Registration method and device of fiber bundle tracking data and structure image and medical system

Abstract

The invention discloses a registration method and device for fiber bundle tracking data and a structure image and a medical system. The method comprises the steps that an anatomical structure image and an initial fiber bundle tracking image obtained by a target part of a patient through nuclear magnetic resonance imaging (MRI) equipment are obtained; based on the image data of the anatomical structure image, performing transformation processing on the image data of the initial fiber bundle tracking image to obtain a first affine transformation matrix and a transformed initial fiber bundle tracking image; the first affine transformation matrix is used for enabling the initial fiber bundle tracking image and the anatomical structure image to partially coincide in the same coordinate system; packaging the image data of the transformed initial fiber bundle tracking image to obtain a packaged fiber bundle tracking image; and registering the packaged fiber bundle tracking image and the anatomical structure image according to a preset registration rule to obtain a registered image.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical image processing and analysis, and in particular relates to a method, a device and a medical system for registering fiber bundle tracking data with structural images. Background Art

[0002] Diffusion tensor imaging (DTI) is a neuroimaging technique based on MRI (Magnetic Resonance Imaging), which is mainly used to study the microstructure and connectivity of brain white matter. DTI provides information about the direction and integrity of white matter fiber bundles in the brain by measuring the diffusion behavior of water molecules in tissues. Anatomical images in magnetic resonance imaging (referred to as "structural images"), such as T1-weighted images, T2-weighted images, etc.

[0003] It is difficult to align the raw data of DTI images with the structural image data, as shown in the following points:

[0004] 1. Since DTI images are 4D MRI scan sequence images, it is difficult to align them with 3D structural images. The original data of DTI images has special algorithm requirements for alignment. Aligning them to structural images only through commonly used affine transformation will produce erroneous results. Using dedicated algorithms for alignment is difficult to operate and has low computational efficiency, which limits its breadth and flexibility in practical applications.

[0005] 2. DTI images have many systematic errors, such as distortion caused by uneven magnetic susceptibility, image distortion caused by patient head movement during scanning, and distortion caused by eddy currents, so that the registration of DTI images with structural images in any single magnetic field gradient direction is incomplete.

[0006] 3. The registration of DTI images and structural images requires a large amount of computation, which is not only inefficient but also places very high demands on processor performance.

[0007] 4. Existing algorithms for DTI image registration with structural images require manual intervention, such as adjusting parameters, which not only increases the complexity of the operation and the risk of error, but is also very time-consuming and places higher demands on the operator's expertise.

[0008] 5 Currently, there is a lack of a unified method on the market to coordinate fiber bundle tracking data from different sources, that is, point and line coordinate data, such as fiber bundle tracking data exported by different software systems such as FSL (FMRIB Software Library) and DSI-studio. Since the fiber bundle tracking data exported by different software systems are different in format and standard, it is very difficult to uniformly align the fiber bundle tracking data from different sources with the corresponding structural imaging data of different sequences.

[0009] After a series of preprocessing and neuroimaging analysis, the raw data of DTI images can generate fiber bundle tracking data.

[0010] To this end, the present invention utilizes fiber bundle tracking data and structural image data for registration to solve at least one of the above problems.

[0011] Note: FSL is a mainstream open source analysis software developed by the Functional Brain Imaging Center (FMRIB) of the University of Oxford that can be used to analyze magnetic resonance brain images; DSI-Studio is a software tool for diffusion magnetic resonance imaging (dMRI) data processing, mainly used for drawing brain connection maps and related research on neuropsychological disorders. It integrates a variety of diffusion MRI methods, including diffusion tensor imaging (DTI), generalized q-sampling imaging (GQI), q-space diffeomorphic reconstruction (QSDR), diffusion MRI connectometry and generalized deterministic fiber tracking. Summary of the invention

[0012] The purpose of the present invention is to provide a method for registering fiber bundle tracking data with structural images, which can accurately register fiber bundle tracking data from different sources with MR structural images of different source sequences, so that the registered images can be better used for medical processing and analysis, and can clearly and accurately display the internal structure and details of the human body, helping doctors to accurately identify and diagnose the diseased area.

[0013] A first aspect of the present invention is a method for registering fiber bundle tracking data with a structural image, the method comprising:

[0014] Obtain anatomical structure images and initial fiber bundle tracking images of the patient's target area using a magnetic resonance imaging (MRI) device;

[0015] Based on the image data of the anatomical structure image, transform the image data of the initial fiber bundle tracking image to obtain a first affine transformation matrix and a transformed initial fiber bundle tracking image;

[0016] Wherein, the first affine transformation matrix is ​​used to make the initial fiber bundle tracking image and the anatomical structure image coincide with each other in the same coordinate system;

[0017] encapsulating the image data of the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image;

[0018] The packaged fiber bundle tracking image and the anatomical structure image are registered according to a preset registration rule to obtain a registered image.

