CN108459286A - Internal Mechanical Properties of Soft Tissues test method based on magnetic resonance imaging and device - Google Patents
Internal Mechanical Properties of Soft Tissues test method based on magnetic resonance imaging and device Download PDFInfo
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- 210000004872 soft tissue Anatomy 0.000 title claims abstract description 102
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Abstract
The invention belongs to technical field of image processing, and in particular to a kind of internal Mechanical Properties of Soft Tissues test method and device based on magnetic resonance imaging.The internal Mechanical Properties of Soft Tissues test method based on magnetic resonance imaging of the present invention includes the following steps：The Displacements Distribution and Strain Distribution of soft tissue are measured by magnetic resonance marker imaging method；The magnetic resonance configurations image information for acquiring soft tissue carries out geometrical reconstruction, and establishes limited element calculation model；Based on the limited element calculation model, calculation optimization is iterated using inverse algorithm, obtains the physico mechanical characteristic parameter distribution of soft tissue.The method of the present invention carries out in-vivo measurement using magnetic resonance marker imaging method to soft tissue physico mechanical characteristic, and noninvasive painless, operability is strong, at low cost, can be widely applied to physical characteristic quantization and analysis to human body soft tissue.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method and a device for testing mechanical properties of soft tissues in vivo based on magnetic resonance imaging.
Background
With the development and progress of medical imaging technology, image processing technology is more and more widely applied in medical research and clinical medicine, and is mainly applied in aspects of radiotherapy planning, interventional therapy, surgical navigation and the like. Currently, clinical medical image-based pathological diagnosis is mainly based on image characteristics and geometric relationships, wherein MR image-based diagnosis is widely applied to brain tissues and soft tissues of abdominal organs. However, the existing imaging method can only judge the spatial geometric position information and the component information represented by the pixels, and cannot test and judge the physical and mechanical characteristics of the soft tissue.
At present, there are two main ways to judge the physical and mechanical properties of soft tissues, one is to test the mechanical properties of pathological tissues in vitro by an in vitro test method; one is an in vivo testing method, including manual palpation by the physician and other methods that employ magnetic resonance elastography or ultrasound elastography. In-vitro testing methods need to sample pathology, and noninvasive testing cannot be realized; the manual palpation method cannot perform accurate and consistent judgment depending on experience, and has large individual difference; the magnetic resonance elastography method needs to adopt a special driving instrument for measurement, and has higher requirements on hardware equipment; the resolution and imaging quality of ultrasound elastography are poor.
Disclosure of Invention
One of the purposes of the invention is to overcome the defects and provide a method which is noninvasive, painless, strong in operability, low in cost and widely applicable to quantification and analysis of physical characteristics of human soft tissues.
In order to solve the technical problem, the invention provides an in-vivo soft tissue mechanical property testing method based on magnetic resonance imaging, which comprises the following steps of:
measuring the displacement distribution and the strain distribution of the soft tissue by a magnetic resonance marker imaging method;
acquiring magnetic resonance structure image information of soft tissue to carry out geometric reconstruction, and establishing a finite element calculation model;
and based on the finite element calculation model, performing iterative calculation optimization by adopting a reverse algorithm to obtain the physical and mechanical characteristic parameter distribution of the soft tissue.
Further, the "measuring displacement distribution and strain distribution of soft tissue by using magnetic resonance marker imaging method" specifically includes:
acquiring a set of magnetic resonance marker image data at a certain frequency;
performing two-dimensional Fourier transform on the collected marked image data, and selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
selecting the intersection points of the marking lines as tracking points, selecting a first frame of marking image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of marking image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm;
and calculating a deformation gradient matrix based on the displacement distribution of the soft tissue, calculating the Lagrange strain of the tracking point, and performing spatial interpolation on the Lagrange strain to obtain the strain distribution of the soft tissue.
Further, the deformation gradient matrixThe calculation formula of (2) is as follows:wherein,is a location vector infinitesimal in the reference image,and the position vector infinitesimal in the deformation image, wherein m is the total frame number of the magnetic resonance marking image data, n is the image frame number, and i is the tracking point number.
Further, lagrangian strain of the tracking pointThe calculation formula of (2) is as follows:whereinIs a matrix of the units,is a deformation gradient matrix.
