CN111145278B - Color coding method, device, equipment and storage medium for diffusion tensor image - Google Patents
Color coding method, device, equipment and storage medium for diffusion tensor image Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
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- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The embodiment of the invention discloses a color coding method, a device, equipment and a storage medium of a diffusion tensor image. According to the method, the dispersion tensor data of the target object are obtained, the correction matrix of the dispersion tensor data and the anatomical coordinate system is determined, the feature vector of the dispersion tensor data is calculated under the dispersion tensor data coordinate system, the feature vector is corrected based on the correction matrix, the corrected feature vector is subjected to color coding under 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 fiber changes with the relative position of the coordinate system in the prior art is solved, the purpose that the feature vector under the image coordinate system is corrected through the correction matrix to change the color coding is achieved, and the effect of improving the uniformity and the accuracy of the color coding of the nerve fiber is 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 the perception of Brownian motion of water molecules, and can display the trend of nerve fibers, reveal how brain tumors affect nerve cell connection, guide medical staff to perform brain surgery and also reveal slight abnormal changes of brain and spinal cord related to stroke, multiple sclerosis, schizophrenia, dyskinesia and the like.
At present, the color coding scheme mainly adopted in the industry directly performs color coding according to the characteristic direction of the diffusion tensor image, or the fiber is globally subjected to the same color coding according to the direction after the main direction of the nerve fiber is calculated, and the direction of the image coordinate system is not considered in the above manner, that is, the color coding scheme adopted in the industry at present has no standardized definition. In the process of carrying out color coding on the characteristic direction of the diffusion tensor image, the scanning angle of the image coordinate system or the same scanning object is changed frequently, so that the relative position of the trend of the nerve fiber and the coordinate system is changed, but the currently adopted color coding scheme cannot change the color coding when the relative position of the trend of the nerve fiber and the coordinate system is changed.
Therefore, the color coding scheme can not show the consistency of the color coding and the relative position when the trend of the nerve fiber changes with the relative position of the coordinate system, so that the color coding of the nerve fiber has larger difference from the actual situation.
Disclosure of Invention
The embodiment of the invention provides a color coding method, device and equipment of a diffusion tensor image and a storage medium, which are used for realizing the effect of improving the uniformity and accuracy of 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, 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 a standard bit direction;
calculating the eigenvectors of the diffusion tensor data under an image coordinate system;
correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under 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, including:
the correction matrix determining module is used for obtaining 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 a standard bit direction;
the feature vector calculation module is used for calculating the feature vector of the diffusion tensor data under an image coordinate system;
and the color coding module is used for correcting the characteristic vector based on the correction matrix and carrying out color coding on the corrected characteristic vector under the anatomical coordinate system according to a preset coding rule.
In a third aspect, an embodiment of the present invention further provides a color encoding device for a diffusion tensor image, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the color encoding method for a diffusion tensor image according to any one of the first aspects when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, implement a color coding method of a diffusion tensor image as in any of the first aspects.
According to the technical scheme provided by the embodiment of the invention, the dispersion tensor data of the target object is obtained, the correction matrix of the dispersion tensor data and the anatomical coordinate system is determined, the feature vector of the dispersion tensor data is calculated under the image coordinate system, the feature vector is corrected based on the correction matrix, and the corrected feature vector is color coded under the anatomical coordinate system according to the preset coding rule, so that the problem that the color coding cannot be changed when the trend of the nerve fiber changes with the relative position of the coordinate system in the prior art is solved, the purpose of correcting the feature vector under the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effect of improving the uniformity and the accuracy of the color coding of the nerve fiber is achieved.
Drawings
FIG. 1 is a flowchart of a color coding method of a diffusion tensor image according to a first embodiment of the present invention;
fig. 2 is a flowchart of a color coding method of a diffusion tensor image according to a second embodiment of the present invention;
FIG. 3 is a 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 view showing the trend of nerve fibers under 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 invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a color coding method for a diffusion tensor image according to a first embodiment of the present invention, where the present embodiment is applicable to a case of color coding feature vectors of diffusion tensor data with an anatomical coordinate system as a standard bit direction, and the method may be performed by a color coding device for a diffusion tensor image, where the device may be implemented by software and/or hardware and is generally integrated in a terminal or a device. 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.
Wherein the target object may be the head, chest or other part. Alternatively, the present embodiment is described taking a head target object as an example. Wherein the anatomical coordinate system is the standard orientation.
