CN110021003B - Image processing method, image processing apparatus, and nuclear magnetic resonance imaging device - Google Patents
Image processing method, image processing apparatus, and nuclear magnetic resonance imaging device Download PDFInfo
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Abstract
The invention relates to a medical imaging technology, in particular to a method for realizing fusion of an anisotropic image, a diffusion envelope image and a nerve distribution image. The method comprises the steps of obtaining corresponding anisotropic images, diffusion envelope images and nerve distribution images according to nuclear magnetic resonance data, and completing image fusion by combining respective characteristics of the three images. The invention is suitable for analyzing and reconstructing complex biological tissue structures in the brain, and has important application value in the aspects of brain science, neuroscience, medical imaging and the like.
Description
Technical Field
The invention relates to the crossing field of neuroscience and medical image processing, in particular to a method for processing anisotropic images in nuclear magnetic resonance imaging, a method for fusing diffusion envelope surface images and nerve distribution images, a corresponding image processing device and magnetic resonance imaging equipment comprising the image processing device. The method and the device are suitable for the research of brain structure and function, the diagnosis and treatment of brain diseases, the preoperative planning of clinical neurosurgery, and the like.
Background
Magnetic Resonance Imaging (MRI) uses the principle of nuclear magnetic resonance, and detects emitted electromagnetic waves by an external gradient magnetic field according to different attenuations of released energy in different structural environments inside a substance, so that the positions and the types of nuclei constituting the substance can be known, and accordingly, a structural image inside the substance can be drawn. Diffusion Tensor Imaging (DTI), a new method for describing brain structure, is a special form of Magnetic Resonance Imaging (MRI). That is, the tissue structure and the distribution of nerve fibers are determined according to the dispersion movement of water molecules in biological tissues such as brain. The diffusion tensor imaging map can reveal how brain tumors affect nerve cell connections and guide medical personnel to carry out brain operations. It may also reveal subtle abnormalities associated with stroke, multiple sclerosis, schizophrenia, reading disorders, and the like. In addition, there are similar methods such as high angular resolution imaging, enhanced diffusion tensor imaging, and the like. The method adopts a high-order tensor model which can select independent variable number to describe the diffuse motion in a voxel and solves the direction of the fiber number in the voxel by combining a high-order tensor decomposition theory.
Dispersion (dispersion) refers to random irregular movement of molecules, which is an important moving form of water molecules in the human body and is also called brownian movement. The dispersion is a three-dimensional process, the dispersion distance of molecules along a certain direction in space is influenced by the structure of biological tissues, and the dispersion mode can be divided into two types: one means that in a completely uniform medium, the movement of molecules is not obstructed, and the movement distances in all directions are equal, and this dispersion mode is called isotropic (isotropic) dispersion, for example, the dispersion of water molecules in pure water is isotropic dispersion, and in human brain tissue, the dispersion of water molecules in cerebrospinal fluid and cerebral gray matter is similar to isotropic dispersion. The other type of diffusion has directional dependence, and in tissues arranged in a certain direction, the distance of diffusion of molecules to each direction is unequal, which is called anisotropic (anistropic) diffusion. In order to describe the degree of anisotropy of water molecule diffusion movement, anisotropy is often quantitatively analyzed using parameters such as Fractional Anisotropy (FA), Relative Anisotropy (RA), volume ratio index (VR), and the like. Images made from anisotropic parameters of various locations within the brain have important applications in the diagnosis of brain diseases. The white matter fiber structure of the brain observed by an FA image is the clearest, the grey white matter boundary is good, and the FA value is positively correlated with the completeness of myelin sheath and the compactness and parallelism of the fiber, so that the FA value is most widely applied. In addition, the dispersion motion envelope surface is also an effective means for describing the dispersion motion situation of water molecules in the voxel, namely: the spatial envelope surface formed by water molecules released from the center of the measurement voxel through the dispersion motion in unit time. Compared with the anisotropic parameters, the dispersion motion enveloping surface more completely and comprehensively reflects the influence of the dispersion motion of water molecules on the internal structure of the biological tissue, and has important physical and clinical significance. In the traditional diffusion tensor imaging, a diffusion motion enveloping surface is an ellipsoid; in high angular coordinate resolution imaging, the dispersion motion is the combination of a series of ellipsoids; in the enhanced diffusion tensor imaging technique, restoring the diffusion motion envelope in voxels is one of the key steps to complete the reconstruction of the nerve fiber bundle.