Further, the "based on the finite element calculation model, using a reverse algorithm to perform iterative calculation optimization to obtain the distribution of physical and mechanical property parameters of the soft tissue" specifically includes:
calculating displacement distribution under a measured strain distribution state according to the finite element model;
and establishing an objective function between the calculated displacement distribution and the measured displacement distribution, and performing parameter optimization on the objective function to obtain the physical and mechanical characteristic parameter distribution of the finite element model.
Further, the objective function is:wherein,in order to calculate the distribution of the displacements,for measuring displacement distribution, m is the total frame number of the magnetic resonance marked image data, n is the image frame number, and i is the tracking point number.
Further, the method for testing the mechanical property of the soft tissue in the body based on the magnetic resonance imaging further comprises the following steps:
and matching the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue with the pathological characteristics or the mapping relation of pathological stages to perform auxiliary diagnosis.
Correspondingly, the invention also provides an in-vivo soft tissue mechanical property testing device based on magnetic resonance imaging, which comprises:
the first processing module is used for measuring the displacement distribution and the strain distribution of the soft tissue by a magnetic resonance marker imaging method;
the second processing module is used for acquiring the magnetic resonance structure image information of the soft tissue to carry out geometric reconstruction and establishing a finite element calculation model;
and the third processing module is used for carrying out iterative calculation optimization by adopting a reverse algorithm based on the finite element calculation model to obtain the physical and mechanical property parameter distribution of the soft tissue.
Further, the first processing module includes:
the first processing unit is used for acquiring a group of magnetic resonance marker image data according to a certain frequency;
the second processing unit is used for carrying out two-dimensional Fourier transform on the collected marked image data and selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
the third processing unit is used for selecting the intersection point of the mark line as a tracking point, selecting the first frame of mark image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of mark image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm;
and the fourth processing unit is used for calculating a deformation gradient matrix based on the displacement distribution of the soft tissue, then calculating the Lagrangian strain of the tracking point, and carrying out spatial interpolation on the Lagrangian strain to obtain the strain distribution of the soft tissue.
Further, the third processing module includes:
the first processing unit is used for calculating displacement distribution under a measured strain distribution state according to the finite element model;
and the second processing unit is used for establishing an objective function between the calculated displacement distribution and the measured displacement distribution, and performing parameter optimization on the objective function to obtain the physical and mechanical property parameter distribution of the finite element model.
Further, the device for testing the mechanical properties of the soft tissue in the body based on the magnetic resonance imaging further comprises:
and the fourth processing module is used for matching the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue with the pathological characteristics or the mapping relation of pathological stages to perform auxiliary diagnosis.
The technical scheme of the invention has the beneficial effects that:
1. by measuring the displacement distribution and the strain distribution of the soft tissue by using the magnetic resonance marker-based imaging method, on one hand, noninvasive and painless in-vivo detection is realized, on the other hand, the natural motion in the human body is adopted for motion acquisition, an external driving device and an auxiliary instrument are not needed, the operability is high, and the cost is low.
2. The physical and mechanical characteristic parameters of the soft tissue are quantified through finite element model calculation, and reliable physical data can be provided for clinical diagnosis and analysis.
3. The pathological mapping can be carried out on the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue, and the accuracy and the universality of diagnosis are improved.
4. The invention is not limited to a specific organ tissue, can be widely applied to all soft tissue organs, is not limited to clinical diagnosis, and can also be widely applied to other scientific and engineering calculations.
Drawings
FIG. 1 is a flow chart of the steps of a method for testing the mechanical properties of soft tissues in vivo based on magnetic resonance imaging.
Fig. 2 is a flowchart of the steps of measuring the displacement distribution and strain distribution of soft tissue by a magnetic resonance marker imaging method according to the present invention.
FIG. 3 is a flowchart of the step of obtaining the distribution of parameters of physical mechanical properties by iterative computation using a reverse algorithm according to the present invention.
Fig. 4 is a structural diagram of an in-vivo soft tissue mechanical property testing device based on magnetic resonance imaging.
FIG. 5 is a block diagram of a first processing module of the present invention.
