Neuropathic pain target spot positioning method, system and device based on DTI

Abstract

The invention provides a DTI-based neuropathic pain target positioning method, system and device, and relates to the technical field of neuropathic pain target positioning. The positioning method comprises the following steps: receiving DTI image data covering symmetrical areas on two sides of a patient in a target path; preprocessing the DTI image data, performing tensor fitting on each voxel point, and calculating a measurement parameter to obtain a parameter graph; performing fiber bundle analysis on the DTI image data to obtain a segment map; based on the segment map, after symmetrically setting regions of interest on the affected side and the uninjured side, extracting measurement parameters of the regions of interest; based on the measurement parameter difference of the symmetric segment atlas between the affected side and the uninjured side and preset pathological characteristics, performing comprehensive judgment, and determining candidate targets; through coordinate transformation, the candidate target spots are mapped back to the three-dimensional space, and position information is output to a navigation system. According to the scheme, the influence of individual differences can be effectively reduced, subjective decision fluctuation is reduced, and the consistency and operability of processes before and during an operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of neuropathic pain target positioning, and in particular to a DTI-based neuropathic pain target positioning method, system and device. BACKGROUND

[0002] Neuropathic pain is chronic pain caused by damage or dysfunction of the nervous system, mainly manifested as burning sensation, electric shock-like pain or needle-like pain. Postherpetic neuralgia (PHN) is a typical neuropathic pain, and its pathological basis often involves dorsal root ganglion (DRG) and related fibers.

[0003] Common treatment methods for neuropathic pain include drug therapy, minimally invasive intervention, nerve regulation therapy and surgical treatment, etc. Except for drug therapy, other treatment methods need to determine the treatment target. The existing clinical target selection mainly depends on the subjective inference of dermatome map and complaint, or uses diagnostic block as verification, which has strong subjectivity, is invasive and lacks objective imaging gold standard.

[0004] Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that can noninvasively display the microscopic diffusion motion of water molecules in living tissues. Among them, fractional anisotropy (FA) and mean diffusivity (MD) are the most commonly used quantitative measurement parameters of DTI. The FA value mainly reflects the integrity and uniformity of nerve fibers, and the closer the value is to 1, the stronger the anisotropy is; the MD value reflects the overall diffusion level of water molecules, which is usually related to tissue edema or cell density. Although these parameters can objectively reflect the microscopic pathological changes of nerves, there is still a lack of effective means to convert these (abstract and objective) parameters into (intuitive navigation) specific surgical targets in clinical practice. SUMMARY

[0005] The purpose of the present application is to provide a DTI-based neuropathic pain target positioning method, system and device to solve at least one of the above technical problems in the prior art.

[0006] In a first aspect, to solve the above technical problems, the present application provides a DTI-based neuropathic pain target positioning method, comprising the following steps: Step S110, receiving DTI image data covering the target pathway symmetrical region of the patient.

[0007] In a feasible implementation, the DTI image data can be derived from standard MRI scanning and transmitted through DICOM protocol.

[0008] In an implementable embodiment, the specific parameters of the MRI scan include: Field strength: 2.0-3.0T; b-value: 600-1000s / mm2; Gradient direction: ≥30 (preferably ≥48); Voxel size: 1.5-3.0mm, and isotropic.

[0009] Step S120, pre-processing the DTI image data, tensor fitting for each voxel, calculating the metric parameters, and obtaining the parameter map.

[0010] In an implementable embodiment, the pre-processing includes performing eddy / distortion / motion artifact correction and spatial registration.

[0011] In an implementable embodiment, the specific calculation formula of the metric parameters includes: ; ; Wherein, represents the tensor eigenvalue; represents the average diffusion rate; represents the anisotropy fraction.

[0012] In an implementable embodiment, the step S120 further includes step S125, quality control for the pre-processed DTI image data, including quality control threshold and quality control strategy; The quality control threshold includes: Registration residual ≤ε (target upper limit of spatial registration residual, used to determine whether the registration quality of the pre-processed data meets the standard) so as to automatically identify and eliminate image data with motion artifact or excessive distortion; Eliminate abnormal frames with phase encoding direction exceeding θ° (abnormal frame angle threshold of phase encoding direction); SNR (signal-to-noise ratio) ≥S0 (average signal intensity of image when b-value is 0); The quality control strategy includes: When SNR For abnormal frames with phase encoding direction exceeding θ°, perform the elimination operation or the weight reduction operation.

