CN110415243B - Angiography image data processing method and image data processing device - Google Patents
Angiography image data processing method and image data processing device Download PDFInfo
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Abstract
The invention discloses an angiography image data processing method and an angiography image data processing device. Based on the processing of the angiography sequence image data, the vascular blood flow differential parameter is calculated from the processed image data, the differential analysis of the blood flow parameter in the whole filling process of the heart is carried out by utilizing the hemodynamics principle and the differential principle, the differential fraction of the whole filling process of the blood flow is calculated and obtained, and the differential fraction can be used for evaluating whether the blood flow is blocked or not and the degree of the blockage in the whole filling process, so that the stenosis degree of the blood vessel can be evaluated.
Description
Technical Field
The present invention relates to the field of medical image processing technologies, and in particular, to an angiographic image data processing method and an angiographic image data processing apparatus.
Background
Cardiovascular diseases are one of the major hazards of human health, wherein cardiovascular stenosis directly affects myocardial blood supply, and severe diseases endangering the life of patients, such as myocardial infarction, are caused.
The Fractional Flow Differential Coefficient (FFD) refers to the ratio of the blood Flow variation of a myocardial region provided by a blood vessel at each moment in the blood Flow filling process to the maximum blood Flow variation, can objectively reflect the blood Flow variation of the blood Flow at each moment in the whole filling process, represents that the blood Flow in the blood vessel is smooth and steady when the blood Flow variation is stable in the whole filling process, and represents that the blood Flow in the blood vessel is blocked when passing through the stenosis part when the blood Flow variation is obviously reduced and then increased within a certain period of time, and the Fractional Flow Differential Coefficient is a method for effectively evaluating the stenosis degree of the blood vessel and has an important guiding significance for the treatment strategy of coronary artery stenosis.
At present, the method mainly used for evaluating the stenosis degree of a blood vessel is to obtain the pressure difference ratio of the normal part and the stenosis part of the blood vessel through a pressure sensor of an interventional blood vessel to determine the fractional flow reserve of the blood vessel, such as an interventional catheter or a guide wire, and the method for obtaining the fractional flow reserve has the risk of injuring the blood vessel, has no prediction mechanism and has higher clinical cost; another evaluation method obtains fractional flow reserve by analyzing an angiographic sequence image and calculating a pressure difference between a normal part and a stenotic part of blood flow, and can obtain fractional flow reserve in a general case, but the fractional flow reserve of a complicated and tortuous multi-segment branched blood vessel lacks accuracy.
Disclosure of Invention
The invention aims to overcome the defects that the method for acquiring the fractional flow reserve by adopting the interventional blood vessel has the risk of injuring the blood vessel, has no prediction mechanism and higher clinical cost, and the method for analyzing the pressure difference by adopting an angiography sequence image cannot accurately acquire the fractional flow reserve of complex and tortuous multi-section branched blood vessels in the prior art, and provides an angiography image data processing method, an image data processing device, an image display method, a storage medium and equipment.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for processing data of an angiographic image,
extracting angiographic sequence image data within a predetermined time period, the sequence image data comprising a number of frames, the predetermined time period comprising at least one vessel filling cycle;
calculating the blood flow of the frame corresponding to the moment in the blood vessel filling process according to the pixel variation in the sequence images and the time interval of the frame;
and calculating the differential fraction of the blood flow at the corresponding moment relative to the time.
Preferably, the sequence of image data is noise-canceled and image-enhanced.
Preferably, the method of treatment,
comprising the step of processing angiographic image data into the sequence of image data;
the method comprises the steps of denoising, image enhancement and image identification on the sequence images, wherein the image identification is to distinguish blood vessels of each frame of image in the sequence images from the background;
a step of forming a new sequence of images of the blood vessel during the blood flow filling process from each frame of images in the sequence of images;
calculating the time interval between each frame of image in the new sequence of images, calculating the pixel variation of the cross section in a plurality of adjacent frames of blood vessel images according to a certain cross section specified in the new sequence of images, and calculating the blood flow of the cross section according to the pixel variation and the time interval;
comprising the step of calculating a differential fraction of blood flow at each instant of the vessel from said blood flow at each instant of the entire filling process of the vessel.
