Method, apparatus, and device for planning multi-fiber laser ablation treatment of brain gliomas

Abstract

The application relates to a multi-fiber laser ablation treatment brain glioma planning method, device and equipment. The method comprises the following steps: according to pre-acquired image information and segmentation results of a tumor region of a target patient, sequentially performing segmentation sampling processing on the image information to obtain a candidate fiber path set; according to a fiber planning objective function and a fiber planning constraint condition, performing screening processing on the candidate fiber path set to obtain an intermediate fiber path set, and determining the number of fibers required for tumor ablation according to the intermediate fiber path set; and according to the number of fibers required for tumor ablation and a pre-determined path evaluation score, determining a target fiber path set from the intermediate fiber path set; and the target fiber path set at least comprises position information corresponding to each target fiber path. The method can improve the tumor ablation rate and reduce the use of fibers.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a planning method, apparatus, and equipment for multi-fiber laser ablation therapy of glioma. Background Technology

[0002] Malignant gliomas are among the most common malignant tumors of the central nervous system. High-grade gliomas, in particular, are difficult to treat and have a poor prognosis, posing a serious threat to human health. Currently, surgical resection is the preferred treatment. However, due to factors such as tumor recurrence or its location deep within the brain, some patients are not suitable candidates for craniotomy.

[0003] In this context, MRI-guided laser interstitial thermal therapy (LITT) is gaining attention as a safe and effective alternative treatment. During LITT treatment, doctors use a stereotactic surgical platform to insert optical fibers into the predetermined treatment area within the skull, and ablate the tumor tissue through laser heating.

[0004] However, currently, LITT relies heavily on doctors manually determining the fiber location based on a three-dimensional visualization image of the tumor and performing ablation based on the manually determined location. This ablation range may not be able to completely cover the tumor, thus affecting the full ablation of the tumor and limiting the treatment effect. Summary of the Invention

[0005] Therefore, it is necessary to provide a planning method, device, and equipment for multi-fiber laser ablation treatment of glioma that can improve tumor ablation coverage, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a planning method for multi-fiber laser ablation therapy of gliomas, the method comprising:

[0007] Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the image information is sequentially segmented and sampled to obtain a set of candidate fiber optic paths.

[0008] Based on the objective function and constraints of fiber optic planning, the candidate fiber optic path set is screened to obtain the intermediate fiber optic path set, and the number of fibers required for tumor ablation is initially determined based on the intermediate fiber optic path set.

[0009] The target fiber path set is determined from the intermediate fiber path set based on the number of fibers required for tumor ablation and the pre-determined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0010] Secondly, this application also provides a planning device for multi-fiber laser ablation therapy of gliomas, the device comprising:

[0011] The segmentation module is used to perform segmentation and sampling processing on the image information sequentially based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, so as to obtain a set of candidate fiber optic paths.

[0012] The filtering module is used to filter the candidate fiber path set according to the fiber planning objective function and fiber planning constraints, obtain the intermediate fiber path set, and determine the number of fibers required for tumor ablation based on the intermediate fiber path set.

[0013] The determination module is used to determine the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and the pre-determined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0014] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0015] Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the fiber optic path set image information is sequentially segmented and sampled to obtain a candidate fiber optic path set.

[0016] Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set, and the number of optical fibers required for tumor ablation is initially determined based on the intermediate optical fiber path set.

[0017] The target fiber path set is determined from the intermediate fiber path set based on the number of fibers required for tumor ablation in the fiber path set and the predetermined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0019] Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the fiber optic path set image information is sequentially segmented and sampled to obtain a candidate fiber optic path set.

[0020] Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set, and the number of optical fibers required for tumor ablation is initially determined based on the intermediate optical fiber path set.

[0021] The target fiber path set is determined from the intermediate fiber path set based on the number of fibers required for tumor ablation in the fiber path set and the predetermined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0022] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, performs the following steps:

[0023] Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the fiber optic path set image information is sequentially segmented and sampled to obtain a candidate fiber optic path set.

[0024] Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set, and the number of optical fibers required for tumor ablation is initially determined based on the intermediate optical fiber path set.

[0025] The target fiber path set is determined from the intermediate fiber path set based on the number of fibers required for tumor ablation in the fiber path set and the predetermined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0026] The aforementioned planning method, apparatus, and equipment for multi-fiber laser ablation treatment of gliomas generate a set of candidate fiber paths based on the target patient's image information and tumor region segmentation results. These paths are then filtered using a fiber planning objective function and constraints. Finally, the target fiber path set is determined based on path evaluation scores, effectively improving the scientific rigor and optimal quantity of fiber placement. Compared to the traditional method of manually planning fiber paths in 3D visualized images by physicians, this method can identify potentially feasible paths and select the best fiber scheme using quantitative evaluation methods, avoiding poor planning results caused by insufficient human experience or visual judgment errors. Furthermore, this helps achieve sufficient and uniform coverage of the tumor region, significantly improving the integrity and therapeutic effect of tumor ablation. Attached Figure Description

[0027] Figure 1 This is an internal structural diagram of a computer device in one embodiment;

[0028] Figure 2 This is a flowchart illustrating a planning method for multi-fiber laser ablation treatment of glioma in one embodiment.

[0029] Figure 3 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0030] Figure 4 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0031] Figure 5 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0032] Figure 6 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0033] Figure 7 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0034] Figure 8 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0035] Figure 9 This is a schematic diagram illustrating the fiber optic path solving algorithm and evaluation in one embodiment;

[0036] Figure 10 This is a flowchart illustrating the planning method for multi-fiber laser ablation treatment of glioma in another embodiment.

[0037] Figure 11 This is a schematic diagram of the resampling generation function f(k) process in one embodiment;

[0038] Figure 12 This is a schematic diagram of a laser interstitial hyperthermia planning system in one embodiment;

[0039] Figure 13 This is a structural block diagram of a planned device for multi-fiber laser ablation treatment of glioma in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0041] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 1 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores relevant data during the planning process of multi-fiber laser ablation for glioma treatment. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a planning method for multi-fiber laser ablation for glioma treatment.

[0042] It will be understood by those skilled in the art that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0043] In one embodiment, such as Figure 2 As shown, a planning method for multi-fiber laser ablation therapy of gliomas is provided, which is then applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0044] Step 201: Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the image information is sequentially segmented and sampled to obtain a set of candidate fiber optic paths.

[0045] The target patient's imaging information refers to image data acquired through medical imaging equipment such as CT and MRI, reflecting the tissue structure within the target patient's body. This imaging information is used to locate the spatial distribution of the tumor region and its surrounding tissues.

[0046] The segmentation result of the tumor region refers to the result of identifying and extracting the tumor region from the whole medical image through image segmentation algorithms (such as artificial intelligence models or image processing technology), which is used for subsequent path planning and fiber optic layout.

[0047] Segmentation sampling processing refers to discretizing three-dimensional image information along specific directions (such as multiple angles or multiple cross-sections) to obtain a series of spatial points or path segments that can be used as candidates for fiber optic path insertion.

[0048] The candidate fiber path set refers to the set of all spatial paths that can be used as fiber insertion paths, generated by sampling in image data.

[0049] In this embodiment, medical imaging information of the target patient is first acquired, and this image information is processed to obtain segmentation results of the tumor region. The image information can be CT images, MRI images, or other three-dimensional medical imaging data. Using segmentation techniques based on deep learning, image processing, or manual annotation, the spatial location and shape of the tumor are extracted, providing basic data for subsequent fiber optic path planning.

[0050] After obtaining the image information and tumor region segmentation results, the image information is segmented and sampled. Specifically, the tumor region and its surrounding tissue can be sampled in three-dimensional space along different directions to generate multiple candidate insertion paths covering the tumor. These paths constitute a set of candidate fiber paths, and each fiber path in the set has a clear start point, end point, insertion direction, and tissue crossing information, possessing potential insertion feasibility.

[0051] Step 202: Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set, and the number of optical fibers required for tumor ablation is determined based on the intermediate optical fiber path set.

[0052] The objective function for fiber optic planning refers to the mathematical function used to evaluate the merits of fiber optic insertion schemes. It typically considers multiple factors, such as: whether the fiber insertion angle is reasonable; whether the fiber path length is the shortest; whether it is far from critical structures (such as blood vessels and nerves); and whether the ablation area coverage is sufficient.

