CN116649995A - Method and device for acquiring hemodynamic parameters based on intracranial medical image - Google Patents
Method and device for acquiring hemodynamic parameters based on intracranial medical image Download PDFInfo
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Abstract
The application relates to a method and a device for acquiring hemodynamic parameters based on intracranial medical images, which are characterized in that three-dimensional blood vessel models and two-dimensional segmentation binary images are correspondingly obtained by processing three-dimensional image data and two-dimensional radiography image data related to intracranial blood vessels, the three-dimensional blood vessel models and the two-dimensional segmentation binary images are registered by adjusting the angle positions of the three-dimensional blood vessel models, the three-dimensional blood vessel models after registration are decomposed, the inlet section and the outlet section of each single blood vessel section are projected into the two-dimensional radiography image data, the corresponding blood flow time is obtained by utilizing the delay between the concentration-time curve of an annoying contrast agent corresponding to the inlet section and the outlet section, the average blood flow velocity and the blood flow volume are correspondingly obtained, so that the average blood flow velocity and the blood flow volume distribution on the three-dimensional blood vessel models are obtained, and finally the hemodynamic parameters are obtained by carrying out hemodynamic simulation calculation. The method can automatically and accurately acquire intracranial vascular hemodynamic parameters.
Description
Technical Field
The application relates to the technical field of medical image processing, in particular to a method and a device for acquiring hemodynamic parameters based on intracranial medical images.
Background
Atherosclerosis is one of the frequently occurring cerebrovascular diseases, and the subsequent development of the atherosclerosis is easy to cause ischemic cerebral apoplexy, thereby seriously threatening the potential patients. In current clinical practice, there are two treatment regimens for this disease, if the intracranial vascular lesions caused by atherosclerosis are of light to moderate stenosis, conservative medication is used, and if the intracranial vascular lesions caused by atherosclerosis are of severe stenosis, balloon dilation or stent implantation intervention is used.
However, recent clinical studies have shown that satisfactory prognosis of patients is not obtained in either the drug or interventional therapy group, which means that there is a limitation to using only the extent of stenosis of the atherosclerotic vessel as a reference index for the selected treatment regime. Other parameters need to be used as reference indicators of the extent of the lesion.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method and apparatus for acquiring hemodynamic parameters based on intracranial medical images, which can automatically calculate the hemodynamic evaluation parameters of intracranial blood vessels.
A method of obtaining hemodynamic parameters based on intracranial medical imaging, the method comprising:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
And carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
In one embodiment, the three-dimensional blood vessel model of the intracranial blood vessel is obtained by processing and constructing the three-dimensional image data by adopting a reconstruction method based on threshold segmentation, or a lofting reconstruction method based on a blood vessel center line, or a reconstruction method based on a deep neural network model.
In one embodiment, generating a two-dimensional segmented binary map of an intracranial vessel from the two-dimensional contrast image data comprises:
counting gray values of pixel points at the same corresponding positions of each frame of contrast image in the two-dimensional contrast image data;
processing a plurality of gray values at each pixel point position to obtain a foreground coefficient of a corresponding pixel point;
performing foreground or background assignment on each pixel point according to the foreground coefficient to generate a rough segmentation binary image of the intracranial blood vessel;
and after the maximum communication domain noise elimination point is extracted from the rough segmentation binary image, closing operation is carried out to fill the cavity, and the two-dimensional segmentation binary image of the intracranial blood vessel is obtained.
In one embodiment, the processing the plurality of gray values at each pixel position to obtain the foreground coefficient of the corresponding pixel includes:
Generating a corresponding gray time curve according to a plurality of gray values at each pixel point position;
translating the gray value of the first time point downwards by each gray time curve, and turning over by taking the abscissa as the axis to obtain a corresponding contrast agent concentration time curve;
and calculating the mean value, standard deviation and maximum value of each contrast agent concentration time curve one by one, and calculating according to the mean value, standard deviation and maximum value to obtain the foreground coefficient of the corresponding pixel point.
In one embodiment, the foreground coefficients of the corresponding pixels are obtained by calculating according to the mean value, the standard deviation and the maximum value, and the following formula is adopted:
in the above-mentioned description of the invention,representing foreground coefficients, ++>Mean value->Represents standard deviation, & lt + & gt>The maximum value is indicated and the maximum value,、/>、/>the weight coefficients respectively represent the mean, standard deviation and maximum value.