It will be appreciated that the diffusion tensor image DTI is an image obtained by imaging the perception of brownian motion of water molecules in the brain by a diffusion tensor imaging technique, the diffusion tensor imaging map being composed of a plurality of diffusion weighted data.
Wherein the anatomical coordinate system is the standard orientation. In general, when a target object is subjected to diffusion tensor imaging, the target object often moves, for example, a head moves in a certain direction, deflects to a certain angle, changes coordinate data of diffusion tensor data caused by movement of a scanning device, and the like, and these movements change the trend of brain nerve fibers. The correction matrix of the anatomical coordinate system of the diffusion tensor data and the standard bit direction can be determined firstly, so that the trend of the nerve fiber can be corrected later, and the nerve fiber with the changed trend can be color coded later.
S120, calculating the eigenvectors of diffusion tensor data under an image coordinate system.
The image coordinate system can be understood as a diffusion tensor data coordinate system. The feature vector can be understood as three orthogonal vectors of the diffusion tensor data under the image coordinate system, and the feature vector of the diffusion tensor data and the feature value corresponding to the feature vector can reflect the trend information of the nerve fiber, so that the trend of the nerve fiber can be determined by calculating the feature vector of the diffusion tensor, and further the color coding of the nerve fiber in each trend can be facilitated.
And S130, correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under an anatomical coordinate system according to a preset coding rule.
Alternatively, the feature vector may be corrected by: acquiring space data of a correction matrix, and correcting the feature vector according to the space data; wherein the spatial data may include at least one of a rotation angle, a rotation direction, and change coordinate data.
Illustratively, the correction matrix includes: diffusion tensor data Y n (X n ，Y n ，Z n P, Q), wherein n is not less than 1, X n ，Y n ，Z n For diffusion tensor data A n Changing coordinate data under anatomical coordinate system, P is A n In the direction of rotation under an anatomical coordinate system, Q is A n Rotation angle under anatomical coordinate system. Alternatively, P may be up, down, front, back, left and right, and Q may be any value from 0 to 360. If the trend of any nerve fiber changes, the diffusion tensor data also changes, at least one of the change coordinate data, the rotation direction and the rotation angle exists in the correction matrix of the calculated diffusion tensor data, the feature vector with the trend changing can be corrected under the anatomical coordinate system through the spatial data of the correction matrix, and then the corrected feature vector is re-correctedColor coding is performed.
According to the technical scheme provided by the embodiment of the invention, the dispersion tensor data of the target object is obtained, the correction matrix of the dispersion tensor data and the anatomical coordinate system is determined, the feature vector of the dispersion tensor data is calculated under the image coordinate system, the feature vector is corrected based on the correction matrix, and the corrected feature vector is color coded under the anatomical coordinate system according to the preset coding rule, so that the problem that the color coding cannot be changed when the trend of the nerve fiber changes with the relative position of the coordinate system in the prior art is solved, the purpose of correcting the feature vector under the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effect of improving the uniformity and the accuracy of the color coding of the nerve fiber is achieved.
Example two
Fig. 2 is a flowchart of a color coding 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 on the basis of the foregoing embodiment, 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 this embodiment may include the following steps:
s210, acquiring diffusion tensor data of the target object.
S220, acquiring an anatomical image under an anatomical coordinate system.
Wherein an anatomical image in an anatomical coordinate system can be understood as a standard anatomical image. For example, the anatomical image is obtained through a high-precision segmentation algorithm, or the anatomical image is obtained through the high-precision segmentation algorithm and manual debugging for a plurality of times.
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 is used as a correction matrix of the diffusion tensor data. The acquired diffusion tensor data may include a DWI image B0 to which no gradient is applied, a DWI image D1 to which a gradient is applied 1 st, a DWI image D2 … to which a gradient is applied 2 nd, and a DWI image Dn to which a gradient is applied n not less than 6. Wherein the registration mode of the diffusion tensor data and the anatomical image is rigid registration. In specific implementation, the B0 data of the diffusion data may be registered with the anatomical image, or the DWI image Di (1 < =i < =n) data of the diffusion data may be registered with the anatomical image, and the diffusion data registered with the anatomical image is not specifically limited in this embodiment.
Alternatively, 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 the original neural network according to diffusion tensor data, the standard anatomical image and the standard correction matrix. Alternatively, the original neural network may be a deep learning model, or may be a convolution model, or other network model, which is not specifically limited in this embodiment. It can be appreciated that the correction matrix obtained by training the diffusion tensor data and the standard anatomical image has higher accuracy in extracting the model, so that the correction matrix can be automatically obtained by inputting the diffusion tensor data and the anatomical image into the model.