Fig. 6 is a block diagram of a third process module of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of steps of a method for testing mechanical properties of soft tissues in vivo based on magnetic resonance imaging, which includes the following steps:
step 1, measuring displacement distribution and strain distribution of soft tissue by a magnetic resonance marker imaging method;
magnetic Resonance Imaging (MRI) technology utilizes the nuclear Magnetic Resonance phenomenon of protons contained in a human body in a Magnetic field to collect radio frequency signals, and then forms an image through a spatial coding technology for a doctor to diagnose. The magnetic resonance marker imaging method is a method for tracking the movement of body tissues and performing body tissue displacement and strain analysis, wherein the displacement refers to the moving distance of the body tissues in the movement process, and the strain refers to the deformation of the body tissues.
Fig. 2 is a flowchart of the steps of measuring the displacement distribution and the strain distribution of the soft tissue by the mri method according to the present invention, which includes the following steps:
step 201, collecting a group of magnetic resonance marking image data according to a certain frequency;
acquiring a group of image data in the process of soft tissue deformation movement according to a certain time interval, wherein the image data can be natural deformation of human tissues and organs, such as myocardial movement, and rhythm of internal organs based on cardiac motion or respiration; or an externally applied regular motion.
202, performing two-dimensional Fourier transform on the collected marked image data, and selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
in order to obtain the position of the marking line, the technical scheme of the invention comprises the following steps of: first, a two-dimensional fourier transform is performed, which mathematically processes a function into a series of periodic functions. From the physical effect, the fourier transform is to convert the image from the spatial domain to the frequency domain, i.e. to transform the gray distribution function of the image into the frequency distribution function of the image. And then, selecting a first main frequency peak value after two-dimensional Fourier transform to perform band-pass filtering, wherein the band-pass filtering refers to passing frequencies in a certain range and filtering other frequencies. In a preferred embodiment, the bandwidth of the band pass filter is 5-10 pixels. And thirdly, performing two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering, and converting the image from a frequency domain to a space domain to obtain complex variable two-dimensional information. And finally, calculating a sine function value of a corresponding phase according to the characteristic that the phase diagram of the complex variable changes in the interval [ -pi, pi ], and calculating the position of the mark line according to the function peak value.
Step 203, selecting the intersection points of the marking lines as tracking points, selecting a first frame of marking image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of marking image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm;
in the image data with the obtained positions of the marking lines, the cross points of the marking lines are selected as tracking points, and the number of the tracking points of each frame of image can be several, and the positions areWherein n is the image frame number and i is the mark point number. Selecting a first frame (n is 1) to mark an image I1Calculating the displacement of tracking point for each frameAnd spatial interpolation is carried out to calculate the displacementIn the distribution in space, a linear interpolation algorithm or a spline interpolation algorithm is commonly used as a spatial interpolation algorithm.
And 204, calculating a deformation gradient matrix based on the displacement distribution of the soft tissue obtained in the previous step, then calculating Lagrange strain of the tracking point, and performing spatial interpolation on the Lagrange strain to obtain the strain distribution of the soft tissue.
In a preferred embodiment, a deformation gradient matrix is calculatedThe calculation formula of (2) is as follows:wherein,is a location vector infinitesimal in the reference image,and the position vector infinitesimal in the deformation image, wherein m is the total frame number of the magnetic resonance marking image data, n is the image frame number, and i is the tracking point number. Computing Lagrange strain for tracking pointsThe calculation formula of (2) is as follows:whereinIs a matrix of the units,is a deformation gradient matrix.
After the Lagrange strain result is obtained, the strain distribution of the soft tissue can be obtained by performing spatial interpolation by adopting a linear interpolation algorithm or a spline interpolation algorithm. For example, to track point strainAnd the position in spaceIs input; calculating the required spatial position point by linear interpolation or spline interpolationStrain of
Step 2, collecting the magnetic resonance structure image information of the soft tissue to carry out geometric reconstruction, and establishing a finite element calculation model;
the geometry of the soft tissue organ to be measured is imaged and acquired using conventional magnetic resonance structural image acquisition methods, such as T1W or T2W. According to clinical needs, two-dimensional or three-dimensional reconstruction is carried out on the geometric form of a target tissue organ, wherein the geometric reconstruction refers to extraction of the geometric form of the tissue organ in an acquired image, and an extraction algorithm which is often adopted is a threshold segmentation method or a deformation registration method.
After the geometrical morphology of the soft tissue is reconstructed, a Finite Element calculation model for calculating the physical and mechanical characteristic parameter distribution of the soft tissue can be established, and Finite Element Analysis (FEA) is to simulate a real physical system by using a mathematical approximation method, approach the real system of infinite unknown quantity by using a Finite number of unknown quantities, namely solve a complex problem by using a simpler problem. Because the structures of different soft tissue organs are different, the finite element model is not necessarily the same in assumption, and can be modeled according to actual conditions.