[0013] Step S130, fiber bundle analysis is performed on the DTI image data to obtain a segment atlas (i.e., segmentation reference is performed according to the symmetrical anatomical landmarks of the nerve root-DRG-peripheral nerve trunk / branch, so as to standardize the ROI placement and the subsequent symmetrical comparison process); after the ROIs are symmetrically set on the affected side and the healthy side based on the segment atlas, the measurement parameters of the ROIs are extracted.

[0014] In an implementable embodiment, the fiber bundle analysis includes sampling according to a preset step length (e.g., 10-20 mm) within a range of 5-120 mm along the nerve running direction.

[0015] In an implementable embodiment, the segment atlas specifically refers to a three-dimensional voxel mask including information of the nerve root, DRG, sciatic nerve and peroneal nerve proximal segment, and with anatomical coordinate constraints; the proximal segment can refer to a range of 5-15 cm from the distal end of the anatomical landmark (such as the inferior piriform aperture or the ischial tuberosity) along the nerve running direction.

[0016] In an implementable embodiment, the symmetrical setting refers to setting ROIs of the same shape and size at the same anatomical position and layer on the affected side and the healthy side after rigid registration and / or affine registration and / or elastic registration (e.g., B-Spline) based on anatomical reference, so as to automatically extract measurement parameters with comparability.

[0017] In an implementable embodiment, the anatomical reference includes bony landmarks (such as vertebral body / sacrum, etc.) and the midaxial plane of the spine.

[0018] In an implementable embodiment, the shape of the ROI is circular or elliptical or spherical.

[0019] In an implementable embodiment, the symmetrical setting further includes: when there is a problem of insufficient symmetry (caused by anatomical variation), the most adjacent layer is taken as a symmetrical substitute along the nerve running direction.

[0020] Step S140, based on the difference in the measurement parameters of the symmetrical segment atlas between the affected side and the healthy side and the preset imaging pathological features, comprehensive determination is performed to determine a candidate target point (i.e., a suspected target position of neuropathic pain), so as to subsequently select a treatment target point most suitable for incision operation from the candidate target point; The imaging pathological features include: FA value reduction, reflecting that the integrity of the myelin sheath is destroyed; MD value increase, reflecting the presence of edema or inflammation.

[0021] In an implementable embodiment, the step S140 specifically includes: Step 701, extracting the metric parameters of the voxel points from the ROI of the symmetric segment atlas; Step 702, calculating the symmetric difference index , the specific expression includes (taking the FA value as an example): ; Wherein, represents the healthy side anisotropy fraction; represents the affected side anisotropy fraction; Step 703, calculating the relative diffusion ratio , the specific expression includes (taking the MD value as an example): ; Wherein, represents the average diffusion rate of the affected side; represents the average diffusion rate of the healthy side; Step 704, calculating the fiber continuity index , the specific expression includes: ; Wherein, represents the healthy side nerve fiber tracking length (or continuity index representative quantity); represents the affected side nerve fiber tracking length (or continuity index representative quantity); Step 705, normalizing and aggregating , and ; in this way, the interference of artifacts can be effectively eliminated (simple FA / MD value change may be caused by artifacts, and fiber bundle fracture is a strong specificity index of nerve injury), and the specificity of target positioning is improved; Step 706, calculating the comprehensive score , and then comparing with the comprehensive threshold value to remove the voxel points lower than the comprehensive threshold value, the specific expression includes: ; Wherein, , and are weight coefficients, and ; Step 707, in the remaining voxel points, threshold method and / or statistical test method are used for judgment to obtain the candidate target point; Step 708, outputting the candidate target point.

[0022] Preferably, =0.5, =0.3, =0.2.

[0023] In an embodiment, the threshold method includes: and / or ; wherein, represents a preset coefficient (e.g., 1-3); represents a standard deviation of the healthy side anisotropy fraction (or an empirical constant); represents a standard deviation of the healthy side average diffusion rate (or an empirical constant).

