CN115797486A - CT image linear artifact eliminating method and device and storage medium - Google Patents
CT image linear artifact eliminating method and device and storage medium Download PDFInfo
- Publication number
- CN115797486A CN115797486A CN202211377306.XA CN202211377306A CN115797486A CN 115797486 A CN115797486 A CN 115797486A CN 202211377306 A CN202211377306 A CN 202211377306A CN 115797486 A CN115797486 A CN 115797486A
- Authority
- CN
- China
- Prior art keywords
- data
- reliability
- correction
- correction result
- result data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The embodiment of the application discloses a method, a device and a storage medium for eliminating a linear artifact of a CT image, wherein the method for eliminating the linear artifact of the CT image comprises the following steps: acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data; performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on the corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level; and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
Description
Technical Field
The present application relates to the field of medical image processing technologies, and in particular, to a method and an apparatus for eliminating a linear artifact in a CT image, and a storage medium.
Background
In CT imaging, image noise increases as the tube current decreases, and when a body image is scanned, linear banding artifacts are caused by decrease in the tube current to a certain extent. In the CT detection process, in order to reduce the dose as much as possible without affecting the image quality, an automatic tube current adjustment technique is usually adopted, and different mA (tube current) is used for exposure when different parts are scanned, and even the automatic tube current adjustment technique is difficult to meet the requirement of being actually compatible with low-dose high-quality images, for example, the dose required when scanning the lung is very low, but the bone at the entrance of the chest often needs a larger current ratio, and is limited by the current adjustment influenced by the conditions of high voltage and bulb hardware. Therefore, in the case of low dose scanning, since there are a large number of bones in the shoulder, a line artifact due to a low signal is easily caused, and the quality of the shoulder image is affected.
Disclosure of Invention
An embodiment of the present application provides a method, an apparatus, and a storage medium for removing a linear artifact in a CT image, so as to solve a problem in the prior art that a linear artifact is generated due to a low signal in a CT imaging process.
In order to achieve the above object, an embodiment of the present application provides a method for removing a line artifact in a CT image, including: acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data;
performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on the corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level;
and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
Optionally, the method for classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain the third data includes:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained values of the confidence levels, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level are arranged in the order from high to low;
when the second data is classified as the first credibility, the second data obtained at this time is the third data;
when the second data is classified into the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
Optionally, the method for performing the first correction includes:
correcting initial data according to information of adjacent channels in the column direction of a detector array of the data acquisition system to obtain column direction correction result data, wherein the initial data comprises the second data or the third correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the initial data to obtain first target correction result data, wherein the first target correction result data comprises the first correction result data.
Optionally, the method for performing the second correction includes:
correcting the first target correction result data obtained after the first correction according to the information of the adjacent channels in the row direction of the detector array of the data acquisition system to obtain row direction correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the first target correction result data to obtain second target correction result data, wherein the second target correction result data comprises the second correction result data or the fourth correction result data.
Optionally, the method for performing the third correction includes:
negative correction is carried out on the negative number in the second data to obtain negative correction result data;
performing ray attenuation compensation correction on the basis of the negative number correction result data to obtain compensation correction result data;
and processing the compensation correction result data by using an equal average filtering method to obtain third correction result data.
Optionally, the method for classifying the second data into the first confidence level, the second confidence level, the third confidence level, or the fourth confidence level, which are arranged in order of the value of the confidence level from high to low, includes:
using the formula:
calculating a value for said confidence level, wherein N represents an average of said electronic noise levels of said data acquisition system, C (i, j) represents said confidence level, D 2 (i, j) represents the second data,i is the ith row of the detector array and j is the jth column of the detector array;
when the calculated value of the reliability is 1.00, classifying the second data into the first reliability;
when the calculated value of the degree of reliability is 0.75, classifying the second data as the second degree of reliability;
when the calculated value of the degree of reliability is 0.50, classifying the second data as the third degree of reliability;
when the calculated value of the degree of reliability is 0.25, the second data is classified as the fourth degree of reliability.