As can be seen from the above S210-S230, the correction matrix is obtained from the diffusion tensor data and the anatomical image. In this embodiment, S220 may be modified as follows: acquiring an anatomical image and a current anatomical image in an anatomical coordinate system, S230 may be modified as: and registering the anatomical image with the current anatomical image to obtain a second registration matrix, registering the current anatomical image with 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 and the current anatomical image and the third registration matrix may be understood as the current anatomical image and diffusion tensor data, such that combining these two mappings together may determine the correction matrix. It will be appreciated that when determining the correction matrix from the anatomical image, the current anatomical image and the diffusion tensor data, the anatomical image, the current anatomical image, and the like may be input into a trained correction matrix extraction model to obtain the correction matrix, in addition to the registration method described above. The training manner of the correction matrix extraction model is the same as that of the foregoing embodiment, and will not be described in detail herein.
Alternatively, the present embodiment may not employ an anatomical image and a current anatomical image, and the correction matrix may be determined by registering diffusion tensor data with standard diffusion tensor data. Thus, S320 may be modified as: obtaining standard diffusion tensor data of the target object, S330 may be modified as follows: registering the standard diffusion tensor data with 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. Wherein the standard diffusion tensor data can be determined from a plurality of DWI images B0 to which no gradient is applied. It will be appreciated that when determining the correction matrix from 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 into a trained correction matrix extraction model to obtain the correction matrix. The training manner of the correction matrix extraction model is the same as that of the foregoing embodiment, and will not be described in detail herein.
By the method, the flexibility of correction matrix determination can be improved.
S240, calculating the eigenvectors of the diffusion tensor data under the image coordinate system.
S250, correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under an anatomical coordinate system according to a preset coding rule.
Example III
Fig. 3 is a flowchart of a color coding 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 foregoing embodiment. Optionally, the correcting the feature vector based on the correction matrix includes: acquiring spatial data of the correction matrix, and correcting the feature vector according to the spatial data; wherein the spatial data includes at least one of a rotation angle, a rotation direction, and change coordinate data. Referring specifically to fig. 3, the method of this 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 an anatomical coordinate system.
S320, calculating the eigenvectors of diffusion tensor data under the image coordinate system.
Alternatively, the eigenvectors of the diffusion tensor data may be calculated by: determining a matrix of the 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 the eigenvector corresponding to each eigenvalue, and taking the eigenvector corresponding to the eigenvalue with the largest numerical value as the eigenvector of the diffusion tensor data.
On the basis of the above steps, the diffusion tensor data may include a DWI image B0 to which no gradient is applied, or may include a DWI image Di (1 < =i < =n) to which a gradient is applied. In this embodiment, the symmetric matrix may include a plurality of diffusion tensor data that apply a gradient. For example, the symmetric matrix includes a 1 st gradient-applied DWI image D1, a 2 nd gradient-applied DWI image D2 …, and a 6 th gradient-applied DWI image D6, and the pair of matrices includes six directions, respectively: xx, xy, xz, yx, yy, yz and zz, diagonalizing the symmetric matrix to obtain three eigenvalues (main dispersion coefficients) lambda 1, lambda 2 and lambda 3 and eigenvectors e1, e2 and e3 corresponding to each eigenvalue, extracting the eigenvalue with the largest value and the eigenvector corresponding to the eigenvalue with the largest value, and taking the eigenvector corresponding to the eigenvalue with the largest value as the eigenvector of the dispersion tensor data.
S330, spatial data of the correction matrix is obtained, and the feature vector is corrected according to the spatial data.
Wherein the spatial data includes at least one of a rotation angle, a rotation direction, and a change coordinate data.
And S340, correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under an anatomical coordinate system according to a preset coding rule.
Optionally, color coding the corrected feature vector under the anatomical coordinate system according to a preset coding rule may be achieved by: and carrying out color coding on the corrected feature vector under an anatomical coordinate system according to the color coding and the spatial data of the feature vector before correction.
Optionally, the preset encoding rule is defined as: the front-back direction of the anatomical coordinate system is the first color, the left-right direction of the anatomical coordinate system is the second color, and the up-down direction of the anatomical coordinate system is the 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. Note that the first color, the second color, and the third color are not limited to the above-mentioned colors, and the present embodiment is not particularly limited.