[0053] Fiber optic planning constraints refer to the safety and clinical constraints that need to be met during the planning process, such as: a minimum safe distance must be maintained between the fiber optic cable and critical tissues; the fiber optic insertion angle must not exceed the angle allowed by human tissues; and the fiber optic length must not exceed the maximum length of the device.

[0054] The intermediate fiber path set refers to the set of fiber paths with high feasibility that are retained from the candidate fiber path set after objective function evaluation and constraint condition screening. It is used for subsequent objective set screening.

[0055] The number of optical fibers required for tumor ablation is calculated based on the size, shape, and distribution of the tumor, representing the minimum or optimized number of optical fibers needed to ensure treatment effectiveness.

[0056] In this embodiment, a set of candidate fiber optic paths is screened based on a fiber optic path planning objective function and preset constraints. The objective function may include multiple indicators such as minimizing path length, the rationality of the puncture angle, and minimizing the physiological risks of tissue crossing; while the constraints may include avoiding crossing critical structures such as blood vessels and nerves, and limiting the range of insertion angle and depth. Through this screening process, paths that do not meet the safety and effectiveness requirements are eliminated, resulting in a set of intermediate fiber optic paths.

[0057] Next, based on the coverage relationship between the intermediate fiber path set and the tumor region, the number of fibers required for tumor ablation is determined. This process can be estimated based on tumor volume, target ablation coverage, and the effective ablation range of a single fiber, thereby ensuring that the treatment goal of complete tumor coverage is achieved with the minimum number of fibers used.

[0058] In a specific embodiment, the following method can be used to determine the number of optical fibers required for tumor ablation: the multi-fiber ablation problem of laser interstitial thermal therapy (LITT) is modeled in the form of combinatorial optimization, and the planning objective is determined to be the minimum number of optical fibers. The constraints include coverage constraints and safety constraints. The coverage constraint is that the combination of ablation ranges can cover the tumor area, and the safety constraint is that the path cannot damage critical tissues, there is no conflict between paths, and there is no risk of path placement. The final planning problem can be expressed as shown in equation (1).

[0059]

[0060] In the last line of formula (1), set P represents all possible single ablation operations, where I and J are the candidate point sets for the fiber entry point and target point. The domain of the plan is the possible combinations of ablation operations, i.e., all subsets of P. The first row of formula (1) represents the planning objective, which is to complete the surgical objective using fewer ablation operations, i.e., minimizing the number of elements in the candidate subset min|X|. The second to fourth rows are the planning constraints, mainly considering the requirements of tumor coverage and safety. By reasonably discretizing the three-dimensional tumor range into several coverage target points s, the tumor coverage is equivalent to the combination of single ablation operations covering all target points, i.e., ∑ ( i,j,k )∈X C ijks ≥1, Surgical safety requirements include ensuring the placement path avoids damage to important brain anatomical structures, and minimizing the minimum distance d between the path and critical tissues. ij Greater than the safety threshold; no conflicts occur between paths, and the distance between paths is [missing information]. Greater than the safety threshold; to prevent slippage during placement, the angle θ between the path and the normal to the skull surface is... ij Less than the safety threshold.

[0061] To facilitate further evaluation, the planning problem can be mathematically transformed. First, based on the second and third constraints in problem (1), paths in set P that do not meet these constraints are removed. Then, the candidate paths in set P are encoded as 1-n, and the vector x∈R is used. n This indicates the selection status of these paths. A value of 1 for the k-th component of x indicates that the k-th path in the selection set P is chosen, while a value of -1 indicates that the path is not selected. (diag(xx)) T ) = e ensures that vector x satisfies the above value requirements. At this point, the constraint covering the tumor is equivalent to C m (x+e)≥2e, matrix C m Let be the coverage matrix, where the i-th column represents the coverage of the target point by the i-th path. The constraint to avoid collisions between paths can be written as (x+e). T P(x+e)=0, matrix C m Let P be the collision matrix. ij =1 indicates that there is a collision between path i and path j, while P ij =0 indicates that there is no conflict between the two. When c is a vector of all 1s, the optimization objective is min(c T x+c T e) / 2 is the number of paths to be minimized. Finally, the transformed formula is shown in equation (2).

[0062]

[0063] For each target patient, the parameters in planning problem (1) need to be calculated, and planning problem (2) is obtained by following the above transformation process. Based on preoperative imaging information, key structures are segmented and sampled to obtain the set of entry points I and the set of endpoints J for the paths, thus obtaining all possible fiber optic paths. The insertion angle θ of all fiber optic paths is further calculated. ij d, distance from anatomical structure ij Coverage of tumor target points c ijs Then, the possible combinations of two optical fibers are traversed to calculate whether there are any collisions between the fibers, and the parameters are obtained.

[0064] Step 203: Determine the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and the pre-determined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0065] Among them, the path evaluation score refers to the evaluation index that quantitatively scores each intermediate fiber path. It is usually a comprehensive score that reflects the overall performance of the path in terms of puncture angle, path length, safety and treatment effectiveness.

[0066] The target fiber path set is the final set of fiber paths selected for insertion into the tumor region to perform thermal ablation. This set should meet all planning objectives and clinical requirements and have specific three-dimensional location information (start point, end point, angle, etc.).

[0067] In this embodiment, the final set of target fiber optic paths for implementation is selected from the intermediate set of fiber optic paths based on the path evaluation score. The path evaluation score can be comprehensively evaluated by combining factors such as path safety, path length, puncture angle, and tissue crossing complexity, with paths having higher scores being given priority. Based on the required number of fibers, several optimal paths are selected to form the final set of target fiber optic paths.

[0068] Finally, the output provides detailed information about the target fiber path set, including the spatial location information of each fiber, such as the three-dimensional coordinates of the start and end points, insertion direction, and path length.

[0069] In the aforementioned planning method for multi-fiber laser ablation treatment of gliomas, a set of candidate fiber paths is generated based on the target patient's image information and tumor region segmentation results. These paths are then screened using a fiber planning objective function and constraints. Finally, the target fiber path set is determined based on path evaluation scores, effectively improving the scientific rigor and optimal quantity of fiber placement. Compared to the traditional method that relies on doctors manually planning fiber paths in 3D visualized images, this method can identify potentially feasible paths and select the best fiber scheme using quantitative evaluation methods, avoiding poor planning results caused by insufficient human experience or visual judgment errors. Furthermore, this helps achieve sufficient and uniform coverage of the tumor region, significantly improving the integrity and therapeutic effect of tumor ablation.

[0070] In one embodiment, such as Figure 3 As shown, the above-mentioned "determining the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and the predetermined path evaluation score" includes:

[0071] Step 301: For each intermediate fiber path in the set of intermediate fiber paths, substitute the path information corresponding to the intermediate fiber path into the pre-constructed path evaluation relation to determine the path evaluation score of the intermediate fiber path; the path evaluation relation is used to characterize the correspondence between the path evaluation score and the path information; the path information includes at least the fiber insertion angle, the distance between the fiber and the key tissue in the brain, the distance variance of the fiber covering the tumor target point, the coverage rate of the fiber to the tumor, and the fiber length.

[0072] Path information refers to the set of characteristic parameters used to describe the spatial insertion of an intermediate fiber optic path, and is used to evaluate the feasibility and quality of the path. Specifically, it includes the following elements:

[0073] (1) Fiber insertion angle: refers to the angle between the fiber insertion path and the skin surface or standard direction (such as the vertical direction), which affects the difficulty and safety of the operation.

[0074] (2) Distance between optical fiber and key brain tissues: This refers to the minimum safe distance between the optical fiber path and key structures in the brain (such as blood vessels, nerve bundles, functional areas, etc.) in three-dimensional space. The greater this distance, the higher the path safety.

[0075] (3) Distance variance of fiber optic coverage of tumor target points: refers to the variance of the distance distribution between the fiber optic path and multiple target points in the tumor area, reflecting the uniformity of the path's coverage of the tumor area. The smaller the variance, the more concentrated the coverage and the more uniform the ablation effect.

[0076] (4) Coverage of the tumor by the optical fiber: refers to the proportion of the tumor volume that the optical fiber can cover within its effective ablation range to the total tumor volume, which is an important indicator reflecting the integrity of the treatment.

[0077] (5) Fiber length: refers to the path length from the insertion start point to the end point. It is usually required to be moderate. Too long may cause puncture risk, and too short may not be able to penetrate into the target area.