In one embodiment, the performing angular position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model onto an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by using a maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image includes:
Transforming the three-dimensional blood vessel model according to the translation matrix and the rotation matrix to obtain a transformed three-dimensional blood vessel model;
projecting the transformed three-dimensional blood vessel model to an imaging plane according to the projection parameters of the two-dimensional contrast image data to generate a projection binary image;
calculating an overlap coefficient according to the projection binary image and the two-dimensional segmentation binary image, and if the calculation result reaches a preset maximum value, completing space registration;
if the calculated result does not reach the preset maximum value, updating the translation matrix and the rotation matrix until the overlapping coefficient reaches the preset maximum value, so as to complete the space registration.
In one embodiment, the method for estimating the blood dynamics based on computational fluid dynamics, the method for estimating the blood dynamics based on the pipeline flow principle, or the method for estimating the blood dynamics based on the vascular resistance are adopted when the blood dynamics simulation is carried out on the intracranial blood vessel based on the three-dimensional blood vessel model and the average blood flow speed and the blood flow distribution on the model.
In one embodiment, the three-dimensional image data comprises CT angiography image data, magnetic resonance arterial vessel imaging data, or 3D digital subtraction angiography image data.
A hemodynamic parameter acquisition apparatus based on intracranial medical imaging, the apparatus comprising:
the system comprises an intracranial blood vessel three-dimensional model and a two-dimensional image generation module, wherein the intracranial blood vessel three-dimensional model and the two-dimensional image generation module are used for acquiring three-dimensional image data and two-dimensional contrast image data related to the intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
the image data registration module is used for carrying out angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and carrying out space registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
the single blood vessel section blood flow time obtaining module is used for decomposing the three-dimensional blood vessel model after the space registration is completed according to the single blood vessel section and the bifurcation check, projecting the inlet section and the outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining the blood flow time of the corresponding single blood vessel section according to the delay between the two contrast agent concentration-time curves;
The three-dimensional blood vessel model blood flow parameter distribution module is used for obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and the blood flow dynamics parameter calculation module is used for carrying out blood flow dynamics simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain blood flow dynamics parameters.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
Decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
Performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
According to the method and the device for acquiring the hemodynamic parameters based on the intracranial medical image, the three-dimensional image data and the two-dimensional contrast image data are processed respectively, the three-dimensional vascular model and the two-dimensional segmentation binary image of the intracranial blood vessel are correspondingly obtained, the three-dimensional vascular model is subjected to angle position transformation, the transformed three-dimensional vascular model is projected to an imaging plane to generate a projection binary image, the three-dimensional vascular model and the two-dimensional contrast image data are subjected to spatial registration by utilizing the overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image, the three-dimensional vascular model and the two-dimensional contrast image data are decomposed according to the three-dimensional vascular model after the spatial registration is completed by single vascular segment and bifurcation check, the inlet section and the outlet section of each single vascular segment are projected to the two-dimensional contrast image data, the corresponding contrast agent concentration-time curve is respectively generated by utilizing the average gray values on the inlet section and the outlet section, the blood flow time corresponding to the single vascular segment is obtained according to the delay between the two contrast agent concentration-time curves, the corresponding average blood flow velocity and the blood flow volume on the basis of each single vascular segment is obtained, and the blood flow velocity on the basis of the length, the lumen volume and the blood flow time of each single vascular segment is calculated, and the blood flow velocity on the basis of the three-dimensional vascular model is calculated, and the blood flow dynamic flow velocity and the blood flow velocity is calculated on the three-dimensional vascular model. The method is simple in operation, noninvasive and low in cost, and simultaneously can accurately acquire the intracranial blood vessel hemodynamic parameters so as to carry out ischemia evaluation on the intracranial blood vessels.
Drawings
FIG. 1 is a flow chart of a method for acquiring hemodynamic parameters based on intracranial medical imaging, in one embodiment;
FIG. 2 is a schematic illustration of a three-dimensional model of an intracranial blood vessel in another embodiment;
FIG. 3 is a two-dimensional digital contrast image and a corresponding segmentation binary image according to another embodiment, wherein (a) is a two-dimensional digital contrast raw image and (b) is a segmentation binary image of an intracranial vessel obtained according to the two-dimensional vessel segmentation method in the method;
FIG. 4 is a series of consecutive multi-frame intracranial angiography images, E1 through E9 being 9 frames of intracranial angiography images taken over a continuous period of time, in another embodiment;
FIG. 5 is a schematic diagram of gray scale time curves of two-dimensional digital contrast images and corresponding pixel positions according to another embodiment, wherein (a) is a two-dimensional digital contrast original image and (b) is a gray scale time curve of each pixel position obtained according to the method;
FIG. 6 is a schematic diagram of spatial matching of two-dimensional image data and a three-dimensional vessel model in another embodiment, wherein (a) is a two-dimensional digital radiography raw image, (b) is a schematic diagram of a three-dimensional vessel model presented after spatial matching according to (a);
FIG. 7 is a schematic diagram of a blood flow time calculation in another embodiment;
FIG. 8 is an exploded view of a three-dimensional vascular model in another embodiment;
FIG. 9 is a block diagram of a hemodynamic parameter acquisition apparatus based on intracranial medical imaging, in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, a method for obtaining hemodynamic parameters based on intracranial medical images is provided, which specifically includes the following steps:
step S100, three-dimensional image data and two-dimensional contrast image data related to intracranial blood vessels are obtained, and a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessels are correspondingly obtained by processing the three-dimensional image data and the two-dimensional contrast image data respectively;
step S110, carrying out angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and carrying out space registration on the three-dimensional blood vessel model and two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
Step S120, decomposing the three-dimensional blood vessel model after the space registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
step S130, obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
step S140, performing a hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, and the average blood flow velocity and the blood flow distribution on the model, and calculating to obtain a hemodynamic parameter.