Further preferably, the processing method comprises the following steps:
A. angiography, acquiring a series of image data of blood vessels, converting the image data into a first sequence of images I according to a uniform formatK，M×NWherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
B. determining the near end and the far end of a blood vessel in the region of interest of the image, determining the near end of the blood vessel as a normal blood flow passing position and determining the far end of the blood vessel as a blood vessel narrow part according to the shape and the change condition of the blood vessel;
C. applying Laplace Transform (Laplace Transform) and/or applying Partial Differential Equation (PDE) -based nonlinear filtering method to the IK，M×NCarrying out noise elimination treatment;
D. using edge sharpening process and/or adaptive histogram equalization method to the IK，M×NCarrying out image enhancement processing to improve the identification degree of blood vessels;
E. scale Invariant Feature Transform (SIFT) pairsThe following formula IK，M×NCarrying out transformation and reduction of a plurality of scales;
F. to the I after reductionK，M×NCarrying out image graying, image enhancement and image identification processing on each frame of image to form a second sequence image V in the blood vessel blood flow filling processK，M×NWherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
G. calculating the time interval and the frame number of the contrast agent from the image frame at the near end of the blood vessel to the image frame at the far end of the blood vessel, and calculating the time interval T between each frame of image according to the time interval and the frame number of the sequence imageK；
H. At the VK，M×NCalculating the pixel variation of the cross section in several adjacent frames of the blood vessel image, and determining the cross section according to the pixel variation and the TKCalculating the blood flow of the cross-section over the time period;
I. calculating the blood flow differential at each moment, calculating the blood flow differential quantity at each moment according to the blood flow at each moment in the whole blood vessel filling process, and calculating the ratio of the blood flow differential quantity at each moment to the maximum blood flow differential quantity, namely calculating the blood flow differential fraction at each moment, so as to obtain the change process of the blood flow in the whole blood vessel filling process.
Further preferably, the edge sharpening process employs Gamma Correction (Gamma Correction).
Further preferably, after the step B is executed, the step I is firstly executedK，M×NAnd C, carrying out graying treatment and then executing the step C.
Further preferably, the step E comprises the steps of:
e1 for the IK，M×NPerforming convolution operation by using a Gaussian kernel, and forming images with different scale parameters according to different variances selected by the Gaussian kernel;
e2 for the IK，M×NEach frame of image is processed with several level transformation, each level transformation is extracted from the original image at intervalsTaking pixel points to form a new image, and generating a first conversion sequence image after the final level conversion;
e3, carrying out a plurality of level reduction on each frame of image in the first conversion sequence image, in each level conversion, sequentially extracting pixel points from the original image, forming a new image at intervals, carrying out linear interpolation on the pixel points which are not arranged in the new image and are obtained by adjacent pixel points, and finally generating a second conversion sequence image after the level reduction.
Further preferably, the linear Interpolation is performed by using a Bilinear Interpolation method (Bilinear Interpolation) for image restoration.
The present invention also provides an image data processing apparatus comprising:
the image receiving module is used for receiving a series of image data of the blood vessel and processing the image data into a sequence image;
the image processing module is used for denoising, image enhancement and image identification on the sequence images to form a new sequence image in the blood vessel blood flow filling process, calculating the blood flow of the cross section according to the time interval between each frame of image in the new sequence image and the pixel variation of a specified certain blood vessel cross section in a plurality of adjacent frames of blood vessel images to obtain the blood flow differential fraction of the blood vessel at each moment according to the blood flow;
and the human-computer interface module is used for displaying the sequence image and the blood flow differential fraction.
Preferably, the processing device further comprises an image storage module for storing the image data, the sequence images, the new sequence images and the blood flow differential score.
Preferably, the processing device further comprises a digital subtraction angiography machine for angiography acquiring a series of image data of the blood vessel and providing the image data to the image receiving module.
Further preferably, the image receiving module includes an image transmission interface, a cable for receiving an image, an image receiving interface, and an image converter, the image transmission interface is used for connecting the digital subtraction angiography machine, and the image converter is a processing unit that converts the image data transmitted by the digital subtraction angiography machine according to a specified data format.
Further preferably, the image converter is an image acquisition card.
Further preferably, the image storage module includes control logic, physical storage media, such as image storage software, memory, and a hard disk.
Further preferably, the human interface module comprises a display, a printer, a user interface, a mouse and a keyboard.
Further preferably, the image processing module comprises control logic, image processing logic, a physical processing unit, such as an image processing chip, and image processing software.
The present invention also provides an image display method, wherein the corresponding blood flow differential score range is represented by a transition color selected from red, orange, yellow, green, cyan, blue and purple according to the blood flow differential score obtained by the processing method, the blood flow differential score is greater than or equal to 0.00 and less than or equal to 1.00, the degree of the blood flow differential score being less than 0.70 is represented by red, and the degree of the blood flow differential score being close to 1.00 is represented by purple.
Preferably, the color is displayed on the blood vessel image or on the blood flow differential fraction value.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a processing method as set forth in any one of the above or implements a display method as set forth above.
The present invention also provides an electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the program in the memory to implement the processing method as described in any one of the above or to implement the display method as described above.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method is based on processing of angiography sequence image data, the differential parameters of blood flow of blood vessels are calculated from the processed image data, the differential analysis of the blood flow parameters in the whole filling process of the heart is carried out by utilizing the hemodynamic principle and the differential principle, the differential fraction of the whole filling process of the blood flow is calculated and obtained, and the corresponding fractional flow reserve can be obtained; due to the adoption of a differential mode corresponding to a time sequence and an image frame sequence, the degree of the hindered blood flow can be reflected on the whole by the change process of pixels in a sequence image along with time, the degree of the hindered blood flow of a certain section can be reflected locally, and even the blood flow reserve fraction at a complicated and tortuous multi-section blood vessel can be analyzed and obtained, so that the defects of wound risk and unpredictability caused by the adoption of an interventional blood vessel mode are overcome, and the defect that the process evaluation cannot be carried out by adopting other blood vessel image analysis methods is overcome; compared with other methods, the image processing method based on partial differential equation, adaptive histogram equalization, and scale invariant feature decomposition and restoration, which conforms to the nonlinear diffusion characteristics of contrast agent and blood flow, can effectively eliminate noise, enhance blood vessel images, and is easy to identify.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
Example 1
The invention discloses an angiography image data processing method, which comprises the following steps:
step one, angiography is carried out, and a series of angiography image data of blood vessels are obtained;
step two, a series of contrast imagesAccording to a uniform format, converting the image into a first sequence image IK，M×NWherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
the sequence of images is a series of images of contrast agent from the proximal end to the distal end of the vessel over a period of time, i.e. the entire filling process of the blood flow in the vessel, including the anatomically significant spatial and temporal relationship of the blood flow in the vessel;
step three, storing IK，M×N；
Step four, displaying IK，M×NDetermining the near end and the far end of a blood vessel in the region of interest of the image, determining the near end of the blood vessel as a normal blood flow passing position and determining the far end of the blood vessel as a blood vessel narrow part according to the shape and the change condition of the blood vessel;
step five, for IK，M×NCarrying out graying treatment;
if the image is an RGB color image, pair IK，M×NCarrying out gray processing to reduce the storage amount of image processing and improve the image processing rate, wherein the calculation formula is as follows:
I(i，j)＝0.299×I(i，j，r)+0.758×I(i，j，g)+0.114×I(i，j，b)
wherein r, g and b are RGB encoding values of image pixel points;
if the image is a gray image, skipping the fifth step after executing the fourth step, and executing the subsequent steps;
step six, pair IK，M×NCarrying out noise elimination treatment;
the laplacian transform is used to perform denoising processing, and in this embodiment, a 3-order laplacian is selected:
by using the Laplace operator for IK，M×NFiltering is carried out to eliminate high-frequency noise in the image, which is beneficial to improving the subsequent enhancement processing of the blood vessel edge, and the calculation formula is as follows:
wherein G is a gray value matrix composed of adjacent pixel points centered on the currently processed pixel point, Iin(I, j) and Iout(i, j) are the pixel points currently processed before and after calculation, respectively;
according to IK，M×NThe method comprises the following steps of (1) carrying out further denoising processing on an image by using a nonlinear filtering method based on a partial differential equation, wherein the nonlinear diffusion characteristic of the flowing of a contrast agent in a blood vessel is included, namely the flowing of the contrast agent from high concentration to low concentration along with the change of space and time is presented:
wherein the content of the first and second substances,
gradient of image pixel point, div is diffusion operator, fdiffuse(x) As a diffusion function, the intensity coefficient A is in the range of 0.01-1.0 in this embodiment, the time step t is in the range of 0.01-0.2 in this embodiment, and I is0(i, j) initializing the image;
wherein λ is1、λ2And omega is a threshold value of a gradient mode of an image pixel point, the value range is 0-10, the values are 3, 6 and 2 in the embodiment, the coefficients B and C are constants, the values are 4 and 2 in the embodiment, and the coefficients
In this example, the value is 0.2;
iteratively calculating I using partial differential equation based nonlinear diffusion filteringK，M×NIn the embodiment, the value range of the number of iterations is 10-30 times;
when the diffusion function fdiffuse(x) Gradient mode of pixel points along with image
When the diffusion is gradually increased while the diffusion is gradually decreased, the diffusion tends to be smooth;
when gradient mode of image pixel point
At [ lambda ]2-ω，λ2+ω]In time between, the diffusion function fdiffuse(x) If the value is negative, the sharpening enhancement processing of the blood vessel edge is realized;
when gradient mode of image pixel point
When the value is larger, the diffusion function fdiffuse(x) The value is reduced, so that the edge of the blood vessel is well reserved;
the processed image not only effectively inhibits noise, but also well retains the edge and detail characteristics of blood vessels;
step seven, for IK，M×NCarrying out image enhancement processing;
edge sharpening is carried out on the blood vessel image with complex morphology and pathological changes, so that the trunk of the blood vessel with slight bifurcation in morphology is clearer, the identification degree is higher, and the calculation error caused by the slight bifurcation of the blood vessel is avoided;
in this embodiment, the edge sharpening is performed by using gamma correction, or using other algorithms with similar effects, so that the edges of the blood vessel image and the background image are clearer, and support is provided for subsequent enhancement processing, and the calculation formula is as follows:
g＝max[0，min[255，I(i，j)]] 1≤i≤M，1≤j≤N
I(i，j)＝Gamma[g]
wherein Gamma is a transformation coefficient set, the power exponent r is set to be in a value range of 0.1-10.0 according to image quality, and g is a gray value of an image pixel point;
sharpening the edge of the image to obtain the I with enhanced blood vessel edgeK，M×N；
Because the contrast between the background of the angiographic image and the blood vessel image is usually low, when the blood vessel image is subsequently segmented, the situation of mistaken segmentation or excessive segmentation is easily caused, and the angiographic image contains a low-frequency blood vessel signal and a high-frequency interference signal;
using adaptive histogram equalization method to IK，M×NAnd further implementing enhancement processing to ensure that the blood vessel image has more obvious difference with the background image and improve the identification degree of the blood vessel image, wherein the calculation formula of the self-adaptive window for self-adaptive histogram equalization is as follows:
Im×n(k，l)＝Hvalue[Im×n(k，l)]
wherein HvalueFor the adaptive window measurement of the image, m and n are the pixel height and width of the adaptive window, respectively, which in this embodiment is taken to be 32, the above adaptive window operation is iteratively performed for each pixel in the image by applying to IK，M×NCarrying out adaptive histogram equalization processing to ensure that the interested blood vessel region has stronger contrast and identification degree with the background region;
step eight, clearly and accurately identifying the blood vessel image from the background image;
because the heart has the characteristic of periodic motion, the position of the blood vessel image also shows dynamic change and movement, the blood vessel image needs to be clearly and accurately identified from the dynamically changed background image, and the local characteristics of the blood vessel image can be kept;
the blood vessel image is transformed and restored in a plurality of scales by adopting scale-invariant feature transformation, so that the blood vessel image can be effectively identified, the integral form of the blood vessel is kept, and the local features of the blood vessel are effectively kept;
to IK，M×NThe convolution operation is carried out by using a Gaussian kernel, so that the noise is eliminated, the image is smoothed, the images with different scale parameters are formed according to the difference of the variance of the Gaussian kernel, and the formula is as follows:
I(i，j，σ)＝Fgauss(i，，σ)*I(i，j)
in the present embodiment, 3, 5, and 7-order gaussian matrices are used to convolve the images, so as to form 3-scale images, for example:
g is a gray value matrix formed by adjacent pixel points taking the currently processed pixel point as the center;
the method comprises the steps of converting an image into a plurality of hierarchical images, extracting pixel points from an original image at intervals according to different converted hierarchies to form a new image, namely, performing hierarchical conversion on a blood vessel image, wherein after the hierarchical conversion is performed on the image, detail interference in the image can be inhibited, and the shape and the characteristics of blood vessels in the image can be enhanced;
to IK，M×NEach frame of image is subjected to a plurality of level transformations to generate a new first transformation sequence image;
in the embodiment, the interval of the extracted pixel points is 1, the number of the pixels of the converted new image is 1/4 of the original image, the image can be converted into 3-4 hierarchical images, and excessive scale space conversion can weaken or even eliminate the blood vessel image, so that excessive conversion and false identification are caused;
restoring and fusing a plurality of hierarchical images, sequentially extracting pixel points from the original image according to different transformed hierarchies, forming a new image at intervals, and performing linear interpolation on the pixel points which are not arranged in the new image from the adjacent pixel points of the pixel points, namely performing hierarchical restoration on the blood vessel image;
in this embodiment, a bilinear interpolation method is used for image restoration, and other interpolation methods may also be used;
fusing the images restored by the different layers to obtain a new image, wherein the new image can effectively identify the blood vessel image, maintain the integral shape of the blood vessel and effectively maintain the local characteristics of the blood vessel;
performing a plurality of levels of reduction on each frame of image in the first conversion sequence image to generate a new second conversion sequence image;
in the embodiment, the interval of the interpolation pixel points is 1, the number of the pixels of the restored new image is 4 times that of the original image, and the image can be converted into 3-4 hierarchical images;
step nine, carrying out the image enhancement of the step seven and the image identification processing of the step eight on each frame of image in the second conversion sequence image, and carrying out the image enhancement of the step eightUntil all the images of the sequence have been processed, a second sequence of images V is formed of the filling of the blood vessel with blood (the sequence of images itself contains the filling of the blood vessel since the contrast process is that of the contrast agent during the filling of the blood vessel and the sequence of images is acquired during this process)K，M×NWherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
step ten, calculating the time interval and the frame number of the contrast agent from the image frame at the near end of the blood vessel to the image frame at the far end of the blood vessel, and calculating the time interval T between each frame of image according to the time interval and the frame number of the sequence imageK；
Different models of digital subtraction angiography machines have different frame rates of the contrast images, so that the calculated time interval TKThe method also differs, and generally speaking, the frame rate of the contrast sequence images is 15-30 frames/second;
step eleven, calculating the blood flow volume of each moment in the whole filling process of the blood vessel through the blood vessel image and the time interval;
in the blood vessel image VK，M×NAt a certain specified cross section, calculating the pixel variation of a plurality of adjacent frame blood vessel images, wherein the formula is as follows:
wherein S is the lumen cross-sectional area of the blood vessel, t0And tnThe pixel variation P [ t ] of the blood vessel is respectively the initial frame and the end frame of a plurality of specified blood vessel image frames]The flow of contrast agent in blood vessel can be approximated by the blood flow Ft of a certain cross section specified in the blood vessel in a time interval]；
Step twelve, calculating the blood flow differential at each moment, and calculating the blood flow differential at each moment through the blood flow at each moment in the whole filling process of the blood vessel, wherein the formula is as follows:
the change process of the blood flow in the whole filling process of the blood vessel is obtained by calculating the ratio of the blood flow differential quantity at each moment to the maximum blood flow differential quantity, namely calculating the blood flow differential fraction FFD at each moment, and the blood flow at a certain moment can be evaluated according to the change process of the blood flow, so that the stenosis degree of the blood vessel position at the moment can be evaluated, and the formula for calculating the blood flow differential fraction is as follows:
step thirteen, dynamically evaluating the stenosis moment, the stenosis part and the stenosis degree in the whole filling process of the blood vessel through the blood flow differential fraction of the blood vessel;
the Fractional Flow Reserve (FFR) is obtained by calculating the ratio of the blood pressure at the near end and the far end of the blood vessel so as to judge whether the blood vessel has stenosis between the near end and the far end and the stenosis degree, the Fractional Flow Differential Coefficient (FFD) is obtained by calculating the ratio of the Fractional Flow at the near end and the far end of the blood vessel so as to judge whether the blood vessel has stenosis and the stenosis degree, both the FFR and the FFD can judge the stenosis degree of the blood vessel, and the FFR is based on the blood pressure change and the FFD is based on the blood Flow change;
the differential blood flow score provides a relatively direct evaluation criterion for the degree of the stenosis of the blood vessel, when the differential blood flow score FFD is greater than 0.80, the degree of the stenosis of the blood vessel is within an acceptable range, when the differential blood flow score FFD is less than 0.70, the risk of representing the degree of the stenosis of the blood vessel is possibly higher, and when the differential blood flow score FFD is within a range of 0.70-0.80, the degree of the stenosis of the blood vessel needs to be observed further.
The differential fraction of blood flow can also be used to assess the degree of recovery of the vessel after surgery.
Example 2
An image data processing apparatus of the present invention for executing an angiographic image data processing method of embodiment 1, includes:
the digital subtraction angiography machine is used for executing the step one and acquiring a series of angiography image data of the blood vessel;
an image receiving module connected with the digital subtraction angiography machine and used for executing the step two, receiving the angiography image and converting the angiography image into a first sequence image I according to the format required by the deviceK，M×NWherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image; specifically, the image receiving module comprises an image transmission interface connected with the digital subtraction angiography machine, a cable for receiving images, an image receiving interface, an image converter and the like; the image converter is a processing unit for converting image data transmitted by the digital subtraction angiography machine according to a specified data format, and is, for example, an image acquisition card or the like;
the image storage module is connected with the image receiving module and used for executing the step three and storing IK，M×NB, carrying out the following steps of; specifically, the image storage module includes control logic, physical storage media, and the like, such as image storage software, a memory, a hard disk, and the like;
a man-machine interface module connected with the image storage module and used for executing the step four and displaying the IK，M×N(ii) a An operator can determine the near end and the far end of a blood vessel in the region of interest of the image through the human-computer interface module, determine the near end of the blood vessel as a normal blood flow passing part and determine the far end of the blood vessel as a blood vessel narrow part according to the shape and the change condition of the blood vessel; specifically, the human-computer interface module comprises a display, a printer, a user interface, a mouse, a keyboard and the like;
an image processing module connected with the image storage module and the man-machine interface module and used for executing the step five to the step twelve and the step IK，M×NProcessing, calculating a blood flow differential parameter to obtain a blood flow differential fraction; specifically, the image processing module comprises control logic and image processing logicPhysical processing units, etc., such as CPU, GPU, NPU, image processing software, etc.;
the image storage module is also used for storing contrast image data, blood flow differential fraction data and process data between the contrast image data and the blood flow differential fraction data, and the human-computer interface module is also used for outputting and displaying the blood flow differential fraction.
As a preferable scheme of this embodiment, the human-machine interface module can display a clear independent blood vessel image after the blood vessel sequence image data processing.
As a preferable scheme of the embodiment, the human-machine interface module can display a virtual blood vessel three-dimensional image.
Example 3
An image display method of the present invention is to additionally represent the magnitude of a differential fraction of blood flow in a blood vessel by using a difference in color.
The differential blood flow fraction is obtained according to the processing method as in example 1, and is greater than or equal to 0.00 and less than or equal to 1.00.
The corresponding blood flow differential fraction range is represented by transition colors from red, orange, yellow, green, cyan, blue and purple, red and orange represent the degree of the blood flow differential fraction lower than 0.70, the risk of representing the degree of the blood vessel stenosis is high, yellow represents the degree of the blood flow differential fraction between 0.70 and 0.80, green, cyan, blue and purple represent the degree between 0.80 and 1.00, and the colors are displayed on the blood vessel image or the numerical value of the blood flow differential fraction.
Example 4
A computer-readable storage medium of the present invention has stored thereon a computer program that, when executed by a processor, implements a processing method as embodiment 1 or implements a display method as embodiment 3.
Example 5
An electronic device of the present invention includes:
a memory having a computer program stored thereon;
a processor for executing the program in the memory to realize the processing method as embodiment 1 or to realize the display method as embodiment 3.
As a preferable aspect of the present embodiment, the electronic device may include: a processor, a memory, the electronic device may further include one or more of a multimedia component, an input/output (I/O) interface, and a communication component.
The processor is used for controlling the overall operation of the electronic equipment so as to complete all or part of the steps in the processing method or the display method.
The memory is used to store various types of data to support operation at the electronic device, which may include, for example, instructions for any application or method operating on the electronic device, as well as application-related data; the Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The multimedia components may include a screen, which may be, for example, a touch screen, and an audio component for outputting and/or inputting audio signals; for example, the audio component may include a microphone for receiving external audio signals, and the received audio signals may be further stored in the memory or transmitted through the communication component; the audio assembly also includes at least one speaker for outputting audio signals.
The I/O interface provides an interface between the processor and other interface modules, wherein the other interface modules can be a keyboard, a mouse, buttons and the like; these buttons may be virtual buttons or physical buttons.
The communication component is used for carrying out wired or wireless communication between the electronic equipment and other equipment; wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G or 5G, or a combination of one or more of them, so that the corresponding Communication component may comprise: Wi-Fi module, bluetooth module, NFC module, cell-phone communication module.
As a preferred embodiment of the present invention, the electronic Device may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is configured to perform the Processing method or the display method.
In addition, the computer-readable storage medium provided by the embodiments of the present disclosure may be the above-mentioned memory including program instructions, and the program instructions may be executed by a processor of an electronic device to implement the above-mentioned processing method or the above-mentioned display method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. An angiographic image data processing method characterized by,
extracting angiographic sequence image data within a predetermined time period, the sequence image data comprising a number of frames, the predetermined time period comprising at least one vessel filling cycle;
calculating the blood flow of the frame corresponding to the moment in the blood vessel filling process according to the pixel variation in the sequence images and the time interval of the frame;
calculating the differential fraction of blood flow of the blood flow at the corresponding moment relative to the time; the method comprises the following specific steps: calculating the differential of blood flow at each moment, calculating the differential of blood flow at each moment according to the blood flow at each moment in the whole filling process of the blood vessel, and calculating the ratio of the differential of blood flow at each moment to the maximum differential of blood flow, namely calculating the differential fraction of blood flow at each moment.
2. The processing method according to claim 1,
comprising the step of processing angiographic image data into the sequence of image data;
the method comprises the steps of denoising, image enhancement and image identification on the sequence images;
a step of forming a new sequence of images of the blood vessel during the blood flow filling process from each frame of images in the sequence of images;
calculating the time interval between each frame of image in the new sequence of images, calculating the pixel variation of the cross section in a plurality of adjacent frames of blood vessel images according to a certain cross section appointed in the new sequence of images, and calculating the blood flow of the cross section according to the pixel variation and the time interval;
comprising the step of calculating a differential fraction of blood flow at each instant of the vessel from said blood flow at each instant of the entire filling process of the vessel.
3. The process of claim 2, comprising the steps of:
A. angiography, acquiring a series of said image data of the blood vessel, converting them into a first sequence of images according to a uniform format
Wherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
B. determining the near end and the far end of a blood vessel in the region of interest of the image, determining the near end of the blood vessel as a normal blood flow passing position and determining the far end of the blood vessel as a blood vessel narrow part according to the shape and the change condition of the blood vessel;
C. using Laplace transformAnd/or applying a nonlinear filtering method based on partial differential equation to the filter
Carrying out noise elimination treatment;
D. using edge sharpening process and/or adaptive histogram equalization method to correct the above-mentioned errors
Carrying out image enhancement processing to improve the identification degree of blood vessels;
E. using scale invariant feature transformation to
Carrying out transformation and reduction of a plurality of scales;
F. after reduction, the
Each frame of image is processed by image graying, image enhancement and image identification to form a second sequence image of the blood flow filling process in the blood vessel
Wherein K represents the number of image sequence frames, M represents the number of horizontal pixels of each frame of image, and N represents the number of longitudinal pixels of each frame of image;
G. calculating the time interval and the frame number of the contrast agent from the image frame at the near end of the blood vessel to the image frame at the far end of the blood vessel, and calculating the time interval T between each frame of image according to the time interval and the frame number of the sequence imageK；
H. In the above-mentioned
Calculating the pixel variation of the cross section in several adjacent frames of the blood vessel image, and determining the cross section according to the pixel variation and the TKCalculating the cross-section in several phasesThe blood flow of the adjacent frame blood vessel image in a corresponding period;
I. calculating the differential of blood flow at each moment, calculating the differential of blood flow at each moment according to the blood flow at each moment in the whole filling process of the blood vessel, and calculating the ratio of the differential of blood flow at each moment to the maximum differential of blood flow, namely calculating the differential fraction of blood flow at each moment.
4. The process of claim 3, wherein said step B is performed prior to said step C
And C, carrying out graying treatment and then executing the step C.
5. The process according to any one of claims 3 to 4, characterized in that said step E comprises the steps of:
e1 to
Performing convolution operation by using a Gaussian kernel, and forming images with different scale parameters according to different variances selected by the Gaussian kernel;
e2 to
In each frame of image, carrying out a plurality of level transformations, in each level transformation, extracting pixel points from the original image at intervals to form a new image, and finally generating a first transformation sequence image after the level transformation;
e3, carrying out a plurality of level reduction on each frame of image in the first conversion sequence image, in each level conversion, sequentially extracting pixel points from the original image, forming a new image at intervals, carrying out linear interpolation on the pixel points which are not arranged in the new image and are obtained by adjacent pixel points, and finally generating a second conversion sequence image after the level reduction.
6. An image data processing apparatus characterized by comprising:
the image receiving module is used for receiving a series of image data of the blood vessel and processing the image data into a sequence image;
the image processing module is used for denoising, image enhancement and image identification on the sequence images to form a new sequence image in the blood vessel blood flow filling process, calculating the blood flow of the cross section according to the time interval between each frame of image in the new sequence image and the pixel variation of a specified certain blood vessel cross section in a plurality of adjacent frames of blood vessel images to obtain the blood flow differential fraction of the blood vessel at each moment according to the blood flow; the specific steps of calculating the differential fraction of blood flow at each moment of the blood vessel according to the blood flow comprise: calculating the differential of blood flow at each moment, calculating the differential of blood flow at each moment according to the blood flow at each moment in the whole filling process of the blood vessel, and calculating the ratio of the differential of blood flow at each moment to the maximum differential of blood flow, namely calculating the differential fraction of blood flow at each moment;
and the human-computer interface module is used for displaying the sequence image and the blood flow differential fraction.
7. The processing apparatus according to claim 6, further comprising an image storage module for storing the image data, the sequence images, the new sequence images and the differential blood flow score.
8. An image display method, wherein the differential blood flow score obtained by the processing method according to any one of claims 1 to 5 is represented by a transition color selected from red, orange, yellow, green, cyan, blue, and purple, and the corresponding differential blood flow score range is greater than or equal to 0.00 and less than or equal to 1.00, and the degree of the differential blood flow score being less than 0.70 is represented by red, and the degree of the differential blood flow score being close to 1.00 is represented by purple.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the processing method of any one of claims 1 to 5 or carries out the display method of claim 8.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the program in the memory to implement the processing method of any one of claims 1 to 5 or to implement the display method of claim 8.
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