In order to achieve the above object, the present application further provides a line artifact removing apparatus for a CT image, comprising: a memory; and
a processor coupled to the memory, the processor configured to:
acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data;
performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on a corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level;
and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
Further, the processor is further configured to:
the method for classifying the second data according to the obtained value of the credibility and correspondingly processing the second data based on different classifications to obtain the third data comprises the following steps:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained values of the confidence levels, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level are arranged in the order from high to low;
when the second data is classified into the first credibility, the second data obtained at this time is the third data;
when the second data is classified as the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
Further, the processor is further configured to:
the method for classifying the second data into the first degree of reliability, the second degree of reliability, the third degree of reliability, or the fourth degree of reliability, which is arranged in order of the value of the degree of reliability from high to low, includes:
using the formula:
calculating a value for said confidence level, wherein N represents an average of said electronic noise levels of said data acquisition system, C (i, j) represents said confidence level, D 2 (i, j) represents the second data, i is the ith row of the detector array, and j is the detectorColumn j of the array;
when the calculated value of the reliability is 1.00, classifying the second data as the first reliability;
when the calculated value of the degree of reliability is 0.75, classifying the second data as the second degree of reliability;
when the calculated value of the degree of reliability is 0.50, classifying the second data as the third degree of reliability;
when the calculated value of the degree of reliability is 0.25, the second data is classified as the fourth degree of reliability.
To achieve the above object, the present application also provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a machine, implements the steps of the method as described above.
The embodiment of the application has the following advantages:
the embodiment of the application provides a method for eliminating linear artifacts in CT images, which comprises the following steps: acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data; performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on a corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level; and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
By the method, the reliability of the preprocessed X-ray light intensity signal is analyzed according to a threshold value, and if the reliability is high, the ray is not starved and is not processed; if the reliability is low, different processing steps are carried out according to different reliabilities to restore and restore the image, so that linear artifacts caused by ray starvation are eliminated, original texture features of the image are not changed, and the diagnosis confidence of doctors is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary and that other implementation drawings may be derived from the provided drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of a method for removing a linear artifact in a CT image according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a target image obtained by a method for removing a linear artifact in a CT image according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a linear artifact removing apparatus for CT images according to an embodiment of the present disclosure.
Detailed Description
The present disclosure is not intended to be limited to the particular embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the technical features mentioned in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.
An embodiment of the present application provides a method for removing a line artifact of a CT image, and referring to fig. 1, fig. 1 is a flowchart of a method for removing a line artifact of a CT image provided in an embodiment of the present application.
In step 101, first data obtained by X-ray scanning is acquired, and the first data is preprocessed to obtain second data.
Specifically, first data D1 obtained by X-ray scanning is acquired; and preprocessing the first data to obtain preprocessed second data D2, wherein D1 and D2 are both original projection data, and the preprocessing comprises the processing of detector dead pixel correction, air correction and the like.
At step 102, performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, where the reliability analysis includes comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on a corresponding relationship between a comparison result and a preset threshold, and the higher the second data is than the electronic noise level, the higher the reliability is.
In some embodiments, the method for classifying the second data according to the obtained value of the credibility, and performing corresponding processing on the second data based on different classifications to obtain the third data includes:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained confidence level values, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level is arranged in the order from high confidence level to low confidence level;
when the second data is classified as the first credibility, the second data obtained at this time is the third data;
when the second data is classified as the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
In some embodiments, the method of classifying said second data into said first confidence level, second confidence level, third confidence level, or fourth confidence level in order of high to low values of said confidence levels comprises:
using the formula:
calculating a value for said confidence level, wherein N represents said average electronic noise level of said data acquisition system, C (i, j) represents said confidence level, and D represents 2 (i, j) represents the second data, i is the ith row of the detector array, and j is the jth column of the detector array;
when the calculated value of the reliability is 1.00, classifying the second data as the first reliability;
when the calculated value of the degree of reliability is 0.75, classifying the second data as the second degree of reliability;
classifying the second data as the third reliability when the calculated value of the reliability is 0.50;
when the calculated value of the degree of reliability is 0.25, the second data is classified as the fourth degree of reliability.
