CN112508875A

CN112508875A - Establishment method of simulated three-dimensional vascular stenosis analysis model

Info

Publication number: CN112508875A
Application number: CN202011324141.0A
Authority: CN
Inventors: 贾思琪
Original assignee: Xian Cresun Innovation Technology Co Ltd
Current assignee: Xian Cresun Innovation Technology Co Ltd
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2021-03-16
Also published as: CN114240841A

Abstract

The invention discloses a method for establishing a simulated three-dimensional vascular stenosis analysis model, which comprises the following steps: acquiring a bright blood image group and an enhanced black blood image group of a blood vessel part; carrying out image registration on each bright blood image by taking the corresponding enhanced black blood image as a reference by using a registration method based on mutual information and an image pyramid to obtain a registered bright blood image group; establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group; aiming at each section of blood vessel in the blood vessel simulation three-dimensional model, segmenting from three preset positions to obtain a two-dimensional sectional view of each position; carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of each direction, and recording the target corrosion times when the blood vessel is corroded to a single pixel; obtaining a numerical value of a target parameter representing the stenosis degree of the section of the blood vessel according to the target corrosion times of the section of the blood vessel in the three directions respectively; and marking the blood vessel simulation three-dimensional model by using the numerical value of the target parameter of each section of blood vessel to obtain the simulated three-dimensional blood vessel stenosis analysis model.

Description

Establishment method of simulated three-dimensional vascular stenosis analysis model

Technical Field

The invention belongs to the field of image processing, and particularly relates to a method for establishing a simulated three-dimensional vascular stenosis analysis model.

Background

Stenosis refers to the condition of increased thickness of the wall of a blood vessel, narrowing of the lumen of the blood vessel, and ischemia of the organ or tissue that the blood vessel innervates, caused by atherosclerosis, trauma, or other congenital factors. Coronary artery stenosis can lead to coronary heart disease, etc.; cerebral infarction and cerebral ischemia caused by carotid artery stenosis and intracranial artery stenosis; lower extremity arterial stenosis can lead to lower extremity arteriosclerotic occlusion, such as ischemic symptoms in the foot.

Currently, for the clinical assessment of the degree of vascular lesions, lumen-based imaging methods such as Digital Subtraction Angiography (DSA), CT Angiography (CTA), Magnetic Resonance Angiography (MRA), and High-Resolution Magnetic Resonance Angiography (HRMRA) are generally used.

However, the images obtained by the above imaging methods cannot intuitively and quickly obtain analysis data about the degree of stenosis of the blood vessel, and are not favorable for the positioning analysis of the lesion region of the blood vessel in clinical practice.

Disclosure of Invention

To obtain analysis data about the degree of stenosis of a blood vessel intuitively and rapidly in clinic. The embodiment of the invention provides a method for establishing a simulated three-dimensional vascular stenosis analysis model. The method comprises the following steps:

acquiring a bright blood image group and an enhanced black blood image group of a blood vessel part; wherein the bright blood image group and the enhanced black blood image group respectively include K bright blood images and K enhanced black blood images; the images in the bright blood image group and the enhanced black blood image group correspond to each other one by one; k is a natural number greater than 2;

aiming at each bright blood image in the bright blood image group, carrying out image registration by using a registration method based on mutual information and an image pyramid by taking a corresponding enhanced black blood image in the enhanced black blood image group as a reference to obtain a registered bright blood image group comprising K registered bright blood images;

establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group;

segmenting each section of blood vessel in the blood vessel simulation three-dimensional model from three preset directions to obtain a two-dimensional sectional view of each direction;

carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of each direction, and recording the target corrosion times when the blood vessel is corroded to a single pixel;

obtaining a numerical value of a target parameter representing the stenosis degree of the section of the blood vessel according to the target corrosion times of the section of the blood vessel in the three directions respectively;

and marking the blood vessel simulation three-dimensional model by using the numerical value of the target parameter of each section of blood vessel to obtain a simulated three-dimensional blood vessel stenosis analysis model.

In the scheme provided by the embodiment of the invention, firstly, the bright blood image and the enhanced black blood image obtained by scanning the magnetic resonance blood vessel imaging technology are subjected to image registration by adopting a registration method based on mutual information and an image pyramid, so that the registration efficiency can be improved, and the registration accuracy of the images is improved layer by layer from low resolution to high resolution. The bright blood image and the enhanced black blood image can be unified under the same coordinate system through the image registration. And thirdly, establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group. The blood vessel simulation three-dimensional model simulates the shape of a blood vessel by utilizing blood information, and realizes the three-dimensional visualization of the blood vessel. And finally, carrying out corrosion operation by using the two-dimensional sectional graphs in three directions to obtain the numerical value of the target parameter for representing the stenosis degree of the blood vessel, and marking the blood vessel simulation three-dimensional model by using the numerical value of the target parameter of each section of the blood vessel to obtain the simulated three-dimensional stenosis analysis model. The simulated three-dimensional vascular stenosis analysis model can provide visual vascular three-dimensional space information without the need of a doctor to restore vascular tissue structures, disease characteristics and the like through imagination, is convenient for visual observation and is convenient for positioning and displaying a narrow focus area. The analytical data about the stenosis degree of the blood vessel can be intuitively and quickly obtained clinically.

Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.

The present invention will be described in further detail with reference to the accompanying drawings and examples.

Drawings

In order to more clearly illustrate the embodiments of the present invention 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 is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a method for establishing a simulated three-dimensional vascular stenosis analysis model according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of gray scale linear transformation and parameter setting according to an embodiment of the present invention

FIG. 3 is a schematic diagram of coordinate transformation of an intracranial vascular magnetic resonance image according to an embodiment of the invention;

FIG. 4 shows the results of registration comparison of two search strategies according to an embodiment of the present invention;

FIG. 5 is a graph of pre-registered results of intracranial vascular magnetic resonance images according to an embodiment of the invention;

FIG. 6 is a schematic diagram of a region to be registered of an intracranial vascular magnetic resonance image in accordance with an embodiment of the invention;

FIG. 7 is a bright blood Gaussian pyramid and a black blood Gaussian pyramid of an intracranial vascular magnetic resonance image in accordance with an embodiment of the present invention;

FIG. 8 is a bright blood Laplacian pyramid and a dark blood Laplacian pyramid of an intracranial vascular magnetic resonance image in accordance with an embodiment of the present invention;

FIG. 9 is a result of registration of Laplacian pyramid images of intracranial vascular magnetic resonance images according to an embodiment of the invention;

fig. 10 is a schematic diagram of a gaussian pyramid image registration step based on mutual information for an intracranial vascular magnetic resonance image according to an embodiment of the present invention;

FIG. 11 is a normalized mutual information for different iterations according to an embodiment of the present invention;

FIG. 12 is a registration result of intracranial vascular magnetic resonance images of multiple registration methods;

FIG. 13 is a graph showing the result of linear gray scale transformation according to an embodiment of the present invention;

FIG. 14 is a diagram of the effect of a vascular simulation three-dimensional model of an intracranial blood vessel in accordance with an embodiment of the invention;

FIG. 15 is a graph showing the effect of a simulated three-dimensional vascular stenosis analysis model of an intracranial blood vessel in accordance with an embodiment of the present invention;

fig. 16 is a simulated three-dimensional angiostenosis analysis model of intracranial vessels and a sectional view display effect diagram according to an embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.

it should be noted that an implementation subject of the method for establishing a simulated three-dimensional vascular stenosis analysis model according to the embodiment of the present invention may be an apparatus for establishing a simulated three-dimensional vascular stenosis analysis model, and the apparatus may be implemented in an electronic device. The electronic device may be a blood vessel imaging device or an image processing device, but is not limited thereto.

As shown in fig. 1, a method for establishing a simulated three-dimensional vascular stenosis analysis model according to an embodiment of the present invention may include the following steps:

s1, acquiring a bright blood image group and an enhanced black blood image group of the blood vessel part;

the bright blood image group and the enhanced black blood image group respectively comprise K bright blood images and K enhanced black blood images; the images in the bright blood image group and the enhanced black blood image group correspond to each other one by one; k is a natural number greater than 2;

in the embodiment of the present invention, the blood vessel may be a blood vessel of a tissue portion such as an intracranial blood vessel, a cardiovascular blood vessel, an ocular fundus blood vessel, and the like, and the blood vessel portion in the embodiment of the present invention is not limited herein.

