threshold segmentation algorithm, a maximum inter-class variance segmentation algorithm, and a maximum entropy threshold segmentation algorithm.
S3, filtering the divided image, including:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
s31, calculating a neighborhood coefficient of the pixel point;
s32, filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values.
In some embodiments, the neighborhood coefficients are calculated as:
neicoef a neighborhood coefficients representing pixel a, neia representing a set of pixels in the partitioned image, numgray a Represents the number of pixels in the divided image, the gray value of which is the same as the pixel a, numsu represents the total number of pixels in the divided image, and gray b Grayneia representing gray value of pixel b ma Represents the maximum value, η, of the gray values of the pixel points in neia 1 、η 2 And eta 3 Respectively representing a gray level weight, a gray value fluctuation weight and a gray value weight set in advance.
In the above embodiment, the neighborhood coefficient can reflect the importance degree of the pixel point on the one hand, and the fluctuation degree of the gray value around the pixel point on the other hand, so that the pixel point for performing the filtering processing can be accurately screened out by comprehensively considering two different aspects.
Under the condition that the second term in the formula is kept unchanged, the more the number of other pixels with the same gray value as the pixel and the larger the gray value of the pixel are, the more information which belongs to the coronary artery imaging area and is carried on the pixel is represented, and therefore, the larger the corresponding neighborhood coefficient is.
And in the case where the first term and the third term in the formula remain unchanged, the larger the value of the second term, the greater the degree of fluctuation of the gradation value around the pixel point is represented, the greater the probability of containing noise is, and thus the greater the neighborhood coefficient is.
Under the condition that all items are changed, the invention can comprehensively express the importance degree of the pixel points and the possibility of noise, and is favorable for selecting the pixel points which are really needed to be subjected to filtering processing.
In some embodiments, the gray level weight, gray value fluctuation weight, and gray value weight are
In some embodiments, filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values includes:
if the neighborhood coefficient is larger than or equal to a preset neighborhood coefficient threshold value, adopting a first type filtering algorithm to filter the pixel points to obtain a filtered gray value;
if the neighborhood coefficient is smaller than the preset neighborhood coefficient threshold value, adopting a second type filtering algorithm to filter the pixel points to obtain a filtered gray value;
the operation time of the first type of filtering algorithm is longer than that of the second type of filtering algorithm.
In the above embodiment, whether the pixel point needs to be filtered is accurately determined based on the neighborhood coefficient threshold and the neighborhood coefficient of the pixel point, which is favorable for selecting the first type filtering algorithm with better filtering capability but more time-consuming to filter when the noise probability is high. And when the probability of containing noise is smaller, a filtering algorithm which takes less time is selected for filtering. By the calculation mode, on one hand, filtering operation on all pixel points by using a more time-consuming algorithm can be avoided, filtering efficiency is improved, and on the other hand, accuracy of a filtering result can be guaranteed. Because an algorithm with slightly poorer filtering capability can obtain sufficiently accurate filtering results when the neighborhood coefficients are smaller.
In some embodiments, when the gray level weight, the gray value fluctuation weight, and the gray value weight are bothWhen the neighborhood coefficient threshold is set to 0.6.
In some embodiments, the first type of filtering algorithm comprises any one of a bilateral filtering algorithm, a wavelet transform algorithm, a non-local mean filtering algorithm, a mean shift filtering algorithm, a Kuwahara filtering algorithm, a kalman filtering algorithm;
the second type of filtering algorithm includes any one of a gaussian filtering algorithm, a median filtering algorithm, and a mean filtering algorithm.
S4, reconstructing the filtered image to obtain a CT scanning image.
In some embodiments, reconstructing the filtered image to obtain a CT scan image includes:
establishing a blank image with the same resolution as CT projection data;
for any one pixel point d in the filtered image;
acquiring coordinates (x) of a pixel point corresponding to the pixel point d in CT projection data d ,y d )；
Filling gray values of pixel points d in the filtered image into (x) d ,y d ) A place;
after the gray values of the pixel points in the filtered image are filled in the blank image, setting the gray value of the pixel point without the gray value in the blank image to be 0, and obtaining an image to be reconstructed;
reconstructing the image to be reconstructed to obtain a CT scanning image.
Because the filtered image only comprises partial pixel points of the CT projection data, in order to be suitable for the existing CT image reconstruction algorithm, the invention obtains an image to be reconstructed, which has the same resolution as the CT projection data, based on the blank image. Therefore, the resolution ratios of the finally obtained images to be reconstructed at all scanning angles are kept consistent, and the image reconstruction can be performed by directly utilizing the existing reconstruction algorithm. By the arrangement mode, the application range of the invention is improved.