[0019] In a possible implementation of the first aspect, the method further includes:

[0020] Based on the preset registration rule, generating a second affine transformation matrix;

[0021] Wherein, the second affine transformation matrix is ​​used to align the packaged fiber bundle tracking image and the anatomical structure image in the coordinate system;

[0022] Obtaining a target affine transformation matrix based on the first affine transformation matrix and the second affine transformation matrix;

[0023] Wherein, the target affine transformation matrix = the second affine transformation matrix * the first affine transformation matrix;

[0024] Registering the target image based on the target affine transformation matrix;

[0025] The images to be registered include a target anatomical structure image and a target fiber bundle tracking image of a target part of the patient.

[0026] In a possible implementation of the first aspect, transforming the image data of the initial fiber bundle tracking image based on the image data of the anatomical structure image to obtain the first affine transformation matrix includes:

[0027] Determine the reference space coordinate system;

[0028] Acquire a first geometric center of the anatomical structure image in the reference space coordinate system;

[0029] Acquire the second geometric center of the initial fiber bundle tracking image in the reference space coordinate system;

[0030] The first geometric center and the second geometric center are overlapped to make the anatomical structure image and the initial fiber bundle tracking image register and coincide, and obtain a first affine transformation matrix.

[0031] In a possible implementation of the first aspect, acquiring the first geometric center of the anatomical structure image in the reference space coordinate system includes:

[0032] Acquiring voxel coordinate data of the anatomical structure image;

[0033] Based on the voxel coordinate data, calculating the voxel geometric center of the anatomical structure image in the voxel coordinate system;

[0034] Based on the voxel geometric center, a first geometric center corresponding to the anatomical structure image in the reference space coordinate system is determined.

[0035] In a possible implementation of the first aspect, acquiring the second geometric center of the initial fiber bundle tracking image in the reference space coordinate system includes:

[0036] Acquire second coordinate data of the initial fiber bundle tracking image in the reference space coordinate system;

[0037] Based on the second coordinate data, a second geometric center of the initial fiber bundle tracking image in the reference space coordinate system is calculated.

[0038] In a possible implementation manner of the first aspect, overlapping the first geometric center with the second geometric center so that the anatomical structure image and the initial fiber bundle tracking image are registered and overlapped, and obtaining a first affine transformation matrix includes:

[0039] Transforming the coordinate origin of the reference space coordinate system by translating the first geometric center of the anatomical structure image to obtain a coordinate data set of the anatomical structure image after the first transformation;

[0040] Determine a first translation matrix based on the coordinate data set of the anatomical structure image after the first transformation;

[0041] Transforming the geometric center of the anatomical structure image after the first transformation to the second geometric center of the initial fiber bundle tracking image to obtain the anatomical structure image after the second transformation;

[0042] determining a second translation matrix based on the anatomical structure image after the second transformation;

[0043] A first affine transformation matrix is ​​determined according to the first translation matrix and the second translation matrix.

[0044] In a possible implementation of the first aspect, encapsulating the image data of the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image comprises:

[0045] Acquire the third coordinate data of the transformed initial fiber bundle tracking image in the reference space coordinate system;

[0046] Based on the voxel coordinate data, determining first coordinate data of the corresponding anatomical structure image in the reference space coordinate system;

[0047] Constructing an empty three-dimensional array of the array where the first coordinate data is located;

[0048] The third coordinate data is written into the empty three-dimensional array for packaging to obtain a packaged fiber bundle tracking image.

[0049] In a possible implementation of the first aspect, writing the third coordinate data into the empty three-dimensional array for encapsulation includes:

[0050] Traversing each coordinate point in the third coordinate data;

[0051] Write each coordinate point into each coordinate position in the empty three-dimensional array;

[0052] In the current coordinate position, each time a coordinate point is written, the brightness value of the voxel corresponding to the coordinate position is marked.

[0053] A second aspect of the present invention provides a registration device, comprising:

[0054] Image acquisition module: used to obtain anatomical structure images and initial fiber bundle tracking images of the patient's target part using magnetic resonance imaging (MRI);

[0055] A first processing module is used for transforming the image data of the initial fiber bundle tracking image based on the image data of the anatomical structure image to obtain a first affine transformation matrix and a transformed initial fiber bundle tracking image;

[0056] Wherein, the first affine transformation matrix is ​​used to make the initial fiber bundle tracking image and the anatomical structure image partially overlap in the same coordinate system;

[0057] The second processing module is used to encapsulate the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image;

[0058] Image registration module: used to register the packaged fiber bundle tracking image and the anatomical structure image according to a preset registration rule to obtain a registered image.

[0059] A third aspect of the present invention provides a visualized medical system, comprising:

[0060] The processor is configured to communicate with the medical acquisition device and execute the steps of applying the above-mentioned registration method, including:

[0061] The display is configured to display a medical image of the patient, wherein the medical image at least includes the registered image described above.