[0078] The path evaluation relation is a mathematical function used to quantify the mapping relationship between path information and path evaluation scores. Its form can be a weighted linear function, a neural network model, or a logistic regression function, etc. The path information of each intermediate fiber path is used as input, and after being substituted into this relation, the corresponding path evaluation score is output.

[0079] The pathway assessment score is a numerical indicator that evaluates the quality of a particular fiber optic pathway, typically a normalized score. This score is a comprehensive evaluation considering safety, treatment effectiveness, and operability, and is used to compare and rank all intermediate fiber optic pathways.

[0080] In this embodiment, after screening the intermediate fiber path set, each intermediate fiber path in the intermediate fiber path set is further evaluated to determine the final target fiber path set. Specifically, for each intermediate fiber path in the fiber path set, its corresponding path information is extracted, and this path information is substituted into a pre-constructed path evaluation formula to calculate the path evaluation score of the intermediate fiber path.

[0081] Path information is used to characterize the spatial characteristics and ablation potential of each intermediate fiber path, including at least the following parameters: fiber insertion angle, i.e., the angle between the fiber insertion path and the standard reference plane, used to reflect the operational feasibility of insertion and the direction of tissue penetration; distance between the fiber and key tissues in the brain, used to measure the safety of the path in avoiding key tissues (such as blood vessels, functional areas, etc.); distance variance of the fiber covering the tumor target points, reflecting the uniformity of the fiber path's coverage distribution of each target point within the tumor; fiber coverage of the tumor, i.e., the ratio of the effective range of the fiber to the total tumor volume, used to reflect the integrity of treatment; and fiber length, used to limit the actual feasibility of the path and device compatibility.

[0082] Based on the aforementioned path information, it is substituted into the path evaluation formula for scoring. This formula is used to establish the mapping relationship between the path evaluation score and the path information. It can be constructed through expert experience modeling, linear weighted formulas, machine learning models, etc., to quantify the quality of each path and output the path evaluation score as a unified evaluation standard. In a specific embodiment, the path evaluation formula can be represented by equation (3).

[0083] score ij =w1g θ (θ ij )+w2f d (d ij )+w3g var (var ij )+w4f c (c ij )+w5g I (l ij )

[0084]

[0085] Where, θ ij For the fiber insertion angle, d ij The distance between the optical fiber and key tissues in the brain, var ij The distance variance of the target tumor point covered by the optical fiber, l ij The fiber optic coverage of the tumor and the fiber length are given. Weights w1 to w5 are the weighting coefficients for each clinical indicator, g and f are the functional relationships, and x is the function variable, such as θ. ij d ij var ij and l ij .

[0086] Step 302: Sort each intermediate fiber path in the intermediate fiber path set according to the path evaluation score to obtain the sorting result.

[0087] The ranking result is the order in which all intermediate fiber paths are arranged from highest to lowest (or from best to worst) according to their path evaluation scores. This result is used to determine which paths are preferentially selected as the final implementation fibers.

[0088] In this embodiment, after obtaining the path evaluation scores of all intermediate fiber paths, all paths in the intermediate fiber path set are sorted according to their evaluation scores to generate a ranking result. The ranking can be arranged from high to low scores, so that fiber paths with higher scores and better overall performance are placed first, facilitating subsequent priority selection.

[0089] Step 303: Based on the number of optical fibers required for tumor ablation and the sorting results, determine the target optical fiber path set from the intermediate optical fiber path set.

[0090] In this embodiment, based on the determined number of optical fibers required for tumor ablation and the ranking results, a corresponding number of optical fiber paths are sequentially selected from the top-ranked intermediate optical fiber paths to determine the target optical fiber path set. The final selected target optical fiber path set is the path set to be used in the actual ablation operation, possessing high ablation coverage, safety, and feasibility.

[0091] In one embodiment, such as Figure 4 As shown, the above-mentioned "determining the target fiber path set from the intermediate fiber path set based on the number and sorting results of the fibers required for tumor ablation" includes:

[0092] Step 401: If the number of optical fibers required for tumor ablation is not greater than a preset value, select all feasible combinations of intermediate optical fiber paths from the set of intermediate optical fiber paths according to the predetermined coverage matrix.

[0093] The coverage matrix describes the coverage relationship of each fiber in the intermediate fiber path set to each target point within the tumor region. It is typically a two-dimensional matrix with the following structure:

[0094] Rows: represent different intermediate fiber optic paths;

[0095] Column: Represents multiple target points (or voxels) divided within the tumor region;

[0096] Element value: Indicates whether a certain optical fiber can cover the corresponding target point. For example, 1 indicates coverage; 0 indicates no coverage; or a floating-point number can be used to represent the coverage strength / probability. This coverage matrix serves as the basic data structure for path combination filtering, used to determine whether a certain intermediate optical fiber path combination can cover all or a preset proportion of the tumor area.

[0097] Intermediate fiber path combinations refer to multiple subsets of fiber paths formed by combining intermediate fiber path sets according to certain rules. The number of fibers in each subset is less than or equal to a preset value, and they jointly cover the tumor region spatially. These combinations must meet the coverage integrity requirements defined by the coverage matrix, that is, all fiber paths within the combination must jointly cover all key target points in the tumor region or achieve a coverage rate of a preset threshold (e.g., 90%). All "feasible combinations" will be generated based on these conditions.

[0098] Selecting intermediate fiber paths to determine the required number of fibers for tumor ablation involves further ranking all combinations of intermediate fiber paths that meet coverage requirements, choosing the fiber path with the same number of fibers needed for tumor ablation based on path evaluation results. Prioritizing higher-ranked fibers during the selection process aims to improve path quality and surgical outcomes while ensuring coverage.

[0099] In this embodiment of the application, when the number of optical fibers required for tumor ablation is not greater than a preset value, all feasible path combinations are selected from the intermediate optical fiber path set based on the pre-constructed coverage matrix as candidate optical fiber combinations.

[0100] The preset value for the fiber optic path set is the maximum acceptable number of fiber optic insertions, typically determined based on clinical operational limits, patient tolerance, or navigation system channel limitations. For example, in some applications, a maximum of 4 or 6 fibers may be allowed to be inserted simultaneously, so the preset value is 4 or 6. If the currently calculated number of fibers required for tumor ablation does not exceed this value, the process proceeds to the combination optimization stage.

[0101] The coverage matrix is ​​a one- or two-dimensional structure matrix. Its rows represent each fiber in the set of intermediate fiber paths, and its columns represent the target points (or voxel units) divided within the tumor region. Matrix elements indicate whether a specific fiber can effectively cover a target point; for example, a Boolean value of "1" indicates coverage and "0" indicates non-coverage, or real values ​​express the degree of coverage. This coverage matrix is ​​constructed based on the three-dimensional spatial mapping of fiber paths and the tumor region, and can quantify the spatial coverage relationship under multiple path combinations.

[0102] Based on the coverage matrix, all intermediate fiber path combinations not exceeding a preset limit are traversed, and feasible fiber combinations that can jointly cover all or a preset proportion of target points are selected. For example, if a coverage rate of 95% is required, only combinations that cover the required number of target points under combined action are selected. This yields a set of all feasible fiber combinations that meet the condition of complete tumor coverage.

[0103] Step 402: Based on the sorting results, select the intermediate fiber paths from the intermediate fiber path combinations that require the number of fibers for tumor ablation, and determine the selected intermediate fiber paths as the target fiber path set.

[0104] In this embodiment, after obtaining the aforementioned feasible combinations, these combinations are further screened based on the calculated path evaluation scores and ranking results. Priority is given to selecting intermediate fiber optic paths from the top-ranked paths, equal to the number of fibers required for tumor ablation, to form the final target fiber optic path set. This set not only meets the spatial coverage requirements of the tumor region but also possesses advantages in multiple dimensions such as insertion angle, path length, and safety, making it suitable for subsequent surgical procedures or navigation guidance. The final determined target fiber optic path set will include the spatial location information and ablation parameters of each fiber.

[0105] In one specific embodiment, the aforementioned preset value can be 2. For cases where the required number of optical fibers is less than or equal to 2, all feasible path combinations are first screened based on the coverage matrix, and then the path combination that best meets clinical preferences is selected based on the path evaluation score. Taking into account factors such as damage to normal tissues, insertion angle, and distance from critical tissues, searching based on this evaluation score helps obtain path results that meet clinical constraints.