Fiber bundle tracing imaging (FT) is a new technology developed on the basis of diffusion tensor imaging and can be used for nondestructive testing of the direction and integrity of nerve Fiber bundles in the brain. The basic principle is to start from any measured voxel (i.e. the seed point), travel a specified length in the direction of the nerve fiber bundle within that voxel to the next voxel, and continue the above operation. Until the boundary of the measurement space is reached; or the anisotropy parameter within the voxel is below a threshold; or the angle of the nerve fiber bundles in the two voxels to which they are connected is greater than a threshold value (typically 60 degrees). The directions of the nerve fiber bundles in the series of voxels are connected in space, so that the overall direction of the nerve fiber bundles in the space is obtained. The fiber bundle tracing imaging technology can realize the three-dimensional reconstruction of the distribution of nerve fibers in the brain, and has extremely important significance for the research of structures and functions in the brain, the diagnosis of clinical brain diseases, surgical navigation and preoperative planning.
Anisotropic images can give the overall distribution of grey-white material in the slice plane; in comparison, the image of the diffusion envelope surface is more complete, and the anisotropy of the diffusion motion at each position in the slice plane can be comprehensively reflected, so that the tissue structure at the position can be roughly estimated; the nerve distribution image can help doctors and researchers to visually observe the spatial distribution and trend of the brain nerve fibers, and delicately and completely depict the structure of the nerve tissue in the brain and the connection condition of each functional area. The three images reflect the complex biological tissue structure in the brain from three different aspects of grey-white distribution, local anisotropy and nerve fiber distribution. In order to make full use of these three kinds of information, a certain fusion process needs to be performed on the three kinds of images. But the dimensions of the three images are different: the anisotropic image is a two-dimensional image, the nerve distribution image is a three-dimensional image, and the dispersion envelope surface image is between two and three dimensions (the voxel to be measured is distributed on a two-dimensional plane, and the dispersion envelope surface image in each voxel is three-dimensional), which causes certain difficulty in image fusion.
Disclosure of Invention
In order to solve the problems, the invention provides a processing method for effectively completing the fusion of an anisotropic image, a diffusion envelope image and a nerve distribution image. Specifically, the present invention is realized as such.
An image processing method is used for realizing the fusion processing of an anisotropic image, a diffusion envelope image and a nerve fiber distribution image, and is characterized by comprising the following steps:
step 1, acquiring nuclear magnetic resonance data of a subject;
step 2, processing the nuclear magnetic resonance data to obtain a dispersion motion envelope surface in each voxel of the object;
step 3, calculating the direction and the anisotropic parameters of the nerve fiber bundle in each voxel;
step 4, generating an anisotropic image in the selected slice plane according to the calculated anisotropic parameters;
step 5, selecting a dispersion motion envelope surface in a volume element with anisotropic parameters larger than a threshold value in the selected slice plane to generate a dispersion envelope surface image;
step 6, selecting a voxel with an anisotropic parameter larger than a threshold value as a seed point in the selected slice plane, and reconstructing a nerve fiber distribution image according to the direction of the nerve fiber bundle from the seed point;
and 7, fusing the anisotropic image, the diffusion envelope image and the nerve distribution image in the selected slice plane.
