CN111145278A - Color coding method, device and equipment of diffusion tensor image and storage medium - Google Patents
Color coding method, device and equipment of diffusion tensor image and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a color coding method, a device, equipment and a storage medium of a diffusion tensor image. By acquiring diffusion tensor data of a target object, determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, calculating an eigenvector of the diffusion tensor data in the diffusion tensor data coordinate system, correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule, the problem that the color coding cannot be changed when the trend of nerve fibers and the relative position of the coordinate system change in the prior art is solved, the purpose of correcting the eigenvector in the image coordinate system through the correction matrix to change the color coding is achieved, and the effects of improving the uniformity and the accuracy of the color coding of the nerve fibers are achieved.
Description
Technical Field
The embodiment of the invention relates to a color coding technology, in particular to a color coding method, a device, equipment and a storage medium of a diffusion tensor image.
Background
The diffusion tensor image is an image obtained by imaging by using the perception of Brownian motion of water molecules, and the diffusion tensor image can show the trend of nerve fibers, reveal how brain tumors influence nerve cell connection, guide medical personnel to carry out brain surgery, and reveal subtle abnormal changes of the brain and the spinal cord related to stroke, multiple sclerosis, schizophrenia, reading disorder and the like.
At present, the color coding scheme mainly adopted in the industry is to directly perform color coding according to the characteristic direction of a diffusion tensor image, or calculate the main walking direction of a nerve fiber and globally adopt the same color coding for the fiber according to the direction. In the process of color coding the characteristic direction of the diffusion tensor image, the image coordinate system or the scanning angle of the same scanning object is changed frequently, so that the direction of the nerve fiber and the relative position of the coordinate system are changed, but the color coding scheme adopted at present cannot change the color coding when the direction of the nerve fiber and the relative position of the coordinate system are changed.
Therefore, the color coding scheme can not reflect the consistency of the color coding and the relative position when the trend of the nerve fiber and the relative position of the coordinate system are changed, so that the difference between the color coding of the nerve fiber and the actual situation is large.
Disclosure of Invention
The embodiment of the invention provides a color coding method, a device, equipment and a storage medium of a diffusion tensor image, so as to realize the effect of improving the uniformity and the accuracy of the color coding of nerve fibers.
In a first aspect, an embodiment of the present invention provides a color coding method for a diffusion tensor image, where the method includes:
acquiring diffusion tensor data of a target object, and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard bit direction;
calculating an eigenvector of the diffusion tensor data under an image coordinate system;
and correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule.
In a second aspect, an embodiment of the present invention further provides a color coding apparatus for a diffusion tensor image, where the apparatus includes:
the correction matrix determining module is used for acquiring diffusion tensor data of a target object and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard position direction;
the eigenvector calculation module is used for calculating eigenvectors of the diffusion tensor data in an image coordinate system;
and the color coding module is used for correcting the eigenvector based on the correction matrix and carrying out color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule.
In a third aspect, an embodiment of the present invention further provides a color coding apparatus for a diffusion tensor image, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the color coding method for the diffusion tensor image according to any one of the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions, when executed by a computer processor, implement the color coding method for the diffusion tensor image according to any one of the first aspect.
According to the technical scheme provided by the embodiment of the invention, by acquiring the diffusion tensor data of the target object, determining the diffusion tensor data and the correction matrix of the anatomical coordinate system, calculating the eigenvector of the diffusion tensor data in the image coordinate system, correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to the preset coding rule, the problem that the color coding cannot be changed when the trend of the nerve fibers and the relative position of the coordinate system change in the prior art is solved, the purpose of correcting the eigenvector in the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effects of improving the uniformity and the accuracy of the color coding of the nerve fibers are achieved.
Drawings
Fig. 1 is a schematic flowchart of a color coding method of a diffusion tensor image according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a color coding method of a diffusion tensor image according to a second embodiment of the present invention;
fig. 3 is a schematic flowchart of a color coding method of a diffusion tensor image according to a third embodiment of the present invention;
FIG. 4 is a schematic diagram of the orientation of nerve fibers in an anatomical coordinate system according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a color coding apparatus for a diffusion tensor image according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a color coding apparatus for a diffusion tensor image according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow chart of a color coding method for a diffusion tensor image according to an embodiment of the present invention, which is applicable to a case where an anatomical coordinate system is used as a standard bit direction, and feature vectors of diffusion tensor data are color coded. Referring specifically to fig. 1, the method may include the steps of:
s110, acquiring diffusion tensor data of the target object, and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system.