And 3, based on the finite element calculation model, performing iterative calculation optimization by adopting a reverse algorithm to obtain the physical and mechanical characteristic parameter distribution of the soft tissue.
Fig. 3 is a flowchart of a step of obtaining distribution of parameters of physical and mechanical properties by iterative computation using a reverse algorithm, and the method includes the following steps:
step 301, calculating displacement distribution under a measured strain distribution state according to a finite element model;
the basic steps of calculating the displacement distribution under the measured strain distribution state according to the finite element model are as follows: performing mesh division on a two-dimensional or three-dimensional geometric structure established by the soft tissue; setting boundary conditions according to the image and the physiological structure, and initializing physical parameters; and calculating the output of the model under the condition of the initial physical parameters, including the displacement distribution of the soft tissue organ under the deformation state.
Step 302, an objective function is established between the calculated displacement distribution and the measured displacement distribution, and the objective function is subjected to parameter optimization to obtain the physical and mechanical property parameter distribution of the finite element model.
In a specific embodiment, the objective function established between calculating the displacement distribution and measuring the displacement distribution may be:wherein,in order to calculate the distribution of the displacements,for measuring displacement distribution, m is the total frame number of the magnetic resonance marked image data, n is the image frame number, and i is the tracking point number.
In a preferred embodiment, the method for testing the mechanical properties of the soft tissue in the body based on the magnetic resonance imaging of the present invention may further include: and 4, matching the displacement distribution, the strain distribution and the mapping relation between the physical and mechanical characteristic parameters and the pathological characteristics or pathological stages of the soft tissues to perform auxiliary diagnosis. The mapping relationship may be a mapping relationship between the individual parameters of the displacement distribution, the strain distribution and the physical and mechanical characteristics and the pathological characteristics or pathological stages, or a mapping relationship between the comprehensive data of any two or three parameters and the pathological characteristics or pathological stages. For example, the mapping relationship between the physical and mechanical parameters and pathological characteristics of soft tissues is illustrated by taking the diagnosis of liver cirrhosis as an example: the liver cirrhosis is classified into five grades F0-F4 according to different pathological characteristics, wherein F0 is free from liver fibrosis symptoms, F4 is severe liver cirrhosis, and the existing research data show that the shear elastic modulus threshold values of liver tissues corresponding to the five grades F0-F4 are respectively 2.8,3.1,3.6,4.5 and 7.8kPa.
Fig. 4 is a structural diagram of an in-vivo soft tissue mechanical property testing device based on magnetic resonance imaging according to the present invention, including:
the first processing module is used for measuring the displacement distribution and the strain distribution of the soft tissue by a magnetic resonance marker imaging method; the displacement refers to the distance of movement of the body tissue during movement, and the strain refers to the deformation of the body tissue.
The second processing module is used for acquiring the magnetic resonance structure image information of the soft tissue to carry out geometric reconstruction and establishing a finite element calculation model; firstly, a conventional magnetic resonance structure image acquisition method, such as T1W or T2W, is adopted to image and acquire the geometric form of a soft tissue organ to be measured, and then the geometric form of the tissue organ in the acquired image is extracted, wherein the commonly adopted extraction algorithm is a threshold segmentation method or a deformation registration method. Then, a finite element calculation model for calculating the distribution of the physical and mechanical characteristic parameters of the soft tissue is established, and due to the fact that the structures of different soft tissue organs are different, the assumptions of the finite element model are not necessarily the same, and modeling can be carried out according to actual conditions.
And the third processing module is used for carrying out iterative calculation optimization by adopting a reverse algorithm based on the finite element calculation model to obtain the physical and mechanical property parameter distribution of the soft tissue.
In a preferred embodiment, the device for testing the mechanical properties of the soft tissue in vivo based on magnetic resonance imaging further comprises: and the fourth processing module is used for matching the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue with the pathological characteristics or the mapping relation of pathological stages to perform auxiliary diagnosis. According to the technical scheme, displacement distribution, strain distribution and physical and mechanical characteristic parameters can be calculated, and then the noninvasive diagnosis and analysis of the focus part can be realized by comparing the pathological characteristics or pathological stage mapping relation table generated according to experience.