According to an aspect of the present invention, there is provided an effective image processing apparatus for fusing an anisotropic image, a diffusion envelope image, and a nerve fiber distribution image, characterized by comprising the following units:
a sampling unit for acquiring nuclear magnetic resonance data of a subject;
the calculating unit is used for obtaining a dispersion motion envelope surface in each voxel of the object and calculating the direction and the anisotropic parameters of the nerve fiber bundle;
the image generating unit is used for respectively generating an anisotropic image, a diffusion envelope image and a nerve fiber distribution image;
and the fusion unit is used for realizing the fusion of the anisotropic image, the diffusion envelope image and the nerve fiber distribution image.
According to another aspect of the invention, a magnetic resonance imaging apparatus is provided, comprising an image processing device according to an embodiment of the invention.
Drawings
FIG. 1 is a flow chart of an image fusion method according to the present invention;
fig. 2 is a block diagram showing a configuration example of an image fusion processing apparatus according to the present invention;
fig. 3 is a block diagram of a configuration example of a magnetic resonance imaging apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of the anisotropic image in step S150 of the practice of the present invention;
FIG. 5 is a partial schematic view of the fusion of the anisotropic image and the diffusion envelope image in step S180 according to the present invention;
FIG. 6 is a partial schematic view of the fusion of the three images in step S180 according to the present invention;
FIG. 7 is a schematic diagram of the fusion of the anisotropic image, the diffusion envelope image and the nerve distribution image that is the final implementation of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in 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 invention and are not intended to limit the invention.
As shown in fig. 1, it is a flow chart of the image fusion method of the present invention, which includes the following steps:
step S110, nuclear magnetic resonance data of the object is acquired. And selecting a limited number of sampling directions on the unit spherical surface, and measuring the attenuation intensity of signals in the sampling directions, namely nuclear magnetic resonance data, in each body element by using a nuclear magnetic resonance technology.
And S120, selecting a nuclear magnetic resonance data processing method to obtain a dispersion motion envelope surface in each voxel of the object. Such as: diffusion Tensor Imaging (DTI), high angular resolution imaging (HARDI), Enhanced Diffusion Tensor Imaging (EDTI), and the like. Taking Enhanced Diffusion Tensor Imaging (EDTI) as an example now:
(a) calculating dispersion coefficients D in each direction according to the signal attenuation intensity obtained in step S110,
b is the instrument parameter, S0Is the raw signal intensity and S is the measured intensity.
(b) Calculating the dispersion displacement x in unit time (1s) in each direction according to the dispersion coefficient D,
(c) reconstructing a diffusion motion envelope in the voxel from diffusion displacements in a finite number of directions in (b). For a three-dimensional envelope surface, taking one point inside the envelope surface to establish a coordinate system, the graph can be expressed as a radial length r and a direction unit vector
Functional relationship between
I.e. given direction
Can be composed of
The radial length r in that direction is determined. And function of
Can be unfolded into the following forms:
wherein D is1,D2...DrRespectively, a vector (first order tensor), a second order tensor … r order tensor. The form of the decomposition is similar to Taylor expansion (x is replaced by x
The higher derivative is replaced with a higher tensor). According to the irreducible decomposition in the theory of higher-order tensor decomposition, any higher-order tensor can be decomposed into a combination of a series of irreducible tensors,
can ultimately be represented as a series of non-contractable sheetsQuantity and unit vector
Sum of shrinkage of (c).