The target object may be a head, a chest or other parts. Optionally, the present embodiment takes the head target object as an example for description. Wherein the anatomical coordinate system is a standard position direction.
It can be understood that the diffusion tensor image DTI is an image obtained by imaging the perception of brownian motion of water molecules in the brain by the diffusion tensor imaging technique, and the diffusion tensor imaging diagram is composed of a plurality of diffusion weighted data.
Wherein the anatomical coordinate system is a standard position direction. In general, when diffusion tensor imaging is performed on a target object, the target object often moves, for example, the head moves in a certain direction, the head deflects to a certain angle, and coordinate data of diffusion tensor data changes due to movement of a scanning device, and the movement changes the orientation of brain nerve fibers. In this embodiment, the diffusion tensor data and the correction matrix of the anatomical coordinate system in the standard bit direction may be determined first, so as to correct the following trend of the nerve fiber, and facilitate the following color coding of the nerve fiber with the changed trend.
And S120, calculating the eigenvector of the diffusion tensor data in the image coordinate system.
The image coordinate system can be understood as a diffusion tensor data coordinate system. The eigenvector can be understood as three orthogonal vectors of the diffusion tensor data in the image coordinate system, and the eigenvector of the diffusion tensor data and the eigenvalue corresponding to the eigenvector can reflect the trend information of the nerve fiber, so that the trend of the nerve fiber can be determined by calculating the eigenvector of the diffusion tensor, and the subsequent color coding of the nerve fiber in each trend is facilitated.
And S130, correcting the eigenvector based on the correction matrix, and carrying out color coding on the corrected eigenvector in an anatomical coordinate system according to a preset coding rule.
Optionally, the feature vector may be corrected by: acquiring spatial data of the correction matrix, and correcting the eigenvector according to the spatial data; wherein the spatial data may include at least one of a rotation angle, a rotation direction, and changed coordinate data.
Illustratively, the correction matrix includes: diffusion tensor data An(Xn，Yn，ZnP, Q), wherein n is not less than 1, Xn，Yn，ZnIs diffusion tensor data AnIn dissectionChanging coordinate data under a mathematical coordinate system, P is AnDirection of rotation in an anatomical coordinate system, Q being AnRotation angle in anatomical coordinate system. Alternatively, P can be up, down, front, back, left, and right, and Q can be any value from 0 to 360. If the trend of any nerve fiber changes and the diffusion tensor data also change, at least one of changed coordinate data, a rotation direction and a rotation angle of a correction matrix of the calculated diffusion tensor data exists, the feature vector with the changed trend can be corrected in an anatomical coordinate system through the spatial data of the correction matrix, and then the corrected feature vector is subjected to color coding again.
According to the technical scheme provided by the embodiment of the invention, by acquiring the diffusion tensor data of the target object, determining the diffusion tensor data and the correction matrix of the anatomical coordinate system, calculating the eigenvector of the diffusion tensor data in the image coordinate system, correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to the preset coding rule, the problem that the color coding cannot be changed when the trend of the nerve fibers and the relative position of the coordinate system change in the prior art is solved, the purpose of correcting the eigenvector in the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effects of improving the uniformity and the accuracy of the color coding of the nerve fibers are achieved.
Example two
Fig. 2 is a schematic flow chart of a color encoding method of a diffusion tensor image according to a second embodiment of the present invention. The technical solution of this embodiment adds a new step to the above embodiment, and optionally, before the determining the correction matrix of the diffusion tensor data and the anatomical coordinate system, the method further includes: an anatomical image in the anatomical coordinate system is acquired. Referring specifically to fig. 2, the method of the present embodiment may include the following steps:
and S210, acquiring diffusion tensor data of the target object.
S220, obtaining an anatomical image in an anatomical coordinate system.
The anatomical image in the anatomical coordinate system is understood to be a standard anatomical image. For example, the anatomical image is obtained by a high-precision segmentation algorithm, or the anatomical image is obtained by a high-precision segmentation algorithm and manual multiple debugging.
And S230, determining a correction matrix of the diffusion tensor data and the anatomical coordinate system.