Specifically, reliability analysis is performed on the second data D2, and different processing branches are entered according to different reliabilities. If the credibility of the data is high, the ray starvation phenomenon does not exist, the data with low credibility is divided into three levels of low level, low level and ultra-low level, and corresponding different processing is carried out on different levels. Data reliability analysis this application adopts and compares with the electronic noise level of data acquisition system, classifies the credibility of data, and is categorised as first credibility, second credibility, third credibility and fourth credibility, and first credibility is that the credibility is high, and second credibility is that the credibility is low, and third credibility is that the credibility is low, and fourth credibility is that the credibility is ultra-low, and the second data D2 is higher than the electronic noise level, then the credibility is higher. The data acquisition system refers to the part of the CT used for data acquisition, which is converted into digital signals by X-rays, and mainly refers to the detector part. The classification method is as follows:
wherein N represents the average electronic noise of the data acquisition system, C (i, j) represents the confidence level, D 2 (i, j) represents the second data, i being the ith row of the detector array and j being the jth column of the detector array.
If the reliability of the data is high, namely C (i, j) =1.0, the data is free from ray starvation and is not processed.
If the reliability of the data is low and C (i, j) =0.75, the data is corrected once, namely, the first correction is performed, and the correction is completed through three steps.
In some embodiments, the method of making the first correction comprises:
correcting initial data according to information of adjacent channels in the column direction of a detector array of the data acquisition system to obtain column direction correction result data, wherein the initial data comprises the second data or the third correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the initial data to obtain first target correction result data, wherein the first target correction result data comprises the first correction result data.
Specifically, the implementation method of the first modification includes: a) Firstly, correcting a low signal according to information of adjacent channels in the column direction of a detector array, and constructing a method for filtering in the column direction as follows;
wherein: d3 (i, j) is column direction correction result data corrected in the column direction, C (i, j) represents the reliability of the data (reliability value is 0.75), D 2 (i, j) represents the preprocessed second data, i is the ith row of the detector array, j is the jth column of the detector array, F c1 For the channel direction (column direction) filter function, n1 is the filter order dependent parameter.
B) On the basis of column direction correction, correcting according to the row direction information of the detector array, and constructing a filtering mode in the row direction as follows;
wherein: d4 (i, j) is row-column direction correction result data corrected along the row-column direction of the detector array, D3 (i, j) is column direction correction result data corrected along the row-column direction of the detector array, C (i, j) represents the reliability of the data, i is the ith row of the detector array, j is the jth column of the detector array, F s1 And n2 is a filter order related parameter in the line direction filtering function.
C) And performing weighted fusion on the result filtered along the row and column directions of the detector array and the preprocessed data to obtain corrected data.
The fused weight coefficients are calculated as follows:
wherein: μ 1 (i, j) is the weight coefficient for image fusion, D 2 (i, j) represents the preprocessed second data, C (i, j) represents the reliability of the data, i is the ith row of the detector array, j is the jth column of the detector array, and N represents the average electronic noise of the data acquisition system.
And fusing the result of the row and column correction of the detector and the preprocessed second data through the weighting coefficients to obtain first corrected result data, wherein a fusion calculation formula is as follows:
D5(i,j)＝(1-μ1(i,j))*D2(i,j)+μ1(i,j)*D4(i,j)
if the reliability of the data is low, i.e., C (i, j) =0.5, the data is corrected twice, the data is corrected once by the first correction method as described above (the value of the reliability substituted in the formula is still 0.75), and the correction result of the first time is used as the input of the second iteration correction (i.e., the second correction is performed).