In this embodiment, a three-dimensional blood vessel model is obtained by reconstructing three-dimensional blood vessels of an intracranial artery by using three-dimensional image data, a two-dimensional blood vessel segmentation image of a single contrast angle is obtained according to the two-dimensional image data, the position angle of the three-dimensional blood vessel model is adjusted to be in spatial registration with the two-dimensional blood vessel segmentation image, then, after the three-dimensional blood vessel model is segmented, an inlet and outlet section of a single blood vessel section is projected into the two-dimensional image data, as time data of a contrast agent flowing through each blood vessel section is clearly recorded by multi-frame contrast image data in the two-dimensional image data, a corresponding pixel value of the projection position of the inlet section on the multi-frame contrast image can be utilized, a contrast agent concentration-time curve is obtained by combining shooting time of each frame, blood flow time corresponding to the single blood vessel section is obtained according to delay between the two contrast agent concentration-time curves, then, average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model can be obtained, and finally, blood flow dynamic parameters are calculated on the basis of the three-dimensional blood vessel model and the average flow velocity and the blood flow distribution on the model. The method is simple to operate, noninvasive, low in cost and accurate in parameter calculation, and effective data are effectively provided for subsequent evaluation of intracranial arterial stenosis.
In step S100, the three-dimensional image data related to the intracranial blood vessel may be any one of medical image data of CT angiography image data, magnetic resonance arterial vessel imaging data, or 3D digital subtraction angiography image data. Two-dimensional digital subtraction angiography image data may be used for two-dimensional image data relating to intracranial vessels.
In this embodiment, the three-dimensional blood vessel model of the intracranial blood vessel is obtained by processing and constructing the three-dimensional image data by adopting a reconstruction method based on threshold segmentation, or a lofting reconstruction method based on a blood vessel center line, or a reconstruction method based on a deep neural network model.
Specifically, a reconstruction method based on threshold segmentation, namely, automatically or manually setting an image signal intensity interval corresponding to an intracranial blood vessel, extracting intracranial tissues corresponding to the image signal intensity interval from a corresponding three-dimensional medical image, and obtaining a final intracranial blood vessel three-dimensional segmentation result through extracting a connected domain, opening and closing operation and the like, thereby generating a spatial three-dimensional model of the intracranial blood vessel.
Specifically, the method for reconstructing the lofting based on the blood vessel center line comprises the following implementation steps: firstly, marking the starting point and the end point of a target vessel segment on a three-dimensional medical image by an operator, thereby generating a three-dimensional central line of the vessel segment, dispersing the three-dimensional central line into a certain number of central points, resampling an original image on a normal plane by the central points, generating a lumen boundary on the section by applying a region growing or dynamic contour algorithm on the resampled image, and finally superposing the lumen boundaries on all the central points according to a vessel central line path to obtain the final three-dimensional model of the target vessel space.
Specifically, the implementation steps of the reconstruction method based on the deep neural network model comprise: firstly, marking intracranial blood vessels in a three-dimensional medical image by a professional neurosurgeon, generating a corresponding marking data set, and then training based on the marking data set to obtain an AI segmentation model. The AI segmentation model takes the three-dimensional medical image as input, outputs the three-dimensional segmentation result of the intracranial blood vessel, and obtains the spatial three-dimensional model of the intracranial blood vessel by proper treatment.
In other embodiments, other reconstruction methods besides the three-dimensional vascular model reconstruction methods described above may be used, which are all within the scope of the present method.
In this embodiment, a two-dimensional segmentation binary image of the intracranial blood vessel is generated according to the two-dimensional contrast image data, and the intracranial blood vessel is actually segmented based on the two-dimensional contrast image data, so as to obtain a two-dimensional segmentation result corresponding to the intracranial blood vessel, and the result is stored in a binary image format, so that the two-dimensional segmentation binary image of the intracranial blood vessel is obtained.