Illustratively, as shown in fig. 4, which is an acquired diffusion tensor image, the arrow represents a feature vector of a nerve fiber, and if the feature vector of the backward and forward direction of the brain is oriented back and forth under the anatomical coordinate system, the color coding of the feature vector under the anatomical coordinate system is determined to be green 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, a correction matrix of the diffusion tensor data is determined according to the diffusion tensor data deflected by an angle and the anatomical image under the anatomical coordinate system, then the correction matrix contains the diffusion tensor data with the P being right, the Q being 90 degrees, that is, the rotation direction being right, and the rotation angle being 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, so that the purpose of adaptively changing the color coding when the trend of the feature vector changes is achieved.
According to the technical scheme provided by the embodiment of the invention, the space data of the correction matrix is obtained, the feature vector is corrected according to the space data, and then the corrected feature vector is color coded under the anatomical coordinate system according to the color coding and the space data of the feature vector before correction, so that the problem that the color coding cannot be changed when the trend of the nerve fiber changes with the relative position of the coordinate system in the prior art is solved, the purpose of correcting the feature vector under the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effect of improving the uniformity and the accuracy of the color coding of the nerve fiber is realized.
Example IV
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: correction matrix determination module 41, feature vector calculation module 42, and color coding module 43.
The correction matrix determining module 41 is configured to obtain diffusion tensor data of a 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; a feature vector calculation module 42, configured to calculate feature vectors of the diffusion tensor data under an image coordinate system; the color coding module 43 is configured to correct the feature vector based on the correction matrix, and color-code the corrected feature vector under the anatomical coordinate system according to a preset coding rule.
On the basis of the technical schemes, the device further comprises: a first acquisition module; the first acquiring module is configured to acquire an anatomical image in the anatomical coordinate system, and the corresponding 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 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 the original neural network according to diffusion tensor data, the standard anatomical image and the standard correction matrix.
Based on the above aspects, optionally, the feature vector calculation module 42 is further configured to determine a 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 the eigenvector corresponding to each eigenvalue, and taking the eigenvector corresponding to the eigenvalue with the largest numerical value as the eigenvector of the 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 feature vector according to the spatial data; wherein the spatial data includes at least one of a rotation angle, a rotation direction, and a change coordinate data.
On the basis of the above aspects, optionally, the color coding module 43 is further configured to color code the corrected feature vector under an 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, a preset encoding rule is defined as: the front-back direction of the anatomical coordinate system is the first color, the left-right direction of the anatomical coordinate system is the second color, and the up-down direction of the anatomical coordinate system is the third color.
According to the technical scheme provided by the embodiment of the invention, the dispersion tensor data of the target object is obtained, the correction matrix of the dispersion tensor data and the anatomical coordinate system is determined, the feature vector of the dispersion tensor data is calculated under the image coordinate system, the feature vector is corrected based on the correction matrix, and the corrected feature vector is color coded under the anatomical coordinate system according to the preset coding rule, so that the problem that the color coding cannot be changed when the trend of the nerve fiber changes with the relative position of the coordinate system in the prior art is solved, the purpose of correcting the feature vector under the anatomical coordinate system through the correction matrix to change the color coding is achieved, and the effect of improving the uniformity and the accuracy of the color coding of the nerve fiber is achieved.
Example five
Fig. 6 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 shows a block diagram of a color coding device 12 suitable for use in implementing an exemplary diffusion tensor image according to an embodiment of the present invention. The color-coding device 12 of the diffusion tensor image shown in fig. 6 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 6, the color-coding device 12 of the diffusion tensor image is 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, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The color-coding device 12 of the diffusion tensor image typically includes a variety of computer system readable media. Such media may be any available media that can be accessed by the color encoding device 12 of the diffusion tensor image, including volatile and nonvolatile 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-coding device 12 of the diffusion tensor image may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard disk drive"). Although not shown in fig. 6, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The memory 28 may comprise at least one program product having a set of program modules (e.g. correction matrix determination module 41, feature vector calculation module 42 and color coding module 43 of the color coding device of the diffusion tensor image) configured to perform the functions of the various embodiments of the invention.
A program/utility 44 having a set of program modules 46 (e.g., correction matrix determination module 41, feature vector calculation module 42, and color coding module 43 of the color coding 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 or some combination of which may include an implementation of a network environment. Program modules 46 generally perform the functions and/or methods of the embodiments described herein.
