CN113920173A - Heart blood flow vorticity ring identification method based on optical flow and Lagrangian vorticity deviation - Google Patents
Heart blood flow vorticity ring identification method based on optical flow and Lagrangian vorticity deviation Download PDFInfo
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Abstract
The invention discloses a heart blood flow vorticity ring identification method based on optical flow and Lagrange vorticity deviation. The method comprises the following steps: estimating optical flow between two successive phase-contrast magnetic resonance imaging PC-MRIs, wherein the intermediate optical-flow field is approximated by a process of warping the constructed continuous velocity field; the lagrangian mean vorticity deviation, LAVD, is calculated using the synthetic velocity field to extract the lagrangian vorticity kernel and the region within the target chamber to identify the cardiac blood flow vorticity ring. The invention accurately identifies the vortex ring and the continuous velocity field based on the optical flow information, thereby representing the region of the main Lagrange vortex ring in the heart chamber more accurately.
Description
Technical Field
The invention relates to the technical field of medical image analysis, in particular to a heart blood flow vorticity ring identification method based on optical flow and Lagrangian vorticity deviation.
Background
Measuring the characteristics of the heart's blood flow vortices helps facilitate hemodynamic analysis that regulates heart function. However, due to the complexity of the cardiac blood flow and other physical limitations, it is difficult to accurately identify the major vortices in the heart chamber, which play an important role in regulating the heart's function.
The formation of blood flow vortex in the heart has important significance for representing the function of a blood flow mechanism and energy transfer to the heart cavity, and is an important index for quantifying the overall function of the heart. In helping blood circulation to various regions of the ventricle, it was observed that the fluid flow delivered by vortex ring formation was more effective than the fluid flow delivered by a steady, straight jet. It is well known that the myocardium has helical fibers intertwined with each other. Thus, when the heart muscle contracts, the ventricles are distorted, which is the way blood is effectively expelled from the ventricles. This ventricular distortion results in the formation of high vorticity vortex rings in the ventricular blood flow. In contrast, cardiomyopathies suffer from impaired contractility of the heart and are therefore less distorted, and therefore they have smaller vortex rings and lower vorticity. Therefore, the formation of the vortex ring is closely related to the heart function and the health condition of an individual, and the vortex analysis can deeply analyze the heart function and help to distinguish a normal subject from a heart disease patient by combining the structural parameters of the heart. Therefore, a method capable of quantifying characteristics of the vortex ring and dynamic changes of the vortex ring in the cardiac cycle is developed, so that physiological functions of the vortex ring can be known, and the exploration of heart diagnosis and pathological changes of the heart can be promoted.
Interestingly, phase contrast magnetic resonance imaging (PC-MRI, or phase contrast magnetic resonance imaging) allows three-dimensional MR (magnetic resonance) velocity mapping based on the inherent sensitivity of MRI (magnetic resonance imaging) to blood flow and provides a unique tool for measuring complex blood flow patterns in the body. A study was performed earlier by Wong et al in which the vorticity of the vortices was measured using a flow field obtained from a scanning PC-MRI to characterize the location and intensity of the vortices within the heart chamber. The main limitation of this study is that it is based on global estimation and does not include the function of extracting local information from the eddy current regions to elucidate the dominant eddy currents. The identification of the dominant vortex ring is the key to fully describe the outflow of blood caused by the vortical flow generated in the heart chamber (e.g., the left ventricle).
Currently, because there is no uniform definition of vortex flow, different vortex flow criteria have been used to extract vortex flow information within the core. Elbaz et al used the Left Ventricular (LV) eddy current detection criterion λ _2 in early and late diastolic blood flow. Krauter et al calculated a non-divergent portion of the velocity vector of the identified vortex based on the Q-criterion throughout the cardiac cycle. The above method is region-based for extracting transient vortex rings in a single frame, which prevents a correct exploration of the vortex ring formation process.
Et al believe that Lyapunov (Lyapunov) index values above 50% identify vortex boundaries as lagrange vortex rings. Lagrangian vortex rings can describe developmental processes more accurately, but the method is conservative and does not include vortex nuclei. In the Yang et al study, Lagrangian mean vorticity deviation (LAVD) was used to identify the core regions of Lagrangian and Euler vortices for measuring vorticity and vorticity in LV blood flow to more accurately quantify vortex formation. Wong et al determined two oppositely rotating primary vortices in the Right Atrium (RA), but applied an unsupervised data clustering algorithm without taking into account blood flow characteristics.