[0024] In an embodiment, the statistical test method includes: performing a t-test on the difference of the paired affected side and healthy side measurement parameters, and determining the candidate target point when a preset significance level (e.g., 0.05) is met, thereby excluding random error interference and ensuring positioning accuracy. The specific calculation formula includes: ; wherein, represents a segment atlas logarithm; represents a measurement parameter difference mean value; represents a measurement parameter difference standard deviation; represents the measurement parameter difference; represents the healthy side measurement parameter value; represents the affected side measurement parameter value; Step S150: mapping the candidate target point back to the three-dimensional space through coordinate transformation, and outputting the position information to the navigation system.

[0025] In an embodiment, the specific expression of the coordinate transformation includes: ; wherein, represents a (4*4) homogeneous transformation matrix (rigid / affine); represents a device coordinate system; represents a world coordinate system (patient coordinate system); represents an image coordinate system; represents a column vector of the three-dimensional coordinates of the voxel point, i.e., (x, y, z, 1).

[0026] In an embodiment, the mapping accuracy of the mapping in step S150 is a positioning error ≤3 mm; the positioning error is derived from the registration error, the navigation calibration error, and the image resolution. In an embodiment, the format output in step S150 includes DICOM-RT and / or JSON and / or XML.

[0027] In an implementable embodiment, the navigation system in step S150 comprises presenting the difference between the candidate target point and the healthy side for comparison on the three-dimensional reconstruction view of the fiber tract tracing, so as to intuitively assist navigation.

[0028] In an implementable embodiment, the positioning method further comprises step S160, when the healthy side cannot be used as a comparison, using the atlas of the adjacent non-involved segment of the affected side as a comparison, determining the candidate target point of the affected side by longitudinal self-contrast.

[0029] In an implementable embodiment, the baseline value in the population baseline library refers to the standard value of the measurement parameter of the healthy population at the preset nerve segment collected and counted, and is stratified according to age, gender, site and protocol.

[0030] In an implementable embodiment, step S160 specifically comprises: Step 801, determining whether the healthy side can be used as a comparison based on the population baseline library; if not, performing step 811; if yes, performing step S140; Step 811, using the atlas of the adjacent non-involved segment of the affected side instead of the healthy side as a comparison; Step 812, performing longitudinal self-contrast by regularly repeating MRI scanning (or follow-up) on the patient, iteratively performing step 811 until the iteration termination condition is reached; Step 820, performing comprehensive determination to determine the candidate target point of the affected side according to steps 702-707; Step 821, outputting the candidate target point.

[0031] In a second aspect, based on the same inventive concept, the present application also provides a DTI-based neuropathic pain target positioning system, comprising a data interface module, an image preprocessing module, a parameter calculation module, a symmetry analysis module, a target decision module and a visualization and output module; The data interface module is configured to provide an interface for receiving DTI image data covering the symmetric regions of the target pathway of the healthy and affected sides and outputting position information externally. The image preprocessing module is configured to preprocess the DTI image data. The parameter calculation module is configured to perform tensor fitting on each voxel based on the preprocessed DTI image data, calculate the measurement parameters and obtain a parameter map. The symmetry analysis module is configured to symmetrically set a region of interest based on the parameter map and the segment atlas, and extract the measurement parameters of the region of interest. The target point decision module determines the candidate target point based on the difference between the measurement parameters of the symmetric segment atlas of the affected side and the healthy side and the preset imaging pathological characteristics. The visualization and output module maps the candidate target point back to the three-dimensional space through coordinate transformation and outputs the position information.

[0032] In a third aspect, based on the same inventive concept, the present application also provides a DTI-based neuropathic pain target point positioning device, which comprises a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute the positioning method as described above, and the bus is connected between each functional component for transmitting information.