[0106] In one embodiment, such as Figure 5 As shown, the above-mentioned "determining the target fiber path set from the intermediate fiber path set based on the number and sorting results of the fibers required for tumor ablation" includes:

[0107] Step 501: If the number of optical fibers required for tumor ablation is greater than a preset value, select the initial path for the number of optical fibers required for tumor ablation from the intermediate optical fiber path set according to the sorting results.

[0108] In this embodiment, when the tumor area is large or the lesion distribution is complex, the number of optical fibers required for tumor ablation may exceed a preset value. To address the path optimization problem under such high-dimensional combinations, this embodiment further introduces an iterative optimization algorithm to obtain an optical fiber layout scheme that achieves a balance between accuracy and efficiency.

[0109] Specifically, when the number of optical fibers required for tumor ablation in the set of optical fiber paths exceeds a preset value, the initial path set is first selected based on the generated intermediate optical fiber path set and its corresponding path evaluation and ranking results. The number of optical fiber paths equal to the number of optical fibers required for tumor ablation in the set of optical fiber paths is then directly selected. This initial path set provides the basic solution for subsequent optimization algorithms, thereby accelerating the convergence of the solution process.

[0110] Step 502: For each initial path, the simulated annealing algorithm is used to iteratively optimize the initial path to obtain the intermediate path.

[0111] In this embodiment, for each initial path, a simulated annealing algorithm is used for multiple rounds of iterative optimization. The simulated annealing algorithm is a heuristic optimization algorithm with global search capabilities. By simulating the gradual temperature decrease and stabilization mechanism during physical annealing, it locally perturbs the path and accepts some non-optimal solutions, thereby avoiding getting trapped in local optima.

[0112] In each iteration, new intermediate paths are generated by random replacement, adjusting the insertion angle, or fine-tuning the path point set, and their path evaluation scores are calculated. Based on the difference between the current temperature and the path score, a probabilistic acceptance strategy is used to decide whether to accept the new path. As the number of iterations increases and the temperature gradually decreases, the algorithm tends to stabilize.

[0113] Step 503: If the preset number of iterations is met, select the optimal path from the initial path and multiple intermediate paths, and determine the optimal path as the target fiber path set.

[0114] In this embodiment, after reaching a preset number of iterations, a set of paths with the highest comprehensive score is selected from the initial path and the multiple intermediate paths generated, based on the path evaluation score, as the final set of target fiber optic paths. This set of target fiber optic paths ensures the integrity and quantity of tumor coverage while also considering path security, operability, and equipment compatibility.

[0115] In one specific embodiment, for cases requiring coverage with more than two optical fibers, an initial solution is first selected using a greedy strategy based on the path evaluation score to meet a certain ablation rate requirement. Subsequently, iterative optimization is performed based on this initial solution to reduce the number of paths and further improve the ablation rate of the surgical procedure.

[0116] Through the above methods, even in scenarios with a large number of optical fibers and complex combination spaces, efficient and reliable path optimization and target path output can still be achieved, effectively improving the scientific nature and individualization of ablation solutions.

[0117] In one embodiment, such as Figure 6 As shown, the above-mentioned "using simulated annealing algorithm to iteratively optimize the initial path to obtain the intermediate path" includes:

[0118] Step 601: Construct a neighborhood path set based on the initial path; the neighborhood path set consists of paths distributed around the initial path in three-dimensional space; the neighborhood path set satisfies the condition that the Euclidean distance between the starting point of all neighborhood paths and the starting point of the initial path is less than a first distance threshold, and the Euclidean distance between the ending point of all neighborhood paths and the ending point of the initial path is less than a second distance threshold.

[0119] In this embodiment, during the iterative optimization of the initial path using the simulated annealing algorithm to obtain intermediate paths, a set of neighboring paths with a similar spatial distribution in three-dimensional space is first constructed based on the current initial path. The neighboring path set refers to a group of paths that satisfy the following spatial constraints: the Euclidean distance between the starting point of each path and the starting point of the initial path is less than a first distance threshold, and the Euclidean distance between the ending point and the ending point of the initial path is less than a second distance threshold. This neighborhood path construction strategy ensures that the generated paths are spatially similar to the original path, thus possessing similar puncture directions and surgical accessibility, which is beneficial for improving diversity while maintaining the rationality of local searches.

[0120] Step 602: In the neighborhood path set, based on the equal probability selection strategy, select the path that covers the most target treatment areas or the path with the smallest path coverage variance from the neighborhood path set to obtain candidate paths.

[0121] In this embodiment, after constructing a neighborhood path set, a probabilistic selection strategy is used to select candidate paths. Specifically, among paths that meet spatial constraints, the path with the highest coverage in the target treatment area or the smallest path coverage distance variance is preferentially selected as the candidate path to balance coverage rate and uniformity. The path coverage quantity is used to measure the absolute coverage capability of the path to the tumor target point, while the path coverage variance reflects whether the spatial distribution among the target points is balanced, which helps to improve the uniformity of heat distribution and the comprehensiveness of treatment effect.

[0122] Step 603: If the tumor coverage rate of the candidate path is less than the preset coverage threshold, the candidate path is deleted, and the deleted candidate path is evaluated according to the pre-constructed energy function to obtain the intermediate path. The energy function includes a path number term, a coverage variance correlation term, and an overall ablation rate penalty term. The energy function is used to evaluate the optimization degree of the candidate path. The optimization objective of the energy function is to minimize the required number of paths and achieve a balanced spatial distribution of the coverage area while meeting the clinically required ablation rate.

[0123] In this embodiment, if the tumor coverage of a candidate path is less than a preset coverage threshold, the candidate path is deleted. For the deleted candidate paths, a pre-constructed energy function is introduced to optimize and evaluate them, thereby obtaining the intermediate path corresponding to the current iteration. This energy function comprehensively considers factors such as the number of paths, coverage balance, and overall ablation rate, and specifically includes the following three aspects:

[0124] (1) Number of paths: This measures the total number of fiber optic paths in the current scheme, with the goal of reducing the number of interventional paths, thereby reducing surgical trauma and operational complexity;

[0125] (2) Coverage variance related terms: used to quantify the spatial distribution uniformity of the covered target area. The smaller the variance, the more balanced the covered area, thus improving the consistency of the ablation effect.

[0126] (3) Overall ablation rate penalty: When the path combination fails to achieve the preset tumor area coverage (such as 90% or 95%), a penalty is introduced to reduce the energy function score of the path combination and encourage the algorithm to converge toward higher coverage.

[0127] The global optimization objective of this energy function is to minimize the number of required paths and achieve a spatially balanced distribution of the coverage area while meeting the minimum ablation rate constraint required clinically. The energy function values ​​of candidate paths are evaluated in each round of simulated annealing iterations, and an annealing probability strategy is used to determine whether to accept the candidate path. Finally, after multiple iterations, optimized intermediate paths are obtained for further construction of the target fiber path set.

[0128] In a specific embodiment, iterative optimization uses simulated annealing. To solve the LITT planning problem, specific strategies for neighborhood construction, new path construction, and energy function calculation are designed. The neighborhood set of path k is defined as all paths whose endpoints are less than a certain distance from the endpoints of path k. The neighborhood of each path is updated after each iteration. For new path construction, in the neighborhood set N, the path with the most covered target points or the smallest variance is selected with equal probability, representing the optimization trend towards optimal coverage and optimal coverage efficiency in the iterative process, respectively. The energy function f consists of three parts: the number of paths, a second term related to efficiency (g in the fractional term). var (var ij The ablation rate and the number of pathways are considered, with the first factor ensuring that the iteration yields the result with the fewest optical fibers, and the second and third factors ensuring compliance with clinical constraints.

[0129] In one embodiment, such as Figure 7 As shown, the above "determining the number of optical fibers required for tumor ablation based on the set of intermediate optical fiber paths" includes:

[0130] Step 701: Based on the path information corresponding to the intermediate fiber path, the paths in the intermediate fiber path set are combined to obtain multiple path combinations.

[0131] In this embodiment, based on the path information corresponding to each fiber in the intermediate fiber path set, the paths in the set are first combined to construct multiple possible fiber path combination schemes. The path combination construction process considers the spatial relationship and coverage effect between the paths, aiming to explore the adaptability and effect of different path configurations on tumor ablation coverage.

[0132] Step 702: For each path combination, if there is no spatial conflict between any two fiber paths in the path combination and the preset tumor area ablation coverage requirements are met, the number of fibers in the path combination is determined as the number of fibers required for tumor ablation.