In some embodiments, the method of making the second correction comprises:
correcting the first target correction result data obtained after the first correction according to the information of the adjacent channels in the row direction of the detector array of the data acquisition system to obtain row direction correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the first target correction result data to obtain second target correction result data, wherein the second target correction result data comprises the second correction result data or the fourth correction result data.
Specifically, the method of performing the second correction includes: a) For data (first target correction result data, namely first correction result data) meeting the second iterative correction condition, filtering along the direction of the detector column is performed, and the filtering is structured in the following way:
wherein: d6 (i, j) is column direction correction result data corrected in the column direction, C (i, j) represents the reliability of the data (the reliability value is 0.5), D5 (i, j) is first correction result data corrected for the first time, i is the ith row of the detector array, j is the jth column of the detector array, F c2 For the second column-wise filtering function, n3 is a filter order dependent parameter.
B) And for the data meeting the second iterative correction condition, filtering along the row direction of the detector on the basis of the result after correction along the column direction of the detector, wherein the filtering is constructed in the following way:
wherein: d7 (i, j) is row-column direction corrected result data corrected along the row-column direction of the detector, D6 (i, j) is the corrected result along the column direction, C (i, j) represents the reliability of the data, i is the ith row of the detector array, j is the jth column of the detector array, F s2 And n4 is a filter order related parameter in the line direction filtering function.
C) And weighting and fusing the result of the row and column correction of the detector and the result after the first correction to the data meeting the second iterative correction condition to obtain the final correction result of the second time.
The weight coefficients are calculated as follows:
wherein: μ 2 (i, j) is a weight coefficient of image fusion, D5 (i, j) is a first correction result, C (i, j) represents the reliability of data, i is the ith row of the detector array, j is the jth column of the detector array, and N represents the average electronic noise of the data acquisition system.
And fusing the result D7 (i, j) of the second detector row-column correction and the first data correction result D5 (i, j) through a weighting coefficient mu 2 (i, j) to obtain final data D8 (i, j) (second correction result data) after the second correction, wherein the fusion calculation formula is as follows:
D8(i,j)＝(1-μ2(i,j))*D5(i,j)+μ2(i,j)*D7(i,j)
if the reliability of the data is ultra-low, i.e., C (i, j) =0.25, noise completely drowns out the signal, and even the data collected due to the influence of random noise has negative values. Also, the attenuation of X-rays tends to be underestimated when the signal is so low that it is overwhelmed by noise. Therefore, the compensation correction (third correction) is performed for the negative number and the underestimated portion, and then the above-described two data corrections are performed.
In some embodiments, the method of making the third correction comprises:
negative correction is carried out on the negative number in the second data to obtain negative correction result data;
performing ray attenuation compensation correction on the basis of the negative number correction result data to obtain compensation correction result data;
and processing the compensation correction result data by using an equal average filtering method to obtain third correction result data.
Specifically, the implementation steps include: a) The X-ray collected by the detector should theoretically have no negative value, but under the condition that the X-ray received by the detector is very weak, the collected data has a negative value due to the influence of electronic noise of the collection system, so the negative number in the second data is corrected first, and the correction mode is as follows;
wherein D 2 And (i, j) is the preprocessed second data, i is the ith row of the detector array, j is the jth column of the detector array, DD1 (i, j) is the negative correction result data after negative correction, and N represents the average electronic noise of the data acquisition system.
B) On the basis of the negative number correction, the ray attenuation compensation correction is carried out, and the correction method is as follows;
and DD2 (i, j) is compensation correction result data after ray attenuation compensation correction, i is the ith row of the detector array, j is the jth column of the detector array, DD1 (i, j) is negative correction result data after negative correction, and N represents the average electronic noise of the data acquisition system.