[0062] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a second processor, the steps of the above-mentioned registration method are implemented.

[0063] According to a fourth aspect of the present invention, a computer program product is provided. The computer program product comprises instructions, and when the instructions are executed on an electronic device, the electronic device implements the steps of the above-mentioned registration method.

[0064] Compared with the prior art, the beneficial effects of the present invention include at least:

[0065] 1. The present application provides a method for directly aligning MRI fiber bundle tracking data with corresponding MRI structural images, which not only enables high-precision alignment of fiber bundle tracking data and structural images, but also allows rapid, comprehensive and accurate alignment of the original data of DTI images that generate MRI fiber bundle tracking data with MRI structural image data.

[0066] 2. The registration method of the present application can abstract MRI fiber bundle tracking data from different sources into point-line coordinate data, accurately align with any sequence of structural images, coordinate fiber tracking data exported by different tools or software, and process them through a unified interface.

[0067] 3. The registration method of the present application can realize the visual transformation of fiber bundle tracking data into traditional structural image data, thereby enhancing the applicability, flexibility and computational efficiency of the visual transformation; by converting the fiber bundle tracking data into graphic data similar to the structural image, a more conventional method can be used to align the fiber bundle tracking data with the graphic data of the structural image. Compared with the registration method using the dedicated affine transformation of the DTI image, the complexity of aligning the 4D original data of the DTI image with the 3D structural image is cleverly avoided, thereby realizing direct and high-precision registration of the fiber bundle tracking data with the structural image.

[0068] 4. The registration method of the present application reduces manual intervention and adjustment, provides an automated registration processing flow, reduces operational difficulty and error risk, and improves work efficiency.

[0069] 5. The registration method of the present application utilizes the coarse alignment of the first affine transformation matrix pair to ensure the robustness of the entire registration algorithm, making the entire registration process less prone to errors; the fine alignment of the second affine transformation matrix is ​​utilized to reduce the alignment error and provide a more accurate registration result, taking into account both robustness and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flowchart of the overall steps of the registration method of fiber bundle tracking data and structural images according to an embodiment of the present invention;

[0071] Figure 2 A flowchart of the overall steps of obtaining a first affine transformation matrix according to an embodiment of the present invention;

[0072] Figure 3 A flowchart of the steps of obtaining a first geometric center according to an embodiment of the present invention;

[0073] Figure 4 A flowchart of the steps of obtaining the second geometric center according to an embodiment of the present invention;

[0074] Figure 5 A flowchart of the detailed steps of obtaining a first affine transformation matrix according to an embodiment of the present invention;

[0075] Figure 6 A flowchart of the steps of obtaining a packaged fiber bundle tracking image according to an embodiment of the present invention;

[0076] Figure 7 A flowchart of the packaging steps of writing the third coordinate data into an empty three-dimensional array according to an embodiment of the present invention;

[0077] Figure 8 is a flow chart of the registration steps of the target image according to an embodiment of the present invention;

[0078] Fig. 9 It is a structural schematic diagram of a registration device according to an embodiment of the present invention;

[0079] Fig.10 It is a schematic diagram of the structure of a visualized medical system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided to make the present invention more comprehensive and complete and to fully convey the concepts of example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their repeated description will be omitted.

[0081] Combined with Figure 1 As shown, this embodiment provides a registration method for fiber bundle tracking data and structural images, including steps S1 to S4.

[0082] Step S1: Acquire an anatomical structure image and an initial fiber bundle tracking image of a target part of a target object using a magnetic resonance imaging (MRI) device.

[0083] In the implementation manner of the present application, the target object can be a human organism, which can be an adult or a minor, and can be a man, a woman, or a hermaphrodite; it can also be a non-human organism, such as cats, dogs, cows, sheep and other animals, and the present application does not limit the age, gender and species of the target object; a body part can be the brain, spinal cord, kidney, heart, etc., and the present application does not limit the body part.

[0084] In this embodiment, the target object is an adult male patient, and the target part is the brain.

[0085] It should be noted that anatomical structure imaging can be T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), fluid inversion recovery attenuation sequence (FLAIR), T1WI enhanced scanning, magnetic susceptibility-weighted imaging (SWI), cerebral arterial imaging (MRA), cerebral venography (MRV), etc. T1-weighted images and T2-weighted images are introduced below:

[0086] T1-weighted images mainly focus on anatomical structures, using short repetition time (TR) and short echo time (TE). In T1-weighted images, the longitudinal relaxation differences of tissues are highlighted. For example, in a transverse image of the brain, white matter has a high signal, gray matter has a medium signal, and cerebrospinal fluid has a low signal. T2-weighted images focus on pathological structures or pathological changes, using long TR and long TE. T2-weighted images highlight the transverse relaxation differences of tissues. For example, in a transverse image of the brain, cerebrospinal fluid has a high signal, gray matter has a medium signal, and white matter has a low signal.