The bright blood image group is an image group obtained by performing bright blood sequence scanning on a blood vessel part by using a magnetic resonance blood vessel imaging technology. The enhanced black blood image group is an image group obtained by injecting paramagnetic contrast agent into a patient and then performing black blood sequence scanning on a blood vessel part by using a magnetic resonance blood vessel imaging technology. The magnetic resonance angiography technique includes MRA and HRMRA, and in the embodiment of the present invention, the magnetic resonance angiography technique is preferably HRMRA.

In clinical practice, paramagnetic contrast agent is usually injected into a patient to more clearly reflect the real anatomical morphology of a blood vessel cavity, the T1 value of blood is reduced, the influence of the blood state on the imaging result is reduced, the contrast of the blood and static tissues is enhanced, and thus the blood vessel structure obtained by scanning the black blood sequence can be more clearly shown. By injecting paramagnetic contrast agent, the enhanced black blood image obtained by black blood sequence scanning is more clear in vascular wall structure display compared with the black blood image obtained by directly using black blood sequence scanning, and can clearly reflect the inflammatory reaction and instability of arterial plaque, so that the method is an effective method for measuring the thickness of the vascular wall and identifying the pathological characteristics of the vascular wall.

The K images in the group of bright blood images and the group of enhanced black blood images are in one-to-one correspondence in such a manner that the order of the images formed in accordance with the scanning time is the same.

It should be noted that, in order to improve the accuracy of the subsequent registration of the embodiment of the present invention, the body motion of the patient should be minimized during the scanning imaging process, so as to avoid causing too large spatial position error and generating too many motion artifacts.

S2, aiming at each bright blood image in the bright blood image group, carrying out image registration by using a registration method based on mutual information and an image pyramid by taking a corresponding enhanced black blood image in the enhanced black blood image group as a reference to obtain a registered bright blood image group comprising K registered bright blood images;

the step is to actually complete the image registration of each bright blood image, that is, to use the bright blood image to be registered as a floating image, use the enhanced black blood image corresponding to the bright blood image as a reference image, and perform the image registration by using the similarity measurement based on mutual information and introducing an image pyramid method.

In an alternative embodiment, S2 may include S21-S27:

s21, preprocessing each bright blood image and the corresponding enhanced black blood image to obtain a first bright blood image and a first black blood image;

this step may be understood as a pre-processing procedure of the image, and optionally, the pre-processing of this step may include: and performing image enhancement operations such as cutting, denoising, smoothing, filtering, edge filling and the like on the bright blood image and the corresponding enhanced black blood image so as to enhance the interesting characteristics in the image.

In an alternative embodiment, S21 may include S211 and S212:

s211, aiming at each bright blood image, taking the corresponding enhanced black blood image as a reference, carrying out coordinate transformation and image interpolation on the bright blood image, and obtaining a pre-registered first bright blood image by using similarity measurement based on mutual information and a preset search strategy;

the step S211 is actually image pre-registration of the bright blood image with reference to the enhanced black blood image.

The enhanced black blood image is imaged by coronal plane scanning, while the bright blood image is imaged by axial plane scanning, and the difference of the sequence scanning direction causes the difference of the two final magnetic resonance imaging layers, so that the magnetic resonance images of different imaging layers need to be observed under a standard reference coordinate system through coordinate transformation.

For the blood vessel image, the coordinate transformation of the image can be realized by using the direction information in the DICOM (Digital Imaging and Communications in Medicine) file. The DICOM file is an image storage format for medical devices such as CT and nuclear magnetic resonance, and the contents stored in the DICOM standard include personal data of a patient, an image layer thickness, a time stamp, medical device information, and the like, in addition to image information. The DICOM3.0 format image file contains orientation label information related to the imaging direction, which briefly introduces the orientation relationship between the patient and the imaging instrument, and the accurate position information of each pixel in the image can be obtained through the data in the orientation label information.

Specifically, the enhanced black blood image and the bright blood image are to-be-registered images, and the enhanced black blood image is used as a reference image, the bright blood image is used as a floating image, and the bright blood image is subjected to coordinate transformation according to the orientation tag information in the DICOM file of the bright blood image, so that the purpose of rotating the bright blood image to the same coordinate system as the enhanced black blood image is achieved, and the scanning direction of the rotated bright blood image is also changed into a coronal plane.

To facilitate an understanding of the method of the embodiments of the present invention, a brief description is provided below in connection with an image registration process, which can be understood by referring to the related art.

For the registration of the two images a and B, each coordinate position in the image a is actually mapped to the image B through a mapping relationship. Specific coordinate transformation methods may include rigid body transformation, affine transformation, projective transformation, nonlinear transformation, and the like, and in practical use, different coordinate transformation methods may be selected according to different application scenarios, and herein, the coordinate transformation method in the embodiment of the present invention is not limited.

However, in the coordinate transformation process, the coordinate system of the floating image may stretch or deform, the image pixel coordinate after the coordinate transformation does not completely coincide with the sampling grid of the original image, that is, the original integer pixel coordinate point may not be an integer after the coordinate transformation, which causes some areas of the image to lose part of pixels, therefore, in the image coordinate transformation process, the image needs to be resampled and interpolated at the same time to determine the gray value of the image pixel coordinate point after the coordinate transformation, which is convenient for the subsequent processing. Specifically, the coordinates of the bright blood image after coordinate transformation may be mapped to non-integer coordinates of the original image, and therefore, image interpolation needs to be performed on the bright blood image at the same time. The image interpolation method may adopt any one of methods such as nearest neighbor interpolation, bilinear interpolation, bicubic interpolation and the like.

After image restoration is carried out on missing pixel points by using an image interpolation method, certain similarity measurement is needed to be used for calculating the similarity between a reference image and a changed floating image, then the optimal similarity measurement is found by using a search strategy, iteration optimization is carried out repeatedly until the similarity measurement of two images reaches the optimal value, iteration is stopped, and finally coordinate conversion is carried out on the floating image according to a determined space transformation matrix (rotation matrix) so as to realize complete image registration. After the images to be registered are optimized by an iterative algorithm, the spatial position registration relationship and the registered images of the two images can be calculated, so that the similarity between the registered floating images and the reference images is the highest.

The scale for measuring the feature similarity between the two images is the similarity measurement, and the selection of the proper similarity measurement can improve the registration accuracy, effectively inhibit noise and the like, and has very important function in the registration of the images. The common similarity measurement mainly includes three categories, namely distance measurement, correlation measurement and information entropy, and in the embodiment of the present invention, the similarity measurement based on mutual information is adopted, specifically, the similarity measurement may be mutual information or normalized mutual information.

Mutual information and normalized mutual information are one of information entropies. Mutual Information (MI), which measures the correlation between two images, or the amount of Information contained in each other, is used to explain whether the two images have reached optimal registration, and the larger the value of Mutual Information, the more similar the two images. Or Normalized Mutual Information (NMI) can be selected, which is an improvement of Mutual Information measurement, and when the pixel gray scale levels of two images to be registered are similar, the NMI is used as similarity measurement, so that the obtained registered image has higher accuracy and is more reliable. The value range of NMI is [0,1], and the closer the value is to 1, the more similar the two images are. The concept of normalization mutual information solves the problems that when the overlapping part of two images is small or most of the overlapping area is background information, the image registration based on the mutual information is not high in precision and poor in registration effect, and reduces the sensitivity of the mutual information to the image overlapping area.

Image registration is essentially a multi-parameter optimization problem, namely, spatial coordinate change is performed on images by using a certain search strategy, and finally, the similarity measurement of the two images is optimized, wherein the search strategy and the spatial coordinate change are performed in a mutual intersection manner in the actual calculation process. The algorithm idea is to calculate the similarity measurement between two images in each iteration, adjust the floating image through the operations of coordinate transformation such as translation or rotation and the like, and interpolate the images at the same time until the similarity measurement of the two images is the maximum. Currently, commonly used search strategies include a gradient descent optimizer, (1+1) -ES based on an Evolution Strategy (ES), and the like, and the predetermined search Strategy in the embodiment of the present invention may be selected as needed.

Through the pre-registration of the step, the magnetic resonance images of the same scanning layer can be compared under the same coordinate system preliminarily, but because the scanning time of the bright blood sequence and the scanning time of the black blood sequence are different, and the patient possibly moves slightly before and after the scanning, the operation is only a rough coordinate transformation, the complete registration of the multi-mode magnetic resonance images can not be realized only through the pre-registration, but the step can omit unnecessary processing procedures for the subsequent accurate registration link, and the processing speed is improved.

S212, the same area content as the scanning range of the first bright blood image is extracted from the corresponding enhanced black blood image, and a first black blood image is formed.