C) The compensation method causes that the data of the adjacent detector units are changed greatly, so that the local image unevenness is caused, and therefore, the method of equal-mean filtering is used for further processing, and the method for realizing the equal-mean filtering is as follows;
Δ＝DD2(i,j)-D2(i,j)
when Δ =0, DD3 (i, j) = D2 (i, j)
When Δ! When =0, DD3 (i, j) = D2 (i, j) + Δ
DD3(i+1,j)＝DD2(i+1,j)-Δ/4
DD3(i-1,j)＝DD2(i-1,j)-Δ/4
DD3(i,j+1)＝DD2(i,j+1)-Δ/4
DD3(i,j-1)＝DD2(i,j-1)-Δ/4
Where DD3 (i, j) is the result of the mean filtering (third corrected result data), DD2 (i, j) is the result of the ray attenuation compensation correction, and D 2 And (i, j) is the preprocessed second data, i is the ith row of the detector array, j is the jth column of the detector array, and delta is the difference value between the result after the ray attenuation correction and the preprocessed second data.
D) In addition to the equal-mean filtering, the result of the equal-mean filtering (third correction result data) is input, and data correction is performed twice (first correction and second correction) as described above to obtain corresponding first target correction result data and second target correction result data, respectively, and the result after the two corrections (fourth correction result data) is a processing result in the case where the data reliability is out of tolerance.
In step 103, the third data is subjected to image reconstruction to obtain a target image with linear artifacts removed.
Specifically, according to different credibility of the data, the third data is obtained by processing the second data in the foregoing steps, and the target image with the linear artifact removed can be obtained by performing image reconstruction on the third data. Refer to fig. 2.
By the method, the reliability of the preprocessed X-ray light intensity signal is analyzed according to a threshold, and if the reliability is high, the ray is not starved and is not processed; if the reliability is low, different processing steps are carried out according to different reliabilities to restore and restore the image, so that linear artifacts caused by ray starvation are eliminated, original texture features of the image are not changed, and the diagnosis confidence of doctors is improved.
Fig. 3 is a block diagram of a CT image line artifact removing apparatus according to an embodiment of the present disclosure. The device comprises:
a memory 201; and a processor 202 coupled to the memory 201, the processor 202 configured to: acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data;
performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on the corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level;
and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
In some embodiments, the processor 202 is further configured to: classifying the second data according to the obtained value of the credibility, and performing corresponding processing on the second data based on different classifications, so as to obtain the third data, wherein the method comprises the following steps:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained confidence level values, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level is arranged in the order from high confidence level to low confidence level;
when the second data is classified as the first credibility, the second data obtained at this time is the third data;
when the second data is classified as the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
In some embodiments, the processor 202 is further configured to: the method of performing the first correction includes:
correcting initial data according to information of adjacent channels in the column direction of a detector array of the data acquisition system to obtain column direction correction result data, wherein the initial data comprises the second data or the third correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the initial data to obtain first target correction result data, wherein the first target correction result data comprises the first correction result data.
In some embodiments, the processor 202 is further configured to: the method of performing the second correction includes:
correcting the first target correction result data obtained after the first correction according to the information of the adjacent channels in the row direction of the detector array of the data acquisition system to obtain row direction correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the first target correction result data to obtain second target correction result data, wherein the second target correction result data comprises the second correction result data or the fourth correction result data.
In some embodiments, the processor 202 is further configured to: the method of performing the third correction includes:
negative correction is carried out on the negative number in the second data to obtain negative correction result data;
performing ray attenuation compensation correction on the basis of the negative number correction result data to obtain compensation correction result data;
and processing the compensation correction result data by using an equal average filtering method to obtain third correction result data.
In some embodiments, the processor 202 is further configured to: the method for classifying the second data into the first degree of reliability, the second degree of reliability, the third degree of reliability, or the fourth degree of reliability, which is arranged in order of the value of the degree of reliability from high to low, includes:
using the formula:
calculating a value of said confidence level, whereinN represents the average of the electronic noise level of the data acquisition system, C (i, j) represents the confidence level, D 2 (i, j) represents the second data, i is the ith row of the detector array, and j is the jth column of the detector array;
when the calculated value of the reliability is 1.00, classifying the second data as the first reliability;
when the calculated value of the degree of reliability is 0.75, classifying the second data as the second degree of reliability;
classifying the second data as the third reliability when the calculated value of the reliability is 0.50;
when the calculated value of the degree of reliability is 0.25, the second data is classified as the fourth degree of reliability.