[0087] The anatomical structure image in this embodiment may be T1-weighted imaging.

[0088] Initial fiber bundle tracking images refer to images of the fiber orientation of organs and / or tissues such as the brain obtained using diffusion tensor imaging methods.

[0089] Step S2: Based on the image data of the anatomical structure image, the image data of the initial fiber bundle tracking image is transformed to obtain a first affine transformation matrix and the transformed initial fiber bundle tracking image.

[0090] It should be noted that the first affine transformation matrix is ​​used to make the initial fiber bundle tracking image and the anatomical structure image intersect in the same coordinate system, that is, partially overlap.

[0091] Since the initial fiber bundle tracking image and the anatomical structure image belong to different modal images under different scanning sequences, the image data of the anatomical structure image and the image data of the initial fiber bundle tracking image do not overlap in space, which means that the two are completely separated. The image data of the anatomical structure image and the image data of the initial fiber bundle tracking image are roughly aligned in the same coordinate system through the first affine transformation matrix, so that the initial fiber bundle tracking image and the anatomical structure image are partially aligned, which ensures the robustness of the entire registration algorithm and makes it less likely to make errors in the entire registration process.

[0092] Combined with Figure 2 As shown, the method for obtaining the first affine transformation matrix includes steps S21 to S25.

[0093] Step S21: Determine the reference space coordinate system. In the present application, the reference space coordinate system can be a global coordinate system or an anatomical coordinate system; the global coordinate system is a Cartesian coordinate system Q, in which the medical image mode (such as an MRI scanner or a CT device) is located, and each medical mode has its own coordinate system, but there is only one global coordinate system to define the position and direction of each mode.

[0094] The most important model coordinate system in medical imaging technology is the anatomical space (also called the patient coordinate system), which consists of three planes and is used to describe the standard anatomical position of the human body. The three planes are the axial plane, the sagittal plane, and the coronal plane.

[0095] In this embodiment, the reference space coordinate system is a global coordinate system.

[0096] Step S22: Acquire the first geometric center of the anatomical structure image in the reference space coordinate system.

[0097] In one embodiment, the combination Figure 3 As shown, the first geometric center is obtained through steps S221 to S223.

[0098] Step S221: Acquire voxel coordinate data of the anatomical structure image.

[0099] Voxel coordinate data refers to the coordinates of each voxel in the anatomical structure image under the voxel coordinate system of the image, denoted as (i 1 , j 1 , k 1 ).

[0100] Step S222: Calculate the voxel geometric center of the anatomical structure image in the voxel coordinate system based on the voxel coordinate data.

[0101] Using the coordinates of each voxel in the voxel coordinate system of the image (i 1 , j 1 , k 1 ), calculate the geometric center of the voxel in the voxel coordinate system of the anatomical structure image, and its coordinates are marked as (i 0 , j 0 , k 0 ), the method of calculating the voxel geometric center using the voxel coordinates of each voxel is a conventional technical means and will not be described in detail here.

[0102] Step S223: Based on the voxel geometric center, determine the first geometric center of the corresponding anatomical structure image in the reference space coordinate system.

[0103] The geometric center of the voxel in the voxel coordinate system (i 0 , j 0 , k 0 ), determine the corresponding first geometric center in the reference space coordinate system, denoted as (x 0 ,y 0 、z 0 ).

[0104] The voxel geometric center is determined by calculating the position of each voxel in the anatomical structure image, which can provide very accurate spatial position information. Since the voxel is the basic unit in three-dimensional space, each voxel has a corresponding coordinate position. In the process of medical image reconstruction and mapping, by calculating the position of all voxels, the first geometric center of the anatomical structure image in the reference space coordinate system can be determined very accurately.

[0105] It should be noted that, since each voxel has a corresponding coordinate position, based on the voxel coordinate data of the anatomical structure image, the first coordinate data of the corresponding anatomical structure image in the reference space coordinate system can be obtained, which is recorded as (x 1 ,y 1 、z 1 ).

[0106] Step S23: obtaining a second geometric center of the initial fiber bundle tracking image in the reference space coordinate system.

[0107] In one embodiment, the combination Figure 4 As shown, the second geometric center is obtained through steps S231 and S232.

[0108] Step S231: obtaining second coordinate data of the initial fiber bundle tracking image in the reference space coordinate system.

[0109] The second coordinate data is recorded as (x 2 ,y 2 , z 2 ).

[0110] Step S232: Calculate the second geometric center of the initial fiber bundle tracking image in the reference space coordinate system based on the second coordinate data.

[0111] Using the second coordinate data (x 2 ,y 2 , z 2 ) Calculate the coordinates of the second geometric center of the initial fiber bundle tracking image in the reference space coordinate system, denoted as (x 00 ,y 00 , z 00 ).