In summary, although the existing eddy current quantification method can identify the main eddy current in the heart cavity to a certain extent, the problems of low precision, no description of the eddy current deformation process, neglect of the eddy current center and the like still exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a heart blood flow vorticity ring identification method based on optical flow and Lagrangian vorticity deviation, which comprises the following steps:
step S1: estimating optical flow between two successive phase-contrast magnetic resonance imaging PC-MRIs, wherein the intermediate optical-flow field is approximated by a process of warping the constructed continuous velocity field;
step S2: the lagrangian mean vorticity deviation, LAVD, is calculated using the synthetic velocity field to extract the lagrangian vorticity kernel and the region within the target chamber to identify the cardiac blood flow vorticity ring.
Compared with the prior art, the method has the advantages that the Optical flow Lagrange average vorticity deviation (or called Optical flow-LAVD) method is provided, and the Lagrange average vorticity deviation is used for realizing more accurate identification and quantification of the blood flow vortex ring, so that the description of the vortex characteristics in the heart is improved.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram of a method for cardiac blood flow vorticity ring identification based on optical flow and Lagrangian vorticity bias, according to an embodiment of the invention;
FIG. 2 is a representation of an MRI scan image through the heart and a PC-MRI based velocity image in the heart chamber according to one embodiment of the present invention;
FIG. 3 is a graphical representation of experimental results according to one embodiment of the present invention;
in the figure, Medical Imaging-Medical images; phase Contrast MRI reconstraction-Phase Contrast MRI Reconstruction; central axis-Central axis; selected slice-Selected slice; organization image-oriented image; velocity image-Velocity image; enhanced background scan velocity image-Enhanced background scan velocity image; number of Skipped Image-Number of images Skipped; scan Time Frame Index-Scan Time Frame Index.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Briefly, in one embodiment of the present invention, blood flow in the Right Atrium (RA) of a participant's heart is examined using a set of scans associated with slices in the short axis direction of the two chambers. To fully extract the vortex ring features, a new method driven by the Lagrangian Average Vorticity Deviation (LAVD) was implemented and the trace-integral (PC-MRI) data set related to vorticity deviation and ring-space mean was characterized as a case study by using phase-contrast magnetic resonance imaging. To insert temporal frames between each larger discrete frame and minimize errors due to constructing a continuous velocity field for the LAVD integration process, the optical flow is implemented as an interpolator and inverse warping is introduced as an intermediate frame synthesis basis, which is then used to generate a higher quality continuous velocity field.
Specifically, referring to fig. 1, the provided cardiac blood flow vorticity loop identification method based on optical flow and lagrangian vorticity deviation comprises the following steps.
In step S110, a velocity encoded magnetic resonance image is acquired.
For example, to verify the performance of vortex ring identification and feature extraction, normal and healthy subjects aged around 22 years were recruited for the PC-MRI dataset required for the optical flow labd model test. The blood pressure of the volunteers is normal, and no history of cardiovascular diseases is found through preliminary examination. Subsequently, the blood flow in the Right Atrium (RA) of the participant's heart was examined using a set of scans relating to slices in the short axis direction of the two chambers. In the present example, the right atrium is taken as an example, since the effectiveness of the new method can be verified more accurately by referring to our previous work on intracardiac flow analysis. Meanwhile, the invention aims to analyze the optical flow-LAVD and understand the development process of the heart Lagrange vortex ring. The study was approved by the local ethical review board and informed written consent was obtained from the subjects.
In one embodiment, velocity-encoded magnetic resonance imaging is performed by using Siemens Avanto, 1.5 Tesla, model Syngo MRB15 scanner and Numaris-4, serial number: 26406 is performed by software. More precisely, the encoding is set to 100cm/s in all directions, this configuration being suitable for aliasing situations. In addition, other parameters such as echo Time (TR) 47.1ms, repetition Time (TE) 1.6ms, and field of view (FOV) (298 × 340) [ (mm) ] mm 2 are used to configure the pixel matrix at (134 × 256). In addition, an in-plane resolution of 1.54 mm/pixel, which is determined by the pixel pitch, and an in-plane resolution of 6 mm based on the slice interval are employed. All images were acquired by retrospective gating and 25 phases or time frames per slice (for time frame indices from Nt-1 to 25).