For the specific implementation method, reference is made to the foregoing method embodiments, which are not described herein again.
The present application may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present application.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is noted that, unless expressly stated otherwise, all features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. Where used, further, preferably, still further and more preferably is a brief introduction to the description of the other embodiment based on the foregoing embodiment, the combination of the contents of the further, preferably, still further or more preferably back strap with the foregoing embodiment being a complete construction of the other embodiment. Several further, preferred, still further or more preferred arrangements of the back tape of the same embodiment may be combined in any combination to form a further embodiment.
Although the present application has been described in detail with respect to the general description and the specific examples, it will be apparent to those skilled in the art that certain changes and modifications may be made based on the present application. Accordingly, such modifications and improvements are intended to be within the scope of this invention as claimed.
Claims (10)
1. A method for eliminating a line artifact of a CT image is characterized by comprising the following steps:
acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data;
performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on a corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level;
and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
2. The method of eliminating line artifact in a CT image according to claim 1, wherein the step of classifying the second data according to the obtained value of the confidence level and performing corresponding processing on the second data based on different classifications to obtain the third data comprises:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained values of the confidence levels, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level are arranged in the order from high to low;
when the second data is classified as the first credibility, the second data obtained at this time is the third data;
when the second data is classified as the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
3. The method of removing line artifact in a CT image according to claim 2, wherein the method of performing the first correction includes:
correcting initial data according to information of adjacent channels in the row direction of a detector array of the data acquisition system to obtain row direction correction result data, wherein the initial data comprises the second data or the third correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the initial data to obtain first target correction result data, wherein the first target correction result data comprises the first correction result data.
4. The method of eliminating line artifact in a CT image according to claim 3, wherein the second correction method comprises:
correcting the first target correction result data obtained after the first correction according to the information of the adjacent channels in the row direction of the detector array of the data acquisition system to obtain row direction correction result data;
on the basis of the column direction correction result data, correcting according to the row direction of the detector array to obtain row and column direction correction result data;
and performing weighted fusion on the row and column direction correction result data and the first target correction result data to obtain second target correction result data, wherein the second target correction result data comprises the second correction result data or the fourth correction result data.
5. The method of removing line artifact in a CT image according to claim 2, wherein the third correction method comprises:
negative correction is carried out on the negative number in the second data to obtain negative correction result data;
performing ray attenuation compensation correction on the basis of the negative number correction result data to obtain compensation correction result data;
and processing the compensation correction result data by using an equal average filtering method to obtain third correction result data.
6. The method of eliminating a line artifact in a CT image according to claim 2, wherein the step of classifying the second data into the first confidence level, the second confidence level, the third confidence level, or the fourth confidence level in order of the value of the confidence level from high to low comprises:
using the formula:
calculating a value for said confidence level, wherein N represents an average of said electronic noise levels of said data acquisition system, C (i, j) represents said confidence level, D 2 (i, j) represents the second data, i is the ith row of the detector array, and j is the jth column of the detector array;
when the calculated value of the reliability is 1.00, classifying the second data into the first reliability;
when the calculated value of the reliability is 0.75, classifying the second data as the second reliability;
when the calculated value of the degree of reliability is 0.50, classifying the second data as the third degree of reliability;
when the calculated value of the reliability is 0.25, the second data is classified as the fourth reliability.