[0133] In this embodiment, for each path combination, it is further determined whether there is a spatial conflict between any two fiber optic paths in the combination. A spatial conflict refers to the overlap or excessive proximity of the path trajectories of two fiber optic paths in three-dimensional space, which may lead to physical interference or safety risks during surgical procedures. If no such conflict exists in the path combination, the combination is deemed feasible in terms of spatial layout.

[0134] Furthermore, it is necessary to confirm that the combination of fiber optic pathways meets the preset tumor region ablation coverage requirements. Specifically, the tumor target area covered by the combined pathways must reach or exceed the clinically set coverage threshold to ensure treatment efficacy. Coverage requirements include coverage rate indicators and coverage uniformity indicators to ensure the integrity and balance of the ablation area.

[0135] When the path combination meets the above requirements of spatial non-conflict and ablation coverage, the computer equipment uses the number of optical fibers in the path combination as the basis for determining the number of optical fibers required for the current tumor ablation.

[0136] The above embodiments enable reasonable selection of fiber optic path configuration, ensuring that the number of selected fibers meets both surgical safety requirements and achieves ideal treatment coverage.

[0137] In one embodiment, such as Figure 8 As shown, the above method also includes:

[0138] Step 801: Based on the pre-built Lagrange relaxation optimization model, determine the minimum number of fiber optic paths under the condition of complete tumor coverage.

[0139] In this embodiment, a pre-constructed Lagrangian relaxation optimization model is used to determine the minimum number of fiber optic paths required for complete tumor coverage. This model transforms the original complex path optimization problem into a solvable mathematical optimization problem by relaxing constraints, aiming to find the minimum number of fiber optic paths required to achieve complete tumor coverage. This theoretical lower bound reflects the minimum number of paths required to achieve full tumor region coverage under ideal conditions, providing a reference benchmark for fiber optic planning.

[0140] Step 802: If the number of optical fibers required for tumor ablation is consistent with the minimum number of optical fiber paths and the target set of optical fiber paths can achieve complete coverage of the tumor area, the target set of optical fiber paths is determined to be a valid set of optical fiber paths.

[0141] In this embodiment, when the number of optical fibers required for tumor ablation is consistent with the minimum number of optical fiber paths mentioned above, it is further determined whether the selected set of target optical fiber paths can achieve complete coverage of the tumor region. Complete coverage means that the tumor target points covered by the set of target optical fiber paths meet the clinically set coverage threshold, ensuring the adequacy and safety of the treatment effect.

[0142] Provided that the above two conditions are met—that is, the number of optical fibers required for tumor ablation equals the theoretical minimum number of paths and the target optical fiber path set achieves complete coverage—the target optical fiber path set is determined to be an effective optical fiber path set. An effective optical fiber path set represents the current path planning scheme achieving optimal results in terms of both the number of paths and coverage, possessing high clinical application value and feasibility.

[0143] In one specific embodiment, after obtaining the path through iteration, Lagrange relaxation is used to quickly calculate the lower bound of the fiber and evaluate the mathematical optimality of the result. Existing technical solutions have not yet used this method to verify the optimality of the solution. The iterative process is shown in equation (4).

[0144]

[0145] When c = e, the optimal solution corresponds to the minimum number of paths. The optimal solution to the primal problem is denoted as Z. p0 The optimal solution to the relaxation problem is denoted as Z. p1 Taking λ = 0, the problem can be simplified to a standard set covering problem, whose optimal solution is denoted as Z. p2 If the number of paths obtained by the iterative algorithm is Z h If the tumor is completely ablated, then the following inequality holds: Z h ≥Z p0 ≥Z p1 ≥Z p2 When the upper bound Z of the problem h With the lower boundary Z p2When the results are equal, the mathematical optimality of the method can be verified, and the location of the optimal fiber optic path can be output.

[0146] In a specific embodiment, such as Figure 9 As shown, Figure 9 This technology is used to precisely generate safe, efficient, and disease-adaptive fiber optic pathways for fiber optic ablation therapy of tumors such as gliomas, achieving a deep integration of clinical needs and technological feasibility.

[0147] The process prioritizes determining whether more than two optical fibers are needed for tumor ablation, and then implements strategies based on clinical characteristics such as tumor size and morphological complexity.

[0148] When the number of optical fibers required for tumor ablation is ≤2 (suitable for scenarios with small tumor area and simple path constraints), we first traverse all feasible path combinations of 1-2 optical fibers based on medical images such as T1ce enhanced magnetic resonance imaging and safety constraints such as avoiding key brain structures. Then, we select the path that best meets clinical needs and output it directly by weighted scoring of evaluation parameters such as placement angle compliance, tumor coverage integrity, and safety distance.

[0149] If the required number of optical fibers for tumor ablation is greater than 2 (to adapt to scenarios with large tumor volume and irregular shape), then an initial path is first constructed heuristically using greedy rules (such as prioritizing coverage of the tumor center), and then a loop optimization of "neighborhood update - new path construction - iterative termination judgment" is entered:

[0150] The search space is narrowed by dynamically defining paths with endpoint distances less than 5mm. Within the neighborhood, paths are replaced with equal probability based on the dual objectives of covering the most unablated areas and minimizing path variance. The results are output only after multiple iterations when the path parameters have no optimization.

[0151] Meanwhile, regardless of the strategy employed, Lagrange relaxation, set coverage problem modeling, and branch-and-bound methods are used to calculate the theoretical lower bound of the number of optical fibers. This verifies whether the path is mathematically optimal (i.e., the actual number of optical fibers used matches the theoretical minimum). Finally, the three-dimensional coordinates of the fiber entry point and target point are output for surgical navigation, achieving a technical closed loop of "clinically driven path planning, mathematical verification ensuring optimality, and iterative optimization adapting to complex conditions." This solves the problem of insufficient adaptability of single strategies, balances treatment safety and efficiency, and provides core technical support for fiber optic ablation surgery. The number of optical fibers refers to the number of optical fibers required for tumor ablation.

[0152] In one embodiment, such as Figure 10 As shown, the above method also includes:

[0153] Step 901: For each target fiber path, based on the spatial orientation of the target fiber path, model the ablation range corresponding to the target fiber path as a rotating ellipsoid with the target fiber path as the axis of symmetry.

[0154] Among them, the spatial orientation of the target optical fiber path refers to the specific location and direction of the target optical fiber path in three-dimensional space, including the path trajectory of the optical fiber from entering the human body to the termination position, which is used to determine the spatial distribution of the ablation effect.

[0155] The rotating ellipsoid is a three-dimensional geometric shape formed by rotating around the target optical fiber path as an axis of symmetry. This model is used to approximate the diffusion range of ablation energy around the optical fiber, reflecting the spatial coverage area of ​​the ablation treatment.

[0156] In this embodiment, for each fiber path set target fiber path, based on its spatial orientation in three-dimensional space—that is, the specific path and direction of the target fiber path from its entry into the human body to its termination position—the ablation range corresponding to that fiber is modeled as a rotating ellipsoid with the target fiber path as its axis of symmetry. The rotating ellipsoid of the fiber path set refers to a three-dimensional ellipsoid generated by rotating around the fiber path axis, used to approximately describe the spatial distribution range of ablation energy around the fiber, thereby reflecting the coverage of the tumor by the fiber during actual treatment.

[0157] Step 902: Obtain the spatial intersection between the target fiber optic path and the tumor region, which will be used as the corresponding target treatment area.

[0158] The target treatment area refers to the spatial intersection of the rotating ellipsoid and the tumor area, which is the tumor volume area that the optical fiber can actually affect and treat, and is the effective range of optical fiber ablation.

[0159] In this embodiment, the spatial intersection of the rotating ellipsoid and the tumor region is obtained as the target treatment region of the target optical fiber path. This region represents the subset of tumor volume in which the optical fiber can effectively act and achieve ablation.

[0160] Step 903: Perform equidistant sampling along the path direction of the target optical fiber to obtain multiple candidate ablation points, and construct a two-dimensional cross-section perpendicular to the target optical fiber path at each candidate ablation point.

[0161] In this process, equidistant sampling refers to selecting several points at fixed intervals along the fiber optic path as candidate ablation points. These points represent potential ablation locations, facilitating step-by-step control of the ablation process and enabling precise treatment of tumors.

[0162] The two-dimensional cross-section is a plane perpendicular to the fiber path direction at each candidate ablation point, used to analyze the coverage of the tumor area near that point, facilitating the calculation and evaluation of the local ablation effect.