[0033] By adopting the above technical solution, the present application has the following beneficial effects: The DTI-based neuropathic pain target point positioning method, system and device provided by the present application combine the segment atlas with the automatic determination engine to output the spatialized candidate target point, which can effectively reduce the influence of individual differences, reduce the subjective decision fluctuation, and improve the consistency and operability of the preoperative and intraoperative procedures. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the following specific embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0035] Figure 1 A DTI-based neuropathic pain target point positioning method flowchart is provided for the embodiments of the present application. Figure 2 A contrast schematic diagram of the FA parameter map is provided for the embodiments of the present application, wherein a diagram is a healthy side FA parameter map, and b diagram is an affected side FA parameter map. Figure 3 A contrast schematic diagram of the FA parameter map is provided for the embodiments of the present application, wherein a diagram is a healthy side FA parameter map, and b diagram is an affected side FA parameter map. Figure 1 A specific flowchart of S140 in the method is provided. Figure 4 A contrast schematic diagram of the FA parameter map is provided for the embodiments of the present application, wherein a diagram is a healthy side FA parameter map, and b diagram is an affected side FA parameter map. Figure 5 Another DTI-based neuropathic pain target point positioning method flowchart is provided for the embodiments of the present application. Figure 6 A DTI-based neuropathic pain target point positioning system diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] The present invention will be further explained below with reference to specific embodiments.

[0040] It should also be noted that the specific embodiments or implementation methods described below are a series of optimized settings listed by the present invention to further explain the specific content of the invention, and these settings can be combined or used in conjunction with each other.

[0041] like Figure 1 As shown in the figure, this embodiment provides a DTI-based method for locating neuropathic pain targets, which includes the following steps: Step S110: Receive DTI image data covering the bilateral symmetrical regions of the target pathway in both healthy and patient patients.

[0042] Furthermore, the DTI image data can originate from standard MRI scans and be transmitted via protocols such as DICOM.

[0043] Furthermore, the specific parameters of the MRI scan include: Field strength: 2.0–3.0T; b-value: 600–1000 s / mm²; Gradient direction: ≥30 (preferably ≥48); Voxel size: 1.5–3.0 mm, and isotropic.

[0044] Step S120: Preprocess the DTI image data, perform tensor fitting on each voxel point, calculate metric parameters, and obtain a parameter map.

[0045] Furthermore, the preprocessing includes performing conventional eddy current / distortion / motion artifact correction and spatial registration.

[0046] Furthermore, the specific calculation formula of the metric parameters includes: ; ; where represents the tensor eigenvalue; represents the mean diffusivity; represents the fractional anisotropy; Exemplarily, as Figure 2 shown, where Figure a is the FA parameter map of the healthy side, and Figure b is the FA parameter map of the affected side; the small white circle icon in Figure a indicates a high FA value at this voxel point, and the small black circle icon in Figure b indicates a low FA value at the same voxel point.

[0047] Furthermore, step S120 further includes step S125 of performing quality control on the preprocessed DTI image data, including a quality control threshold and a quality control strategy; The quality control threshold includes: Registration residual ≤ ε (the target upper limit of the spatial registration residual, used to determine whether the registration quality after preprocessing meets the standard), so as to automatically identify and remove image data with excessive motion artifacts or distortions; Remove abnormal frames with a phase encoding direction exceeding θ° (the abnormal frame angle threshold in the phase encoding direction); SNR (signal-to-noise ratio) ≥ S0 (the average signal intensity of the image when the b value is 0); The quality control strategy includes: When SNR < S0 or the registration residual > ε, trigger an MRI scan reconstruction operation or an MRI scan reconfiguration operation; For abnormal frames with a phase encoding direction exceeding θ°, perform a removal operation or a downweighting operation.

[0048] Step S130: Perform fiber bundle analysis on the DTI image data to obtain a segmental atlas (i.e., define the symmetrical anatomical landmarks of nerve root-DRG-peripheral nerve trunk / branch, and make segmental references to standardize ROI placement and subsequent symmetrical comparison process); based on the segmental atlas, symmetrically set the region of interest (ROI) on the affected side and the healthy side, and extract the measurement parameters of the region of interest.

[0049] Furthermore, the fiber bundle analysis includes sampling within a range of 5-120 mm along the nerve course, according to a preset step size (e.g., 10-20 mm).

[0050] Furthermore, the segmental atlas specifically refers to a three-dimensional voxel mask that includes information such as nerve roots, DRGs, sciatic nerves, and proximal segments of the common peroneal nerve, and is constrained by anatomical coordinates.