Because the scanning ranges of the blood vessel imaging in different magnetic resonance sequences are different, after the bright blood image is subjected to image coordinate transformation, the information of the coronal plane of the bright blood image is not rich in the information of the enhanced black blood image, so that the same scanning area can be extracted from the enhanced black blood image according to the scanning area of the first bright blood image, and the registration range of the subsequent image is reduced.

Optionally, S212 may include the following steps:

1. obtaining edge contour information of a blood vessel in the first bright blood image;

specifically, the edge contour information may be obtained by using a Sobel edge detection method or the like. The edge profile information contains coordinate values of the respective edge points.

2. Extracting the minimum value and the maximum value of the abscissa and the ordinate from the edge profile information, and determining an initial extraction frame based on the obtained four coordinate values;

in other words, in the edge profile information, extracting a minimum abscissa value, a maximum abscissa value, a minimum ordinate value and a maximum ordinate value, and determining four vertexes of the square frame by using the four coordinate values, thereby obtaining an initial extracted frame;

3. in the size range of the first bright blood image, the size of the initial extraction frame is respectively enlarged by a preset number of pixels along four directions to obtain a final extraction frame;

wherein, the four directions are respectively the positive and negative directions of the horizontal and vertical coordinates; the preset number is reasonably selected according to the type of the blood vessel image, so as to ensure that the expanded final extraction frame does not exceed the size range of the first bright blood image, for example, the preset number may be 20.

4. And extracting the corresponding area content in the final extracted frame from the corresponding enhanced black blood image to form a first black blood image.

And extracting the content of the corresponding area in the corresponding enhanced black blood image according to the coordinate range defined by the final extraction frame, and forming the extracted content into a first black blood image. The step obtains the common scanning range of the magnetic resonance images under the two modes by extracting the region to be registered, thereby being beneficial to subsequent rapid registration.

The two-step preprocessing process of the embodiment of the invention plays a very important role, the preprocessed image can pay more attention to useful information and exclude irrelevant information, and in actual use, the image preprocessing can be used for improving the reliability of image registration and identification.

In the embodiment of the invention, in order to improve the accuracy of image registration and avoid the convergence of an image to a local maximum value in the registration process, a multi-resolution strategy is selected to solve the problem of a local extreme value, and meanwhile, the multi-resolution strategy is utilized to improve the algorithm execution speed and increase the robustness under the condition of meeting the image registration accuracy. Thus, an image pyramid approach is employed. The method is an effective way to improve the registration accuracy and speed by increasing the complexity of the model, namely, in the registration process, the registration is performed in the order from coarse registration to fine registration, firstly, the registration is performed on the low-resolution image, and then, on the basis of the completion of the registration of the low-resolution image, the registration is performed on the high-resolution image. Optionally, the following steps may be employed:

s22, obtaining a bright blood Gaussian pyramid from the first bright blood image and obtaining a black blood Gaussian pyramid from the first black blood image based on downsampling processing; the bright blood Gaussian pyramid and the black blood Gaussian pyramid comprise m images with resolution ratios which are sequentially reduced from bottom to top; m is a natural number greater than 3;

in an alternative embodiment, S22 may include the following steps:

obtaining an input image of an ith layer, filtering the input image of the ith layer by using a Gaussian kernel, and deleting even rows and even columns of the filtered image to obtain an image G of the ith layer of the Gaussian pyramid_iAnd the ith layer image G_iObtaining an i +1 layer image G of a Gaussian pyramid as an i +1 layer input image_i+1；

Wherein i is 1, 2, …, m-1; when the gaussian pyramid is a bright blood gaussian pyramid, the input image of the 1 st layer is a first bright blood image, and when the gaussian pyramid is a black blood gaussian pyramid, the input image of the 1 st layer is a first black blood image.

Specifically, the multiple images in the gaussian pyramid are corresponding to the same original image with different resolutions. The Gaussian pyramid acquires an image through Gaussian filtering and downsampling, and each layer of construction steps can be divided into two steps: firstly, smoothing filtering is carried out on an image by using Gaussian filtering, namely filtering is carried out by using a Gaussian kernel; and then deleting even rows and even columns of the filtered image, namely reducing the width and height of the lower layer image by half to obtain the current layer image, so that the current layer image is one fourth of the size of the lower layer image, and finally obtaining the Gaussian pyramid by continuously iterating the steps.

The gaussian filter is actually a low-pass filter, and the image frequency range in the gaussian pyramid is very wide, wherein the cut-off frequency of the image of the lower layer is 2 times that of the image of the higher layer.

Gaussian filtering first uses a gaussian function to calculate a weight matrix, and then uses the weight matrix to perform convolution operation on the original image, which can be performed by using a two-dimensional gaussian template. Although the effect of blurring the image can be achieved by using the two-dimensional Gaussian template, when one point is on the boundary and there are not enough points around, the edge image is lost due to the relationship of the weight matrix, so the embodiment of the invention optimizes the two-dimensional Gaussian template. The two-dimensional Gaussian filter can be split into two independent one-dimensional Gaussian filters, and image filtering is performed in the horizontal direction and the vertical direction respectively. The Gaussian function is separated, so that the edge generated by the two-dimensional Gaussian template can be eliminated, and the running speed of the program can be greatly accelerated. Compared with other blurring filters, the Gaussian filtering can not only realize the blurring effect of the image, but also better keep the marginal effect.

In this step, the first bright blood image and the first black blood image after the preprocessing are subjected to the processing, so that a bright blood gaussian pyramid and a black blood gaussian pyramid can be obtained. Wherein the number of picture layers m may be 4.

Since the gaussian pyramid is downsampled, i.e., the image is reduced, a portion of the data of the image is lost. Therefore, in order to avoid data loss of the image in the zooming process and recover detailed data, the Laplacian pyramid is used in the subsequent steps, image reconstruction is realized by matching with the Gaussian pyramid, and details are highlighted on the basis of the Gaussian pyramid image.

S23, based on the upsampling processing, utilizing the bright blood Gaussian pyramid to obtain a bright blood Laplacian pyramid, and utilizing the black blood Gaussian pyramid to obtain a black blood Laplacian pyramid; wherein the bright blood Laplacian pyramid and the black blood Laplacian pyramid comprise m-1 images with resolution which is sequentially reduced from bottom to top;

in an alternative embodiment, S23 may include the following steps:

for the i +1 layer image G of the Gaussian pyramid_i+1Performing upsampling, and filling the newly added rows and columns with data 0 to obtain a filled image;

performing convolution on the filling image by utilizing a Gaussian kernel to obtain an approximate value of the filling pixel to obtain an amplified image;

the ith layer image G of the Gaussian pyramid_iSubtracting the amplified image to obtain the ith layer image L of the Laplacian pyramid_i；

When the gaussian pyramid is the bright blood gaussian pyramid, the laplacian pyramid is the bright blood laplacian pyramid, and when the gaussian pyramid is the black blood laplacian pyramid, the laplacian pyramid is the black blood laplacian pyramid.

Since the laplacian pyramid is a residual between the image and the original image after downsampling, the laplacian pyramid is compared from bottom to top, and the laplacian pyramid has one layer of higher-level image less than the laplacian pyramid structure.

Specifically, the mathematical formula for generating the Laplacian pyramid structure is shown as (1), wherein L_iIndicating the Laplacian pyramid (bright blood Laplacian pyramid or black blood Laplacian pyramid) of the i-th layer G_iRepresenting the i-th level gaussian pyramid (bright blood gaussian pyramid or black blood gaussian pyramid), and the UP operation is an UP-sampled magnified image, symbol

Is a sign of the convolution of the symbols,

is the gaussian kernel used in constructing the gaussian pyramid. The formula shows that the Laplacian pyramid is formed by subtracting residual data of an image which is firstly reduced and then enlarged from an original image, and is a residual prediction pyramid, and the core idea is to useThe difference between the image and the original image after the down-sampling operation is stored, and the high-frequency information of the image is reserved, so that the image before the down-sampling operation is performed on each level can be completely restored. Since a part of information lost in the previous downsampling operation cannot be completely restored by upsampling, that is, downsampling is irreversible, the display effect of the image after downsampling and upsampling is blurred compared with the original image. By storing the residual between the image and the original image after the down-sampling operation, the detail can be added to the images of different frequency layers on the basis of the Gaussian pyramid image, and the detail and the like can be highlighted.

Corresponding to the gaussian pyramid with 4 layers, the step can obtain a bright blood laplacian pyramid and a black blood laplacian pyramid with 3 image layers.