Alternatively, the diffusion tensor data and the anatomical image may be registered to obtain a registration matrix, and the registration matrix may be used as a correction matrix of the diffusion tensor data. It should be noted that the acquired diffusion tensor data may include the undegraded DWI image B0, the 1 st graded DWI image D1, and the 2 nd graded DWI image D2 …, and the nth graded DWI image Dn, n is not less than 6. The registration mode of the diffusion tensor data and the anatomical image is rigid registration. In a specific implementation, B0 data of the diffusion data may be registered with the anatomical image, or DWI image Di (1< ═ i < ═ n) data of the diffusion data may be registered with the anatomical image.
Optionally, the correction matrix may also be determined by: inputting diffusion tensor data and an anatomical image into a trained correction matrix extraction model to obtain a correction matrix; the correction matrix extraction model is obtained by training an original neural network according to the diffusion tensor data, the standard anatomical image and the standard correction matrix. Optionally, the original neural network may be a deep learning model, a convolution model, or other network models, and this embodiment is not particularly limited. It can be understood that the accuracy of the correction matrix extraction model obtained by training the diffusion tensor data and the standard anatomical image is high, and therefore, the correction matrix can be automatically obtained by inputting the diffusion tensor data and the anatomical image into the model.
From the above S210 to S230, the correction matrix is obtained from the diffusion tensor data and the anatomical image. In this embodiment, S220 may be further modified as follows: acquiring the anatomical image and the current anatomical image in the anatomical coordinate system, then S230 may be modified to: the method comprises the steps of registering an anatomical image and a current anatomical image to obtain a second registration matrix, registering the current anatomical image and diffusion tensor data to obtain a third registration matrix, and determining a correction matrix based on the second registration matrix and the third registration matrix. It will be appreciated that the second registration matrix may be understood as a mapping of the anatomical image to the current anatomical image and the third registration matrix may be understood as the current anatomical image and the diffusion tensor data, such that a combined analysis of these two mappings may determine the correction matrix. It is understood that when the correction matrix is determined from the anatomical image, the current anatomical image, and the diffusion tensor data, in addition to the above-described registration method, the diffusion tensor data, the anatomical image, and the current anatomical image may be input to a trained correction matrix extraction model to obtain the correction matrix. The training mode of the correction matrix extraction model is the same as that of the above embodiment, and is not described in detail here.
Alternatively, the embodiment may not use the anatomical image and the current anatomical image, and the correction matrix may be determined by registering the diffusion tensor data with the standard diffusion tensor data. Thus, S320 may be modified to: acquiring the standard diffusion tensor data of the target object, then, S330 may be modified to: and registering the standard diffusion tensor data and the diffusion tensor data to obtain a fourth registration matrix, and taking the fourth registration matrix as a correction matrix of the diffusion tensor data. Therein, standard diffusion tensor data may be determined from the plurality of non-gradient-applied DWI images B0. It can be understood that, when the correction matrix is determined by the diffusion tensor data and the standard diffusion tensor data, in addition to the above-described registration method, the diffusion tensor data and the standard diffusion tensor data may be input to a trained correction matrix extraction model to obtain the correction matrix. The training mode of the correction matrix extraction model is the same as that of the above embodiment, and is not described in detail here.
By the method, flexibility of determining the correction matrix can be improved.
And S240, calculating the eigenvector of the diffusion tensor data in the image coordinate system.
And S250, correcting the eigenvector based on the correction matrix, and carrying out color coding on the corrected eigenvector in an anatomical coordinate system according to a preset coding rule.
EXAMPLE III
Fig. 3 is a schematic flow chart of a color encoding method of a diffusion tensor image according to a third embodiment of the present invention. The technical solution of this embodiment refines the steps of the above embodiment. Optionally, the correcting the feature vector based on the correction matrix includes: acquiring spatial data of the correction matrix, and correcting the eigenvector according to the spatial data; wherein the spatial data includes at least one of a rotation angle, a rotation direction, and changed coordinate data. Referring specifically to fig. 3, the method of the present embodiment may include the following steps:
s310, acquiring diffusion tensor data of the target object, and determining a correction matrix of the diffusion tensor data and the anatomical coordinate system.
And S320, calculating the eigenvector of the diffusion tensor data in the image coordinate system.
Alternatively, the eigenvectors of the diffusion tensor data may be calculated by: determining an array matrix based on the at least one diffusion tensor data; and carrying out diagonalization operation on the symmetric matrix to obtain at least one eigenvalue of the matrix and an eigen direction corresponding to each eigenvalue, and taking the eigen direction corresponding to the eigenvalue with the maximum numerical value as an eigenvector of diffusion tensor data.