[0163] In this embodiment, equidistant sampling is performed along the path direction of the target optical fiber, i.e., multiple candidate ablation points are selected sequentially at fixed spatial intervals. These points represent potential ablation locations, which helps to achieve precise point-by-point treatment of tumors. At each candidate ablation point, a two-dimensional cross-section perpendicular to the optical fiber path direction is constructed. The two-dimensional cross-section of the optical fiber path is a plane passing through the candidate point and perpendicular to the optical fiber direction, which facilitates the analysis of the spatial coverage of the tumor near that location.

[0164] Step 904: For each candidate ablation point, calculate the farthest distance function from the candidate ablation point to the target treatment area within the cross-section. The farthest distance function is used to characterize the maximum treatment demand of the candidate ablation point in the local area.

[0165] Among them, the farthest distance function is a function value that measures the maximum distance from the candidate ablation point to the boundary of the target treatment area, reflecting the maximum treatment demand of the ablation point in the local cross-section, and helping to determine the ablation energy coverage range.

[0166] In this embodiment of the application, for each candidate ablation point, the farthest distance function from the point to the target treatment area in the corresponding two-dimensional cross-section is calculated. This function quantifies the maximum treatment requirement of the candidate ablation point in the local area, that is, the farthest distance from the candidate point to the tumor boundary, reflecting the coverage range that the ablation energy needs to achieve in this plane.

[0167] Step 905: Based on the farthest distance function, iteratively select the target ablation point and the corresponding minor axis radius of the target ablation point from multiple candidate ablation points to form the rotating ellipsoid, so that the target treatment area is completely covered in the cross-sectional direction, and the number of optical fibers required is minimized while satisfying the coverage integrity.

[0168] The minor axis radius is half the length of the minor axis of the ellipsoid in a cross-section perpendicular to the fiber path. This parameter is used to adjust the lateral coverage of the ellipsoid to meet the coverage requirements of tumor treatment.

[0169] In this embodiment, based on the aforementioned farthest distance function, an iterative method is used to select the target ablation point and its corresponding minor axis radius from multiple candidate ablation points to form a rotating ellipsoid. The minor axis radius refers to half the length of the minor axis of the ellipsoid in a cross-section perpendicular to the fiber path direction. This iterative selection process aims to ensure complete coverage of the target treatment area in the cross-sectional direction, while optimizing the required number of optical fibers to achieve the optimal design of the ablation path while ensuring coverage integrity.

[0170] In one specific embodiment, an ablation region is planned based on the optimal path to evaluate the path.

[0171] Considering the ablation range of a single procedure as an axisymmetric ellipsoid, the ablation zone planning involves placing multiple ellipsoid centers along the axis of symmetry to achieve complete coverage of the ablation area. Therefore, the preoperative segmentation results can be resampled, simplifying the planning problem from a three-dimensional space to a two-dimensional coverage problem. The resampling process is shown in the figure below. First, the intersection T of the ablation range of a single path and the tumor segmentation region is selected as the treatment area, such as... Figure 11 As shown, the point where the ablation range of a single path intersects with the tumor range is T. Subsequently, isometric sampling is performed along the path to obtain candidate ablation points k, and a plane P perpendicular to the path (i, j) is constructed at candidate ablation points k. k ,like Figure 11 As shown. If P k The region of intersection with T is denoted as A. k Then the function is as shown in equation (5).

[0172]

[0173] f(k) represents the distance from candidate ablation point k to region A. k The farthest distance from any point within the interior, such as Figure 11 As shown. After obtaining the function f(k), the ablation zone planning is equivalent to placing several symmetrical ellipse centers along the x-axis path (i, j) to ensure that the function f(x) is completely covered. The center position and minor axis radius of the ellipse correspond to the ablation point and ablation radius in the LITT procedure, respectively.

[0174] Following Steps 1-4, use the greedy algorithm to solve for the ablation point {o} and ablation radius {b}, which facilitates the evaluation of the planning results.

[0175] Step 1: Set F = U{f(k)};

[0176] Step 2: Take b = min(max(F) + 1, r thre ), o = argmax(F);

[0177] Step 3: Construct an ellipse T with center o and minor semi-perimeter b, and calculate F. t =∪ k∈T {f(k)};

[0178] Step 4, F = F\F t ,if Repeat Steps 2-4; otherwise, end.

[0179] Step 1: Construct the set of distance requirements to be covered.

[0180] First, Step 1 is executed, which uses F = U{f(k)} to aggregate the farthest coverage distance function f(k) corresponding to all candidate ablation points k into a set F. f(k) describes the farthest distance of the tumor region that needs to be covered when ablation is performed with candidate point k as the center. After aggregation, F represents the distance requirements of all tumors that need to be ablated and covered, which is equivalent to integrating the scattered ablation requirements into a set, which facilitates subsequent planning.

[0181] Step 2: Determine the core parameters of the first ablation point

[0182] Proceed to Step 2 and calculate b = min(max(F) + 1, r thre ), o = argmax(F). Where max(F) is the distance in the set that is most difficult to cover (the point in the tumor region that most needs a large ablation area), and b is taken as max(F) + 1 (the theoretical coverage radius) and the clinical safety threshold r. thre The smaller value ensures that the ablation covers the needs without damaging normal tissue; o is the candidate point corresponding to the maximum value of F, and it is selected as the center of the first ablation point to prioritize the most critical coverage needs.

[0183] Step 3: Cover the area with an ellipse and mark the covered areas.

[0184] In Step 3, an axisymmetric ellipse T is drawn along the fiber path with o as the center and b as the minor semi-axis. This ellipse represents the area covered by the current ablation point. Next, all f(k) contained within ellipse T are found and merged into F. t This means that the tumor regions corresponding to these f(k) are already covered by the current ablation point, and there is no need for repeated planning. This step transforms the abstract distance requirement into the actual physical area that can be covered by ablation, clarifying the requirements that have been addressed.

[0185] Step 4: Update the set to be overwritten, looping or ending the process.

[0186] Finally, in Step 4, we use F = F\F t The requirement F that is already covered by ellipse T in F t After removing these, the remaining F represents the tumor areas that still require ablation coverage. If... This indicates there are still uncovered areas. Return to Step 2 and repeat the process of selecting a new ablation point, calculating the radius, and drawing the ellipse; if... Then all ablation needs in the tumor area are covered, and the planning is complete. This iterative process is repeated to cover the entire tumor with the fewest possible combinations of ablation points, balancing treatment effectiveness and safety costs.

[0187] In one embodiment, the above method further includes:

[0188] Step 1: Based on the pre-acquired image information of the target patient and the segmentation results of the tumor region, the image information is sequentially segmented and sampled to obtain a set of candidate fiber optic paths.

[0189] Step 2: Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set.

[0190] Step 3: Based on the path information corresponding to the intermediate fiber path, the paths in the intermediate fiber path set are combined to obtain multiple path combinations.

[0191] Step 4: For each path combination, if there is no spatial conflict between any two fiber paths in the path combination and the preset tumor area ablation coverage requirements are met, the number of fibers in the path combination is determined as the number of fibers required for tumor ablation.

[0192] Step 5: For each intermediate fiber path in the set of intermediate fiber paths, substitute the path information corresponding to the intermediate fiber path into the pre-constructed path evaluation relation to determine the path evaluation score of the intermediate fiber path; the path evaluation relation is used to characterize the correspondence between the path evaluation score and the path information; the path information includes at least the fiber insertion angle, the distance between the fiber and the key tissue in the brain, the variance of the distance between the fiber and the target point of the tumor, the coverage rate of the fiber to the tumor, and the fiber length.

[0193] Step 6: Sort each intermediate fiber path in the intermediate fiber path set according to the path evaluation score to obtain the sorting result.

[0194] Step 7: If the number of optical fibers required for tumor ablation is not greater than a preset value, select all feasible combinations of intermediate optical fiber paths from the set of intermediate optical fiber paths according to the predetermined coverage matrix.

[0195] Step 8: Based on the sorting results, select the intermediate fiber paths from the intermediate fiber path combinations that require the number of fibers for tumor ablation, and determine the selected intermediate fiber paths as the target fiber path set.

[0196] Step 9: If the number of optical fibers required for tumor ablation is greater than the preset value, select the initial path for the number of optical fibers required for tumor ablation from the intermediate optical fiber path set according to the sorting results.