[0051] Furthermore, the symmetrical setup refers to setting up ROIs of the same shape and size at the same anatomical location and anatomical level on the affected and healthy sides after rigid registration and / or affine registration and / or elastic registration based on anatomical reference, so as to extract comparable measurement parameters.

[0052] Furthermore, the anatomical references include bony landmarks (vertebral bodies / sacrum, etc.) and the axial plane of the spine.

[0053] Furthermore, the shape of the ROI is circular, elliptical, or spherical.

[0054] Furthermore, the symmetry setting also includes: when there is a problem of insufficient symmetry (caused by anatomical variations), the nearest layer along the nerve course is preferentially taken as the symmetry replacement.

[0055] Step S140: Based on the difference in measurement parameters of the symmetrical segmental atlas between the affected and healthy sides and the preset imaging pathological features, a comprehensive judgment is made to determine the candidate target points (i.e., the suspected target locations of neuropathic pain) so that the most suitable treatment target point for subsequent incision can be selected.

[0056] Furthermore, such as Figure 3 As shown, step S140 specifically includes: Step 701: Extract the metric parameters of voxel points from the ROI of the symmetric segment map; Step 702: Calculate the symmetry difference index The specific expressions include (taking the FA value as an example): ; in, Indicates the anisotropy fraction of the healthy side; Indicates the fraction of anisotropy on the affected side; Step 703: Calculate the relative diffusion ratio , the specific expression includes (taking MD value as an example): ; wherein, represents the average diffusion rate of the affected side; represents the average diffusion rate of the healthy side; Step 704, calculating the fiber continuity index , the specific expression includes: ; wherein, represents the healthy side nerve fiber tracking length (or continuity index representative quantity); represents the affected side nerve fiber tracking length (or continuity index representative quantity); Step 705, normalizing (such as Min-Max Scaling) and aggregating (such as weighted summation) the , and ; in this way, the interference of artifacts can be effectively eliminated (simple FA / MD value change may be caused by artifacts, while fiber bundle rupture is a strong specificity index of nerve injury), and the specificity of target positioning can be improved; Step 706, calculating the comprehensive score and comparing it with the comprehensive threshold value (for example, the FA value of the affected side is lower than the “healthy side mean minus K times standard deviation”, or the MD value of the affected side is higher than the “healthy side mean plus K times standard deviation”), removing the voxel points lower than the comprehensive threshold value, the specific expression includes: ; wherein, , and are weight coefficients, and have ; Step 707, in the remaining voxel points, the threshold method and / or statistical test method are used for judgment to obtain the candidate target point; Step 708, outputting the candidate target point.

[0057] Preferably, = 0.5, = 0.3, = 0.2.

[0058] Further, the specific calculation formula of the threshold method includes: and / or ; wherein, represents a preset coefficient (for example, 1-3); represents the standard deviation (or empirical constant) of the healthy side anisotropy fraction. standard deviation of the average diffusion rate of the healthy side (or an empirical constant).

[0059] Further, the statistical test method comprises: performing a t-test on the difference of the measurement parameters of the paired affected side and healthy side, and determining the candidate target point when a preset significance level (for example, 0.05) is met, and the specific calculation formula comprises: wherein, denotes the logarithm of the segment atlas; denotes the mean of the difference of the measurement parameters; denotes the standard deviation of the difference of the measurement parameters; denotes the first difference of the measurement parameters; denotes the first measurement parameter value of the healthy side; denotes the first measurement parameter value of the affected side.

[0060] Step S150: mapping the candidate target point back to the three-dimensional space through coordinate transformation, and outputting the position information to the navigation system.

[0061] Further, the specific expression of the coordinate transformation comprises: wherein, denotes a (4*4) homogeneous transformation matrix (rigid / affine); denotes the device coordinate system; denotes the world coordinate system (patient coordinate system); denotes the image coordinate system; denotes a column vector of the three-dimensional coordinates of the voxel point, i.e., (x, y, z, 1).

[0062] Further, the mapping accuracy of the mapping in the step S150 is a positioning error ≤3 mm; the positioning error is derived from the registration error, the navigation calibration error and the image resolution. Further, the format output in the step S150 comprises DICOM-RT and / or JSON and / or XML.

[0063] Further, the navigation system in the step S150 comprises presenting the difference of the candidate target point and the healthy side on a three-dimensional reconstruction view of fiber tract tracing, so as to intuitively assist navigation, as shown in Figure 4 .