The color-coding device 12 of the diffusion tensor image may also be in communication with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the color-coding device 12 of the diffusion tensor image, and/or any device (e.g., network card, modem, etc.) that enables the color-coding device 12 of the diffusion tensor image to communicate with one or more other computing devices. Such communication may occur 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, such as 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 encoding device 12 of the diffusion tensor image via the bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection 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, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running a program stored in the system memory 28, for example, to implement 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 a standard bit direction;
calculating a feature vector of diffusion tensor data under an image coordinate system;
correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under an anatomical coordinate system according to a preset coding rule.
The processing unit 16 executes various functional applications and data processing by running 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.
Of course, it will be understood by those skilled in the art that the processor may also implement the technical solution of the color coding method of the diffusion tensor image provided by any embodiment of the present invention.
Example six
The fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a color coding method for a diffusion tensor image as provided by the embodiments of the present invention, the method 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 a standard bit direction;
calculating a feature vector of diffusion tensor data under an image coordinate system;
correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under an anatomical coordinate system according to a preset coding rule.
Of course, the computer-readable storage medium provided by the embodiments of the present invention, on which the computer program stored, is not limited to the above method operations, but may also perform the related operations in the color coding method of a diffusion tensor image provided by any of the embodiments of the present invention.
The computer storage media of embodiments of the invention may take the form of 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any 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 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.
The computer readable signal medium may include diffusion tensor data, an anatomical coordinate system, a correction matrix, a feature vector, and the like, having computer readable program code embodied therein. Such propagated diffusion tensor data, anatomical coordinate system, correction matrix, eigenvectors, and the like. 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 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 ++ and 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that, in the above embodiment of the color coding apparatus for a diffusion tensor image, each included module is only divided according to the functional logic, but not limited to the above division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. 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, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A color coding method of 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 a standard bit direction;
calculating the eigenvectors of the diffusion tensor data under an image coordinate system;
correcting the feature vector based on the correction matrix, and performing color coding on the corrected feature vector under the anatomical coordinate system according to a preset coding rule;
the correcting the feature vector based on the correction matrix includes:
acquiring spatial data of the correction matrix, and correcting the feature vector according to the spatial data, wherein the spatial data comprises at least one of rotation angle, rotation direction and change coordinate data;
the step of performing color coding on the corrected feature vector under the anatomical coordinate system according to a preset coding rule comprises the following steps:
and carrying out color coding on the corrected feature vector under the anatomical coordinate system according to the color coding of the feature vector before correction and the spatial data.
2. The method of claim 1, further comprising, prior to determining the correction matrix for the diffusion tensor data and anatomical coordinate system:
acquiring an anatomical image in the anatomical coordinate system;
accordingly, the determining a correction matrix of the diffusion tensor data and an anatomical coordinate system includes:
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 the determining a correction matrix of the diffusion tensor data and an anatomical coordinate system comprises:
inputting the diffusion tensor data and the anatomical image into a trained correction matrix extraction model to obtain the correction matrix; the correction matrix extraction model is obtained by training an original neural network according to diffusion tensor data, a standard anatomical image and a standard correction matrix.
4. The method according to claim 1, wherein said calculating feature vectors of said diffusion tensor data under an image coordinate system comprises:
determining a symmetry 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 characteristic value of the symmetric matrix and a characteristic direction corresponding to each characteristic value, and taking the characteristic direction corresponding to the characteristic value with the largest numerical value as the characteristic vector of the diffusion tensor data.
5. The method of claim 1, wherein the preset encoding rule is defined as: the front-back direction of the anatomical coordinate system is a first color, the left-right direction of the anatomical coordinate system is a second color, and the up-down direction of the anatomical coordinate system is a third color.
6. A color coding apparatus for diffusion tensor images, comprising:
the correction matrix determining module is used for obtaining 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 a standard bit direction;
the feature vector calculation module is used for calculating the feature vector of the diffusion tensor data under an image coordinate system;
the color coding module is used for correcting the characteristic vector based on the correction matrix and carrying out color coding on the corrected characteristic vector under the anatomical coordinate system according to a preset coding rule;
the color coding module is also used for acquiring spatial data of the correction matrix and correcting the feature vector according to the spatial data, wherein the spatial data comprises at least one of a rotation angle, a rotation direction and change coordinate data;
the color coding module is also used for carrying out color coding on the corrected feature vector under an anatomical coordinate system according to the color coding and the space data of the feature vector before correction.
7. A color coding device for diffusion tensor images, 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 diffusion tensor images according to any one of claims 1-5 when executing the computer program.
8. A storage medium containing computer executable instructions which, when executed by a computer processor, implement the color coding method of a diffusion tensor image according to any one of claims 1-5.
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