[0112] Step S24: Overlapping the first geometric center with the second geometric center so that the anatomical structure image and the initial fiber bundle tracking image partially overlap in the reference space coordinate system, and obtaining a first affine transformation matrix.

[0113] By overlapping the second geometric center with the first geometric center, the initial fiber bundle tracking image and the anatomical structure image can be quickly and accurately tracked and positioned in the reference space coordinate system, thereby achieving partial alignment, reducing the amount of calculation in the coarse alignment process, and improving the calculation efficiency of the coarse alignment.

[0114] In some embodiments, in combination with Figure 5 As shown, the first affine transformation matrix is ​​obtained through steps S241 to S245.

[0115] Step S241: Transform the first geometric center of the anatomical structure image to the coordinate origin of the reference space coordinate system, and obtain a coordinate data set of the anatomical structure image after the first transformation.

[0116] It can be understood that, since the first geometric center is translated and transformed to the coordinate origin of the reference space coordinate system, its first coordinate data is also transformed synchronously, and the coordinate data set of the anatomical structure image after the first transformation, that is, the fourth coordinate data, is obtained, which is recorded as (x 4 ,y 4 , z 4 ).

[0117] Step S242: Determine a first translation matrix based on the coordinate data set of the anatomical structure image after the first transformation.

[0118] It can be understood that based on the first coordinate data (x 1 ,y 1 、z 1 ) and the fourth coordinate data (x 4 ,y 4 , z 4 ), the first translation matrix can be determined.

[0119] Step S243: Translating the geometric center of the first transformed anatomical structure image to the second geometric center of the initial fiber bundle tracking image to obtain the second transformed anatomical structure image.

[0120] It can be understood that since the geometric center of the anatomical structure image after the first transformation, that is, the coordinate origin, is translated to the second geometric center of the initial fiber bundle tracking image, its fourth coordinate data is also transformed synchronously to obtain the coordinate data set of the anatomical structure image after the second transformation, that is, the fifth coordinate data, recorded as (x 5 ,y 5 , z 5 ).

[0121] Step S244: determining a second translation matrix based on the anatomical structure image after the second transformation.

[0122] It can be understood that based on the fourth coordinate data (x 4 ,y 4 , z 4 ) and the fifth coordinate data (x 5 ,y 5 , z 5 ), the second translation matrix can be determined.

[0123] Step S245: Determine a first affine transformation matrix according to the first translation matrix and the second translation matrix. Specifically, the first affine transformation matrix = the first translation matrix * the second translation matrix.

[0124] It can be understood that the first geometric center and the second geometric center can be overlapped through the above-mentioned first affine transformation matrix, so that the anatomical structure image and the initial fiber bundle tracking image have an intersection in the reference space coordinate system to achieve partial overlap.

[0125] The advantages of this application of translation transformation through the coordinate origin mainly include the following points:

[0126] By performing a translation transformation between the first geometric center and the second geometric center through the coordinate origin of the reference space coordinate system, the calculation process can be simplified, and there is no need to calculate the translation of each point separately, which reduces the amount of calculation and improves the registration efficiency of the image data. In addition, by performing a translation transformation through the coordinate origin, the relative positions between the points in the space can be kept unchanged. This ensures that the shape and size of each organ and / or tissue in the image will not change during the translation process.

[0127] Furthermore, translation transformation through the coordinate origin can unify the processing method, which can simplify programming and algorithm design and make different operations more consistent and coordinated.

[0128] Step S3: encapsulating the image data of the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image.

[0129] In medical imaging systems, NIfT (Neuroimaging Informatics Technology Initiative) files are a standard file format commonly used to store neuroimaging data. Anatomical structure images usually use NIfTI files. Therefore, in order to achieve the unification of the image data of anatomical structure images and fiber bundle tracking data in format, it is necessary to encapsulate the image data of the transformed initial fiber bundle tracking images to obtain NIfTI files that are consistent with the format of anatomical structure images. When used in visualization medical systems, it can realize the visualization conversion of fiber bundle tracking data to traditional structure image data, enhancing the applicability, flexibility and computational efficiency of visualization conversion.

[0130] In some embodiments, in combination with Figure 6 As shown, the encapsulated fiber bundle tracking image is obtained through steps S31 to S33.

[0131] Step S31: obtaining the third coordinate data of the transformed initial fiber bundle tracking image in the reference space coordinate system.

[0132] The third coordinate data of the transformed initial fiber bundle tracking image represents the coordinates of each point on each fiber, recorded as (x 3 ,y 3 , z 3 ).

[0133] Step S32: construct an empty three-dimensional array of the array where the first coordinate data is located.

[0134] In medical imaging systems, three-dimensional array is an important data structure used to represent and process three-dimensional medical imaging data. This data structure can effectively store and manage volumetric image data obtained from medical imaging devices such as MRI devices.