[0064] Further, as shown in Figure 5 ​​​As shown, the positioning method further comprises a step S160 of judging based on the population baseline library, when the healthy side cannot be used as a control, using the atlas of the adjacent non-involved segment of the affected side as a control, determining the candidate target point of the affected side by longitudinal self-contrast.

[0065] Further, the baseline value in the population baseline library refers to the standard value (such as FA mean value, MD mean value, fiber bundle diameter mean value, etc.) of the measurement parameter of the healthy population collected and statistically obtained at the preset nerve segment, and is stratified according to age, gender, site and protocol.

[0066] Further, the step S160 specifically comprises: Step 801, determining whether the healthy side can be used as a control based on the population baseline library; if not, performing step 811; if yes, performing step S140; Step 811, using the atlas of the adjacent non-involved segment of the affected side as a control instead of the healthy side; for example, if the lesion is at the distal end of the nerve, using the proximal segment (proximal normal segment) of the nerve as a control; if the lesion is at the proximal end of the nerve, using the distal segment (distal normal segment) of the nerve as a control; Step 812, performing longitudinal self-contrast by periodically repeating MRI scanning (or follow-up) on the patient; iteratively performing step 811 until the iteration termination condition is reached; Step 820, referring to steps 702-707, performing comprehensive judgment to determine the candidate target point of the affected side; Step 821, outputting the candidate target point (including coordinates and DICOM-RT structure set, etc.).

[0067] Embodiment Two: As Figure 6 shown, the embodiment provides a DTI-based neuropathic pain target point positioning system, which comprises a data interface module 501, an image preprocessing module 502, a parameter calculation module 503, a symmetry analysis module 504, a target point decision module 505, and a visualization and output module 506. The data interface module 501 is configured to provide an interface for receiving DTI image data covering the symmetric regions of the target pathway of the healthy and affected sides and outputting position information externally. The image preprocessing module 502 is configured to preprocess the DTI image data. The parameter calculation module 503 is configured to perform tensor fitting on each voxel based on the preprocessed DTI image data, calculate the measurement parameters, and obtain a parameter map. The symmetry analysis module 504 is configured to symmetrically set a region of interest based on the parameter map and the segment atlas, and extract the measurement parameters of the region of interest. The target point decision module 505 determines the candidate target point based on the difference between the measurement parameters of the symmetric segment atlas of the affected side and the healthy side and the preset imaging pathological characteristics. The visualization and output module 506 maps the candidate target point back to the three-dimensional space through coordinate transformation and outputs the position information.

[0068] Embodiment three: The embodiment provides a DTI-based neuropathic pain target point positioning device, which comprises a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is used for calling the instructions and data in the memory to execute the positioning method as described above, and the bus is connected between various functional components for transmitting information.

[0069] In another embodiment, the present scheme can also be implemented by an integrated device, which can include corresponding modules for performing one or more steps in the above embodiments. The modules can be one or more hardware modules specially configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer readable medium for implementation by a processor, or implemented by some combination.

[0070] The processor performs various methods and processes described above. For example, the method embodiments in the present scheme can be implemented as a software program tangibly embodied in a machine readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via the memory and / or communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps of the above-described methods can be performed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (e.g., by means of firmware).

[0071] The device can be implemented with a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application of the hardware and the overall design constraints. The bus will connect various circuits of the one or more processors, memories, and / or hardware modules. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, power management circuits, external antennas, and the like.

[0072] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like, and can be divided into an address bus, a data bus, a control bus, and the like.

[0073] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for locating neuropathic pain targets based on DTI, characterized in that, include: Step S110: Receive DTI image data covering the bilateral symmetrical regions of the target pathway in both healthy and patient patients; Step S120: Preprocess the DTI image data, perform tensor fitting on each voxel point, calculate the metric parameters, and obtain the parameter map; Step S130: Perform fiber bundle analysis on DTI image data to obtain segmental atlases; based on the segmental atlases, symmetrically set regions of interest on the affected and healthy sides, and extract the measurement parameters of the regions of interest. Step S140: Based on the difference in measurement parameters of the symmetrical segmental atlas between the affected and healthy sides and the preset imaging pathological features, a comprehensive judgment is made to determine the candidate target points. Step S150: Through coordinate transformation, the candidate target points are mapped back to three-dimensional space, and the position information is output to the navigation system.