[0197] Step 10: For each initial path, construct a neighborhood path set based on the initial path; the neighborhood path set consists of paths distributed around the initial path in three-dimensional space; the neighborhood path set satisfies the condition that the Euclidean distance between the starting point of all neighborhood paths and the starting point of the initial path is less than a first distance threshold, and the Euclidean distance between the ending point of all neighborhood paths and the ending point of the initial path is less than a second distance threshold.

[0198] Step 11: In the neighborhood path set, based on the equal probability selection strategy, select the path that covers the most target treatment areas or the path with the smallest path coverage variance from the neighborhood path set to obtain candidate paths.

[0199] Step 12: If the tumor coverage of a candidate path is less than a preset coverage threshold, the candidate path is deleted, and the deleted candidate path is evaluated according to a pre-constructed energy function to obtain an intermediate path. The energy function includes a path quantity term, a coverage variance correlation term, and an overall ablation rate penalty term. The energy function is used to evaluate the optimization degree of the candidate path. The optimization objective of the energy function is to minimize the required number of paths and achieve a balanced spatial distribution of the coverage area while meeting the clinically required ablation rate.

[0200] Step 13: If the preset number of iterations is met, select the optimal path from the initial path and multiple intermediate paths, and determine the optimal path as the target fiber path set.

[0201] Step 14: Based on the pre-built Lagrange relaxation optimization model, determine the minimum number of fiber optic paths under the condition of complete tumor coverage.

[0202] Step 15: If the number of optical fibers required for tumor ablation is consistent with the minimum number of optical fiber paths and the target set of optical fiber paths can achieve complete coverage of the tumor area, the target set of optical fiber paths is determined to be a valid set of optical fiber paths.

[0203] Step 16: For each target fiber path, based on the spatial orientation of the target fiber path, model the ablation range corresponding to the target fiber path as a rotating ellipsoid with the target fiber path as the axis of symmetry.

[0204] Step 17: Obtain the spatial intersection between the target fiber optic path and the tumor region, which will be used as the corresponding target treatment area.

[0205] Step 18: Perform equidistant sampling along the path direction of the target optical fiber to obtain multiple candidate ablation points, and construct a two-dimensional cross-section perpendicular to the target optical fiber path at each candidate ablation point.

[0206] Step 19: For each candidate ablation point, calculate the farthest distance function from the candidate ablation point to the target treatment area within the cross-section. The farthest distance function is used to characterize the maximum treatment demand of the candidate ablation point in the local area.

[0207] Step 20: Based on the farthest distance function, iteratively select the target ablation point and the corresponding minor axis radius of the target ablation point from multiple candidate ablation points to form the rotating ellipsoid, so that the target treatment area is completely covered in the cross-sectional direction, and the number of optical fibers required is minimized while satisfying the coverage integrity.

[0208] It should be noted that the execution order of steps 7-8 and steps 9-13 is not limited. In actual execution, steps 7-8 or steps 9-13 can be executed according to the number of optical fibers required for tumor ablation.

[0209] In one embodiment, such as Figure 12 As shown, the method also includes:

[0210] The first stage is the input information phase, which uses the patient's preoperative T1-weighted enhanced magnetic resonance imaging (T1ce images) and tumor region segmentation results as the basic data.

[0211] Next, the planning model is established. First, planning constraints are confirmed based on clinical safety requirements (such as avoiding key structures such as the brainstem and major blood vessels). Then, planning parameters such as path angle and safety distance are calculated by combining imaging data, and finally, the fiber optic planning model is constructed.

[0212] The next stage is path finding and evaluation. The "fiber optic path search and optimization" algorithm is used to find possible fiber optic paths under model constraints. Then, mathematical methods such as "Lagrange relaxation" are used to verify the optimality of the path (to ensure that the tumor is covered with the fewest fibers and the safest path), and finally the precise fiber optic location is determined.

[0213] Finally, in the ablation plan evaluation stage, the ablation zone is first planned based on the fiber optic location (simulating the ablation range to see if it completely covers the tumor), and then the "planning visualization" (such as 3D model display) allows doctors to make an intuitive judgment; finally, a "clinical evaluation" is conducted in combination with clinical experience to confirm whether the plan is safe and effective.

[0214] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0215] Based on the same inventive concept, this application also provides a planning device for multi-fiber laser ablation of glioma to implement the planning method for multi-fiber laser ablation of glioma described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more planning devices for multi-fiber laser ablation of glioma provided below can be found in the limitations of the planning method for multi-fiber laser ablation of glioma described above, and will not be repeated here.

[0216] In one embodiment, such as Figure 13 As shown, a planning device for multi-fiber laser ablation therapy of gliomas is provided, comprising: a segmentation module 1001, a screening module 1002, and a determination module 1003, wherein:

[0217] The segmentation module 1001 is used to perform segmentation and sampling processing on the image information in sequence according to the pre-acquired image information of the target patient and the segmentation results of the tumor region to obtain a set of candidate optical fiber paths;

[0218] The screening module 1002 is used to screen the candidate fiber path set according to the fiber planning objective function and fiber planning constraints, obtain the intermediate fiber path set, and determine the number of fibers required for tumor ablation based on the intermediate fiber path set.

[0219] The determination module 1003 is used to determine the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and the pre-determined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path.

[0220] In one embodiment, the determining module 1003 is specifically used to, for each intermediate fiber path in the intermediate fiber path set, substitute the path information corresponding to the intermediate fiber path into a pre-constructed path evaluation formula to determine the path evaluation score of the intermediate fiber path; the path evaluation formula is used to characterize the correspondence between the path evaluation score and the path information; the path information includes at least the fiber insertion angle, the distance between the fiber and the key tissue in the brain, the distance variance of the fiber covering the tumor target point, the coverage rate of the fiber to the tumor, and the fiber length; according to the path evaluation score, each intermediate fiber path in the intermediate fiber path set is sorted to obtain a sorting result; according to the number of fibers required for tumor ablation and the sorting result, the target fiber path set is determined from the intermediate fiber path set.

[0221] In one embodiment, the determining module 1003 is specifically used to, when the number of optical fibers required for tumor ablation is not greater than a preset value, select all feasible optical fiber path combinations from the set of intermediate optical fiber paths according to a pre-determined coverage matrix; select the intermediate optical fiber path of the number of optical fibers required for tumor ablation from the intermediate optical fiber path combinations according to the sorting result, and determine the selected intermediate optical fiber path as the target optical fiber path set.

[0222] In one embodiment, the determining module 1003 is specifically used to select an initial path for the number of optical fibers required for tumor ablation from the intermediate optical fiber path set according to the sorting result when the number of optical fibers required for tumor ablation is greater than a preset value; for each initial path, the initial path is iteratively optimized using a simulated annealing algorithm to obtain an intermediate path; and when the preset number of iterations is met, the optimal path is selected from the initial path and multiple intermediate paths, and the optimal path is determined as the target optical fiber path set.

[0223] In one embodiment, the determining module 1003 is specifically used to construct a neighborhood path set of the initial path based on the initial path; the neighborhood path set consists of paths distributed around the initial path of the fiber optic path set in three-dimensional space; the neighborhood path set satisfies the condition that the Euclidean distance between the starting point of all neighborhood paths and the starting point of the initial path is less than a first distance threshold, and the Euclidean distance between the ending point of the neighborhood paths and the ending point of the initial path is less than a second distance threshold; in the neighborhood path set, based on an equal probability selection strategy, the path with the largest number of coverage areas of the target treatment region or the path with the smallest path coverage variance is selected from the neighborhood path set to obtain candidate paths; if the coverage rate of the candidate paths in the fiber optic path set to the tumor is less than a preset coverage threshold, the candidate paths in the fiber optic path set are deleted, and the deleted candidate paths are evaluated according to a pre-constructed energy function to obtain intermediate paths; wherein, the energy function includes a path quantity term, a coverage variance related term, and an overall ablation rate penalty term, the energy function is used to evaluate the optimization degree of the candidate paths, and the optimization objective of the energy function is to minimize the required number of paths and achieve a balanced spatial distribution of the coverage area while meeting the clinically required ablation rate.

[0224] In one embodiment, the screening module 1002 is specifically used to combine the paths in the intermediate fiber path set based on the path information corresponding to the intermediate fiber path to obtain multiple path combinations; for each path combination, if there is no spatial conflict between any two fiber paths in the path combination and the preset tumor area ablation coverage requirements are met, the number of fibers in the path combination is determined as the number of fibers required for tumor ablation.