See fig. 2, which is a representation of an MRI scan through the heart and a PC-MRI based velocity image in the heart chamber. The PC-MRI belongs to the foot-head (F-H) and anterior-posterior (A-P) directions, respectively, and the intensity of its pixels indicates the magnitude of the velocity component in a given direction. Combining two orthogonal velocity encoded images can produce a velocity flow field.
And step S120, identifying Lagrangian vortex rings in the heart based on the Lagrangian average vorticity deviation.
To identify the lagrangian vortex ring within the Right Atrium (RA), the Lagrangian Average Vorticity Deviation (LAVD) is calculated in the plane and continuous time phase of each image. For example, Haller et al (Haller, G. "Dynamic Rotation and Stretch transducers from a Dynamic Polar Decomposition", Journal of the Mechanics & Physics of Solids86:70-93,2016.) derive LAVD from the Dynamic Polar Decomposition of the deformation gradient to define the vortex. The time-dependent motion trajectory of the fluid particles generated by v (x, t) within the heart is governed by the following differential equation:
where x represents the position of the particle in the flow field, t represents the time, and v (x, t) represents the velocity of the particle at this position at this time. Defining a cardiac blood flow map as:
where δ represents the derivative, x (x)0,t0And t) represents t0Flow mapping to time t. It should be noted that it is preferable that,
the tensor does not provide an objective indication of the rotational component of the deformation, because the polar rotation angle extracted from the map depends on the observer. To address this problem, a technique based on Dynamic Polar Decomposition (DPD) may be used, which will be described in more detail below
Decomposition into dynamic rotation tensor
And right dynamic tension tensor
Is represented as follows:
then, the user can use the device to perform the operation,
can be further decomposed into two tensors. Specifically, the following expressions are given:
wherein the content of the first and second substances,
is the relative rotation of the two parts,
the average rotation is described.
With dynamic consistency it is meant that the tensor is dynamically consistent over the total angle swept around its axis of rotation. Due to the nature of any physical rigid body motion, this angle will satisfy the relationship:
wherein s and t represent the interval [ t0，t1]The intermediate time of (c).
Is the natural rotation angle. Using the results obtained in the reference (Dynamic Rotation and Stretch variables from a Dynamic Polar Decomposition), it can be calculated as:
where ω (x)0S), s) is the vorticity along the trajectory of the material,
the spatial average of vorticity is described. LAVD is defined by the integral component of equation (7) above, as follows:
based on equation (8) above, the rotating coherent Lagrangian vortices are defined as a set of nested outwardly decreasing LAVD tubular level sets. LAVD is objective, and we note that this metric is relative to its neighborhood, whose range of values depends on the watershed. In this study, the area relative to the vortex was large based on manual segmentation. The boundary of Lagrangian vortex is a closed convex horizontal plane of the outermost layer of LAVD, which satisfies the deficiency of convexity, and the vortex core is the local maximum of LAVD surrounded by the boundary. Thus, in the above embodiments, the boundary of the intracardiac lagrange vortices is defined as the outermost member of the closed family of LAVD level curves, which is below the under-convexity threshold.
In conclusion, the Lagrangian vortex nucleus and related region data in the heart are accurately extracted based on PC-MRI, the track integration of the normalized deviation of the vorticity and the spatial average value of the vorticity is realized, and the method is used for accurately identifying the Lagrangian vortex ring of the heart and tracking the characteristics of the vortex ring of the heart based on LAVD.
LAVD also causes a difficulty. In particular, Lagrangian vortices are inherently related to certain finite time intervals within which they exert an influence on nearby trajectories. Short-term changes in blood flow within the heart chamber are generally considered important when calculating LAVD values. Thus, in a preferred embodiment, intermediate PC-MRI velocity data is synthesized temporally and spatially using Horn-Schunck (or simply HS) optical flow to produce a more accurate continuous velocity field v (x, t). The Horn-Schunck optical flow algorithm uses a global approach to estimate the dense optical flow field of an image (i.e., computing light for each pixel in the image). Intermediate PC-MRI data are synthesized by adopting Horn-Schunck brightness constraint, and a high-quality continuous velocity field can be generated so as to reduce errors brought by an LAVD integration process.