Fig. 5 is a structural diagram of a first processing module according to the present invention, which includes:
the first processing unit is used for acquiring magnetic resonance marking image data in a group of tissue and organ movement processes according to a certain frequency; can be natural deformation of human tissue and organs, such as heart muscle movement, rhythm of internal organs based on heart beat or respiration; or an externally applied regular motion.
The second processing unit is used for carrying out two-dimensional Fourier transform on the collected marked image data, transforming a gray distribution function of the image into a frequency distribution function of the image, and then selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering, and converting the image from a frequency domain to a space domain to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
the third processing unit is used for selecting the intersection point of the mark line as a tracking point, selecting the first frame of mark image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of mark image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm; wherein, the number of the tracking points of each frame image can be several, and the positions areWherein n is an image frame encodingAnd the number i is a mark point number. Selecting a first frame (n is 1) to mark an image I1Calculating the displacement of tracking point for each frameAnd spatial interpolation is carried out to calculate the displacementIn the distribution in space, a linear interpolation algorithm or a spline interpolation algorithm is commonly used as a spatial interpolation algorithm.
And the fourth processing unit is used for calculating a deformation gradient matrix based on the displacement distribution of the soft tissue, then calculating the Lagrangian strain of the tracking point, and carrying out spatial interpolation on the Lagrangian strain to obtain the strain distribution of the soft tissue.
In a specific embodiment, a deformation gradient matrix is calculatedThe calculation formula of (2) is as follows: is a location vector infinitesimal in the reference image,and the position vector infinitesimal in the deformation image, wherein m is the total frame number of the magnetic resonance marking image data, n is the image frame number, and i is the tracking point number. Computing Lagrange strain for tracking pointsThe calculation formula of (2) is as follows:whereinIs a matrix of the units,is a deformation gradient matrix.
After the Lagrange strain result is obtained, the strain distribution of the soft tissue can be obtained by performing spatial interpolation by adopting a linear interpolation algorithm or a spline interpolation algorithm. For example, to track point strainAnd the position in spaceIs input; calculating the required spatial position point by linear interpolation or spline interpolationStrain of
Fig. 6 is a structural diagram of a third processing module according to the present invention, including:
the first processing unit is used for calculating displacement distribution under a measured strain distribution state according to the finite element model; the method comprises the following basic steps: performing mesh division on a two-dimensional or three-dimensional geometric structure established by the soft tissue; setting boundary conditions according to the image and the physiological structure, and initializing physical parameters; and calculating the output of the model under the condition of the initial physical parameters, including the displacement distribution of the soft tissue organ under the deformation state.
And the second processing unit is used for establishing an objective function between the calculated displacement distribution and the measured displacement distribution, and performing parameter optimization on the objective function to obtain the physical and mechanical property parameter distribution of the finite element model. The objective function established between the calculation displacement distribution and the measurement displacement distribution may be:wherein,in order to calculate the distribution of the displacements,for measuring displacement distribution, m is the total frame number of the magnetic resonance marked image data, n is the image frame number, and i is the tracking point number.
The above embodiments are merely illustrative of the technical solutions of the present invention, and the present invention is not limited to the above embodiments, and any modifications or alterations according to the principles of the present invention should be within the protection scope of the present invention.
Claims (11)
1. A method for testing mechanical properties of soft tissues in vivo based on magnetic resonance imaging is characterized by comprising the following steps:
measuring the displacement distribution and the strain distribution of the soft tissue by a magnetic resonance marker imaging method;
acquiring magnetic resonance structure image information of soft tissue to carry out geometric reconstruction, and establishing a finite element calculation model;
and based on the finite element calculation model, performing iterative calculation optimization by adopting a reverse algorithm to obtain the physical and mechanical characteristic parameter distribution of the soft tissue.
2. The in-vivo soft tissue mechanical property testing method based on magnetic resonance imaging as claimed in claim 1, wherein the "measuring displacement distribution and strain distribution of soft tissue by magnetic resonance marker imaging method" specifically comprises:
acquiring a set of magnetic resonance marker image data at a certain frequency;
performing two-dimensional Fourier transform on the collected marked image data, and selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
selecting the intersection points of the marking lines as tracking points, selecting a first frame of marking image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of marking image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm;
and calculating a deformation gradient matrix based on the displacement distribution of the soft tissue, calculating the Lagrange strain of the tracking point, and performing spatial interpolation on the Lagrange strain to obtain the strain distribution of the soft tissue.