[0225] In one embodiment, the planning device for multi-fiber laser ablation therapy of glioma is further used to determine the minimum number of fiber paths under the condition of complete tumor coverage based on a pre-constructed Lagrange relaxation optimization model; and to determine the target fiber path set as an effective fiber path set when the number of fibers required for tumor ablation in the fiber path set is consistent with the minimum number of fiber paths in the fiber path set and the target fiber path set can achieve complete coverage of the tumor area.

[0226] In one embodiment, the planning device for multi-fiber laser ablation treatment of glioma is further configured to, for each target fiber path, model the ablation range corresponding to the target fiber path as a rotating ellipsoid with the target fiber path as the axis of symmetry based on the spatial orientation of the target fiber path; obtain the spatial intersection between the target fiber path and the tumor region as the corresponding target treatment region; perform equidistant sampling along the path direction of the target fiber path to obtain multiple candidate ablation points, and construct a two-dimensional cross-section perpendicular to the target fiber path at each candidate ablation point; for each candidate ablation point, calculate the farthest distance function from the candidate ablation point to the target treatment region within the cross-section, the farthest distance function being used to characterize the maximum treatment demand of the candidate ablation point in the local region;

[0227] Based on the farthest distance function, the target ablation point and the corresponding minor axis radius of the target ablation point are iteratively selected from multiple candidate ablation points to form the rotating ellipsoid, so that the target treatment area is completely covered in the cross-sectional direction, and the number of optical fibers required is minimized while satisfying the coverage integrity.

[0228] The modules in the aforementioned multi-fiber laser ablation device for treating gliomas can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0229] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0230] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0231] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0232] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0233] Those skilled in the art can understand the underlying principles to implement all or part of the processes in the above embodiments. This can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0234] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0235] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A planning method for multi-fiber laser ablation therapy of glioma, characterized in that, The method includes: The image information of the target patient acquired in advance is sequentially segmented and sampled to obtain a set of candidate fiber optic paths; Based on the optical fiber planning objective function and optical fiber planning constraints, the candidate optical fiber path set is screened to obtain the intermediate optical fiber path set, and the number of optical fibers required for tumor ablation is initially determined based on the intermediate optical fiber path set. The target fiber path set is determined from the intermediate fiber path set based on the number of optical fibers required for tumor ablation and the predetermined path evaluation score; the target fiber path set includes at least the location information corresponding to each target fiber path. The step of determining the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and a pre-determined path evaluation score includes: For each intermediate fiber path in the set of intermediate fiber paths, the path information corresponding to the intermediate fiber path is substituted into a pre-constructed path evaluation formula to determine the path evaluation score of the intermediate fiber path; the path evaluation formula is used to characterize the correspondence between the path evaluation score and the path information. Based on the path evaluation score, each intermediate fiber path in the intermediate fiber path set is sorted to obtain a sorting result. If the number of optical fibers required for tumor ablation is not greater than a preset value, all feasible combinations of intermediate optical fiber paths are selected from the set of intermediate optical fiber paths according to a predetermined coverage matrix. Based on the sorting results, intermediate fiber paths for the number of fibers required for tumor ablation are selected from the intermediate fiber path combinations, and the selected intermediate fiber paths are determined as the target fiber path set.

2. The method according to claim 1, characterized in that, The step of determining the target fiber path set from the intermediate fiber path set based on the number of fibers required for tumor ablation and the sorting result includes: If the number of optical fibers required for tumor ablation is greater than a preset value, an initial path for the number of optical fibers required for tumor ablation is selected from the intermediate optical fiber path set according to the sorting result. For each initial path, the simulated annealing algorithm is used to iteratively optimize the initial path to obtain intermediate paths; If a preset number of iterations is met, the optimal path is selected from the initial path and the multiple intermediate paths, and the optimal path is determined as the target fiber optic path set.

3. The method according to claim 2, characterized in that, The step of iteratively optimizing the initial path using the simulated annealing algorithm to obtain the intermediate path includes: Based on the initial path, a set of neighborhood paths is constructed for the initial path; the set of neighborhood paths consists of paths distributed around the initial path in three-dimensional space; the set of neighborhood paths satisfies the condition that the Euclidean distance between the starting point of the neighborhood path and the starting point of the initial path is less than a first distance threshold, and the Euclidean distance between the ending point of the neighborhood path and the ending point of the initial path is less than a second distance threshold. In the neighborhood path set, based on an equal probability selection strategy, the path that covers the most target treatment areas or the path with the smallest path coverage variance is selected from the neighborhood path set to obtain candidate paths; If the tumor coverage of the candidate path is less than a preset coverage threshold, the candidate path is deleted, and the deleted candidate path is evaluated according to a pre-constructed energy function to obtain the intermediate path. The energy function includes a path quantity term, a coverage variance correlation term, and an overall ablation rate penalty term. The energy function is used to evaluate the optimization degree of the candidate path. The optimization objective of the energy function is to minimize the required number of paths and achieve a spatially balanced distribution of the coverage area while meeting the clinically required ablation rate.

4. The method according to claim 1, characterized in that, The step of determining the number of optical fibers required for tumor ablation based on the set of intermediate optical fiber paths includes: Based on the path information corresponding to the intermediate fiber path, the paths in the intermediate fiber path set are combined to obtain multiple path combinations. For each path combination, if there is no spatial conflict between any two fiber paths in the path combination and the preset tumor area ablation coverage requirement is met, the number of fibers in the path combination is determined as the number of fibers required for tumor ablation.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Based on a pre-built Lagrange relaxation optimization model, the minimum number of fiber optic paths under the condition of complete tumor coverage is determined. If the number of optical fibers required for tumor ablation is the same as the minimum number of optical fiber paths and the target set of optical fiber paths can achieve complete coverage of the tumor area, the target set of optical fiber paths is determined to be a valid set of optical fiber paths.

6. The method according to any one of claims 1 to 4, characterized in that, The method further includes: For each target fiber path, based on the spatial orientation of the target fiber path, the ablation range corresponding to the target fiber path is modeled as a rotating ellipsoid with the target fiber path as the axis of symmetry; The spatial intersection of the target optical fiber path and the tumor region is obtained as the corresponding target treatment area. Equidistant sampling is performed along the path direction of the target optical fiber to obtain multiple candidate ablation points, and a two-dimensional cross-section perpendicular to the target optical fiber path is constructed at each candidate ablation point. For each candidate ablation point, calculate the farthest distance function from the candidate ablation point to the target treatment area in the cross-section. The farthest distance function is used to characterize the maximum treatment demand of the candidate ablation point in the local area. Based on the farthest distance function, the target ablation point and the minor axis radius corresponding to the target ablation point are iteratively selected from multiple candidate ablation points to form a rotating ellipsoid, so that the target treatment area is completely covered in the cross-sectional direction, and the number of optical fibers required is minimized while satisfying the coverage integrity.

7. A planning device for multi-fiber laser ablation therapy of glioma, characterized in that, The device includes: The segmentation module is used to sequentially segment and sample the pre-acquired image information of the target patient to obtain a set of candidate fiber optic paths; The filtering module is used to filter the candidate fiber path set according to the fiber planning objective function and fiber planning constraints to obtain the intermediate fiber path set, and to preliminarily determine the number of fibers required for tumor ablation based on the intermediate fiber path set. The determination module is used to determine a set of target fiber paths from the set of intermediate fiber paths based on the number of fiber optic cables required for tumor ablation and a pre-determined path evaluation score; the set of target fiber paths includes at least the location information corresponding to each target fiber path; The determining module is specifically used to, for each intermediate fiber path in the set of intermediate fiber paths, substitute the path information corresponding to the intermediate fiber path into a pre-constructed path evaluation formula to determine the path evaluation score of the intermediate fiber path; the path evaluation formula is used to characterize the correspondence between the path evaluation score and the path information. Based on the path evaluation score, each intermediate fiber path in the intermediate fiber path set is sorted to obtain a sorting result. If the number of optical fibers required for tumor ablation is not greater than a preset value, all feasible combinations of intermediate optical fiber paths are selected from the set of intermediate optical fiber paths according to a predetermined coverage matrix. Based on the sorting results, intermediate fiber paths for the number of fibers required for tumor ablation are selected from the intermediate fiber path combinations, and the selected intermediate fiber paths are determined as the target fiber path set.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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