S24, registering images of corresponding layers in the bright blood Laplacian pyramid and the black blood Laplacian pyramid to obtain a registered bright blood Laplacian pyramid;

in an alternative embodiment, S24 may include the following steps:

aiming at each layer of the bright blood Laplacian pyramid and the black blood Laplacian pyramid, taking the corresponding black blood Laplacian image of the layer as a reference image, taking the corresponding bright blood Laplacian image of the layer as a floating image, and realizing image registration by using a similarity measure based on mutual information and a preset search strategy to obtain the registered bright blood Laplacian image of the layer;

forming a registered Laplacian pyramid of the bright blood from bottom to top according to the sequence of the sequential reduction of the resolution by the registered multilayer Laplacian images of the bright blood;

the black blood laplacian image is an image in the black blood laplacian pyramid, and the bright blood laplacian image is an image in the bright blood laplacian pyramid.

The registration process in this step is similar to the pre-registration process, and the registered bright blood laplacian image can be obtained by performing coordinate transformation and image interpolation on the bright blood laplacian image, and using the similarity measurement based on mutual information and a predetermined search strategy to realize image registration.

S25, registering images of each layer in the bright blood Gaussian pyramid and the black blood Gaussian pyramid from top to bottom by using the registered bright blood Laplacian pyramid as superposition information to obtain a registered bright blood Gaussian pyramid;

for S25, the registered leuca laplacian pyramid is used as overlay information to perform top-down registration on images of each layer in the leuca gaussian pyramid and the sanguine gaussian pyramid, and images with different resolutions in the gaussian pyramid need to be registered, and since the registration of low-resolution images can more easily hold the essential features of the images, embodiments of the present invention register high-resolution images on the basis of the registration of low-resolution images, that is, register the gaussian pyramid images from top to bottom, and use the registration result of the previous layer image as the input of the registration of the next layer image.

In an alternative embodiment, S25 may include the following steps:

for the j-th layer from top to bottom in the bright blood Gaussian pyramid and the black blood Gaussian pyramid, taking the black blood Gaussian image corresponding to the layer as a reference image, taking the bright blood Gaussian image corresponding to the layer as a floating image, and using similarity measurement based on mutual information and a preset search strategy to realize image registration to obtain a registered j-th layer bright blood Gaussian image;

performing up-sampling operation on the registered jth layer of bright blood Gaussian image, adding the up-sampling operation to the registered corresponding layer of bright blood Laplacian image, and replacing the jth +1 layer of bright blood Gaussian image in the bright blood Gaussian pyramid by using the added image;

taking the black blood Gaussian image of the j +1 th layer as a reference image, taking the replaced bright blood Gaussian image of the j +1 th layer as a floating image, and using a preset similarity measure and a preset search strategy to realize image registration to obtain a registered bright blood Gaussian image of the j +1 th layer; where j is 1, 2, …, m-1, the black blood gaussian image is an image in the black blood gaussian pyramid, and the bright blood gaussian image is an image in the bright blood gaussian pyramid.

And repeating the operations until the high-resolution registration of the bottom layer Gaussian pyramid image is completed to obtain the registered bright blood Gaussian pyramid. The coordinate system of the bright blood image is consistent with that of the black blood image, and the images have high similarity. The registration process is similar to the pre-registration process described above and will not be described in detail.

S26, obtaining a registered bright blood image corresponding to the bright blood image based on the registered bright blood Gaussian pyramid;

in the step, the bottom layer image in the registered bright blood Gaussian pyramid is obtained to be used as the bright blood image after registration.

And S27, obtaining a group of registered bright blood images by the registered bright blood images corresponding to the K bright blood images respectively.

After all the bright blood images are registered, K registered bright blood images can be used for obtaining a registered bright blood image group. Each post-registration bright blood image and the corresponding enhanced black blood image may be a post-registration image pair.

Through the steps, the image registration of the bright blood image and the enhanced black blood image can be realized, and in the registration scheme provided by the embodiment of the invention, the registration precision can be improved based on mutual information as similarity measurement; meanwhile, an image pyramid algorithm is introduced, which is an effective mode for improving the registration accuracy and speed by increasing the complexity of a model, namely, firstly, the image with lower resolution is subjected to coarse registration, then, the image with higher resolution is subjected to fine registration on the basis of the coarse registration, and the vessel image is decomposed and reconstructed by using the Gaussian pyramid and the Laplace pyramid, so that the effect of observing one image by human eyes at different distances is simulated, and the essential characteristics of the vessel image are more easily obtained. The magnetic resonance bright blood image and the black blood image of the blood vessel part are registered by using a pyramid algorithm, so that the registration efficiency can be improved, and the registration accuracy of the images is improved layer by layer from low resolution to high resolution. The bright blood images and the enhanced black blood images can be unified under the same coordinate system through the image registration, so that doctors can conveniently understand the blood vessel images corresponding to the black blood sequences and the bright blood sequences, comprehensive information required by diagnosis can be simply, conveniently and quickly obtained, and accurate and reliable reference information is provided for subsequent medical diagnosis, operation plan making, radiotherapy plan and the like. The registration scheme provided by the embodiment of the invention can provide a better reference mode for registration of other medical images, and has great clinical application value.

S3, establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group;

in an alternative embodiment, S3 includes S31 and S32:

s31, improving the contrast of each of the registered bright blood images to obtain K contrast enhanced bright blood images;

an optional implementation manner of the step S31 includes:

carrying out gray scale linear transformation on each of the registered bright blood images to obtain K contrast enhanced bright blood images;

according to the embodiment of the invention, according to the characteristic that blood in the bright blood image is in a high signal state and peripheral tissues are in a low signal state, the gray scale linear transformation is carried out on the bright blood image after the registration, the gray scale range of the image is adjusted, and the purpose of improving the contrast of the image is realized.

For example, fig. 2 shows a gray scale linear transformation and parameter setting used for a post-registration bright blood image, and fig. 2 is a schematic diagram of the gray scale linear transformation and parameter setting provided by the embodiment of the present invention. By using the gray scale linear transformation shown in fig. 2, the smaller gray scale value change interval in the original post-registration bright blood image f can be expanded to the larger gray scale value change interval in the new post-registration bright blood image f1 (contrast enhanced bright blood image), the image gray scale range is adjusted, and the purpose of improving the contrast of the post-registration bright blood image is achieved. As the pixel range of the medical image is large and may be-1000- +1000, the pixel range can be normalized to 0-255 through the step, so that the pixel range is in accordance with the general image processing, and the subsequent processing can be facilitated. Through this step, for K registered bright blood images, corresponding K contrast enhanced bright blood images can be obtained.

The specific process of the gray scale linear transformation can be referred to in the related art, and is not described in detail herein.

S32, establishing a blood vessel simulation three-dimensional model by using K contrast enhanced bright blood images;

it can be understood that the bright blood images after registration are two-dimensional images, and although the registration is performed and the corresponding enhanced black blood images are in the same coordinate system, the difficulty of observation by a doctor is reduced, the two-dimensional images have limitations, and the doctor needs to combine a plurality of two-dimensional images to imagine the specific form of the blood vessel, so that the doctor cannot simply, quickly and intuitively obtain the whole state of the blood vessel in clinic.

After the inventors have considered, it is desirable to simulate the morphology of a blood vessel by performing three-dimensional reconstruction on the registered bright blood image, expressing blood information as a three-dimensional structure, and creating a three-dimensional model of blood. The process of obtaining a three-dimensional model with a stereoscopic effect from a two-dimensional image by interpolation is called three-dimensional reconstruction. Current three-dimensional reconstruction techniques include a Marching Cubes (MC) method, a Maximum Intensity Projection (MIP) method, a surface shading cover method (SSD), a Volume Roaming Technique (VRT), a curved surface reconstruction method (CPR), a virtual endoscopy technique (VE), and the like. The embodiment of the invention can adopt any three-dimensional reconstruction method to establish a three-dimensional model related to blood. The three-dimensional model can preliminarily simulate the three-dimensional blood vessel form and visually display the trend of the blood vessel, the focus area and the like.

In an alternative embodiment, S32 may include S321 to S323:

s321, acquiring first three-dimensional volume data formed by K contrast enhanced bright blood images;

it will be understood by those skilled in the art that the K contrast enhanced bright blood images are actually stacked as a three-dimensional cube of data. For the sake of convenience of distinction, this is named as first three-dimensional volume data in the embodiment of the present invention.

S322, calculating a first threshold corresponding to second centered three-dimensional volume data in the first three-dimensional volume data by using a preset image binarization method;

the preset image binarization method, namely the binarization processing of the image, can set the gray scale of the points on the image to be 0 or 255, namely, the whole image can show obvious black and white effect. That is, a gray scale image with 256 brightness levels is selected by a proper threshold value to obtain a binary image which can still reflect the whole and local features of the image. According to the embodiment of the invention, the blood information in the contrast enhanced bright blood image can be highlighted as white and the irrelevant information can be displayed as black through a preset image binarization method, so that the purpose of enhancing the contrast is realized. The preset image binarization method in the embodiment of the invention may include a maximum inter-class variance method OTSU, kittle, and the like. In an optional embodiment, the preset image binarization method is an inter-class maximum variance method (OTSU for short).