[0135] Since the first coordinate data is three-dimensional data and the array in which it is located is a three-dimensional array, the three-dimensional array can be used to describe the position or state of the anatomical structure image in the three-dimensional space. By constructing an empty three-dimensional array of the array in which the first coordinate data is located, the original data in the three-dimensional array is cleared, thereby obtaining an empty three-dimensional array.

[0136] Step S33: writing the third coordinate data into an empty three-dimensional array for packaging to obtain a packaged fiber bundle tracking image.

[0137] This ensures that, apart from the difference in image content, the dimensional information (spatial dimension size, coordinates, pixels, etc.) of the packaged fiber bundle tracking images and the anatomical structure images in the NIfTI file remain consistent, preventing misalignment and scaling. The images can also remain consistent with the affine transformation matrix and even the patient information that comes with the NIfTI file, ensuring that the packaged fiber bundle tracking images and the anatomical structure images can be fully registered and aligned, providing the necessary preparation for the precise alignment of the point and line coordinate data of the packaged fiber bundle tracking images and the anatomical structure images.

[0138] In some embodiments, in combination with Figure 7 As shown, the third coordinate data is written into the empty three-dimensional array for encapsulation through steps S331 to S333.

[0139] Step S331: traverse each coordinate point in the third coordinate data.

[0140] Step S332: Write each coordinate point into each coordinate position in the empty three-dimensional array.

[0141] Step S333: At the current coordinate position, each time a coordinate point is written, the brightness value of the voxel corresponding to the coordinate position is marked.

[0142] Specifically, each time a coordinate point is written, the brightness value of the voxel corresponding to the coordinate position is marked with +1. In this way, different coordinate positions will have different brightness value marks, and the brightness contrast of different fiber bundles will be displayed in the obtained packaged fiber bundle tracking image.

[0143] It should be noted that, because the amount of data in the third coordinate data (generally millions) is much larger than the coordinate position in the empty three-dimensional array (generally hundreds of thousands), many repeated coordinate points will appear in the writing process, that is, the current coordinate position will be written into multiple coordinate points. Through step S333, the density of the fiber bundle can be well restored in the obtained encapsulated fiber bundle tracking image. In the visualized medical system, different brightness value marks can be used to more vividly display the brightness changes of the fiber bundle.

[0144] Step S4: registering the packaged fiber bundle tracking image and the anatomical structure image according to a preset registration rule to obtain a registered image.

[0145] In some embodiments, in combination with Figure 8 As shown, the registration method also includes steps S5-S7.

[0146] Step S5: Generate a second affine transformation matrix based on a preset registration rule.

[0147] The second affine transformation matrix is ​​a universal 4*4 affine transformation matrix under the preset registration rules. The second affine transformation matrix is ​​used to completely align the packaged fiber bundle tracking image and the anatomical structure image in the reference space coordinate system. The precise alignment of the second affine transformation matrix reduces the alignment error, enables the packaged fiber bundle tracking image and the anatomical structure image to be precisely registered, provides a more accurate registration result, and takes into account the robustness and accuracy of the overall registration.

[0148] Step S6: Based on the first affine transformation matrix and the second affine transformation matrix, obtain a target affine transformation matrix.

[0149] Among them, the target affine transformation matrix = the second affine transformation matrix * the first affine transformation matrix.

[0150] Step S7: registering the target image based on the target affine transformation matrix, wherein the image to be registered includes the target anatomical structure image of the target part of the patient and the target fiber bundle tracking image.

[0151] According to the above target affine transformation matrix, the registration of the target anatomical structure image and the target fiber bundle tracking image can be achieved.

[0152] It should be noted that the target anatomical structure image and the target fiber bundle tracking image can be acquired by the same MRI device as that of the present application.

[0153] It is understandable that in a medical imaging system, since the 3D target fiber bundle tracking image is generated by converting the 4D DTI image, there is a corresponding relationship between the image data (coordinate data) of the DTI image and the image data of the target fiber bundle tracking image.

[0154] Therefore, the target affine transformation matrix can not only realize the registration of the target fiber bundle tracking image and the target anatomical structure image, but also realize the registration of the target anatomical structure image and the DTI image through the target fiber bundle tracking image. There is no need to directly register the target anatomical structure image data with the original data of the DTI image, thus avoiding the inaccurate and incomplete registration caused by image distortion. It does not require manual operation and reduces the risk of errors. It not only has high computational efficiency, but also high universality and flexibility.

[0155] The target affine transformation matrix of the present application can abstract MRI fiber bundle tracking data from different sources into point-line coordinate data, accurately align with any sequence of structural images, coordinate fiber tracking data exported by different tools or software, and process them through a unified interface.

[0156] Combined with Fig. 9As shown, in another embodiment, a registration device is provided. The registration method is applied, and the registration device includes an image acquisition module 100, a first processing module 200, a second processing module 300 and an image registration module 400.

