CN112712523A - Automatic brain perfusion data artery candidate vessel screening method - Google Patents
Automatic brain perfusion data artery candidate vessel screening method Download PDFInfo
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Abstract
The invention provides an automatic brain perfusion data artery candidate vessel screening method, which is mainly realized by 4 steps: firstly, registering data in a time dimension; secondly, removing the skull; thirdly, segmenting a 4D data blood vessel; fourthly, screening candidate arterial blood vessels; the fourth step also comprises motion estimation and blood vessel screening; when Sp is greater than 1 or Sk/min (Ky, Kb) >0.1, the obvious motion error is considered to exist, otherwise, the motion error is not considered to exist; when motion errors exist, the Cv and the Ct are respectively normalized to obtain Nv and Nt, and a difference curve D of the Nv and the Nt is calculated. The key point of the invention is a method for screening arterial blood vessel candidates from all blood vessels; the automatic brain perfusion data artery candidate vessel screening method can enable the result to be more accurate and can be compatible with images of which the movement is not completely corrected.
Description
Technical Field
The invention relates to the field of medical treatment, in particular to an automatic cerebral perfusion data artery candidate vessel screening method.
Background
The key of the acute cerebral infarction treatment is to open blood vessels and save ischemic penumbra. However, the judgment criteria and assessment means for infarct core and ischemic penumbra are different. The DAWN and DEFUSE 3 researches take a region with Tmax larger than 6 seconds as an ischemic region, and the Tmax needs to be calculated by carrying out deconvolution on an AIF curve on a time density curve TDC of each pixel point, so that the selection of the AIF curve is crucial to the final ischemic penumbra evaluation. The selection of AIF is performed in a series of arterial vessels, which require screening and filtering.
Currently, there are 2 main ways to extract AIF curves, one is to manually extract AIF, but because perfusion data is 4D data, not only the position is determined in space, but also the curve characteristics are observed in time, and the manual extraction efficiency is extremely low. The other is an automatic extraction mode, and the existing methods are k-means, fuzzy-cmeans and other clustering methods.
In the background art, the main disadvantages are: the k-means and fuzzy-cmeans clustering methods are all methods for all pixel points, the performance of the two methods is unstable, the movement of a patient in the actual data in the scanning process is not considered, and the signal change caused by the movement is regarded as the prominent change characteristic of the contrast agent, so that the misselection is caused.
Disclosure of Invention
The invention discloses an automatic brain perfusion data artery candidate vessel screening method which can improve the performance of AIF extraction, can enable the result to be more accurate and can be compatible with images of which the movement is not completely corrected.
In order to achieve the purpose, the invention provides the following specific technical scheme:
an automatic cerebral perfusion data artery candidate vessel screening method is characterized in that artery vessel candidates are screened from all vessels, so that the result is more accurate and is compatible with images of which the motion is not completely corrected.
The method comprises the following four steps:
firstly, registering data in a time dimension;
secondly, removing the skull;
thirdly, segmenting a 4D data blood vessel;
and fourthly, screening candidate arterial blood vessels.
The first step further comprises registering the 4D data in a time dimension using mutual information; corresponding the volume data of all the periods to the volume data of the first period to ensure the corresponding relation of the organization structure; the second step is to use an active contour method to segment the skull and remove the skull to leave a brain tissue part; the third step further comprises: performing maximum intensity projection on the CT in a time dimension, and segmenting brain tissues higher than a threshold value as blood vessels through a self-adaptive threshold value; for MR, using minimum density projection, segmenting out brain tissue below a threshold value through an adaptive threshold value to serve as a blood vessel; the fourth step includes step 4.1. motion estimation and 4.2 vessel screening.
The step 4.1 specifically comprises:
step 4.1.1: summing the gray levels of all pixel points in the blood vessel, the non-blood vessel brain tissue and the blood vessel brain tissue to obtain gray levels and curves Cv, Ct and Cb;
step 4.1.2: respectively solving peak values Pv, Pt and Pb of the three curves; combining the vectors into a vector P ═ (Pv, Pt and Pb) to obtain the standard deviation Sp of the P vector;
step 4.1.3: respectively solving kurtosis of the curve Cv, Ct and Cb, and combining the kurtosis into a vector K which is (Kv, Kt, Kb) to obtain a standard deviation Sk of the K vector;
when Sp is greater than 1 or Sk/min (Ky, Kb) >0.1, the obvious motion error is considered to exist, otherwise, the motion error is not considered to exist;
when motion errors exist, respectively normalizing Cv and Ct to obtain Nv and Nt, and calculating a difference curve D of the Nv and the Nt;
step 4.1.4: calculating a second derivative Gd of the D, finding a time coordinate with Gd being greater than 0.1, and recording the time coordinate as Tg; calculating the second derivative of Nv, and finding out the time coordinate of Nv < -0.1, which is recorded as Tn; find the position of D <0, denoted as Td. The motion time coordinate Tm is the intersection of Tg n Td.