The digital subtraction radiography is a two-dimensional projection imaging mode, in the process of generating two-dimensional digital subtraction radiography, the flowing state of contrast agent in blood vessels can be clearly observed, and the following two-dimensional blood vessel segmentation mode is provided by combining the principle characteristics of the imaging mode and the physiological characteristics of intracranial blood vessels.
Before processing the two-dimensional digital subtraction image data, spatial and temporal filtering is carried out on each frame of contrast image, so that the influence of image noise is eliminated. And then counting the gray values of pixel points at the same positions corresponding to the image frames, wherein the image frames in the two-dimensional digital subtraction image number are imaged under the same contrast angle, so that the corresponding pixel points at the same positions on the image frames have corresponding relations, a plurality of gray values at the positions of each pixel point are processed to obtain the foreground coefficient of the corresponding pixel point, then the foreground or the background is assigned to each pixel point according to the foreground coefficient, a rough segmentation binary image of the intracranial blood vessel is generated, and after the maximum communication domain elimination noise point is extracted from the rough segmentation binary image, a closing operation is carried out to fill a cavity, so that the two-dimensional segmentation binary image of the intracranial blood vessel is obtained.
Specifically, the step of processing the plurality of gray values at the position of each pixel to obtain the foreground coefficient of the corresponding pixel includes: generating a corresponding gray time curve according to a plurality of gray values at the positions of each pixel point, translating the gray value of the first time point downwards by each gray time curve, turning over by taking the abscissa as an axis to obtain a corresponding contrast agent concentration time curve, namely calculating each pixel point in corresponding contrast image data to obtain a contrast agent concentration time curve, calculating the mean value, standard deviation and maximum value of each contrast agent concentration time curve one by one, and calculating according to the mean value, standard deviation and maximum value to obtain the foreground coefficient of the corresponding pixel point.
In this embodiment, the foreground coefficient of the corresponding pixel point is obtained by calculating using the mean value, the standard deviation and the maximum value, and the following formula is adopted:
（1）
in the case of the formula (1),representing foreground coefficients, ++>Mean value->Represents standard deviation, & lt + & gt>Represents maximum value>、/>、/>The weight coefficients respectively represent the mean, standard deviation and maximum value.
In the present embodiment, the weight coefficient、/>、/>Can be selected according to the specific situation. Preferably, a +>、/>、The values can be respectively: 0.3, 0.3 and 0.4.
Specifically, after the foreground coefficient of each pixel point is obtained through calculation, a statistical histogram of the foreground coefficient of all the pixel points in the whole image area is generated, all the pixel points are divided into a foreground and a background according to a self-adaptive optimal threshold value, the foreground pixel point is assigned with 1, the background pixel point is assigned with 0, and a corresponding rough segmentation binary image is generated.
In step S110, performing angular position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model onto an imaging plane to generate a projected binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by using the maximum overlapping coefficient of the projected binary image and the two-dimensional segmentation binary image includes: firstly, transforming a three-dimensional blood vessel model according to a translation matrix and a rotation matrix to obtain a transformed three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane according to projection parameters of two-dimensional contrast image data to generate a projection binary image, calculating an overlapping coefficient according to the projection binary image and the two-dimensional segmentation binary image, if the calculation result reaches a preset maximum value, completing space registration, and if the calculation result does not reach the preset maximum value, performing space registration on the translation matrix and the rotation matrix until the overlapping coefficient reaches the preset maximum value, thereby completing space registration.
Specifically, the coordinates of the three-dimensional model are recorded asThe intracranial blood vessel segmentation binary image is +.>The translation matrix is C, the rotation matrix is R, and the coordinates of the three-dimensional model after translation and rotation transformation are +.>The projection matrix calculated based on the projection parameters of the 2D DSA image is P, and the projection binary image is +.>Firstly, calculating based on translation and rotation matrixes to obtain coordinates of a transformed three-dimensional model, namely:
（2）
then, the transformed three-dimensional model coordinates are projected to an imaging surface and a projection binary image is generated, namely:
（3）
and calculating an overlap coefficient Dice by combining the segmentation binary image and the projection binary image, namely:
（4）
and iteratively updating the translation matrix C and the rotation matrix R to ensure that the Dice coefficient reaches the maximum value, namely the maximum coincidence degree of the projection binary image and the segmentation binary image is achieved, and the three-dimensional model and the 2D DSA image are subjected to space registration.