On the basis of the above steps, the diffusion tensor data may include the DWI image B0 without applying a gradient, and may also include the DWI image Di (1< ═ i < ═ n) with applying a gradient. In this embodiment, the symmetric matrix may include a plurality of gradient-applied diffusion tensor data. For example, the symmetric matrix includes the 1 st gradient-applied DWI image D1, the 2 nd gradient-applied DWI image D2 …, the 6 th gradient-applied DWI image D6, and the pair matrix includes six directions, respectively: xx, xy, xz, yx, yy, yz, and zz, diagonalizing the symmetric matrix to obtain three eigenvalues (principal dispersion coefficients) λ 1, λ 2, and λ 3 and eigendirections e1, e2, and e3 corresponding to each eigenvalue, extracting an eigenvalue with the largest numerical value and an eigendirection corresponding to the eigenvalue with the largest numerical value, and using the eigendirection corresponding to the eigenvalue with the largest numerical value as an eigenvector of the diffusion tensor data.
S330, acquiring spatial data of the correction matrix, and correcting the eigenvector according to the spatial data.
Wherein the spatial data includes at least one of a rotation angle, a rotation direction, and changed coordinate data.
And S340, correcting the eigenvector based on the correction matrix, and carrying out color coding on the corrected eigenvector in an anatomical coordinate system according to a preset coding rule.
Optionally, color-coding the corrected feature vectors in the anatomical coordinate system according to a preset coding rule may be implemented by: and according to the color coding and the space data of the feature vector before correction, performing color coding on the corrected feature vector in an anatomical coordinate system.
Optionally, the preset encoding rule is defined as: the front and back direction of the anatomical coordinate system is a first color, the left and right direction of the anatomical coordinate system is a second color, and the up and down direction of the anatomical coordinate system is a third color. Optionally, the first color, the second color, and the third color are different. For example, the first color may be green, the second color may be red, and the third direction may be blue. It should be noted that the first color, the second color and the third color are not limited to the above colors, and the embodiment is not particularly limited.
Illustratively, as shown in fig. 4, the diffusion tensor image is obtained, the arrow indicates the feature vector of a nerve fiber, and if the feature vector of the brain posterior to the brain anterior to the brain runs left and right in the anatomical coordinate system, the color coding of the feature vector in the anatomical coordinate system is determined to be red according to the above coding rule. If the head of the target object is deflected by 90 degrees to the right, that is, the eigenvector of the diffusion tensor data is deflected by 90 degrees to the right, and the correction matrix of the diffusion tensor data is determined according to the diffusion tensor data deflected by the angle and the anatomical image in the anatomical coordinate system, then the diffusion tensor data contained in the correction matrix has P as right and Q as 90 degrees, that is, the rotation direction is right and the rotation angle is 90 degrees, so that the direction and the angle of the eigenvector are corrected according to the correction matrix, and the corrected eigenvector is color-coded again. Therefore, the color coding of the corrected feature vector is changed into green, and the purpose of changing the color coding in a self-adaptive manner when the trend of the feature vector is changed is achieved.
According to the technical scheme provided by the embodiment of the invention, the space data of the correction matrix is obtained, the eigenvector is corrected according to the space data, and then the corrected eigenvector is subjected to color coding in the anatomical coordinate system according to the color coding and the space data of the eigenvector before correction, so that the problem that the color coding cannot be changed when the trend of the nerve fiber and the relative position of the coordinate system are changed in the prior art is solved, the aim of correcting the eigenvector in the anatomical coordinate system through the correction matrix to change the color coding is fulfilled, and the effects of improving the uniformity and the accuracy of the color coding of the nerve fiber are achieved.
Example four
Fig. 5 is a schematic structural diagram of a color coding apparatus for a diffusion tensor image according to a fourth embodiment of the present invention.
Referring to fig. 5, the system includes: a correction matrix determination module 41, a feature vector calculation module 42, and a color coding module 43.
The correction matrix determining module 41 is configured to obtain diffusion tensor data of the target object, and determine a correction matrix of the diffusion tensor data and an anatomical coordinate system, where the anatomical coordinate system is a standard bit direction; an eigenvector calculating module 42, configured to calculate eigenvectors of the diffusion tensor data in an image coordinate system; and a color coding module 43, configured to correct the eigenvector based on the correction matrix, and perform color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule.