The step 4.2 specifically comprises:
step 4.2.1: filtering the moving blood vessels;
calculating the time coordinate Tp of peak arrival corresponding to all the pixel points in the blood vessel, and when the Tp belongs to the Tm, the blood vessel considers that motion exists and removes the motion from the candidate blood vessel;
step 4.2.2: filtering a low peak value;
calculating the peak value mean value P of all blood vessels, calculating the peak value Pv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Pv < P;
step 4.2.3: filtering the peak width;
calculating the full width at half maximum W of the peaks of all blood vessels, calculating the peak value Wv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Wv > W;
step 4.2.4: area filtering;
and calculating the curve area Ap and the whole curve area Ac under all the blood vessel peaks, and filtering the blood vessels with Ap less than 0.5 Ac.
And the filtered blood vessel pixel is the AIF candidate extraction point.
An apparatus for automated cerebral perfusion data artery candidate vessel screening, comprising:
module 1, for registering data in time dimension;
a module 2 as an auxiliary module for removing the skull;
a module 3 for 4D data vessel segmentation;
and a module 4 for screening the candidate artery blood vessel.
An apparatus for automated cerebral perfusion data artery candidate vessel screening storage and processing is characterized by comprising a memory and a processor;
the memory for storing a computer program;
the processor is used for realizing the automatic cerebral perfusion data artery candidate vessel screening method when the computer program is executed.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, implements the method for automated cerebral perfusion data artery candidate vessel screening.
The key point of the invention is a method for screening arterial blood vessel candidates from all blood vessels. The automatic brain perfusion data artery candidate vessel screening method can enable the result to be more accurate and can be compatible with images of which the movement is not completely corrected.
Drawings
Fig. 1 is a flow chart of artery candidate vessel screening of cerebral perfusion data.
Detailed Description
The present invention is described in detail below with reference to the drawings and examples, but the present invention is not limited thereto.
The technical scheme of the invention is mainly realized by 4 steps:
firstly, registering data in a time dimension;
secondly, removing the skull;
thirdly, segmenting a 4D data blood vessel;
fourthly, screening candidate arterial blood vessels;
the first step further comprises registering the 4D data in a time dimension using mutual information; and corresponding the volume data of all the periods to the volume data of the first period to ensure the corresponding relation of the organization structure.
The second step uses active contouring to segment the skull and remove, leaving a portion of brain tissue.
The third step further comprises:
performing maximum intensity projection on the CT in a time dimension, and segmenting brain tissues higher than a threshold value as blood vessels through a self-adaptive threshold value;
for MR, brain tissue below a threshold is segmented out as vessels by adaptive thresholding using minimum intensity projections.
The fourth step includes step 4.1. motion estimation and 4.2 vessel screening.
The step 4.1 specifically comprises:
step 4.1.1: summing the gray levels of all pixel points in the blood vessel, the non-blood vessel brain tissue and the blood vessel brain tissue to obtain gray levels and curves Cv, Ct and Cb;
step 4.1.2: respectively solving peak values Pv, Pt and Pb of the three curves; combining the vectors into a vector P ═ (Pv, Pt and Pb) to obtain the standard deviation Sp of the P vector;
step 4.1.3: respectively solving kurtosis of the curve Cv, Ct and Cb, and combining the kurtosis into a vector K which is (Kv, Kt, Kb) to obtain a standard deviation Sk of the K vector;
when Sp is greater than 1 or Sk/min (Ky, Kb) >0.1, the obvious motion error is considered to exist, otherwise, the motion error is not considered to exist;
when motion errors exist, respectively normalizing Cv and Ct to obtain Nv and Nt, and calculating a difference curve D of the Nv and the Nt;
step 4.1.4: calculating a second derivative Gd of the D, finding a time coordinate with Gd being greater than 0.1, and recording the time coordinate as Tg; calculating the second derivative of Nv, and finding out the time coordinate of Nv < -0.1, which is recorded as Tn; find the position of D <0, denoted as Td. The motion time coordinate Tm is the intersection of Tg n Td.
The step 4.2 specifically comprises:
step 4.2.1: filtering the moving blood vessels;
calculating the time coordinate Tp of peak arrival corresponding to all the pixel points in the blood vessel, and when the Tp belongs to the Tm, the blood vessel considers that motion exists and removes the motion from the candidate blood vessel;
step 4.2.2: filtering a low peak value;
calculating the peak value mean value P of all blood vessels, calculating the peak value Pv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Pv < P;
step 4.2.3: filtering the peak width;
calculating the full width at half maximum W of the peaks of all blood vessels, calculating the peak value Wv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Wv > W;
step 4.2.4: area filtering;
and calculating the curve area Ap and the whole curve area Ac under all the blood vessel peaks, and filtering the blood vessels with Ap less than 0.5 Ac. And the filtered blood vessel pixel is the AIF candidate extraction point.