Setting the envelope surface of the dispersion movement as
For spatial parameters, the following decomposition can be performed according to the above theory:
wherein the content of the first and second substances,
a complete set of orthogonal unfolded bases in three-dimensional space. a ismFor coefficient of expansion, from envelope surface
And a given substrate
The integral can be obtained. Specifically, the expanded substrate may be taken as a three-dimensional spherical harmonic whose mathematical expression is as follows:
wherein
The method for reconstructing the dispersion motion envelope surface through dispersion displacement in the limited directions comprises the following specific steps:
(c1) and dividing a grid on the unit spherical surface, wherein the node of the grid is the measuring direction. Basis function
(taking three-dimensional spherical harmonic expansion as an example) and diffuse displacement in the node direction (obtained in (b)) are known, and the expansion coefficient a is calculated by an interpolation method and a dispersion integralm,r,bm,r: in each grid, obtaining the dispersion displacement at the center of the grid by linear interpolation according to the dispersion displacement in the node direction; replacing the whole grid with the dispersion displacement, trigonometric function value and basis function value at the center of the grid
sinr θ, cosr θ and
approximate calculation of a by multiplying function value by grid aream,r,bm,rThe value of the integral in the expression on the grid; traversing all grids, and summing the integral values on the grids to obtain the expansion coefficient am,r,bm,r。
(c2) From a substrate
And a calculated in (c1)m,r,bm,rRestoring a diffusion envelope
For a general basis function, the above equation can be written as:
and n is the expansion order, and factors such as a basis function, precision requirements, calculation cost and the like need to be comprehensively considered for reasonable selection, so that the diffusion motion envelope surface in each voxel is restored by calculating a diffusion coefficient, a diffusion displacement and an expansion coefficient.
Step S130: the direction of the nerve fiber bundles within the voxel is calculated according to the chosen method of processing of the nuclear magnetic resonance data, such as the enhanced diffusion tensor imaging mentioned above. Will be parameter
Value range of
Equally dividing into a plurality of parts to obtain a series of uniformly distributed points on the unit spherical surface. Traverse the envelope surface by
Calculating dispersion displacement of each point in the corresponding direction, and comparing the dispersion displacement of adjacent points in the corresponding direction; if the dispersion displacement in the direction corresponding to a certain point is larger than all adjacent points (dispersion displacement in the corresponding direction), the direction is the direction in which the dispersion displacement on the envelope surface takes the maximum value; can also be formed by directly
To pair
And (5) carrying out derivative calculation and determining the direction of the maximum value. The direction of the maxima is the direction of the nerve fiber bundles within the voxel.
Step S140: the anisotropic parameters are calculated using the obtained envelope of the diffusion motion according to the selected processing method of the nuclear magnetic resonance data, such as the enhanced diffusion tensor imaging mentioned in the present embodiment. Noting that the maximum value of the vector r on the diffusion motion envelope is maxr, the minimum value is minr, and the average value is meanr, the anisotropy parameter f can be defined as:
f ═ maxr-minr)/meanr. Note that: the anisotropy parameter may be calculated in other ways.
Step S150: the slice of the current study is selected and an anisotropic (parametric) image in the slice plane is made. As shown in fig. 4, an anisotropic parameter is calculated according to the restored diffusion motion envelope surface, the anisotropic parameter is defined as S140, a value range of the anisotropic parameter is linearly mapped to 0 to 255 (the minimum value corresponds to 0, and the maximum value corresponds to 255), and an image with the anisotropic parameter as a gray value is output.
Step S160: within the selected slice plane, voxels with an anisotropy parameter greater than a threshold A are extracted. In this example, the threshold value is taken to be 0.2, and for the general case, the threshold value may be any real number from 0 to 1. And drawing the corresponding diffusion motion envelope surface obtained in the step S120 in the selected voxel, namely making a diffusion envelope surface image in the plane.
Step S170: and selecting voxels with the anisotropy parameter in the slice plane larger than a threshold A as seed points, and starting from the seed points in sequence, and completing the reconstruction of the nerve fibers by using a fiber tracing imaging method (note that other reconstruction methods can also be adopted). The process continues with the travel of the specified length in the direction of the nerve fiber bundle within the seed point to the next voxel. Until the boundary of the measurement space is reached; or the anisotropy coefficient within a voxel is less than a threshold A; or the angle between the nerve fiber bundles in the two connected voxels is larger than the threshold value B. In this example, the threshold B is 60 degrees, and in general, the threshold B ranges from 45 degrees to 90 degrees. The directions of the nerve fiber bundles in the series of voxels are connected in space, so that the overall direction of the nerve fiber bundles in the space is obtained.