[0157] The image acquisition module 100 is used to acquire the anatomical structure image and the initial fiber bundle tracking image of the target part of the patient using a nuclear magnetic resonance MRI device.

[0158] The first processing module 200 is used to transform the image data of the initial fiber bundle tracking image based on the image data of the anatomical structure image to obtain a first affine transformation matrix and the transformed initial fiber bundle tracking image.

[0159] The first affine transformation matrix is ​​used to overlap the initial fiber bundle tracking image with the anatomical structure image in the same coordinate system.

[0160] The second processing module 300 is used to encapsulate the image data of the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image.

[0161] The image registration module 400 is used to register the packaged fiber bundle tracking image and the anatomical structure image according to a preset registration rule to obtain a registered image.

[0162] Combined with Fig.10 As shown, in another embodiment, a visualization medical system 1 is provided, including a first processor and a display screen.

[0163] The first processor is configured to be in communication connection with the medical acquisition device and execute the steps of the above-mentioned registration method.

[0164] The display screen is configured to display a medical image of a patient, wherein the medical image at least includes a registered image.

[0165] The visual medical system can realize visual display of the registered images, so that the registered images can be better used for medical processing and analysis. It can clearly and accurately display the internal structure and details of the human body, helping doctors to accurately identify and diagnose the diseased areas.

[0166] The present application provides another embodiment, which provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a second processor, the steps of the above-mentioned registration method are implemented.

[0167] The present application provides another embodiment, which provides a computer program product. The computer program product includes instructions, and when the instructions are executed on an electronic device, the electronic device implements the steps of the above-mentioned registration method.

[0168] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the medical device. In other embodiments of the present application, the medical device may include more or fewer components, or combine certain components, or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0169] The first processor 11 and / or the second processor may include one or more processing units, for example, a central processing unit CPU (Central Processing Unit), a graphics processor GPU (Graphics Processing Unit), a digital signal processor DSP, a microprocessor MCU (Micro-programmed Control Unit), an AI (Artificial Intelligence, artificial intelligence) processor or a programmable logic device FPGA (Field Programmable Gate Array) and the like processing module or processing circuit. Among them, different processing units can be independent devices or integrated in one or more processors. A storage unit may be set in the first processor 11 and / or the second processor for storing instructions and data. In some embodiments, the storage unit in the first processor 11 and / or the second processor is a cache memory.

[0170] The various embodiments of the mechanism disclosed in the present application can be implemented in hardware, software, firmware or a combination of these implementation methods. The embodiments of the present application can be implemented as a computer program or program code executed on a programmable system, and the programmable system includes at least one third processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device and at least one output device.

[0171] Program code can be applied to input instructions to perform the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0172] Program code can be implemented with high-level programming language or object-oriented programming language to communicate with the processing system. When necessary, program code can also be implemented with assembly language or machine language. In fact, the mechanism described in this application is not limited to the scope of any specific programming language. In either case, the language can be a compiled language or an interpreted language.

[0173] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, instructions may be distributed over a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine (e.g., computer) readable form, including, but not limited to, floppy disks, optical disks, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in electrical, optical, acoustic, or other forms of propagation signals. Therefore, machine-readable media include any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a machine (e.g., computer) readable form.

[0174] In the accompanying drawings, some structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be required. Instead, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular figure does not mean that such features are required in all embodiments, and in some embodiments, these features may not be included or may be combined with other features.

[0175] It should be noted that the units / modules mentioned in the various device embodiments of the present application are all logical units / modules. Physically, a logical unit / module can be a physical unit / module, or a part of a physical unit / module, or can be implemented as a combination of multiple physical units / modules. The physical implementation method of these logical units / modules themselves is not the most important. The combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed by the present application. In addition, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units / modules that are not closely related to solving the technical problems proposed by the present application, which does not mean that there are no other units / modules in the above-mentioned device embodiments.

[0176] It should be noted that in the examples and description of this patent, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0177] Although the present application has been illustrated and described with reference to certain preferred embodiments thereof, it will be apparent to those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

[0178] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, substitute and modify the above embodiments within the scope of the invention without departing from the principles and purpose of the present invention. All such changes should fall within the scope of protection of the claims of the present invention.

Claims

1. A method for registering fiber bundle tracking data with structural images, characterized in that: The method comprises: Obtain anatomical structure images and initial fiber bundle tracking images of the patient's target area using a magnetic resonance imaging (MRI) device; Based on the image data of the anatomical structure image, transform the image data of the initial fiber bundle tracking image to obtain a first affine transformation matrix and a transformed initial fiber bundle tracking image; wherein the first affine transformation matrix is ​​used to make the initial fiber bundle tracking image coincide with the anatomical structure image in the same coordinate system; encapsulating the image data of the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image; The packaged fiber bundle tracking image and the anatomical structure image are registered according to a preset registration rule to obtain a registered image.