In step S120, the three-dimensional model of the intracranial blood vessel with spatial registration is decomposed into a single blood vessel and a bifurcation kernel, the inlet section and the outlet section of each single blood vessel are projected into two-dimensional digital subtraction radiography image data, average gray values on the inlet section and the outlet section are calculated frame by frame, a corresponding contrast agent concentration time curve is generated, and the time delay of the contrast agent concentration-time curve of the outlet section and the contrast agent concentration-time curve of the inlet section is calculated, namely, the blood flow time of the blood flow in the single blood vessel from the inlet section to the outlet section is obtained.
In the two-dimensional contrast image data, when the contrast agent does not flow into the blood vessel, the blood vessel region is displayed as highlight, the corresponding pixel gray value is high, and when the contrast agent flows into the blood vessel, the blood vessel region is gradually darkened under the influence of the contrast agent, and the corresponding pixel gray value is gradually lowered. Therefore, when the contrast agent flows through the inlet section or the outlet section of a single blood vessel, the corresponding pixel gray value gradually changes to a certain extent, and the delay of the change of the gray value of the outlet section relative to the change of the gray value of the inlet section is monitored, so that the contrast agent, namely the blood flow time of the blood flow flowing from the inlet section to the outlet section on the section of the single blood vessel, can be obtained.
Further, after the space three-dimensional model of the intracranial blood vessel is obtained based on the three-dimensional medical image segmentation and the decomposition of the single blood vessel and the bifurcation nucleus is completed, the blood vessel length and the lumen volume corresponding to each single blood vessel can be respectively calculated, and the average blood flow velocity and the blood flow corresponding to each single blood vessel can be obtained by dividing the blood vessel length and the lumen volume of each single blood vessel by the corresponding blood flow time. Finally, combining the blood flow information corresponding to each single blood vessel to finally obtain the flow and flow velocity distribution on the whole intracranial blood vessel three-dimensional model.
In step S130, based on the three-dimensional model of the intracranial blood vessel and the flow and flow velocity distribution on the three-dimensional model, the hemodynamic simulation is performed on the intracranial blood vessel, and the hemodynamic evaluation parameter is calculated. Hemodynamic evaluation parameters include pressure differential, distal pressure, pressure ratio, and the like. The pressure difference is the blood pressure difference between the blood vessel outlet and the blood vessel inlet, the remote pressure is the blood pressure value of the blood vessel outlet, and the pressure ratio is the blood pressure ratio of the blood vessel outlet and the blood vessel inlet.
Further, when the intracranial blood vessel is subjected to the hemodynamic simulation based on the intracranial blood vessel model, the average blood flow speed and the blood flow distribution on the model, a hemodynamic evaluation method based on computational fluid dynamics, a hemodynamic evaluation method based on a pipeline flow principle or a hemodynamic evaluation method based on vascular resistance is adopted.
In particular, a Computational Fluid Dynamics (CFD) based hemodynamic assessment method. In the scheme, firstly, grids are divided on the intracranial blood vessel three-dimensional model, the flow of each outlet and inlet is used as a boundary condition, then an N-S equation is solved to obtain a speed field and a pressure field on the whole intracranial blood vessel three-dimensional model, and finally, corresponding hemodynamic evaluation parameters are obtained based on pressure field calculation.
Specifically, the hemodynamic evaluation method based on the pipe flow principle comprises the following steps: in the duct flow, the pressure drop of the fluid is divided into three parts, the first part being a viscous pressure drop, mainly caused by the viscosity of the fluid, the second part being an expanding pressure drop, mainly caused by the lumen narrowing, and the third part being a bernoulli pressure drop, mainly caused by the lumen cross-sectional area variation. In the scheme, based on the intracranial blood vessel three-dimensional model and flow information of each single blood vessel, the viscosity pressure drop, the expansion pressure drop and the Bernoulli pressure drop are solved respectively, so that pressure distribution on the whole intracranial blood vessel three-dimensional model is obtained, and finally, corresponding hemodynamic evaluation parameters are obtained based on pressure distribution calculation.
In particular, a hemodynamic assessment method based on vascular resistance. In the scheme, based on each single blood vessel and bifurcation nuclei obtained by decomposing the intracranial blood vessel three-dimensional model, respectively solving the vascular resistance corresponding to each single blood vessel and bifurcation nuclei, connecting all calculated resistance according to the upstream-downstream relation of blood flow to construct a resistance network of the whole intracranial blood vessel three-dimensional model, finally combining the flow distribution on the intracranial blood vessel three-dimensional model, calculating to obtain the pressure drop value of each outlet relative to the inlet, and obtaining corresponding hemodynamic evaluation parameters based on the pressure drop value.