7. A CT image line artifact removing device is characterized by comprising:
a memory; and
a processor coupled to the memory, the processor configured to:
acquiring first data obtained by X-ray scanning, and preprocessing the first data to obtain second data;
performing reliability analysis on the second data, classifying the second data according to the obtained value of the reliability, and performing corresponding processing on the second data based on different classifications to obtain third data, wherein the reliability analysis comprises the steps of comparing the second data with an electronic noise level of a data acquisition system, and obtaining a corresponding value of the reliability based on a corresponding relation between a comparison result and a preset threshold, and the reliability is higher if the second data is higher than the electronic noise level;
and carrying out image reconstruction on the third data to obtain a target image with linear artifacts eliminated.
8. The CT image line artifact removal device of claim 7, wherein the processor is further configured to:
the method for classifying the second data according to the obtained value of the credibility and correspondingly processing the second data based on different classifications to obtain the third data comprises the following steps:
classifying the second data into a first confidence level, a second confidence level, a third confidence level or a fourth confidence level according to the obtained values of the confidence levels, wherein the first confidence level, the second confidence level, the third confidence level or the fourth confidence level are arranged in the order from high to low;
when the second data is classified as the first credibility, the second data obtained at this time is the third data;
when the second data is classified into the second credibility, performing first correction on the second data to obtain first correction result data, wherein the first correction result data obtained at the moment is the third data;
when the second data is classified as the third credibility, performing the first correction on the second data, and performing the second correction on the obtained first correction result data to obtain second correction result data, wherein the obtained second correction result data is the third data;
and when the second data is classified into the fourth credibility, performing third correction on the second data to obtain third correction result data, performing the first correction on the third correction result data, and then performing the second correction on the obtained correction result data to obtain fourth correction result data, wherein the fourth correction result data obtained at this time is the third data.
9. The CT image line artifact removal device of claim 8, wherein said processor is further configured to:
the method for classifying the second data into the first reliability, the second reliability, the third reliability or the fourth reliability which are arranged in the order of the reliability value from high to low comprises the following steps:
using the formula:
calculating a value for said confidence level, wherein N represents an average of said electronic noise levels of said data acquisition system, C (i, j) represents said confidence level, D 2 (i, j) represents the second data, i is the ith row of the detector array, and j is the jth column of the detector array;
when the calculated value of the reliability is 1.00, classifying the second data as the first reliability;
when the calculated value of the reliability is 0.75, classifying the second data as the second reliability;
classifying the second data as the third reliability when the calculated value of the reliability is 0.50;
when the calculated value of the reliability is 0.25, the second data is classified as the fourth reliability.
10. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a machine, implements the steps of the method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211377306.XA CN115797486B (en) | 2022-11-04 | 2022-11-04 | CT image linear artifact eliminating method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211377306.XA CN115797486B (en) | 2022-11-04 | 2022-11-04 | CT image linear artifact eliminating method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115797486A true CN115797486A (en) | 2023-03-14 |
| CN115797486B CN115797486B (en) | 2023-06-30 |
Family
ID=85435530
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211377306.XA Active CN115797486B (en) | 2022-11-04 | 2022-11-04 | CT image linear artifact eliminating method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115797486B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777177A (en) * | 2009-12-29 | 2010-07-14 | 上海维宏电子科技有限公司 | Attenuation filter-based metal artifact removing mixed reconstruction method for CT images |