2. The registration method according to claim 1, characterized in that: The method further comprises: Based on the preset registration rule, a second affine transformation matrix is ​​generated; wherein the second affine transformation matrix is ​​used to make the packaged fiber bundle tracking image and the anatomical structure image coincide with each other in the coordinate system; Based on the first affine transformation matrix and the second affine transformation matrix, a target affine transformation matrix is ​​obtained; wherein the target affine transformation matrix = the second affine transformation matrix * the first affine transformation matrix; The target image is registered based on the target affine transformation matrix; wherein the image to be registered includes a target anatomical structure image of a target part of the patient and a target fiber bundle tracking image.

3. The registration method according to claim 1 or 2, characterized in that: The image data of the initial fiber bundle tracking image is transformed based on the image data of the anatomical structure image to obtain a first affine transformation matrix including: Determine the reference space coordinate system; Acquire a first geometric center of the anatomical structure image in the reference space coordinate system; Acquire the second geometric center of the initial fiber bundle tracking image in the reference space coordinate system; The first geometric center and the second geometric center are overlapped to make the anatomical structure image coincide with the initial fiber bundle tracking image, and a first affine transformation matrix is ​​obtained.

4. The registration method according to claim 3, characterized in that: Acquiring the first geometric center of the anatomical structure image in the reference space coordinate system comprises: Acquiring voxel coordinate data of the anatomical structure image; Based on the voxel coordinate data, calculating the voxel geometric center of the anatomical structure image in the voxel coordinate system; Based on the voxel geometric center, a first geometric center corresponding to the anatomical structure image in the reference space coordinate system is determined.

5. The registration method according to claim 3, characterized in that: Acquiring the initial fiber bundle tracking image at the second geometric center of the reference space coordinate system comprises: Acquire second coordinate data of the initial fiber bundle tracking image in the reference space coordinate system; Based on the second coordinate data, a second geometric center of the initial fiber bundle tracking image in the reference space coordinate system is calculated.

6. The registration method according to claim 3, characterized in that: Overlapping the first geometric center with the second geometric center so that the anatomical structure image and the initial fiber bundle tracking image are aligned and coincident, and obtaining a first affine transformation matrix includes: Transforming the coordinate origin of the reference space coordinate system by translating the first geometric center of the anatomical structure image to obtain a coordinate data set of the anatomical structure image after the first transformation; Determining a first translation matrix based on the coordinate data set of the anatomical structure image after the first transformation; Transforming the geometric center of the anatomical structure image after the first transformation to the second geometric center of the initial fiber bundle tracking image to obtain the anatomical structure image after the second transformation; Determining a second translation matrix based on the anatomical structure image after the second transformation; A first affine transformation matrix is ​​determined according to the first translation matrix and the second translation matrix.

7. The registration method according to claim 4, characterized in that: The image data of the transformed initial fiber bundle tracking image is packaged to obtain a packaged fiber bundle tracking image; including: Acquire the third coordinate data of the transformed initial fiber bundle tracking image in the reference space coordinate system; Based on the voxel coordinate data, determining first coordinate data of the corresponding anatomical structure image in the reference space coordinate system; Constructing an empty three-dimensional array of the array where the first coordinate data is located; The third coordinate data is written into the empty three-dimensional array for packaging to obtain a packaged fiber bundle tracking image.

8. The registration method according to claim 7, characterized in that: Writing the third coordinate data into the empty three-dimensional array for packaging includes: Traversing each coordinate point in the third coordinate data; Write each coordinate point into each coordinate position in the empty three-dimensional array; In the current coordinate position, each time a coordinate point is written, the brightness value of the voxel corresponding to the coordinate position is marked.

9. A registration device, characterized in that: include: Image acquisition module: used to obtain anatomical structure images and initial fiber bundle tracking images of the patient's target part using magnetic resonance imaging (MRI); A first processing module is used for transforming the image data of the initial fiber bundle tracking image based on the image data of the anatomical structure image to obtain a first affine transformation matrix and a transformed initial fiber bundle tracking image; Wherein, the first affine transformation matrix is ​​used to overlap the initial fiber bundle tracking image with the anatomical structure image in the same coordinate system; The second processing module is used to encapsulate the transformed initial fiber bundle tracking image to obtain an encapsulated fiber bundle tracking image; Image registration module: used to register the packaged fiber bundle tracking image and the anatomical structure image according to a preset registration rule to obtain a registered image.

10. A visual medical system, characterized in that: include: The first processor is configured to communicate with the medical acquisition device and execute the steps of applying the registration method according to any one of claims 1 to 8, including: A display screen is configured to display a medical image of a patient, wherein the medical image at least includes the registered image described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the second processor, the steps of the registration method according to any one of claims 1 to 8 are implemented.

12. A computer program product, characterized in that The computer program product comprises instructions, which, when executed on an electronic device, enable the electronic device to implement the steps of the registration method according to any one of claims 1 to 8.

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