In another embodiment, the method is also performed according to:
firstly, marking the starting point and the end point of a target blood vessel segment on a three-dimensional medical image by an operator, thereby generating a three-dimensional central line of the segment of blood vessel, dispersing the three-dimensional central line into a certain number of central points, resampling an original image on a normal plane by the central points, generating a lumen boundary on the section by applying a dynamic contour algorithm on a resampled image, and finally, superposing the lumen boundaries on all the central points according to a blood vessel central line path to obtain a final three-dimensional model of the target blood vessel segment, namely the intracranial blood vessel three-dimensional model shown in fig. 2.
As shown in fig. 3, the two-dimensional digital radiography image and the corresponding segmentation binary image are shown in fig. 3 (a) which is a two-dimensional digital radiography original image, and fig. 3 (b) which is a segmentation binary image of an intracranial blood vessel obtained according to the two-dimensional blood vessel segmentation method in the method.
As shown in fig. 4, which is a multi-frame intracranial angiography image, it can be seen from the images that the contrast agent gradually flows to the far end of the blood vessel along with time (different frames are reflected in the image) in the intracranial blood vessel, so that the change of pixel values generated in the corresponding image area when the contrast agent flows into the blood vessel can be accurately identified, and the blood flow time can be analyzed and calculated.
Fig. 5 shows a gray scale time curve of a two-dimensional digital contrast image and corresponding positions of each pixel, fig. 5 (a) shows a two-dimensional digital contrast original image, and fig. 5 (b) shows a gray scale time curve of each pixel obtained according to the method. In the two-dimensional digital subtraction contrast image of intracranial blood vessels, the gray-time curve of the foreground region (blood vessel region) has a significant gray value decrease over a continuous time due to the inflow of contrast agent, while the background region has only relatively weak noise fluctuations on the gray-time curve due to the lack of inflow of contrast agent. Based on the difference between the foreground and the background, the two-dimensional blood vessel segmentation method can accurately segment the blood vessel region.
As shown in fig. 6, a schematic of spatial registration is provided. Fig. 6 (a) shows a two-dimensional contrast image, and fig. 6 (b) shows a three-dimensional model of the target intracranial vascular space obtained by reconstructing the MRA image. The inlet section and the outlet section of the three-dimensional model after spatial registration are projected onto a 2D DSA image, the time difference of the contrast agent flowing into and out of the target intracranial blood vessel section can be identified based on the change of gray values on the inlet projection section and the outlet projection section along with time, and the average blood flow velocity and the blood flow in the target blood vessel can be calculated by combining the blood vessel length and the lumen volume of the three-dimensional model.
As shown in fig. 7, for a blood flow time calculation schematic, firstly, an average gray value on an inlet section projection is calculated frame by frame, and a gray-time curve y=f (t) of the inlet section is generated, then, baseline information is eliminated, only gray value changes caused by contrast agents, namely y=f (t) -f (t 0), are reserved, finally, the curve is turned over along a time axis to obtain a final contrast agent concentration-time curve of the inlet section, namely y=f (t 0) -f (t), and similarly, the contrast agent concentration-time curve of the outlet section can be obtained. Fig. 7 shows contrast agent concentration versus time curves corresponding to the inlet and outlet sections in the embodiment, and it is known from the graph that there is a certain time delay between the outlet section and the contrast agent concentration versus time curve of the inlet section, and the delay is the blood flow time of blood flowing from the inlet section to the outlet section. In this embodiment, the time difference between parallel rising segments in two contrast agent concentration-time curves of the inlet section and the outlet sectionAs the time of blood flow from the inlet cross section to the outlet cross section.
Fig. 8 is an exploded view of a three-dimensional vascular model. In this embodiment, the intracranial vessel three-dimensional model has one inlet and two outlets, i.e. there is a bifurcated vessel. As can be seen from fig. 8, the intracranial blood vessel is divided into 4 parts, which are a single blood vessel 1 corresponding to the blood vessel inlet, a branch blood vessel 2 and a branch blood vessel 3 corresponding to the two blood vessel outlets, and a bifurcation nucleus at the intersection of the 3 single blood vessels. After the blood vessel is segmented to obtain 3 corresponding single blood vessels and bifurcation nuclei, the blood flow time of each single blood vessel can be calculated by adopting the method described in fig. 6, and the average blood flow velocity and blood flow of each of the 3 single blood vessels can be obtained by dividing the length and lumen volume of each single blood vessel by the blood flow time.