2. The positioning method according to claim 1, characterized in that, The specific calculation formula for the metric parameter includes: ； ； in, Represents the eigenvalues ​​of a tensor; Indicates the average diffusion rate; This represents the fraction of anisotropy.

3. The positioning method according to claim 1, characterized in that, The fiber bundle analysis includes sampling within a range of 5-120 mm along the nerve pathway, according to a preset step size.

4. The positioning method according to claim 1, characterized in that, The segmental atlas refers to a three-dimensional voxel mask that includes information on nerve roots, DRGs, sciatic nerves, and proximal segments of the common peroneal nerve, and is constrained by anatomical coordinates.

5. The positioning method according to claim 1, characterized in that, The symmetrical setup refers to setting regions of interest of the same shape and size at the same anatomical location and anatomical level on the affected and healthy sides after rigid registration and / or affine registration and / or elastic registration based on anatomical reference.

6. The positioning method according to claim 1, characterized in that, Step S140 specifically includes: Step 701: Extract the metric parameters of voxel points from the ROI of the symmetric segment map; Step 702: Calculate the symmetry difference index The specific expressions include: ； in, Indicates the anisotropy fraction of the healthy side; Indicates the fraction of anisotropy on the affected side; Step 703: Calculate the relative diffusion ratio The specific expressions include: ； in, This indicates the average diffusion rate on the affected side; Indicates the average diffusion rate of the healthy side; Step 704: Calculate fiber continuity index The specific expressions include: ； in, Indicates the length of the nerve fiber traced on the healthy side; Indicates the length of nerve fiber traced on the affected side; Step 705, for , and Perform normalization and aggregation; Step 706: Calculate the overall score Then, it is compared with a comprehensive threshold, and voxel points below the comprehensive threshold are removed. The specific expression includes: ； in, , and All are weighting coefficients, and have ; Step 707: Among the remaining voxel points, candidate target points are obtained by thresholding and / or statistical testing. Step 708: Output candidate target points.

7. The positioning method according to claim 6, characterized in that, The specific calculation formula for the threshold method includes: and / or ; in, Indicates the preset coefficient; The standard deviation of the anisotropy score of the healthy side; The standard deviation represents the average diffusion rate of the healthy side.

8. The positioning method according to claim 6, characterized in that, The statistical test method includes: performing a t-test on the difference in measurement parameters between paired affected and healthy sides; when the preset significance level is met... When determining candidate targets, the specific calculation formula includes: ； in, Represents the logarithm of the segmental graph; This represents the mean of the differences in the measurement parameters; This represents the standard deviation of the difference in the measurement parameter; Indicates the first The difference in the metric parameters; Indicates the first Each healthy side metric parameter value; Indicates the first The measured parameter values ​​for the affected side.

9. A DTI-based neuropathic pain target localization system, characterized in that, It includes a data interface module, an image preprocessing module, a parameter calculation module, a symmetry analysis module, a target decision module, and a visualization and output module; The data interface module is used to provide an interface for receiving DTI image data covering the bilateral symmetrical areas of the target pathway in both healthy and patients and outputting location information to the outside. The image preprocessing module is used to preprocess DTI image data; The parameter calculation module is used to perform tensor fitting on each voxel point based on the preprocessed DTI image data, calculate the metric parameters, and obtain the parameter map. The symmetry analysis module, based on the parameter map and segment map, symmetrically sets the region of interest and extracts the measurement parameters of the region of interest. The target decision module determines candidate targets based on the difference in measurement parameters of the symmetrical segmental atlas between the affected and healthy sides and preset imaging pathological features. The visualization and output module maps candidate target points back to three-dimensional space through coordinate transformation and outputs position information.

10. A DTI-based target localization device for neuropathic pain, characterized in that, It includes a processor, a memory, and a bus. The memory stores instructions and data read by the processor. The processor is used to call the instructions and data in the memory to execute the positioning method as described in any one of claims 1-8. The bus connects the functional components for transmitting information.

Images