Step S180: as shown in fig. 5, the dispersion motion envelope image drawn in step S160 is placed in the anisotropic image in step S150, and the anisotropic image and the dispersion envelope image are fused; as shown in fig. 5, the seed points of the nerve fiber bundles reconstructed in step S170 are sequentially aligned with the corresponding voxels in the anisotropic image in step S150, and the fusion of the anisotropic image, the diffusion envelope image and the nerve distribution image is completed.
As shown in fig. 2, it is a schematic structural diagram of the image processing apparatus of the present invention. The image processing apparatus 200 of the embodiment of the present invention includes a sampling unit 210, a calculating unit 220, a drawing unit 230, and a fusing unit 240. In the above description of the image fusion method, specific implementation procedures of some steps have been disclosed, and hereinafter, an overview of units of the image fusion processing apparatus is given without repeating some details that have been discussed. Specifically, the method comprises the following steps: the image fusion processing apparatus 200 includes:
the sampling unit 210: for acquiring nuclear magnetic resonance data. And selecting a limited number of sampling directions on the unit spherical surface, and measuring the attenuation intensity of signals in the sampling directions in each volume element by using a nuclear magnetic resonance technology.
The calculation unit 220: the method is used for recovering the diffusion motion envelope surface in the voxel and calculating the nerve fiber bundle direction and the anisotropic parameters. Selecting a proper nuclear magnetic resonance data processing method, taking an Enhanced Diffusion Tensor Imaging (EDTI) method as an example:
(1) calculating dispersion coefficients D in each sampling direction in the voxel according to the signal attenuation intensity;
(2) calculating dispersion displacement x in unit time in each sampling direction in the voxel according to the dispersion coefficient D;
(3) selecting basis functions
And the expansion order n is combined with the dispersion displacement x in each sampling direction to restore the dispersion motion envelope surface in the voxel
Setting a real dispersion motion enveloping surface as
Decomposition is performed as follows;
calculating an expansion coefficient a by linear interpolation, quadratic interpolation or polynomial interpolation and other methods and discrete integrals according to the selected expansion order n and the dispersion displacement x in unit time in the sampling directionmIncorporating basis functions
Restoring a diffusion motion envelope in said voxel
(4) Determining the direction of the nerve fiber bundle in the voxel according to the restored diffusion motion envelope surface: searching a maximum value on the restored dispersion motion enveloping surface, and determining the direction of the maximum value; or determining the direction of the maximum value by directly taking the derivative of the restored diffusion envelope surface. The direction of the maximum is the direction of the nerve fiber bundle within the voxel.
(5) Noting that the maximum value of the vector r on the envelope surface is maxr, the minimum value is minr, and the average value is meanr, the anisotropy parameter f can be defined as: f ═ maxr-minr)/meanr.
The image generation unit 230: for rendering an anisotropy (parameter) image, a diffusion envelope image and a nerve distribution image, respectively:
selecting a slice of a current study, and making an image with anisotropic parameters as gray values;
extracting a voxel with anisotropic parameters larger than a threshold value in the selected slice plane, drawing a corresponding dispersion motion envelope surface on the position of the voxel, and making a dispersion envelope surface image;
and selecting voxels with anisotropic parameters larger than a threshold value in the slice plane as seed points, and starting from the seed points in sequence, and completing the reconstruction of the nerve fiber bundles by using a fiber tracing imaging method. The process continues with the specified length of travel in the direction of the nerve fiber bundle within the seed point to the next voxel. Until the boundary of the measurement space is reached; or the anisotropy coefficient within a voxel is less than a threshold A; or the angle between the nerve fiber bundles in the two connected voxels is larger than the threshold value B. In this example, the threshold B is 60 degrees, and for the general case, the threshold value ranges from 45 to 90 degrees. The directions of the nerve fiber bundles in the series of voxels are connected in space, so that the overall direction of the nerve fiber bundles in the space is obtained. And traversing all the seed points to obtain the nerve distribution image.