According to the method for acquiring the hemodynamic parameters based on the intracranial medical image, the three-dimensional image data is utilized to reconstruct three-dimensional blood vessels of the intracranial artery to obtain the three-dimensional blood vessel model, meanwhile, a two-dimensional blood vessel segmentation image of a single contrast angle is acquired according to the two-dimensional image data, the position angle of the three-dimensional blood vessel model is adjusted to enable the three-dimensional blood vessel model to be in spatial registration with the two-dimensional blood vessel segmentation image, then, after the three-dimensional blood vessel model is segmented, the inlet and outlet sections of the single blood vessel section are projected into the two-dimensional image data, as the time data of the contrast agent flowing through each blood vessel section are clearly recorded through the multi-frame contrast image data in the two-dimensional image data, the corresponding pixel value of the projection position of the inlet section on the multi-frame contrast image can be utilized, the shooting time of each frame is combined to obtain a contrast agent concentration-time curve, and then, the blood flow time of the corresponding single blood vessel section is obtained according to the delay between the two contrast agent concentration-time curves, and finally, the average blood flow speed and the blood flow distribution on the three-dimensional blood vessel model can be obtained, and the blood flow dynamics parameters are calculated and calculated on the basis of the average blood flow speed and the blood flow distribution on the three-dimensional blood vessel model and the model. The method can simply and quickly carry out hemodynamic assessment on the intracranial atherosclerosis lesion blood vessel and output corresponding parameters, provides additional reference indexes for medical staff to diagnose the illness state, plays a certain auxiliary decision-making role, can realize the hemodynamic assessment of intracranial arterial stenosis only by using conventional intracranial medical images, does not need additional detection technology and detection equipment, lightens the burden of medical staff and saves medical cost. Compared with the traditional morphological evaluation method, the method has more clinical significance in outputting the hemodynamic evaluation parameters, and can better represent the ischemia condition of the brain tissue of the patient. The hemodynamic evaluation parameters output by the invention can better help clinicians to carry out case screening classification, so that different treatment strategies are adopted, and the prognosis situation of patients can be improved to a certain extent.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 9, there is provided a hemodynamic parameter acquisition apparatus based on intracranial medical imaging, comprising: an intracranial blood vessel three-dimensional model and two-dimensional image generation module 200, an image data registration module 210, a single blood vessel segment blood flow time obtaining module 220, a three-dimensional blood vessel model blood flow parameter distribution module 230 and a blood flow dynamics parameter calculation module 240, wherein:
The intracranial blood vessel three-dimensional model and two-dimensional image generation module 200 is used for acquiring three-dimensional image data and two-dimensional contrast image data related to the intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
the image data registration module 210 is configured to perform angular position transformation on the three-dimensional blood vessel model, project the transformed three-dimensional blood vessel model onto an imaging plane to generate a projection binary image, and spatially register the three-dimensional blood vessel model and the two-dimensional contrast image data by using the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
a single vessel segment blood flow time obtaining module 220, configured to decompose the three-dimensional vessel model after spatial registration is completed according to the single vessel segment and the bifurcation check, project an inlet section and an outlet section of each single vessel segment into two-dimensional contrast image data, respectively generate corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtain the blood flow time of the corresponding single vessel segment according to delay between the two contrast agent concentration-time curves;
A three-dimensional blood vessel model blood flow parameter distribution module 230, configured to obtain a corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, thereby obtaining an average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
the hemodynamic parameter calculation module 240 is configured to perform a hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model and the average blood flow velocity and the blood flow distribution on the model, and calculate a hemodynamic parameter.
For specific limitations on the intracranial medical image-based hemodynamic parameter acquisition apparatus, reference may be made to the above description of the method for acquiring the intracranial medical image-based hemodynamic parameter, which is not repeated here. The modules in the intracranial medical image-based hemodynamic parameter acquisition apparatus can be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for acquiring hemodynamic parameters based on intracranial medical images. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
Decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
Performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration-time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (9)
1. A method for obtaining hemodynamic parameters based on intracranial medical imaging, the method comprising:
acquiring three-dimensional image data and two-dimensional contrast image data related to an intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
Performing angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
decomposing the three-dimensional blood vessel model after the spatial registration is completed according to the single blood vessel section and the bifurcation check, projecting an inlet section and an outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration time curves by using average gray values on the inlet section and the outlet section, and obtaining blood flow time of the corresponding single blood vessel section according to delay between the two contrast agent concentration time curves;
obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment, so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and carrying out hemodynamic simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain hemodynamic parameters.
2. The method according to claim 1, wherein the three-dimensional blood vessel model of the intracranial blood vessel obtained by processing and constructing the three-dimensional image data is a reconstruction method based on threshold segmentation, a lofting reconstruction method based on a blood vessel center line, or a reconstruction method based on a deep neural network model.