On the basis of the above technical solutions, the apparatus further includes: a first acquisition module; the first obtaining module is configured to obtain an anatomical image in the anatomical coordinate system, and the correction matrix determining module 41 is further configured to register the diffusion tensor data with the anatomical image to obtain a registration matrix, and use the registration matrix as a correction matrix of the diffusion tensor data.
On the basis of the above technical solutions, the correction matrix determining module 41 is further configured to input the diffusion tensor data and the anatomical image into a trained correction matrix extraction model to obtain a correction matrix; the correction matrix extraction model is obtained by training an original neural network according to the diffusion tensor data, the standard anatomical image and the standard correction matrix.
On the basis of the foregoing technical solutions, optionally, the eigenvector calculation module 42 is further configured to determine an array matrix based on at least one diffusion tensor data; and carrying out diagonalization operation on the symmetric matrix to obtain at least one eigenvalue of the matrix and an eigen direction corresponding to each eigenvalue, and taking the eigen direction corresponding to the eigenvalue with the maximum numerical value as an eigenvector of diffusion tensor data.
On the basis of the above technical solutions, optionally, the color coding module 43 is further configured to obtain spatial data of a correction matrix, and correct the eigenvector according to the spatial data; wherein the spatial data includes at least one of a rotation angle, a rotation direction, and changed coordinate data.
On the basis of the above technical solutions, optionally, the color coding module 43 is further configured to perform color coding on the corrected feature vector in the anatomical coordinate system according to the color coding and the spatial data of the feature vector before correction.
On the basis of the above technical solutions, optionally, the preset encoding rule is defined as: the front and back direction of the anatomical coordinate system is a first color, the left and right direction of the anatomical coordinate system is a second color, and the up and down direction of the anatomical coordinate system is a third color.
According to the technical scheme provided by the embodiment of the invention, by acquiring the diffusion tensor data of the target object, determining the diffusion tensor data and the correction matrix of the anatomical coordinate system, calculating the eigenvector of the diffusion tensor data in the image coordinate system, correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to the preset coding rule, the problem that the color coding cannot be changed when the trend of the nerve fibers and the relative position of the coordinate system change in the prior art is solved, the purpose of correcting the eigenvector in the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effects of improving the uniformity and the accuracy of the color coding of the nerve fibers are achieved.
EXAMPLE five
Fig. 6 is a schematic structural diagram of a color encoding apparatus for a diffusion tensor image according to a fourth embodiment of the present invention. Fig. 6 shows a block diagram of an exemplary color encoding device 12 of diffusion tensor images suitable for use in implementing embodiments of the present invention. The color encoding device 12 of the diffusion tensor image shown in fig. 6 is only an example, and should not bring any limitation to the functions and the range of use of the embodiment of the present invention.
As shown in fig. 6, the color coding device 12 of the diffusion tensor image is expressed in the form of a general purpose computing device. The components of the color encoding device 12 of the diffusion tensor image may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The color encoding device 12 of the diffusion tensor image typically includes a variety of computer system readable media. These media may be any available media that can be accessed by the color coding device 12 of the diffusion tensor image, including volatile and non-volatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The color encoding device 12 of the diffusion tensor image may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory 28 may include at least one program product having a set of program modules (e.g., a correction matrix determination module 41, an eigenvector calculation module 42, and a color coding module 43 of a color coding apparatus for diffusion tensor images) configured to perform the functions of embodiments of the present invention.
A program/utility 44 having a set of program modules 46 (e.g., the correction matrix determination module 41, the eigenvector calculation module 42, and the color encoding module 43 of the color encoding apparatus of the diffusion tensor image) may be stored, for example, in memory 28, such program modules 46 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 46 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The color-coding device 12 of the diffusion tensor image may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the color-coding device 12 of the diffusion tensor image, and/or with any devices (e.g., network card, modem, etc.) that enable the color-coding device 12 of the diffusion tensor image to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the color-coding device 12 of the diffusion tensor image may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 20. As shown, the network adapter 20 communicates with the other modules of the color coding device 12 of the diffusion tensor image over the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the color encoding device 12 of the diffusion tensor image, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing a program stored in the system memory 28, for example, implementing a color coding method of a diffusion tensor image provided by an embodiment of the present invention, the method including:
acquiring diffusion tensor data of a target object, and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard position direction;
calculating an eigenvector of diffusion tensor data under an image coordinate system;
and correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in an anatomical coordinate system according to a preset coding rule.