In summary, the invention provides a novel heart Lagrange vortex ring identification method based on the combination of LAVD and optical flow, firstly, the optical flow between two continuous PC-MRI is estimated, and the intermediate optical flow field can be approximated by the process of twisting and constructing a continuous velocity field. The LAVD is then calculated by using the synthetic velocity field to accurately extract the Lagrangian vortex kernel and the region within the RA. The invention describes the time evolution of Lagrange vortex ring in RA for the first time; the interpolation method based on optical flow is verified to achieve the best result on a PC-MRI sequence. The method of the invention provides a solution for analysis of cardiac vortex ring formation and improves understanding of hemodynamics in the heart. It should be understood that although the right atrium is described above, the same applies to the left atrium, left ventricle, and other chambers.
To verify the effectiveness of the present invention, experiments were performed. In experiments, the intermediate PC-MRI synthesis method based on HS Optical flow (labeled HS Optical flow) of the present invention was compared to other methods, including Linear interpolation (labeled Linear) and Phase-based interpolation (labeled Phase). They derive from a different understanding of the image. See FIG. 3, where FIG. 3(a) is the quality of the interpolated PC-MRI versus the number of 2 skipped images. Fig. 3(b) shows the influence α of the parameter α on SSIM (structural similarity), which is the ratio of the luminance constant integral to the weight of the spatial smoothness integral. FIGS. 3(c) and 3(d) Performance of IE and SSIM in selected temporal frame indices of optical flow interpolation, phase-based interpolation and 4 linear interpolations, where IE represents interpolation error, is used to handle construction of an intermediate PC-MRI.
The IE score and SSIM score for each selected time frame of the three methods are shown in fig. 3(c) and fig. 3(d), and the overall evaluation is shown in table 1. The optical flow-LAVD model of the present invention achieves the best performance at a selected number of time frame indices. In particular, HS light flow can reach the best IE and SSIM over a large scale vortex cycle where Nt ═ 8 to 10 exists. In contrast, the phase-based interpolated PC-MRI is of slightly inferior quality. This interpolation method captures phase information, but the uncorrelated content and noise of the PC-MRI have negative effects. The change in IE and SSIM is typically decreasing in the selected time frame index. This is due to the larger pixel displacement and weaker turbulent flow pattern at the end of the RA diastole.
Table 1: comparison of the present invention with existing different interpolation methods
In conclusion, the optical flow-LAVD method provided by the invention can accurately identify the vortex ring and the continuous velocity field based on the optical flow information, and obtain a higher reconstruction result. Compared with linear interpolation and frame interpolation method based on phase, the method provided by the invention can generate more accurate synthetic PC-MRI. The accuracy of the eddy current identification method for the synthetic PC-MRI data sequence based on the optical flow LAVD provided by the invention is widely verified, and the method can characterize the region of the main Lagrangian vortex ring in the heart chamber. Through verification, the optical flow LAVD model developed by the method accurately identifies 25 heart vortex rings, minimizes related errors caused by constructing a continuous 26-velocity field, and is helpful for medical experts to understand the hemodynamics in the heart.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + +, Python, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A heart blood flow vorticity ring identification method based on optical flow and Lagrange vorticity deviation comprises the following steps:
step S1: estimating optical flow between two successive phase-contrast magnetic resonance imaging PC-MRIs, wherein the intermediate optical-flow field is approximated by a process of warping the constructed continuous velocity field;
step S2: the lagrangian mean vorticity deviation, LAVD, is calculated using the synthetic velocity field to extract the lagrangian vorticity kernel and the region within the target chamber to identify the cardiac blood flow vorticity ring.
2. The method of claim 1, wherein in step S1, intermediate PC-MRI data is synthesized using a Horn-Schunck luminance constraint.
3. The method of claim 1, wherein the Lagrangian average vorticity deviation, LAVD, is calculated in the plane and continuous time phase of each image for the Lagrangian vorticity rings within the target chamber.