3. The method of claim 1, wherein generating a two-dimensional segmented binary map of an intracranial vessel from the two-dimensional contrast image data comprises:
counting gray values of pixel points at the same corresponding positions of each frame of contrast image in the two-dimensional contrast image data;
processing a plurality of gray values at each pixel point position to obtain a foreground coefficient of a corresponding pixel point;
performing foreground or background assignment on each pixel point according to the foreground coefficient to generate a rough segmentation binary image of the intracranial blood vessel;
and after the maximum communication domain noise elimination point is extracted from the rough segmentation binary image, closing operation is carried out to fill the cavity, and the two-dimensional segmentation binary image of the intracranial blood vessel is obtained.
4. The method of claim 3, wherein the processing the plurality of gray values at each pixel location to obtain the foreground coefficient of the corresponding pixel comprises:
Generating a corresponding gray time curve according to a plurality of gray values at each pixel point position;
translating the gray value of the first time point downwards by each gray time curve, and turning over by taking the abscissa as the axis to obtain a corresponding contrast agent concentration time curve;
and calculating the mean value, standard deviation and maximum value of each contrast agent concentration time curve one by one, and calculating according to the mean value, standard deviation and maximum value to obtain the foreground coefficient of the corresponding pixel point.
5. The method of claim 4, wherein the foreground coefficients of the corresponding pixels are calculated according to the mean, standard deviation and maximum value respectively, and the following formula is adopted:
；
in the above-mentioned description of the invention,representing foreground coefficients, ++>Mean value->Represents standard deviation, & lt + & gt>Represents maximum value>、/>、The weight coefficients respectively represent the mean, standard deviation and maximum value.
6. The method according to claim 1, wherein performing angular position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model onto an imaging plane to generate a projected binary image, and performing spatial registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by using a maximum overlapping coefficient of the projected binary image and a two-dimensional segmentation binary image comprises:
Transforming the three-dimensional blood vessel model according to the translation matrix and the rotation matrix to obtain a transformed three-dimensional blood vessel model;
projecting the transformed three-dimensional blood vessel model to an imaging plane according to the projection parameters of the two-dimensional contrast image data to generate a projection binary image;
calculating an overlap coefficient according to the projection binary image and the two-dimensional segmentation binary image, and if the calculation result reaches a preset maximum value, completing space registration;
if the calculated result does not reach the preset maximum value, updating the translation matrix and the rotation matrix until the overlapping coefficient reaches the preset maximum value, so as to complete the space registration.
7. The method according to claim 1, wherein the method for estimating hemodynamic parameters based on computational fluid dynamics, the method for estimating hemodynamic parameters based on the principle of tube flow, or the method for estimating hemodynamic parameters based on vascular resistance is used for hemodynamic simulation of intracranial blood vessels based on the three-dimensional blood vessel model and the average blood flow velocity and the blood flow distribution on the model.
8. The method of claim 1, wherein the three-dimensional image data comprises CT angiography image data, magnetic resonance arterial vessel imaging data, or 3D digital subtraction angiography image data.
9. A hemodynamic parameter acquisition apparatus based on intracranial medical imaging, the apparatus comprising:
the system comprises an intracranial blood vessel three-dimensional model and a two-dimensional image generation module, wherein the intracranial blood vessel three-dimensional model and the two-dimensional image generation module are used for acquiring three-dimensional image data and two-dimensional contrast image data related to the intracranial blood vessel, and correspondingly acquiring a three-dimensional blood vessel model and a two-dimensional segmentation binary image of the intracranial blood vessel by respectively processing the three-dimensional image data and the two-dimensional contrast image data;
the image data registration module is used for carrying out angle position transformation on the three-dimensional blood vessel model, projecting the transformed three-dimensional blood vessel model to an imaging plane to generate a projection binary image, and carrying out space registration on the three-dimensional blood vessel model and the two-dimensional contrast image data by utilizing the maximum overlapping coefficient of the projection binary image and the two-dimensional segmentation binary image;
the single blood vessel section blood flow time obtaining module is used for decomposing the three-dimensional blood vessel model after the space registration is completed according to the single blood vessel section and the bifurcation check, projecting the inlet section and the outlet section of each single blood vessel section into two-dimensional contrast image data, respectively generating corresponding contrast agent concentration-time curves by using average gray values on the inlet section and the outlet section, and obtaining the blood flow time of the corresponding single blood vessel section according to the delay between the two contrast agent concentration-time curves;
The three-dimensional blood vessel model blood flow parameter distribution module is used for obtaining corresponding average blood flow velocity and blood flow according to the length, lumen volume and blood flow time of each single blood vessel segment so as to obtain average blood flow velocity and blood flow distribution on the three-dimensional blood vessel model;
and the blood flow dynamics parameter calculation module is used for carrying out blood flow dynamics simulation on the intracranial blood vessel based on the three-dimensional blood vessel model, the average blood flow speed and the blood flow distribution on the model, and calculating to obtain blood flow dynamics parameters.
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