An apparatus for automated cerebral perfusion data artery candidate vessel screening, comprising:
module 1, for registering data in time dimension;
a module 2 as an auxiliary module for removing the skull;
a module 3 for 4D data vessel segmentation;
and a module 4 for screening the candidate artery blood vessel.
An apparatus for automated cerebral perfusion data artery candidate vessel screening storage and processing is characterized by comprising a memory and a processor;
the memory for storing a computer program;
the processor is used for realizing the automatic cerebral perfusion data artery candidate vessel screening method when the computer program is executed.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, implements the method for automated cerebral perfusion data artery candidate vessel screening.
The invention has made automated software that has been used by physicians to verify that this approach is more clinically practical.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. An automatic brain perfusion data artery candidate vessel screening method is characterized in that artery vessel candidates are screened from all vessels, so that the result is more accurate and is compatible with images of which the motion is not completely corrected; the method comprises the following four steps: firstly, registering data in a time dimension; secondly, removing the skull; thirdly, segmenting a 4D data blood vessel; fourthly, screening candidate arterial blood vessels; the first step further comprises registering the 4D data in a time dimension using mutual information; corresponding the volume data of all the periods to the volume data of the first period to ensure the corresponding relation of the organization structure; the second step is to use an active contour method to segment the skull and remove the skull to leave a brain tissue part; the third step further comprises: performing maximum intensity projection on the CT in a time dimension, and segmenting brain tissues higher than a threshold value as blood vessels through a self-adaptive threshold value; for MR, using minimum density projection, segmenting out brain tissue below a threshold value through an adaptive threshold value to serve as a blood vessel; the fourth step includes step 4.1. motion estimation and 4.2 vessel screening.
2. The method for automatically screening artery candidate vessels according to claim 1, wherein the step 4.1 specifically comprises:
step 4.1.1: summing the gray levels of all pixel points in the blood vessel, the non-blood vessel brain tissue and the blood vessel brain tissue to obtain gray levels and curves Cv, Ct and Cb;
step 4.1.2: respectively solving peak values Pv, Pt and Pb of the three curves; combining the vectors into a vector P ═ (Pv, Pt and Pb) to obtain the standard deviation Sp of the P vector;
step 4.1.3: respectively solving kurtosis of the curve Cv, Ct and Cb, and combining the kurtosis into a vector K which is (Kv, Kt, Kb) to obtain a standard deviation Sk of the K vector;
when Sp is greater than 1 or Sk/min (Ky, Kb) >0.1, the obvious motion error is considered to exist, otherwise, the motion error is not considered to exist;
when motion errors exist, respectively normalizing Cv and Ct to obtain Nv and Nt, and calculating a difference curve D of the Nv and the Nt;
step 4.1.4: calculating a second derivative Gd of the D, finding a time coordinate with Gd being greater than 0.1, and recording the time coordinate as Tg; calculating the second derivative of Nv, and finding out the time coordinate of Nv < -0.1, which is recorded as Tn; find the position of D <0, denoted as Td. The motion time coordinate Tm is the intersection of Tg n Td.
3. The method for automatically screening artery candidate vessels according to claim 1, wherein the step 4.2 specifically comprises:
step 4.2.1: filtering the moving blood vessels;
calculating the time coordinate Tp of peak arrival corresponding to all the pixel points in the blood vessel, and when the Tp belongs to the Tm, the blood vessel considers that motion exists and removes the motion from the candidate blood vessel;
step 4.2.2: filtering a low peak value;
calculating the peak value mean value P of all blood vessels, calculating the peak value Pv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Pv < P;
step 4.2.3: filtering the peak width;
calculating the full width at half maximum W of the peaks of all blood vessels, calculating the peak value Wv corresponding to all pixel points in the blood vessels, and filtering the blood vessels with Wv > W;
step 4.2.4: area filtering;
and calculating the curve area Ap and the whole curve area Ac under all the blood vessel peaks, and filtering the blood vessels with Ap less than 0.5 Ac. And the filtered blood vessel pixel is the AIF candidate extraction point.
4. An apparatus for automated cerebral perfusion data artery candidate vessel screening, comprising:
module 1, for registering data in time dimension;
a module 2 as an auxiliary module for removing the skull;
a module 3 for 4D data vessel segmentation;
and a module 4 for screening the candidate artery blood vessel.
5. An apparatus for automated cerebral perfusion data artery candidate vessel screening storage and processing is characterized by comprising a memory and a processor;
the memory for storing a computer program;
the processor, when executing the computer program, for implementing the method for automated cerebral perfusion data artery candidate vessel screening according to any one of claims 1 to 5.
6. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method for automated cerebral perfusion data artery candidate vessel screening according to any one of claims 1 to 5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113712581A (en) * | 2021-09-14 | 2021-11-30 | 上海联影医疗科技股份有限公司 | Perfusion analysis method and system |
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