The processing unit 16 executes a program stored in the system memory 28 to execute various functional applications and data processing, for example, to implement a color coding method of a diffusion tensor image provided by an embodiment of the present invention.
Of course, those skilled in the art can understand that the processor may also implement the technical solution of the color coding method for diffusion tensor images provided by any embodiment of the present invention.
EXAMPLE six
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a color coding method for a diffusion tensor image, the method including:
acquiring diffusion tensor data of a target object, and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard position direction;
calculating an eigenvector of diffusion tensor data under an image coordinate system;
and correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in an anatomical coordinate system according to a preset coding rule.
Of course, the computer program stored on the computer-readable storage medium provided by the embodiments of the present invention is not limited to the above method operations, and may also perform related operations in a color coding method of a diffusion tensor image provided by any embodiments of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
A computer readable signal medium may include computer readable program code embodied in diffusion tensor data, an anatomical coordinate system, a correction matrix, and eigenvectors. Such propagated diffusion tensor data, anatomical coordinate systems, correction matrices, and eigenvectors. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code 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).
It should be noted that, in the embodiment of the color coding apparatus for diffusion tensor images, the modules included in the embodiment are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of color coding a diffusion tensor image, comprising:
acquiring diffusion tensor data of a target object, and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard bit direction;
calculating an eigenvector of the diffusion tensor data under an image coordinate system;
and correcting the eigenvector based on the correction matrix, and performing color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule.
2. The method of claim 1, further comprising, prior to determining the correction matrix of the diffusion tensor data and anatomical coordinate system:
acquiring an anatomical image under the anatomical coordinate system;
accordingly, the determining a correction matrix of the diffusion tensor data and the anatomical coordinate system includes:
and registering the diffusion tensor data and the anatomical image to obtain a registration matrix, and taking the registration matrix as a correction matrix of the diffusion tensor data.
3. The method of claim 2, wherein determining the correction matrix of the diffusion tensor data and the anatomical coordinate system comprises:
inputting the diffusion tensor data and the anatomical image into a trained correction matrix extraction model to obtain a correction matrix; and the correction matrix extraction model is obtained by training an original neural network according to the diffusion tensor data, the standard anatomical image and the standard correction matrix.
4. The method of claim 1, wherein computing eigenvectors of the diffusion tensor data in an image coordinate system comprises:
determining an array matrix based on at least one of the diffusion tensor data;
and carrying out diagonalization operation on the symmetric matrix to obtain at least one eigenvalue of the diagonalized matrix and an eigen direction corresponding to each eigenvalue, and taking the eigen direction corresponding to the eigenvalue with the largest numerical value as an eigenvector of the diffusion tensor data.
5. The method of claim 1, wherein the correcting the eigenvector based on the correction matrix comprises:
acquiring spatial data of the correction matrix, and correcting the eigenvector according to the spatial data; wherein the spatial data includes at least one of a rotation angle, a rotation direction, and changed coordinate data.
6. The method according to claim 5, wherein the color coding the corrected feature vectors in the anatomical coordinate system according to a preset coding rule comprises:
and according to the color coding of the feature vector before correction and the spatial data, performing color coding on the corrected feature vector in the anatomical coordinate system.
7. The method of claim 1, wherein the preset encoding rule is defined as: the front and back direction of the anatomical coordinate system is a first color, the left and right direction of the anatomical coordinate system is a second color, and the up and down direction of the anatomical coordinate system is a third color.
8. A color coding apparatus for a diffusion tensor image, comprising:
the correction matrix determining module is used for acquiring diffusion tensor data of a target object and determining a correction matrix of the diffusion tensor data and an anatomical coordinate system, wherein the anatomical coordinate system is in a standard position direction;
the eigenvector calculation module is used for calculating eigenvectors of the diffusion tensor data in an image coordinate system;
and the color coding module is used for correcting the eigenvector based on the correction matrix and carrying out color coding on the corrected eigenvector in the anatomical coordinate system according to a preset coding rule.
9. A color coding apparatus of a diffusion tensor image, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the color coding method of the diffusion tensor image as set forth in any one of claims 1 to 7 when executing the computer program.
10. A storage medium containing computer-executable instructions which, when executed by a computer processor, implement a method of color coding a diffusion tensor image as recited in any one of claims 1-7.
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