Specifically, the step may obtain, as the first threshold, one threshold corresponding to a plurality of contrast enhanced bright blood images in one small cube (referred to as second three-dimensional volume data) located near the middle portion among the large three-dimensional cubes of the first three-dimensional volume data by using a preset image binarization method.

Since the blood information is substantially concentrated in the middle of the image in the contrast enhanced image, the first threshold value is determined by selecting the small cube data (second three-dimensional volume data) centered in the first three-dimensional volume data, so that the calculation amount of the threshold value can be reduced, the calculation speed can be increased, and the first threshold value is accurately applied to all the blood information in the first three-dimensional volume data.

For the size of the second three-dimensional volume data, the size of the second three-dimensional volume data may be determined by first determining a central point of the first three-dimensional volume data and then extending in six directions corresponding to the cube with a preset side length, where the preset side length may be determined according to an empirical value, such as 1/4 that is the side length of the cube of the first three-dimensional volume data.

And S323, processing the first three-dimensional volume data by using the moving cube method by taking the first threshold as an input threshold of the moving cube method to obtain the blood vessel simulation three-dimensional model.

As mentioned above, the moving cube method (MC for short) is a three-dimensional reconstruction method, which can process the first three-dimensional volume data according to a given input threshold to directly obtain a three-dimensional model of blood, i.e. a three-dimensional model of blood vessel simulation.

Compared with other surface drawing algorithms, the method for moving the cube has the advantage of good grid generation quality. For a specific processing procedure of the first three-dimensional volume data by the moving cube method, please refer to related prior art, which is not described herein.

It can be understood that the blood vessel simulation three-dimensional model in the embodiment of the invention simulates the blood vessel form by using the blood information, so that the whole state of the blood vessel can be simply, conveniently, quickly and intuitively obtained clinically, and meanwhile, a doctor can obtain more accurate blood vessel information by combining the two-dimensional and bright blood images and the enhanced black blood image.

In an optional embodiment, after S3, the method may further include:

displaying the blood vessel simulation three-dimensional model, specifically, displaying the blood vessel simulation three-dimensional model on a display screen of a computer and other equipment so as to facilitate observation by a doctor; and, it is reasonable that it can be displayed simultaneously with the bright blood image and the enhanced black blood image.

S4, segmenting each section of blood vessel in the blood vessel simulation three-dimensional model from three preset positions to obtain two-dimensional sectional diagrams of each position;

in this step, the blood vessels in the blood vessel simulation three-dimensional model may be divided, and for each section of blood vessel, the blood vessel may be divided from three preset positions to obtain two-dimensional sectional views in each position.

Wherein, three preset positions include: axial, coronal, and sagittal.

The segmentation of a certain orientation is performed on the blood vessel simulation three-dimensional model to obtain a two-dimensional sectional view of the orientation, which can be implemented by adopting the prior art and is not described herein again.

S5, carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of each direction, and recording the target corrosion times when the blood vessel is corroded to a single pixel;

the corrosion operation is one of morphological operations, and the basic idea of the morphological operation is to extract image data interested by a user by using a structural element in an original image, remove irrelevant information, retain the essential characteristics of an interested region, generally apply to a binary image, generally apply to extracting a connected region or eliminating noise and the like, and have wide application in image processing.

The corrosion operation can eliminate the edge data of the object, the corroded object has a smaller area than the original area and even can completely disappear, and the corrosion can also break some small and long communication areas. The etching operation can be recorded as A theta B and defined as

Wherein B is a structural element and A is an original drawing.

When the blood vessel is thick, a plurality of corrosion operations can be carried out, and when the blood vessel is thin, only a few corrosion operations can be carried out. It will be appreciated by those skilled in the art that the blood vessel erodes to a single pixel, i.e., to the thinnest state, which may be a point or a line. The specific process of the etching operation can be referred to the related art, and is not described herein.

In step S5, performing erosion operation on the blood vessel in the axial two-dimensional sectional view, and recording the target erosion times n corresponding to the erosion of the blood vessel in the axial two-dimensional sectional view to a single pixel₁(ii) a Carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of the coronal position, and recording the corresponding target corrosion times n when the blood vessel in the two-dimensional sectional diagram of the azimuth corrodes to a single pixel₂(ii) a Carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of the sagittal position, and recording the corresponding target corrosion times n when the blood vessel in the two-dimensional sectional diagram of the azimuth corrodes to a single pixel₃；

Namely, the target corrosion times corresponding to the three orientations respectively comprise: target corrosion times n corresponding to axial position₁Target erosion number n corresponding to crown position₂Number of target erosion times n corresponding to sagittal position₃。

S6, obtaining the value of the target parameter representing the stenosis degree of the section of the blood vessel according to the target corrosion times of the section of the blood vessel corresponding to the three directions respectively;

in an alternative embodiment, the target parameter includes stenosis rate and/or flatness; those skilled in the art will appreciate that both of these parameters may be indicative of the degree of vascular stenosis.

When the target parameter includes a stenosis rate, S6 may include:

according to n₁、n₂、n₃Obtaining the value of the stenosis rate of the section of blood vessel by using a stenosis rate formula of the blood vessel; wherein, the stenosis rate formula is:

wherein, the resolution is the resolution of each azimuth two-dimensional sectional image (the resolution of the three azimuth two-dimensional sectional images is the same), and the smaller the numerical value of the stenosis rate is, the narrower the blood vessel is.

When the target parameter includes flatness, S6 may include:

according to n₁、n₂、n₃Obtaining the value of the flatness of the section of the blood vessel by using a blood vessel flatness formula; wherein, the flatness formula is as follows:

a larger value of the degree of flattening indicates a narrower vessel.

And S7, marking the blood vessel simulation three-dimensional model by using the numerical value of the target parameter of each section of blood vessel to obtain the simulated three-dimensional blood vessel stenosis analysis model.

Through the steps, the numerical value of the target parameter of each section of blood vessel can be obtained, and then the numerical values of each section of blood vessel can be marked on the blood vessel simulation three-dimensional model to obtain the simulation three-dimensional blood vessel stenosis analysis model. The numerical value of the target parameter of each point is embedded into the simulated three-dimensional vascular stenosis analysis model, so that the numerical value of the target parameter of each point can be extracted and displayed when needed, a doctor can conveniently obtain the data of the vascular stenosis degree of each position in time when observing the overall three-dimensional vascular state, for example, when the simulated three-dimensional vascular stenosis analysis model is displayed on a display screen of a computer, the numerical value of the stenosis rate and/or the flatness of the mouse position point can be displayed in a blank area of the simulated three-dimensional vascular stenosis analysis model.

In an alternative embodiment, S7 may include:

and marking the blood vessel simulation three-dimensional model by using the numerical values of the target parameters of each section of blood vessel and adopting the color corresponding to each numerical value to obtain the simulated three-dimensional blood vessel stenosis analysis model.

For convenience of visual display, different numerical values can be marked on the blood vessel simulation three-dimensional model by different colors to obtain a simulated three-dimensional blood vessel stenosis analysis model, for example, multiple colors from light to dark can be correspondingly marked for stenosis rate numerical values from small to large, and for flatness numerical values, because the numerical values are fewer and only 2 numerical values are possible, two colors which are distinguished from the stenosis rate can be correspondingly marked. The narrowing degree of the blood vessel can be more intuitively shown by adopting the color display of different tones, so that the attention of a doctor can be attracted.

In a preferred embodiment, the stenosis rate value can be marked by colors corresponding to different values on one blood vessel simulation three-dimensional model, and the flatness value can be marked by colors corresponding to different values on the other blood vessel simulation three-dimensional model, so that a doctor can observe the stenosis rate condition and the flatness condition respectively.

Furthermore, since doctors are used to observe the two-dimensional medical images of the tangent plane, the embodiment of the invention can provide the two-dimensional tangent plane images corresponding to all directions while analyzing the stenosis of the blood vessel, i.e. the images of the coronal plane, the sagittal plane and the axial plane of the current point corresponding to each point in the simulated three-dimensional stenosis analysis model are displayed together. When the simulated three-dimensional vascular stenosis analysis model is displayed, the functions of measuring distance of two points and measuring angles of three points can be realized by using points with three colors, the three points are displayed on the left lower side of the display screen, and the volume size of the currently selected model is displayed on the right lower side of the display screen. So that the doctor can obtain more detailed data of the intracranial blood vessel.