| US8233586B1 (en) * | 2011-02-17 | 2012-07-31 | Franz Edward Boas | Iterative reduction of artifacts in computed tomography images using forward projection and an edge-preserving blur filter |
| CN104318536A (en) * | 2014-10-21 | 2015-01-28 | 沈阳东软医疗系统有限公司 | Method and device for CT image correction |
| CN105528766A (en) * | 2014-12-04 | 2016-04-27 | 沈阳东软医疗系统有限公司 | CT metal artifact processing method and device |
| CN111053568A (en) * | 2019-12-30 | 2020-04-24 | 苏州瑞派宁科技有限公司 | Method and device for correcting ring artifact in CT image and computer storage medium |
| CN111091514A (en) * | 2019-12-20 | 2020-05-01 | 广州华端科技有限公司 | Oral cavity CBCT image denoising method and system |
| CN112150574A (en) * | 2020-09-28 | 2020-12-29 | 上海联影医疗科技股份有限公司 | Method, system and device for automatically correcting image artifacts and storage medium |
| CN115131452A (en) * | 2022-04-19 | 2022-09-30 | 腾讯医疗健康(深圳)有限公司 | Image processing method and device for artifact removal |
-
2022
- 2022-11-04 CN CN202211377306.XA patent/CN115797486B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777177A (en) * | 2009-12-29 | 2010-07-14 | 上海维宏电子科技有限公司 | Attenuation filter-based metal artifact removing mixed reconstruction method for CT images |
| US8233586B1 (en) * | 2011-02-17 | 2012-07-31 | Franz Edward Boas | Iterative reduction of artifacts in computed tomography images using forward projection and an edge-preserving blur filter |
| CN104318536A (en) * | 2014-10-21 | 2015-01-28 | 沈阳东软医疗系统有限公司 | Method and device for CT image correction |
| CN105528766A (en) * | 2014-12-04 | 2016-04-27 | 沈阳东软医疗系统有限公司 | CT metal artifact processing method and device |
| CN111091514A (en) * | 2019-12-20 | 2020-05-01 | 广州华端科技有限公司 | Oral cavity CBCT image denoising method and system |
| CN111053568A (en) * | 2019-12-30 | 2020-04-24 | 苏州瑞派宁科技有限公司 | Method and device for correcting ring artifact in CT image and computer storage medium |
| CN112150574A (en) * | 2020-09-28 | 2020-12-29 | 上海联影医疗科技股份有限公司 | Method, system and device for automatically correcting image artifacts and storage medium |
| CN115131452A (en) * | 2022-04-19 | 2022-09-30 | 腾讯医疗健康(深圳)有限公司 | Image processing method and device for artifact removal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115797486B (en) | 2023-06-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7623728B2 (en) | Method and product for processing digital images | |
| US8009892B2 (en) | X-ray image processing system | |
| KR20200019572A (en) | Image processing apparatus, image processing method, and storage medium | |
| US10672108B2 (en) | Image processing apparatus, image processing method, and image processing program | |
| EP2429401A1 (en) | Radiographic apparatus and control method for the same | |
| US11645736B2 (en) | Image processing methods, apparatuses and systems | |
| US10163206B2 (en) | Apparatus and method for beam hardening artifact correction of CT image | |
| CN109074635B (en) | Apparatus and method for denoising vector image | |
| US7747058B2 (en) | Image processing method for windowing and/or dose control for medical diagnostic devices | |
| US5283736A (en) | Radiographic image processing apparatus | |
| US7680352B2 (en) | Processing method, image processing system and computer program | |
| KR102472464B1 (en) | Image Processing Method and Image Processing Device using the same | |
| JPH1071138A (en) | Method and device for processing radiograph | |
| CN111127371B (en) | Image enhancement parameter automatic optimization method, storage medium and X-ray scanning device | |
| CN115797486A (en) | CT image linear artifact eliminating method and device and storage medium | |
| US20180018759A1 (en) | Image artifact detection and correction in scenes obtained from multiple visual images | |
| US9978132B2 (en) | Radiation image processing device, method, and program | |
| CN105593899B (en) | The method reduced for the noise in image sequence | |
| JP4488772B2 (en) | Radiographic image diagnostic apparatus and data processing method of radiographic image diagnostic apparatus | |
| JP2001148787A (en) | Image processing unit | |
| JP2005210384A (en) | Image processing method, image processor, and image processing program | |
| CN115943420A (en) | Image processing device, image processing method, training device, training method, and program | |
| CN112562030A (en) | Image reconstruction method and device and electronic equipment | |
| WO2015052890A1 (en) | X-ray image processing device and method, x-ray imaging device, and program | |
| Hržić et al. | Information entropy measures and clustering improve edge detection in medical X-ray images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |