CN112568888A - In-vivo fluid flow analysis method, system, terminal and storage medium - Google Patents
In-vivo fluid flow analysis method, system, terminal and storage medium Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
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Abstract
The present application relates to an in vivo fluid flow analysis method, system, terminal and storage medium. The method comprises the following steps: acquiring fluid sensitive images of a part to be inspected at least two different times; carrying out motion estimation on the fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the part to be inspected; the motion field comprises a velocity vector; and calculating the measurement value of the nonlinear fluid velocity of at least one position in the part to be inspected by adopting a nonlinear velocity calculation method based on the velocity vector in the motion field to obtain a fluid flow analysis result of the part to be inspected. The embodiment of the application can quickly and accurately analyze the fluid flow of different parts and different species in the whole body, and reduces the computer processing overhead.
Description
Technical Field
The present application relates to the field of medical image processing technologies, and in particular, to a method, a system, a terminal, and a storage medium for analyzing fluid flow in a body.
Background
To measure fluid flow, the current method for measuring blood flow in the heart is cardiac elastography. The method measures the strain of the heart during contraction and relaxation of the myocardium by acquiring raw data such as tissue displacement from an echocardiogram (cardiac ultrasound). In addition, a two-dimensional slice of the heart can be imaged using standard ultrasound scanning, and its doppler flow data can be used for flow visualization of blood in the cardiac structure, e.g., pulsed or continuous wave doppler ultrasound can be used to measure blood flow in the heart in vivo, allowing assessment of the heart valve area and function, any abnormal communication between the left and right sides of the heart, any blood leakage through the valve (valve regurgitation), and calculation of cardiac output and ejection fraction. However, while doppler examination can quantify linear flow, more detailed analysis of fluid flow is beyond the detection limit of this imaging modality.
Phase contrast MRI is another technique that can be used to analyze blood flow, which uses the property of uniform motion of blood or tissue in magnetic field gradients to produce phase changes in MR signals. The fluid flow rate produced by phase-difference MRI is considered to be very accurate. However, to achieve these speeds requires the MRI machine to be adjusted out of its standard scanning mode, taking a significant amount of extra time over the standard MRI procedure. In addition, phase contrast MRI is also susceptible to errors in susceptibility gradients and higher order motion, and has a small dynamic range, while also requiring a significant amount of computer processing overhead.
Disclosure of Invention
The present application provides an in vivo fluid flow analysis method, system, terminal and storage medium, which aim to solve at least one of the above technical problems in the prior art to some extent.
In order to solve the above problems, the present application provides the following technical solutions:
a method of in vivo fluid flow analysis, comprising:
acquiring fluid sensitive images of a part to be inspected at least two different times;
carrying out motion estimation on the fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the part to be inspected; the motion field comprises a velocity vector;
and calculating the measurement value of the nonlinear fluid velocity of at least one position in the part to be inspected by adopting a nonlinear velocity calculation method based on the velocity vector in the motion field to obtain a fluid flow analysis result of the part to be inspected.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the method for acquiring the fluid sensitive images of the part to be inspected at least at two different times comprises the following steps:
and segmenting the fluid sensitive image based on active contour drawing of a Kass-snake algorithm, and excluding non-fluid areas in the fluid sensitive image.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the motion estimating the fluid sensitive image by a motion estimation algorithm comprises:
performing motion estimation through a pyramid Lucas Kanade optical flow algorithm; the motion estimation includes:
the pixel intensity is represented by I (x, y, t), assuming that the spatio-temporal variation of the intensity signal is:
I(x,y,t)＝I(x+δx,y+δy,t+δt)
in the above formula, δ represents a variable, x represents an abscissa, y represents an ordinate, and t represents time. I (x + δ x, y + δ y, t + δ t) represents the gray-level consistency assumption;
applying chain rules to distinguish:
wherein δ represents a variable, x represents an abscissa, y represents an ordinate, t represents time, and ε represents a second-order infinitesimal term;
if the brightness of a particular point in the pattern is not changed, then follow:
differences with respect to t yield:
definition of
And yield
The optical flow constraint equation is:
(Ix,Iy)·(vx,vy)＝-It
the optical flow vector has two components vxAnd vy，vxAnd vyThe velocity vectors of the point light flow along the directions of the x axis and the y axis respectively, and the spatial gradient of the intensity is as follows:
wherein the content of the first and second substances,
which represents the gradient of the intensity of the light,
representing a velocity vector, ItIndicating the intensity at time t.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the motion estimating the fluid sensitive image by a motion estimation algorithm comprises:
taking MRI slices from axial, sagittal and coronal scans respectively and constructing a three-dimensional stacked grid or gantry, for each MRI slice of axial, sagittal and coronal scans, three processing flows are performed in parallel;
three processing streams are initialized by a parent process, the parent process starts a parallel processing option, each processing stream respectively reads MRI slices of axial, sagittal and coronal scans, and then analysis is carried out on the MRI slices stage by stage;
each iteration, proceeding to the next phase, which is initially the first phase; then, applying a motion estimation algorithm to the respective MRI slices to generate a first stage mid motion field;
when the final stage is reached, exiting the parallel processing, merging the intermediate motion field of each stage by the father process, and adding intermediate vector components to form a final motion field; the motion field is a three-dimensional motion field of three-dimensional velocity vectors comprising a three-dimensional velocity vector for each intersection point, which is located in the middle of the three-dimensional space.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the calculating the value of the measure of the nonlinear fluid velocity in at least one position in the part to be examined by using the nonlinear velocity calculation method based on the velocity vector in the motion field comprises the following steps:
the nonlinear fluid velocity measurement value calculates the vorticity (omega), shear strain (phi) and normal strain (psi) of the fluid according to the velocity vector in the motion field, and displays the vorticity (omega), the shear strain (phi) and the normal strain (psi) through original values or average values of the vorticity (omega), the shear strain (phi) and the normal strain (psi); wherein the vorticity (ω) represents a rotation of blood in the right atrium of the heart, the shear strain (Φ) represents a shear experienced by the blood, and the normal strain (Ψ) determines a pressure experience of the blood at the local location.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the calculating the value of the measure of the nonlinear fluid velocity in at least one position in the part to be examined by using the nonlinear velocity calculation method based on the velocity vector in the motion field comprises the following steps:
based on the velocity profile of the pixel of interest at (i, j), the x and y components are V, respectivelyx(i, j) and Vy(i, j)), N represents the layer number sampling frame of the inner contour, DeltaxAnd ΔyRepresenting the horizontal and vertical distances between adjacent velocities, the vorticity (ω), shear strain (Φ), and normal strain (Ψ) are calculated by:
vorticity (ω):
shear strain (Φ):
normal strain (Ψ):
the technical scheme adopted by the embodiment of the application further comprises the following steps: after the step of calculating the measurement value of the nonlinear fluid velocity of at least one position in the part to be inspected by adopting a nonlinear velocity calculation method, the method further comprises the following steps:
superimposing and displaying a representation or motion field of the nonlinear fluid velocity metric value on the fluid sensitive image.
Another technical scheme adopted by the embodiment of the application is as follows: an in vivo fluid flow analysis system comprising:
magnetic resonance imager: the MR image acquisition device is used for acquiring MR images of a part to be inspected at least two different times;
a motion estimation element: the motion estimation method is used for carrying out motion estimation on the fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the part to be inspected;
a computing element: the device is used for calculating the measurement value of the nonlinear fluid speed of at least one position in the part to be inspected by adopting a nonlinear speed calculation method based on the speed vector in the motion field to obtain the fluid flow analysis result of the part to be inspected;
display element: for superimposing and displaying a representation or motion field of the non-linear fluid velocity metric value on the fluid sensitive image.
The embodiment of the application adopts another technical scheme that: a terminal comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions for implementing the in vivo fluid flow analysis method;
the processor is for executing the program instructions stored by the memory to control in vivo fluid flow analysis.
The embodiment of the application adopts another technical scheme that: a storage medium storing program instructions executable by a processor to perform the in vivo fluid flow analysis method.
Compared with the prior art, the embodiment of the application has the advantages that: the in-vivo fluid flow analysis method, system, terminal and storage medium of the embodiments of the present application obtain a fluid sensitive image of a body by using various mechanisms, generate a motion field from the image by a motion estimation algorithm, calculate a metric value of a nonlinear fluid flow rate of at least one location in the body according to the motion field, and display the calculated metric value by superimposing a representation of the metric value on the body image. The embodiment of the application can quickly and accurately analyze the fluid flow of different parts and different species in the whole body, and reduces the computer processing overhead.
Drawings
FIG. 1 is a flow chart of a method of in vivo fluid flow analysis according to an embodiment of the present application;
FIG. 2 is a schematic MR image of an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating the operation of the pyramid Lucas Kanade optical flow algorithm performed on two pixel groups at different times according to the embodiment of the present application;
FIG. 4 is a representation of a motion field of an embodiment of the present application in which areas of high turbulence are shown as darker areas in the image;
FIG. 5 is a slice schematic of a set of MR images in orthogonal planes in three-dimensional space of an embodiment of the present application;
fig. 6 is a schematic diagram of a three-dimensional motion field generation process according to an embodiment of the present application;
FIG. 7 is a histogram of non-linear fluid velocity metric values for an embodiment of the present application;
FIG. 8 is a graph of an average of nonlinear fluid velocity metric values calculated at different times in accordance with an embodiment of the present application;
FIG. 9 is a schematic diagram showing the superposition of the motion field generated by the pyramid Lucas Kanade optical flow algorithm and the MR image shown in FIG. 2 according to the embodiment of the present application;
FIG. 10 is a schematic block diagram of an in vivo fluid flow analysis system in accordance with an embodiment of the present application;
fig. 11 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Fig. 1 is a flowchart illustrating an in vivo fluid flow analysis method according to an embodiment of the present application. The in vivo fluid flow analysis method of the embodiment of the application comprises the following steps:
s10: acquiring fluid sensitive images of a part to be inspected at least two different times;
in this step, the fluid sensitive images include different types of images of different parts of the body, such as the heart. For convenience of explanation, the embodiments of the present application take only an MR image obtained from a CMR (cardiac magnetic resonance examination) image scan as an example. The image acquisition mode specifically comprises the following steps: steady-state free-precession cine-MR imaging was performed using successive slices in a short axis view through the heart, with 25 phases (from T0-24) per slice being obtained by retrospective gating to provide MR images of the heart at 25 different times. Referring specifically to fig. 2, a schematic MR image of an embodiment of the present application is shown, which shows the chamber (right atrium) outline of the human heart at four different phases of the cardiac cycle.
It can be understood that the fluid sensitive image according to the embodiment of the present application may be stored in any image format such as a bitmap format, and in actual operation, the pre-stored image may also be directly read for fluid analysis without real-time acquisition. In addition, the embodiment of the application can be applied to different types of MR images such as phase contrast, gradient echo or markers, and intensity contrast is used on a standard MR image without a marked MRI program. In addition, the embodiment of the application can also be applied to other types of fluid sensitive images such as Particle Image Velocimetry (PIV) images.
S20: segmenting the fluid sensitive image, and excluding non-fluid areas in the fluid sensitive image;
in this step, in order to avoid the influence of other tissues on the fluid analysis, the embodiment of the present application segments the heart wall by performing active contour rendering based on the Kass-snake algorithm, placing two-dimensional contours that form a computational elastic wall in the heart cavity, and performing migration of contour nodes from their origin to wall regions based on the energy minimization algorithm to define the internal boundaries of the heart so as to exclude non-regions such as walls and substances of the body.
In the embodiment of the present application, the fluid sensitive image segmentation method specifically includes:
profiling a heart chamber as an energy function EcontourReceiving information from a previous contour and based on the internal energy E of the contourintAnd external energy EextEnergy balancing is applied to redefine the contour representation. The fitted profile is the profile corresponding to the minimum of this energy:
Econtour＝∫Eint+∫Eext (1)
the initial curve can be anywhere in the MR image and the internal contours are automatically detected and if the segmentation is poor due to over-expansion of the elastic contours, the inner wall of the heart chamber is manually tracked. Due to the semi-automatic nature of the segmentation, contour tracing can be pre-processed.
S30: carrying out motion estimation on the segmented fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the heart;
in this step, the motion estimation algorithm used in the embodiment of the present application is a pyramid lucas kanade optical flow algorithm. Based on the difference of the MR images at different times, top-down flow estimation is performed using an image pyramid, where the vertices represent the CMR images at a coarse scale (useful for obtaining a global representation of fluid flow), the computation results from this level are passed on to the next, and the process proceeds based on the flow estimated at the previous scale until a substantially fine (e.g., single pixel) resolution scale is reached to estimate the motion field of the fluid at various locations within the body.
MR images are fluid sensitive, and intensity contrast of turbulent and laminar blood flow is a feature of MRI scans, allowing visualization of blood motion based on intensity of movement in the image due to signal voids caused by dephasing of nuclear spins. The high turbulence regions in the MRI scan are darker than the low turbulence regions, and specifically as shown in fig. 3, a schematic diagram of the operation of the pyramidal lucas kanade optical flow algorithm for two different time pixel sets. In (a), a very simple image is divided into four quadrants or regions, with the lower left region being darker than the other regions. In (b), taken later, the upper right area is darker, and then an object that produces a dark area (i.e., a turbulent area) is caused to move diagonally across the image. The motion field comprises the velocity vectors of the fluid turbulence area. As shown in fig. 4, a display of motion fields is shown in which areas of high turbulence are displayed as darker areas on the image, the intensity of which corresponds to the magnitude of the velocity vector. Since the motion field is a collection of velocity vectors at different points throughout the body, typically each of these points corresponding to a pixel of at least one image, embodiments of the present application save computational load by tracking only the movement of the turbulent region, i.e. the motion field only includes the velocity vector of the turbulent region, and not necessarily the velocity vector of each pixel.
The pyramidal lucas kanade optical flow algorithm can be applied to regions of various scales, ranging from fine pixel resolution (i.e., single pixel) to very coarse pixel resolution (i.e., large regional groups). Taking fine pixel resolution as an example, the implementation process of the pyramid lucas kanade optical flow algorithm specifically includes:
the pixel intensity is represented by I (x, y, t), assuming that the spatio-temporal variation of the intensity signal is:
I(x,y,t)＝I(x+δx,y+δy,t+δt) (2)
in the formula (2), δ represents a variable, x represents an abscissa, y represents an ordinate, and t represents time. I (x + δ x, y + δ y, t + δ t) represents the gray-scale uniformity assumption.
The Taylor formula expansion is carried out on the right side of the formula (2), namely:
in the formula (3), δ represents a variable, x represents an abscissa, y represents an ordinate, t represents time, and ∈ represents a second-order infinite term. If the brightness of a particular point in the pattern is not changed, then follow:
differences with respect to t yield:
definition of
And yield
Thus, the optical flow constraint equation can be rewritten as:
(Ix,Iy)·(vx,vy)＝-It (6)
in the formula (6), vxAnd vyThe velocity vectors of the spot light flow along the x-axis and y-axis directions, respectively.
The optical flow vector has two components vxAnd vyFor describing the movement of feature points in the x and y directions, the spatial gradient of intensity is represented by:
in the formula (7), the reaction mixture is,
which represents the gradient of the intensity of the light,
representing a velocity vector, ItIndicating the intensity at time t.
Thus, a linearized version of the luma constancy assumption generates an optical flow constraint.
It is understood that in other embodiments of the present application, other motion estimation algorithms, such as the horns-SchunckOF algorithm (global approach introducing global constraint for smoothness) or block matching method, may also be used.
Fluid flow is generally not limited to a single plane. Thus, to more accurately analyze fluid flow within the heart, MRI slices from axial, sagittal, and coronal scans are taken and a three-dimensional stacked grid or scaffold is constructed and a three-dimensional motion field is generated, respectively, when performing the motion estimation algorithm. As shown in particular in fig. 5, is a slice schematic of a set of MR images in orthogonal planes in three-dimensional space. Multiple planes intersect in the body at different locations, the interception points of the three image slices are represented by spherical anchor points, and the axial planes are hidden to reveal the spherical anchor points.
Referring to fig. 6, a schematic diagram of a three-dimensional motion field generation process according to an embodiment of the present application is shown, which shows a three-dimensional motion field including X, Y and Z velocity vectors from an interception point. The three-dimensional velocity vector comprises the sum of orthogonal velocity component vectors in three-dimensional space through the intersection of the slices. Namely: for each interception point, a composite velocity vector based on the addition of orthogonal velocity components from the two-dimensional slice is calculated. For each MRI slice of axial, sagittal and coronal scans, three processing streams are performed in parallel. Three processing flows are initiated by the parent process, which initiates the parallel processing option. Each processing stream reads MRI slices of axial, sagittal, and coronal scans, respectively, and then performs phase-by-phase analysis for each phase of the cardiac cycle. Each iteration, proceeding to the next phase, which will initially be the first phase. Then, a motion estimation algorithm is applied to the respective MRI slices to generate the mid motion field of the first stage. When the final stage has been analyzed, the parallel process is exited and then the parent process merges the intermediate motion fields of each stage, adding the intermediate vector components to form the final motion field (for each phase). The motion field is a three-dimensional motion field of three-dimensional velocity vectors comprising a three-dimensional velocity vector for each intersection point, which is located in the middle of the three-dimensional space.
Based on the above, flow visualization using MRI scanning is fast, non-invasive and unlimited due to opacity and motion of the body, and (for medical applications) has the advantage of using common techniques. It will be appreciated that the present invention is applicable to any imaging technique. Intensity contrast of turbulent and laminar blood flow is a feature of certain types of MRI scans, which allow visualization of blood motion based on intensity of movement in the image due to signal voids caused by dephasing of nuclear spins.
S40: calculating the measurement value of the nonlinear fluid velocity of at least one position in the part to be inspected by adopting a nonlinear velocity calculation method based on the velocity vector in the motion field to obtain a fluid flow analysis result of the part to be inspected;
in the step, the nonlinear fluid velocity measurement value calculates the nonlinear flow such as the vorticity (omega), the shear strain (phi) and the normal strain (psi) of the fluid according to the velocity vector in the motion field, and displays the nonlinear flow through the original value or the average value of the vorticity (omega), the shear strain (phi) and the normal strain (psi); where vorticity (ω) represents the rotation of blood in the right atrium of the heart, shear strain (Φ) represents the shear experienced by the blood, and normal strain (Ψ) determines the pressure experience of the blood at the local site. (ii) a velocity profile based on the pixel of interest at (i, j) (with x and y components V, respectively)x(i, j) and Vy(i, j)), n represents the layer number sampling frame of the inner contour, ΔxAnd ΔyThe specific calculation of vorticity (ω), shear strain (Φ), and normal strain (Ψ), which represent the horizontal and vertical distances between adjacent velocities, is as follows:
vorticity (ω):
shear strain (Φ):
normal strain (Ψ):
vorticity is a component of turbulent flow, but turbulent flow also includes random or chaotic fluid flow. In the embodiment of the present application, the statistics of the nonlinear fluid velocity measurement value can also be performed by calculating the number, size, or direction of other nonlinear flow rates such as the number, size, or direction of the vortex or turbulent flow regions in the body. Fig. 7 is a histogram of the nonlinear fluid velocity metric values according to the embodiment of the present application. Where vorticity has been calculated for many locations within the body, the vorticity histogram may give guidance as to the general propagation of vortices within the body. FIG. 8 is a graph of the average of nonlinear fluid velocity metric values calculated at different times depicting baseline plots of average calculated vorticity, shear strain and normal strain in the heart at different stages of the heart. In clinical applications, the shape of the graph may be compared to the shape of a healthy heart to look for abnormalities.
S50: superimposing and displaying a representation or motion field of the non-linear fluid velocity metric value on the fluid sensitive image;
in this step, the non-linear fluid velocity metric value may be displayed by superimposing a representation of the value on the corresponding location of the image, which may include a "dot" of a particular intensity or color (depending on the value of the metric value). Furthermore, the motion field may be superimposed on the image simultaneously or separately, wherein each point of the motion field is displayed at a corresponding location on the image. As shown in fig. 9, a schematic diagram is shown for the superposition of the motion field generated by the pyramid lucas kanade optical flow algorithm and the MR image (T ═ 8) shown in fig. 2. In fig. 9, a contour is shown on the image showing the flow pattern of the right atrial blood at the current stage and showing areas of similar vorticity size. The arrows in the figure represent velocity vectors within the motion field, the length of the arrows corresponding to the magnitude of the velocity vectors. The profile can also be used to show areas with similar shear or normal strain, facilitating observation of the center of the major vortex.
It will be appreciated that the information may be displayed in a variety of forms, for example using a color scale or the like, the color of the area superimposed on the image may indicate the magnitude of vorticity and its direction, and the color may distinguish between clockwise rotation (e.g. red) and anti-clockwise movement of blood (e.g. blue).
Based on the above, the in-vivo fluid flow analysis method according to the embodiment of the present application obtains a fluid sensitive image of a body by using various mechanisms, causes the image to generate a motion field by a motion estimation algorithm, calculates a metric value of a nonlinear fluid flow rate of at least one location in the body from the motion field, and displays the calculated metric value by superimposing a representation of the calculated metric value on the body image. Can be used for the auxiliary diagnosis of various diseases related to the pathogenesis of cardiovascular diseases, such as atherosclerosis, arterial diseases, heart defects or turbulent blood flow. Such as septal defects (ventricles or atria), forcing oxygenated blood through holes in the septum from the left to the right of the heart, resulting in too much blood entering the lungs (through the pulmonary arteries) and too little for the body tissue (through the aorta) to be adequately distributed to other parts of the body, resulting in abnormal patterns of blood flow in the heart that can be learned by the clinician as a result of the analysis of the present application to quickly take effective action to rescue the patient.
It is to be understood that the embodiments of the present application can be used for fluid (e.g., cerebrospinal fluid) flow analysis at different locations and of different species throughout the body. In addition, the present application has significant applicability to the design and testing of biomedical devices, such as artificial or mechanical heart valves, can be used for the design and optimization of prosthetic valves, and can also be used to identify risks that may arise after heart valve implantation. It is also widely applicable to non-biological applications requiring flow analysis, such as analyzing fluid flow through manufacturing or in mechanical devices from an engineering perspective, air flow analysis in aerospace engineering (e.g., reducing air flow turbulence on aircraft structures), fluid flow in pipes or ducts (e.g., optimizing ink flow efficiency in printers), and so forth. In fact, the present invention can be applied to any region currently used (Particle Image Velocimetry).
The embodiment of the application can be applied to the heart analysis after the heart operation, and can be used for determining the operation success of the patient and helping management and decision to stabilize the heart disease. In the event of a heart valve failure, the heart may require more energy to pump blood, and the vortices are energy-retaining structures and are observed in the normal heart chamber, increasing the efficiency of the heart. The flow information generated by the present application can be used to examine the amount of energy wasted by the heart, which may require pumping blood through an abnormal heart valve to maintain a desired circulation in the body. The present invention provides the potential for non-invasive flow visualization and quantification in cardiac structures, such as natural and bioprosthetic heart valves in vivo in a beating heart, changing their spatial position over time.
Fig. 10 is a schematic structural diagram of an in-vivo fluid flow analysis system according to an embodiment of the present application. First, MR images taken at two or more different times are obtained by the magnetic resonance imager 210 or from the scanner 220 or disk drive 230 and received by the receiver 240, where they reside on the processor. In case the image needs to be segmented, non-fluid structures are excluded from the MR image by the segmentation element 250. The segmentation element 250 may allow a user to assist in the segmentation process via an input device such as a keyboard 260 or a mouse 270. The motion estimation element 320 then applies a motion estimation algorithm to the MR images to generate the motion field.
The motion field may be displayed with the MR image by a display element 280, and the display element 280 may communicate with a projector 290 or monitor 300 to display the motion field and/or image in a set manner.
The system may further comprise a calculation element 310 for calculating from the playing field a measure of the non-linear fluid flow of at least one location within the body, which measure may also be displayed by the display element 280. The display element 280 may be adapted to display the motion field and the measure values of the non-linear fluid flow in three dimensions.
Experiments and applications have shown that the present application can be used for specific applications for analyzing blood flow in the human heart, as well as for analyzing fluid flow throughout the rest of the body or different species, and has significant applicability to the design and testing of biomedical devices such as artificial hearts or mechanical heart valves.
Please refer to fig. 11, which is a schematic diagram of a terminal structure according to an embodiment of the present application. The terminal 50 comprises a processor 51, a memory 52 coupled to the processor 51.
The memory 52 stores program instructions for implementing the in vivo fluid flow analysis method described above.
The processor 51 is operative to execute program instructions stored in the memory 52 to control in vivo fluid flow analysis.
The processor 51 may also be referred to as a CPU (Central Processing Unit). The processor 51 may be an integrated circuit chip having signal processing capabilities. The processor 51 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Please refer to fig. 12, which is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium of the embodiment of the present application stores a program file 61 capable of implementing all the methods described above, where the program file 61 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of analyzing fluid flow in a body, comprising:
acquiring fluid sensitive images of a part to be inspected at least two different times;
carrying out motion estimation on the fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the part to be inspected; the motion field comprises a velocity vector;
and calculating the measurement value of the nonlinear fluid velocity of at least one position in the part to be inspected by adopting a nonlinear velocity calculation method based on the velocity vector in the motion field to obtain a fluid flow analysis result of the part to be inspected.
2. The in vivo fluid flow analysis method as defined in claim 1, wherein said acquiring fluid sensitive images of the site to be examined at least two different times comprises:
and segmenting the fluid sensitive image based on active contour drawing of a Kass-snake algorithm, and excluding non-fluid areas in the fluid sensitive image.
3. The in vivo fluid flow analysis method as defined in claim 1, wherein said motion estimating the fluid sensitive image by a motion estimation algorithm comprises:
performing motion estimation through a pyramid Lucas Kanade optical flow algorithm; the motion estimation includes:
the pixel intensity is represented by I (x, y, t), assuming that the spatio-temporal variation of the intensity signal is:
I(x,y,t)＝I(x+δx,y+δy,t+δt)
in the above formula, δ represents a variable, x represents an abscissa, y represents an ordinate, t represents time, and I (x + δ x, y + δ y, t + δ t) represents a gray-scale uniformity assumption;
applying chain rules to distinguish:
wherein δ represents a variable, x represents an abscissa, y represents an ordinate, t represents time, and ε represents a second-order infinitesimal term;
if the brightness of a particular point in the pattern is not changed, then follow:
differences with respect to t yield:
definition of
And yield
The optical flow constraint equation is:
(Ix,Iy)·(vx,vy)＝-It
the optical flow vector has twoA component vxAnd vy，vxAnd vyThe velocity vectors of the point light flow along the directions of the x axis and the y axis respectively, and the spatial gradient of the intensity is as follows:
wherein the content of the first and second substances,
which represents the gradient of the intensity of the light,
representing a velocity vector, ItIndicating the intensity at time t.
4. The in vivo fluid flow analysis method as defined in claim 3, wherein said motion estimating the fluid sensitive image by a motion estimation algorithm comprises:
taking MRI slices from axial, sagittal and coronal scans respectively and constructing a three-dimensional stacked grid or gantry, for each MRI slice of axial, sagittal and coronal scans, three processing flows are performed in parallel;
three processing streams are initialized by a parent process, the parent process starts a parallel processing option, each processing stream respectively reads MRI slices of axial, sagittal and coronal scans, and then analysis is carried out on the MRI slices stage by stage;
each iteration, proceeding to the next phase, which is initially the first phase; then, applying a motion estimation algorithm to the respective MRI slices to generate a first stage mid motion field;
when the final stage is reached, exiting the parallel processing, merging the intermediate motion field of each stage by the father process, and adding intermediate vector components to form a final motion field; the motion field is a three-dimensional motion field of three-dimensional velocity vectors comprising a three-dimensional velocity vector for each intersection point, which is located in the middle of the three-dimensional space.
5. The in vivo fluid flow analysis method as defined in claim 4, wherein said calculating a measure of the nonlinear fluid velocity at least one location within the site to be examined using a nonlinear velocity calculation method based on the velocity vectors in the motion field comprises:
the nonlinear fluid velocity measurement value calculates the vorticity (omega), shear strain (phi) and normal strain (psi) of the fluid according to the velocity vector in the motion field, and displays the vorticity (omega), the shear strain (phi) and the normal strain (psi) through original values or average values of the vorticity (omega), the shear strain (phi) and the normal strain (psi); wherein the vorticity (ω) represents a rotation of blood in the right atrium of the heart, the shear strain (Φ) represents a shear experienced by the blood, and the normal strain (Ψ) determines a pressure experience of the blood at the local location.
6. The in vivo fluid flow analysis method as defined in claim 5, wherein said calculating a measure of the nonlinear fluid velocity at least one location within the site to be examined using a nonlinear velocity calculation method based on the velocity vectors in the motion field comprises:
based on the velocity profile of the pixel of interest at (i, j), the x and y components are V, respectivelyx(i, j) and Vy(i, j)), N represents the layer number sampling frame of the inner contour, DeltaxAnd ΔyRepresenting the horizontal and vertical distances between adjacent velocities, the vorticity (ω), shear strain (Φ), and normal strain (Ψ) are calculated by:
vorticity (ω):
shear strain (Φ):
normal strain (Ψ):
7. the in vivo fluid flow analysis method as claimed in any one of claims 1 to 6, wherein said calculating a measure of the nonlinear fluid velocity at least one location within said site to be examined using a nonlinear velocity calculation method further comprises:
superimposing and displaying a representation or motion field of the nonlinear fluid velocity metric value on the fluid sensitive image.
8. An in vivo fluid flow analysis system, comprising:
magnetic resonance imager: the MR image acquisition device is used for acquiring MR images of a part to be inspected at least two different times;
a motion estimation element: the motion estimation method is used for carrying out motion estimation on the fluid sensitive image through a motion estimation algorithm to obtain motion fields of the fluid at all positions in the part to be inspected;
a computing element: the device is used for calculating the measurement value of the nonlinear fluid speed of at least one position in the part to be inspected by adopting a nonlinear speed calculation method based on the speed vector in the motion field to obtain the fluid flow analysis result of the part to be inspected;
display element: for superimposing and displaying a representation or motion field of the non-linear fluid velocity metric value on the fluid sensitive image.
9. A terminal, comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions for implementing the in vivo fluid flow analysis method of any one of claims 1-7;
the processor is for executing the program instructions stored by the memory to control in vivo fluid flow analysis.
10. A storage medium having stored thereon program instructions executable by a processor to perform the in vivo fluid flow analysis method of any one of claims 1 to 7.
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| CN113920173A (en) * | 2021-10-18 | 2022-01-11 | 中国科学院深圳先进技术研究院 | Heart blood flow vorticity ring identification method based on optical flow and Lagrangian vorticity deviation |
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