The following describes an implementation process and an implementation effect of the method for establishing the simulated three-dimensional vascular stenosis analysis model provided by the embodiment of the invention by taking an intracranial blood vessel as an example. The implementation process can comprise the following steps:

acquiring a bright blood image group and an enhanced black blood image group of a blood vessel part;

secondly, aiming at each bright blood image in the bright blood image group, carrying out image registration by using a registration method based on mutual information and an image pyramid by taking a corresponding enhanced black blood image in the enhanced black blood image group as a reference to obtain a registered bright blood image group comprising K registered bright blood images;

the step may include:

preprocessing each bright blood image and the corresponding enhanced black blood image to obtain a first bright blood image and a first black blood image; the pretreatment process can be divided into two main steps:

(1) pre-registration:

because the intracranial blood vessel can be regarded as a rigid body, the rigid body transformation is selected as a coordinate transformation method in the step. For a specific pre-alignment process, see step S211, which is not described herein again.

Referring to fig. 3, fig. 3 is a schematic diagram of coordinate transformation of an intracranial vascular magnetic resonance image according to an embodiment of the present invention, where the first line is an enhanced black blood image and a bright blood image, respectively, and the second line is an enhanced black blood image and a bright blood image after coordinate transformation, respectively.

The embodiment of the invention carries out simulation experiment on the image interpolation method of the bright blood image, reduces the original image by 50%, then obtains an effect image with the same size as the original image by using different interpolation algorithms, and compares the effect image with the original image. The data shown in table 1 is the average value of the results of repeating interpolation operation for 100 times, and 5 evaluation indexes, namely root mean square error RMSE, peak signal-to-noise ratio PSNR, normalized cross-correlation coefficient NCC, normalized mutual information NMI and Time consumption Time, are set in the experiment, wherein the smaller the RMSE, the more accurate the registration, and the higher the PSNR, NCC and NMI values, the more accurate the registration. From the whole experimental data, the precision of bicubic interpolation is obviously better than that of nearest neighbor interpolation and bilinear interpolation, although the interpolation time of bicubic interpolation is slower than that of the former two methods, the interpolation operation of 100 times is only 0.1 second more than that of the fastest nearest neighbor interpolation, namely, each operation is only 0.001 second slower. Therefore, in a trade-off, embodiments of the present invention employ bicubic interpolation with higher image quality.

TABLE 1 analysis of image interpolation results

In the embodiment of the invention, aiming at intracranial blood vessels, the intracranial blood vessels can be regarded as a rigid body, hardly deform, and organs such as heart or lung change along with the movement of human breath and the like, so that compared with other types of blood vessels, the intracranial blood vessels are really more suitable for selecting mutual information as similarity measurement to achieve a more accurate registration effect.

In an experiment, a gradient descent optimizer and two search strategies (1+1) -ES are used for respectively registering 160 bright blood images and 160 enhanced black blood images of corresponding scanning layers, wherein the enhanced black blood images are reference images, the bright blood images are floating images, the registration result is shown in FIG. 4, and FIG. 4 is the registration comparison result of the two search strategies according to the embodiment of the invention; the left image in fig. 4 is the result of the pair-wise display of two images without using optimizer registration, the middle image is the result of the pair-wise display of images using gradient descent optimizer registration, and the right image is the result of the pair-wise display of images using (1+1) -ES optimizer registration. The right image display adopts a montage effect, and a black blood image and a bright blood image are enhanced by using pseudo-color transparency processing, purple is the enhanced black blood image, and green is the bright blood image (the image in the figure is the image after gray processing of the original image, and the color is not shown). As can be seen from the figure, in the images which are not registered by using the optimizer, the enhanced black blood image and the bright blood image are not overlapped and have more shadows; when the gradient descent optimizer is used for registering images, although the registration effect is better than that of a left image, the obvious misalignment phenomenon still occurs at the gray brain matter; in the image using the (1+1) -ES optimizer, the registration result is accurate, and the misaligned shadow part in the image completely disappears. The data shown in table 2 are 3 evaluation indexes of the registration result, namely normalized mutual information NMI, normalized cross correlation coefficient NCC and algorithm Time consumption Time. From the experimental result graph, the registration image effect of (1+1) -ES is displayed more clearly and is better than that of a gradient descent optimizer; from experimental data, the three evaluation indexes all represent good performance of the (1+1) -ES optimizer, so the embodiment of the invention uses (1+1) -ES as a search strategy.

TABLE 2 analysis of results under different search strategies

^aThe value in (1) is based on the mean value of the evaluation indexes of the registration of 160 bright blood images and 160 enhanced black blood images +/-mean square error

Referring to fig. 5, fig. 5 is a diagram illustrating the result of pre-registering the intracranial vascular magnetic resonance image according to the embodiment of the invention. The left image is a pre-registered first bright blood image, wherein the interpolation method adopts bicubic interpolation; the middle image is an enhanced black blood image, both images are coronal planes, the right image is an effect image obtained by directly superimposing the two images, and the right image shows that although the bright blood image and the enhanced black blood image under the current imaging layer can be observed under the same coronal plane after pre-registration, the bright blood image and the enhanced black blood image are still misaligned, so that subsequent image fine registration is required.

(2) Unified scanning area:

the same area content as the scanning range of the first bright blood image is extracted from the corresponding enhanced black blood image, and a first black blood image is formed. For details, refer to step S212, which is not described herein.

Referring to fig. 6, fig. 6 is a schematic diagram of a region to be registered of an intracranial vascular magnetic resonance image according to an embodiment of the present invention, where the left image is a first bright blood image after pre-registration, the right image is an enhanced black blood image, and the box is a region to be extracted in the enhanced black blood image. The region contains the common scanning range of a bright blood sequence and a black blood sequence in an intracranial vascular magnetic resonance image, and useful information can be focused more quickly by determining the region to be extracted.

(II) after the preprocessing, performing image registration on the first bright blood image and the first black blood image by using a registration method based on mutual information and an image pyramid, as described in the foregoing in relation to steps S22-S27. The method specifically comprises the following steps:

obtaining a bright blood Gaussian pyramid from the first bright blood image based on downsampling processing, and obtaining a black blood Gaussian pyramid from the first black blood image;

the bright blood Gaussian pyramid and the black blood Gaussian pyramid comprise 4 images with resolution becoming smaller from bottom to top in sequence; the generation process of the bright blood gaussian pyramid and the black blood gaussian pyramid is referred to in the foregoing S22, and is not described herein again. As shown in fig. 7, fig. 7 is a bright blood gaussian pyramid and a black blood gaussian pyramid of an intracranial vascular magnetic resonance image according to an embodiment of the invention.

These resolutions are gradually reduced, and the images from the same image combined at different resolutions are arranged to resemble a pyramid, and are therefore referred to as an image pyramid, where the highest resolution image is located at the bottom of the pyramid and the lowest resolution image is located at the top of the pyramid. In the aspect of image information processing, the multi-resolution images can more easily acquire the essential characteristics of the images compared with the traditional single-resolution images.

Based on the upsampling processing, utilizing the bright blood Gaussian pyramid to obtain a bright blood Laplacian pyramid, and utilizing the black blood Gaussian pyramid to obtain a black blood Laplacian pyramid;

the bright blood Laplacian pyramid and the black blood Laplacian pyramid comprise 3 images of which the resolutions are sequentially reduced from bottom to top; the generation process of the bright blood laplacian pyramid and the black blood laplacian pyramid is referred to as S23, and is not described herein again. As shown in fig. 8, fig. 8 is a bright blood laplacian pyramid and a black blood laplacian pyramid of an intracranial vascular magnetic resonance image according to an embodiment of the present invention. The image display uses gamma correction to achieve a clearer effect, and the gamma value is 0.5.

Registering images of corresponding layers in the bright blood Laplacian pyramid and the black blood Laplacian pyramid to obtain a registered bright blood Laplacian pyramid;

in the step, the image in the black blood laplacian pyramid is used as a reference image, the image in the bright blood laplacian pyramid is used as a floating image, image registration is respectively carried out on the enhanced black blood image of each layer and the bright blood image of the corresponding layer, mutual information is used as similarity measurement of the two images, a (1+1) -ES is selected as a search strategy, after coordinate transformation is carried out on each image registration, the mutual information of the two images is circularly and iteratively calculated until the mutual information reaches the maximum, and the image registration is completed. See the foregoing S24 for details, which are not described herein.