3. The method for testing mechanical properties of soft tissue in vivo based on magnetic resonance imaging as claimed in claim 2, wherein the deformation gradient matrixThe calculation formula of (2) is as follows:wherein,is a location vector infinitesimal in the reference image,in a deformed imageAnd (4) a position vector infinitesimal, wherein m is the total frame number of the magnetic resonance marked image data, n is the image frame number, and i is the tracking point number.
4. The method for in vivo soft tissue mechanical property testing based on magnetic resonance imaging as claimed in claim 2, wherein lagrangian strain of the tracking pointThe calculation formula of (2) is as follows:whereinIs a matrix of the units,is a deformation gradient matrix.
5. The in-vivo soft tissue mechanical property testing method based on magnetic resonance imaging as claimed in claim 1, wherein the "based on the finite element calculation model, using a reverse algorithm to perform iterative calculation optimization to obtain the physical mechanical property parameter distribution of the soft tissue" is specifically:
calculating displacement distribution under a measured strain distribution state according to the finite element model;
and establishing an objective function between the calculated displacement distribution and the measured displacement distribution, and performing parameter optimization on the objective function to obtain the physical and mechanical characteristic parameter distribution of the finite element model.
6. The method for testing mechanical properties of soft tissue in vivo based on magnetic resonance imaging according to claim 5, wherein the objective function is:wherein,in order to calculate the distribution of the displacements,for measuring displacement distribution, m is the total frame number of the magnetic resonance marked image data, n is the image frame number, and i is the tracking point number.
7. The method for testing mechanical properties of soft tissue in vivo based on magnetic resonance imaging as claimed in claim 1, further comprising the steps of:
and matching the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue with the pathological characteristics or the mapping relation of pathological stages to perform auxiliary diagnosis.
8. An in vivo soft tissue mechanical property testing device based on magnetic resonance imaging, comprising:
the first processing module is used for measuring the displacement distribution and the strain distribution of the soft tissue by a magnetic resonance marker imaging method;
the second processing module is used for acquiring the magnetic resonance structure image information of the soft tissue to carry out geometric reconstruction and establishing a finite element calculation model;
and the third processing module is used for carrying out iterative calculation optimization by adopting a reverse algorithm based on the finite element calculation model to obtain the physical and mechanical property parameter distribution of the soft tissue.
9. The magnetic resonance imaging-based in vivo soft tissue mechanical property testing device according to claim 8, wherein the first processing module comprises:
the first processing unit is used for acquiring a group of magnetic resonance marker image data according to a certain frequency;
the second processing unit is used for carrying out two-dimensional Fourier transform on the collected marked image data and selecting a first main frequency peak value for band-pass filtering; carrying out two-dimensional inverse Fourier transform on the main frequency peak value subjected to band-pass filtering to obtain complex variable two-dimensional information; selecting a phase diagram of the complex variable to obtain the position of the mark line;
the third processing unit is used for selecting the intersection point of the mark line as a tracking point, selecting the first frame of mark image as a reference image, calculating the displacement of the tracking points of other frames relative to the first frame of mark image, and obtaining the displacement distribution of the soft tissue through a spatial interpolation algorithm;
and the fourth processing unit is used for calculating a deformation gradient matrix based on the displacement distribution of the soft tissue, then calculating the Lagrangian strain of the tracking point, and carrying out spatial interpolation on the Lagrangian strain to obtain the strain distribution of the soft tissue.
10. The magnetic resonance imaging-based in vivo soft tissue mechanical property testing device according to claim 8, wherein the third processing module comprises:
the first processing unit is used for calculating displacement distribution under a measured strain distribution state according to the finite element model;
and the second processing unit is used for establishing an objective function between the calculated displacement distribution and the measured displacement distribution, and performing parameter optimization on the objective function to obtain the physical and mechanical property parameter distribution of the finite element model.
11. The magnetic resonance imaging-based in vivo soft tissue mechanical property testing device according to any one of claims 8 to 10, further comprising:
and the fourth processing module is used for matching the displacement distribution, the strain distribution and the physical and mechanical characteristic parameters of the soft tissue with the pathological characteristics or the mapping relation of pathological stages to perform auxiliary diagnosis.
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