4. The method according to claim 1, wherein step S2 includes the sub-steps of:
defining a cardiac blood flow map:
by using
The displacement gradient of (a) describes the time t from which the two particles have passed0To t1One at x0Where the other is at x0+δx(t0) Is adjacent to it, expressed as:
will be provided with
Decomposition into dynamic rotation tensor
And right dynamic tension tensor
Is shown as
Will be provided with
After a further decomposition into two tensors,
expressed as:
wherein the content of the first and second substances,
is the relative rotation of the two parts,
the average rotation is described and satisfies the relation:
wherein
Is the natural rotation angle, calculated as;
the Lagrangian average vorticity deviation LAVD is defined as:
wherein x represents the position of the particle in the flow field, t represents time, and s and t represent the interval [ t0,t1]Intermediate time of (d), ω (x)0S), s) is the vorticity along the trajectory of the material,
the spatial average of vorticity is described.
5. The method of claim 1, wherein the boundary of the Lagrangian vorticity is a closed convex horizontal plane of an outermost layer of the Lagrangian average vorticity deviation LAVD, satisfying the deficiency in convexity, and the vortex kernel is a local maximum of the Lagrangian average vorticity deviation LAVD surrounded by the boundary.
6. The method of claim 1, wherein the target chamber comprises a right atrium, a left ventricle, or a right ventricle.
7. The method of claim 2, wherein Horn-Schunck uses a global approach to estimate the dense optical flow field of an image to compute light for each pixel in the image, and further uses luminance constraints to synthesize intermediate PC-MRI data.
8. The method of claim 4, wherein the boundaries of the Lagrangian vortices within the target chamber are defined as the outermost members of a closed family of Lagrangian Average Vorticity Deviation (LAVD) level curves and are below a set under-convexity threshold.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
10. A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the steps of the method of any of claims 1 to 8 are implemented when the processor executes the program.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040242954A1 (en) * | 2003-05-30 | 2004-12-02 | Moises Calderon | Universal pneumatic ventricular assist device |
| CN102930511A (en) * | 2012-09-25 | 2013-02-13 | 四川省医学科学院（四川省人民医院） | Method for analyzing velocity vector of flow field of heart based on gray scale ultrasound image |
| CN109791617A (en) * | 2017-01-25 | 2019-05-21 | 清华大学 | The real-time phase of low-rank modeling and parallel imaging compares blood flow MRI |
| CN110781446A (en) * | 2019-09-20 | 2020-02-11 | 中国海洋大学 | Method for rapidly calculating average vorticity deviation of ocean mesoscale vortex Lagrange |
| CN112568888A (en) * | 2020-12-08 | 2021-03-30 | 中国科学院深圳先进技术研究院 | In-vivo fluid flow analysis method, system, terminal and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403060A (en) * | 2017-06-20 | 2017-11-28 | 四川省人民医院 | A kind of heart bicuspid valve flow field domain method for numerical simulation |
-
2021
- 2021-10-18 CN CN202111208552.8A patent/CN113920173A/en active Pending
- 2021-12-14 WO PCT/CN2021/138100 patent/WO2023065504A1/en unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040242954A1 (en) * | 2003-05-30 | 2004-12-02 | Moises Calderon | Universal pneumatic ventricular assist device |
| CN102930511A (en) * | 2012-09-25 | 2013-02-13 | 四川省医学科学院（四川省人民医院） | Method for analyzing velocity vector of flow field of heart based on gray scale ultrasound image |
| CN109791617A (en) * | 2017-01-25 | 2019-05-21 | 清华大学 | The real-time phase of low-rank modeling and parallel imaging compares blood flow MRI |
| CN110781446A (en) * | 2019-09-20 | 2020-02-11 | 中国海洋大学 | Method for rapidly calculating average vorticity deviation of ocean mesoscale vortex Lagrange |
| CN112568888A (en) * | 2020-12-08 | 2021-03-30 | 中国科学院深圳先进技术研究院 | In-vivo fluid flow analysis method, system, terminal and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| KE YANG 等: "A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation", 《FRONTIERS IN PHYSIOLOGY》 * |
| KE YANG 等: "Lagrangian-averaged vorticity deviation of spiraling blood flow in the heart during isovolumic contraction and ejection phases", 《MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING(2021)》 * |
| 张超越 等: "主动脉瓣二瓣畸形升主动脉血流动力学变化的4D 磁共振血流动力学对比研究", 《心肺血管病杂志》 * |
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