As shown in fig. 9, fig. 9 is a registration result of laplacian pyramid images of an intracranial vascular magnetic resonance image according to an embodiment of the present invention, where the left image is a reference image in a black blood laplacian pyramid, the middle image is a registered image in a bright blood laplacian pyramid, the right image is an effect image obtained by directly superimposing the left and middle images, and the superimposed image displays a montage effect, and the black blood image and the bright blood image are enhanced by using pseudo-color transparency processing, where purple is the enhanced black blood laplacian pyramid image, and green is the bright blood laplacian pyramid image (the image is an image of an original image subjected to gray processing, and the color is not shown).

Fourthly, registering the images of each layer in the bright blood Gaussian pyramid and the black blood Gaussian pyramid from top to bottom by using the registered bright blood Laplacian pyramid as superposition information to obtain a registered bright blood Gaussian pyramid;

referring to the foregoing step S25, the specific steps of mutual information based gaussian pyramid image registration are shown in fig. 10, and fig. 10 is a schematic diagram of mutual information based gaussian pyramid image registration steps of an intracranial vascular magnetic resonance image according to an embodiment of the present invention. Firstly, registering the low-resolution black blood Gaussian image of the top layer and the low-resolution bright blood Gaussian image of the top layer based on mutual information; then, performing up-sampling operation on the registered bright blood Gaussian image, and adding the up-sampled bright blood Gaussian image and the bright blood Laplacian image of the corresponding layer which retains high-frequency information and is registered according to the operation to be used as a next layer of bright blood Gaussian image; and then, taking the bright blood Gaussian image obtained by the operation as an input image, registering the input image with the black blood Gaussian image of the corresponding layer, and repeating the operation until the high-resolution registration of the bottom layer Gaussian pyramid image is completed.

In the registration of Gaussian pyramid images based on mutual information, the registration of each layer of bright blood Gaussian image and black blood Gaussian image is carried out by taking normalized mutual information as similarity measurement, and the NMI of the two images is calculated through loop iteration until the NMI reaches the maximum. Fig. 11 is normalized mutual information under different iteration times according to the embodiment of the present invention, and when the registration of the first-layer image, that is, the bottom-layer image with the highest resolution in the gaussian pyramid reaches the maximum NMI value and the data is stable, the iteration is stopped.

In addition, in order to verify the effectiveness and the practicability of the image registration method based on the mutual information and the image pyramid, a comparison experiment is also carried out, and intracranial vascular magnetic resonance images of five patients are used together, wherein the enhanced black blood image and the bright blood image of the patient A, B, C, D are 160 respectively, and the enhanced black blood image and the bright blood image of the patient E are 150 respectively; meanwhile, an algorithm which only uses DICOM image orientation label information for registration and a registration algorithm based on mutual information measurement are selected and compared with the registration method based on mutual information and an image pyramid, wherein the algorithm based on mutual information measurement is to search the optimal transformation between a reference image and a floating image by a multi-parameter optimization method, so that the mutual information value of the two images is the maximum, and the image pyramid algorithm is not used.

The experimental platform was Matlab R2016 b. And combining qualitative analysis and quantitative analysis according to the image registration result of the experiment. In the aspect of qualitative analysis, because a large gray difference exists between the multi-modal medical images, and a difference image obtained by subtracting the registration image from the reference image cannot effectively reflect the registration result of the multi-modal medical images, the embodiment of the present invention overlaps the registration image with the reference image to obtain a color overlapped image that can reflect the alignment degree of the registration image and the reference image, and performs qualitative analysis on the registration effect of the multi-modal registration algorithm through the color overlapped image, fig. 12 shows the registration result of the multi-modal intracranial vascular magnetic resonance images, and fig. 12 shows the registration result of the intracranial vascular magnetic resonance images of multiple registration methods. Wherein, (a) is a reference image; (b) is a floating image; (c) is an overlay image based on image orientation label information; (d) is an overlay image based on a mutual information metric; (e) the invention discloses a superposed image of an image registration method based on mutual information and an image pyramid. The figures are gray scale images of the original image, not shown in color. In the aspect of quantitative analysis, since the root mean square error RMSE and the peak signal-to-noise ratio PSNR of the evaluation indexes are not suitable for evaluating images with large gray scale changes, in order to better evaluate the registration result of the multi-modal medical image, the normalized cross-correlation coefficient NCC is adopted, the normalized mutual information NMI is used as the evaluation index, when the values of the normalized cross-correlation coefficient NCC and the normalized mutual information NMI are larger, the higher the image registration accuracy is, and table 3 shows the evaluation index result analysis of different registration algorithms.

TABLE 3 analysis of the results of different registration methods

^aThe value in (1) is the mean value of the evaluation index +/-mean square error based on the registration of a plurality of images of a patient

And (3) qualitative analysis: as is apparent from the overlaid images of fig. 12, the mutual information metric-based method has a large registration shift, and the analysis reason may be that it is easy to fall into a local optimum value rather than a global optimum value only using the mutual information metric-based method; the registration effect based on the image orientation label information is not good enough, and the images are partially not overlapped; the registration method based on mutual information and the image pyramid has good image effect, the image display is clearer, and the images are almost completely overlapped.

Quantitative analysis: as can be seen from table 3, from the two evaluation indexes NCC and NMI, compared with the registration algorithm using only the orientation tag information of the DICOM image and the registration algorithm based on the mutual information metric, the registration method based on the mutual information and the image pyramid provided by the embodiment of the present invention has improved registration accuracy, and can well process the registration of the multi-modal intracranial vascular magnetic resonance image.

Obtaining a registered bright blood image corresponding to the bright blood image based on the registered bright blood Gaussian pyramid;

and acquiring a bottom layer image in the registered bright blood Gaussian pyramid as a registered bright blood image, and taking the registered bright blood image and the corresponding enhanced black blood image as a registered image pair.

And sixthly, obtaining a group of registered bright blood images by the registered bright blood images corresponding to the K bright blood images respectively.

In the embodiment of the invention, an image registration method based on mutual information and an image pyramid is used for registering the magnetic resonance bright blood image and the enhanced black blood image, the correlation of gray information is considered in the registration process, the registration efficiency is improved by using the Gaussian pyramid, the image is from low resolution to high resolution, and the registration accuracy is improved layer by layer.

Step three, establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group;

and aiming at each registered bright blood image, improving the contrast of the registered bright blood image by utilizing gray scale linear transformation to obtain a contrast enhanced bright blood image. As shown in fig. 13, fig. 13 is a graph of the result of the gray scale linear transformation according to the embodiment of the present invention. The left image is the bright blood image after registration, the right image is the result image after gray scale linear transformation, and it can be seen that the contrast of the blood part in the right image is obviously enhanced compared with the surrounding pixels.

And establishing a blood vessel simulation three-dimensional model aiming at the intracranial blood vessel by using the K contrast enhanced bright blood images. Specific results are as follows, referring to fig. 14, fig. 14 is a diagram showing the effect of a blood vessel simulation three-dimensional model of an intracranial blood vessel according to an embodiment of the invention. And the three-dimensional model of the blood vessel simulation can realize basic functions of rotation, magnification, reduction and the like when being displayed, thereby assisting a doctor to position a focus area and making more accurate judgment.

Step four, segmenting each section of blood vessel in the blood vessel simulation three-dimensional model from three preset directions to obtain a two-dimensional sectional view of each direction;

step five, carrying out corrosion operation on the blood vessel in the two-dimensional sectional diagram of each direction, and recording the target corrosion times when the blood vessel is corroded to a single pixel;

step six, obtaining a numerical value of a target parameter representing the stenosis degree of the section of the blood vessel according to target corrosion times of the section of the blood vessel in three directions respectively;

and seventhly, marking the blood vessel simulation three-dimensional model by using the numerical values of the target parameters of each section of blood vessel and adopting colors corresponding to the numerical values to obtain the simulated three-dimensional blood vessel stenosis analysis model.

For the third to seventh steps, refer to steps S3 to S7, which are not described herein again.

Referring to fig. 15, fig. 15 is a graph showing the effect of the simulated three-dimensional vascular stenosis analysis model of the intracranial blood vessel according to the embodiment of the invention. Wherein the left graph is the stenosis rate marking effect and the right graph is the flatness marking effect. In practice, different colors are displayed on the model, so that the degree of narrowing can be distinguished, for example, a thinner part of a blood vessel is warm, the narrowest part is red, a thicker part of the blood vessel is cool, the thickest part is green, and the like, a white arrow indicates abrupt narrowing of the intracranial blood vessel, and color display with different colors can more intuitively show the narrowing of the blood vessel. In the figure are the effects of the grey scale processing, the colours not being shown.