Embodiment two:
in one embodiment as shown in FIG. 3, the invention provides an imaging quality optimization system for CT scanning of coronary artery disease, comprising a scanning module, a dividing module, a filtering module and a reconstruction module;
the scanning module is used for carrying out CT scanning on a coronary artery imaging area of an imaging target to obtain CT projection data;
the dividing module is used for threshold dividing the CT projection data to obtain divided images;
the filtering module is used for filtering the divided image, and comprises the following steps:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
calculating the neighborhood coefficient of the pixel point;
filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values;
the reconstruction module is used for reconstructing the filtered image to obtain a CT scanning image.
In the process of optimizing projection data obtained by CT scanning on a coronary artery imaging area to improve the quality of a reconstructed image, instead of using only one filtering algorithm to carry out filtering processing on the projection data, the invention calculates the neighborhood coefficient firstly and then selects a proper filtering algorithm for different pixel points to carry out filtering based on the neighborhood coefficient, thereby realizing the improvement of the calculation efficiency of the CT imaging process including the quality optimization process and ensuring the quality optimization effect.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. A method for optimizing imaging quality of a CT scan for coronary artery disease, comprising:
s1, CT scanning is carried out on a coronary artery imaging area of an imaging target, and CT projection data are obtained;
s2, threshold division is carried out on CT projection data, and a divided image is obtained;
s3, filtering the divided image, including:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
s31, calculating a neighborhood coefficient of the pixel point;
s32, filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values;
s4, reconstructing the filtered image to obtain a CT scanning image.
2. The method of optimizing imaging quality of a CT scan for coronary artery disease of claim 1, wherein S2 comprises:
and carrying out threshold division on the CT projection data by adopting a preset first threshold value, and only reserving pixel points with gray values larger than the first threshold value in the CT projection data to obtain a division image.
3. The method of optimizing imaging quality of a CT scan for coronary artery disease of claim 1, wherein S2 comprises:
a second threshold of the CT projection data is calculated using a preset threshold segmentation algorithm,
and carrying out threshold division on the CT projection data based on the second threshold value, and reserving only pixel points with gray values larger than the second threshold value in the CT projection data to obtain a division image.
4. The method of claim 3, wherein the predetermined threshold segmentation algorithm comprises any one of a histogram-based threshold segmentation algorithm, a maximum inter-class variance segmentation algorithm, and a maximum entropy threshold segmentation algorithm.
5. The method for optimizing imaging quality of CT scan of coronary artery disease according to claim 1, wherein the calculation formula of the neighborhood coefficients is:
neicoef a neighborhood coefficients representing pixel a, neia representing a set of pixels in the partitioned image, numgray a Represents the number of pixels in the divided image, the gray value of which is the same as the pixel a, numsu represents the total number of pixels in the divided image, and gray b Grayneia representing gray value of pixel b ma Represents the maximum value, η, of the gray values of the pixel points in neia 1 、η 2 And eta 3 Respectively representing a gray level weight, a gray value fluctuation weight and a gray value weight set in advance.
6. The method for optimizing imaging quality of CT scan for coronary artery disease according to claim 1, wherein filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values comprises:
if the neighborhood coefficient is larger than or equal to a preset neighborhood coefficient threshold value, adopting a first type filtering algorithm to filter the pixel points to obtain a filtered gray value;
if the neighborhood coefficient is smaller than the preset neighborhood coefficient threshold value, adopting a second type filtering algorithm to filter the pixel points to obtain a filtered gray value;
the operation time of the first type of filtering algorithm is longer than that of the second type of filtering algorithm.
7. The method of claim 1, wherein the first type of filtering algorithm comprises any one of a bilateral filtering algorithm, a wavelet transform algorithm, a non-local mean filtering algorithm, a mean shift filtering algorithm, a Kuwahara filtering algorithm, and a kalman filtering algorithm;
the second type of filtering algorithm includes any one of a gaussian filtering algorithm, a median filtering algorithm, and a mean filtering algorithm.
8. The imaging quality optimization system for CT scanning of coronary artery disease is characterized by comprising a scanning module, a dividing module, a filtering module and a reconstruction module;
the scanning module is used for carrying out CT scanning on a coronary artery imaging area of an imaging target to obtain CT projection data;
the dividing module is used for threshold dividing the CT projection data to obtain divided images;
the filtering module is used for filtering the divided image, and comprises the following steps:
for each pixel point in the divided image, respectively performing the following processing to obtain a filtered image;
calculating the neighborhood coefficient of the pixel point;
filtering the pixel points based on the neighborhood coefficients to obtain filtered gray values;
the reconstruction module is used for reconstructing the filtered image to obtain a CT scanning image.
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