The fusion unit 240: the method is used for realizing the fusion of the three images. And (3) placing the drawn dispersion motion envelope surface image in an anisotropic image, and aligning the reconstructed seed points of the nerve fiber bundles to corresponding voxels in the anisotropic image in sequence to complete the fusion of the anisotropic image, the dispersion envelope surface image and the nerve distribution image.
In addition, embodiments of the present disclosure also include a magnetic resonance imaging apparatus. As shown in fig. 3, the magnetic resonance imaging apparatus 300 includes an image fusion processing device 200. The image fusion processing apparatus 200 may be the configuration of the embodiment with reference to fig. 2.
As an example, the respective steps of the above-described image fusion method and the respective constituent modules and/or units of the above-described image fusion processing apparatus may be implemented as software, firmware, hardware, or a combination thereof. In the case of implementation by software or firmware, a program constituting software for implementing the above method may be installed from a storage medium or a network to a computer having a dedicated hardware structure, and the computer may execute various functions and the like when various programs are installed.
The invention also provides a program product with machine readable instruction codes stored. The instruction codes can be read and executed by a machine to execute the nerve imaging method according to the embodiment of the invention.
Accordingly, a storage medium carrying the above-described program product having machine-readable instruction code stored thereon is also included in the present disclosure. Including, but not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.
In the foregoing description of specific embodiments of the invention, features described and/or illustrated with respect to one embodiment may be used in the same or similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
In the above embodiments and examples, numerical reference numerals have been used to indicate various steps and/or elements. It will be understood by those of ordinary skill in the art that these reference numerals are for convenience of description and drawing only and do not denote any order or any other limitation. While the present invention has been disclosed above by the description of specific embodiments thereof, it should be understood that all of the embodiments and examples described above are illustrative and not restrictive. Various modifications, improvements and equivalents of the invention may be devised by those skilled in the art within the spirit and scope of the appended claims. Such modifications, improvements and equivalents are also intended to be included within the scope of the present invention.
Claims (6)
1. An image processing method is used for realizing the fusion processing of an anisotropic image, a diffusion envelope image and a nerve fiber distribution image, and is characterized by comprising the following steps:
step 1, acquiring nuclear magnetic resonance data of a subject;
step 2, processing the nuclear magnetic resonance data to obtain a dispersion motion envelope surface in each voxel of the object;
step 3, calculating the direction and the anisotropic parameters of the nerve fiber bundle in each voxel;
step 4, generating an anisotropic image in the selected slice plane according to the calculated anisotropic parameters;
step 5, selecting a dispersion motion envelope surface in a volume element with anisotropic parameters larger than a threshold value in the selected slice plane to generate a dispersion envelope surface image;
step 6, selecting a voxel with an anisotropic parameter larger than a threshold value as a seed point in the selected slice plane, and reconstructing a nerve fiber distribution image according to the direction of the nerve fiber bundle from the seed point;
and 7, fusing the anisotropic image, the diffusion envelope image and the nerve distribution image in the selected slice plane.
2. The image processing method according to claim 1, characterized in that: the anisotropic parameters are calculated from the dispersion motion envelope.
3. The image processing method according to claim 1, characterized in that: the threshold is any real number from 0 to 1.
4. An image processing apparatus for fusing an anisotropic image, a diffusion envelope image and a nerve fiber distribution image by the image fusion processing method according to any one of claims 1 to 3, comprising:
a sampling unit for acquiring nuclear magnetic resonance data of a subject;
the calculating unit is used for obtaining a dispersion motion envelope surface in each voxel of the object and calculating the direction and the anisotropic parameters of the nerve fiber bundle;
the image generating unit is used for respectively generating an anisotropic image, a diffusion envelope image and a nerve fiber distribution image;
and the fusion unit is used for realizing the fusion of the anisotropic image, the diffusion envelope image and the nerve fiber distribution image.
5. The image processing apparatus according to claim 4, wherein in the calculation unit, the anisotropic parameters are calculated from the dispersion motion envelope surface.
6. A magnetic resonance imaging apparatus comprising the image processing according to any one of claims 4-5.
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