Because doctors are used to observe two-dimensional medical images of the tangent plane, the embodiment of the invention can provide a simulated three-dimensional vascular stenosis analysis model and simultaneously provide two-dimensional tangent plane images of three directions, namely images of a coronal plane, a sagittal plane and an axial plane of a current point corresponding to each point in the simulated three-dimensional vascular stenosis analysis model can be displayed. Referring to fig. 16, fig. 16 is a simulated three-dimensional angiostenosis analysis model of intracranial vessels and a sectional view display effect diagram according to an embodiment of the invention. In fig. 16, there may be a blood vessel narrowing at the warm tone of the blood vessel, there is no obvious blood vessel narrowing at the cold tone, and the three two-dimensional images on the right side of the image are respectively imaged from top to bottom on the axial plane, the sagittal plane and the coronal plane where the current point is located; when the simulated three-dimensional vascular stenosis analysis model is displayed, the functions of measuring the distance by two points and measuring the angle by three points can be realized by using the points with three colors such as red, green and blue, the three points are displayed on the left lower side of the display screen, and the volume size of the currently selected model is displayed on the right lower side of the display screen. So that the doctor can obtain more detailed data of the intracranial blood vessel.

In the scheme provided by the embodiment of the invention, aiming at intracranial blood vessels, firstly, a registration method based on mutual information and an image pyramid is adopted for image registration of bright blood images and enhanced black blood images obtained by scanning through a magnetic resonance blood vessel imaging technology, so that the registration efficiency can be improved, and the registration accuracy of the images can be improved layer by layer from low resolution to high resolution. The bright blood image and the enhanced black blood image can be unified under the same coordinate system through the image registration, so that subsequent unified observation is facilitated. And thirdly, establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group. The blood vessel simulation three-dimensional model simulates the shape of a blood vessel by utilizing blood information, and realizes the three-dimensional visualization of the blood vessel. And finally, carrying out corrosion operation by using section diagrams in three directions to obtain values of target parameters representing the degree of the angiostenosis, and marking different values on the blood vessel simulation three-dimensional model by using different colors to obtain the simulated three-dimensional angiostenosis analysis model. The simulated three-dimensional blood vessel stenosis analysis model does not need a doctor to restore blood vessel tissue structures, disease characteristics and the like through imagination, can facilitate the doctor to observe and analyze blood vessel morphological characteristics from any interested angle and level, can provide visual blood vessel three-dimensional space information, is convenient for visual observation, and is convenient for positioning and displaying a narrow focus area. The analytical data about the stenosis degree of the blood vessel can be intuitively and quickly obtained clinically.

Note: the patient experimental data in the embodiment of the invention are all from people hospitals in Shaanxi province, and the images can be used for general scientific research.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for establishing a simulated three-dimensional vascular stenosis analysis model is characterized by comprising the following steps:

2. The method according to claim 1, wherein the performing image registration for each of the group of bright blood images by using a registration method based on mutual information and an image pyramid with reference to a corresponding enhanced black blood image in the group of enhanced black blood images to obtain a group of registered bright blood images including K registered bright blood images comprises:

preprocessing each bright blood image and the corresponding enhanced black blood image to obtain a first bright blood image and a first black blood image;

based on downsampling processing, obtaining a bright blood Gaussian pyramid from the first bright blood image, and obtaining a black blood Gaussian pyramid from the first black blood image; the bright blood Gaussian pyramid and the black blood Gaussian pyramid comprise m images with resolution becoming smaller in sequence from bottom to top; m is a natural number greater than 3;

based on the upsampling processing, obtaining a bright blood Laplacian pyramid by using the bright blood Gaussian pyramid, and obtaining a black blood Laplacian pyramid by using the black blood Gaussian pyramid; the bright blood Laplacian pyramid and the black blood Laplacian pyramid comprise m-1 images with resolution which is sequentially reduced from bottom to top;

registering the images of all layers in the bright blood Gaussian pyramid and the black blood Gaussian pyramid from top to bottom by using the registered bright blood Laplacian pyramid as superposition information to obtain a registered bright blood Gaussian pyramid;

and obtaining a group of registered bright blood images by the registered bright blood images corresponding to the K bright blood images respectively.

3. The method of claim 2, wherein the pre-processing each bright blood image and the corresponding enhanced black blood image to obtain a first bright blood image and a first black blood image comprises:

for each bright blood image, taking the corresponding enhanced black blood image as a reference, performing coordinate transformation and image interpolation on the bright blood image, and obtaining a pre-registered first bright blood image by using a similarity measurement based on mutual information and a preset search strategy;

and extracting the same area content as the scanning range of the first bright blood image from the corresponding enhanced black blood image to form a first black blood image.

4. The method of claim 3, wherein the registering images of corresponding layers of the Laplacian pyramid with bright blood and the Laplacian pyramid with black blood to obtain a registered Laplacian pyramid with bright blood comprises:

aiming at each layer of the bright blood Laplacian pyramid and the black blood Laplacian pyramid, taking a corresponding black blood Laplacian image of the layer as a reference image, taking a corresponding bright blood Laplacian image of the layer as a floating image, and realizing image registration by using a similarity measure based on mutual information and a preset search strategy to obtain a registered bright blood Laplacian image of the layer;

5. The method according to claim 4, wherein the registering the images of the respective layers in the blood-brightening Gaussian pyramid and the black blood Gaussian pyramid from top to bottom by using the registered blood-brightening Gaussian pyramid as overlay information to obtain the registered blood-brightening Gaussian pyramid comprises:

performing upsampling operation on the registered jth layer of bright blood Gaussian image, adding the upsampled operation to the registered corresponding layer of bright blood Laplacian image, and replacing the jth +1 layer of bright blood Gaussian image in the bright blood Gaussian pyramid by using the added image;

taking the black blood Gaussian image of the j +1 th layer as a reference image, taking the replaced bright blood Gaussian image of the j +1 th layer as a floating image, and using a preset similarity measure and a preset search strategy to realize image registration to obtain a registered bright blood Gaussian image of the j +1 th layer;

wherein j is 1, 2, …, m-1, the black blood gaussian image is an image in the black blood gaussian pyramid, and the bright blood gaussian image is an image in the bright blood gaussian pyramid.

6. The method according to claim 1 or 5, wherein the establishing of the blood vessel simulation three-dimensional model by using the registered bright blood image group comprises:

improving the contrast of each of the registered bright blood images to obtain K contrast-enhanced bright blood images;

and establishing a blood vessel simulation three-dimensional model by using the K contrast enhanced bright blood images.

7. The method of claim 6, wherein said creating a three-dimensional model of a blood vessel simulation using said K contrast-enhanced bright blood images comprises:

acquiring first three-dimensional volume data formed by the K contrast enhanced bright blood images;

calculating a first threshold corresponding to second centered three-dimensional volume data in the first three-dimensional volume data by using a preset image binarization method;

and taking the first threshold as an input threshold of a moving cube method, and processing the first three-dimensional volume data by using the moving cube method to obtain a blood vessel simulation three-dimensional model.

8. The method of claim 1 or 7, wherein the preset three orientations comprise: axial, coronal, and sagittal;

the target corrosion times respectively corresponding to the three orientations comprise:

target corrosion times n corresponding to axial position₁Target erosion number n corresponding to crown position₂Number of target erosion times n corresponding to sagittal position₃。

9. The method of claim 8, wherein the target parameters include stenosis rate and/or flatness;

when the target parameter includes a stenosis rate, obtaining a numerical value of the target parameter representing the stenosis degree of the segment of the blood vessel according to the target erosion times corresponding to the segment of the blood vessel in the three directions respectively, including:

according to n₁、n₂、n₃Obtaining the value of the stenosis rate of the section of blood vessel by using a stenosis rate formula of the blood vessel; wherein the stenosis rate formula is:

when the target parameter includes the flatness, obtaining the value of the target parameter representing the stenosis degree of the section of the blood vessel according to the target erosion times of the section of the blood vessel respectively corresponding to the three positions, including:

according to n₁、n₂、n₃Obtaining the value of the flatness of the section of the blood vessel by using a blood vessel flatness formula; wherein the flatness formula is:

10. the method according to claim 1 or 9, wherein the labeling of the vessel simulated three-dimensional model with the values of the target parameters of each segment of the vessel to obtain a simulated three-dimensional vessel stenosis analysis model comprises:

and marking the blood vessel simulation three-dimensional model by using the numerical values of the target parameters of each section of blood vessel and adopting the color corresponding to each numerical value to obtain a simulated three-dimensional blood vessel stenosis analysis model.