A data intelligent analysis system before brain glioma resection

Abstract

The application relates to the fields of medical informatization and medical image data processing, and particularly discloses a data intelligent analysis system before brain glioma resection. The system integrates anatomical and microcosmic metabolic information to construct a structure support through a multi-modal image data fusion unit, a microenvironment topology reconstruction unit, an evolution dynamics simulation unit, an infiltration boundary determination unit and a surgery scheme optimization unit, simulates a space-time evolution process of tumor cells by using evolution game logic, generates an infiltration probability distribution field, and determines an optimal boundary by balancing resection utility and nerve damage risk. The application realizes quantitative auxiliary decision for each patient, improves the scientific nature of surgery boundary prediction, and greatly reduces the recurrence risk and disability rate.

Description

Technical Field

[0001] This invention belongs to the field of medical information technology and medical image data processing, and specifically relates to an intelligent data analysis system for glioma resection. Background Technology

[0002] In the field of neurosurgery, precise resection of gliomas is a core means of improving patient survival rates and quality of life. With the deep integration of precision medicine and medical imaging technology, preoperative decision support systems have become an important technical support for guiding surgical planning, assessing tumor boundaries, and protecting functional neural structures. These systems integrate multimodal medical data to provide doctors with spatial distribution information of lesions and are an indispensable component of modern neurosurgical planning systems.

[0003] This intelligent analysis system for glioma resection aims to utilize advanced algorithms to characterize lesions from multiple dimensions, thereby achieving accurate prediction and simulation of the tumor's infiltration extent. The fundamental goal of this type of system is to identify the core region of the tumor and potential spread pathways through automated processing and mining of neuroimaging data. This assists surgeons in determining the optimal surgical resection area, maximizing tumor removal while minimizing damage to healthy brain tissue.

[0004] Current technologies primarily rely on conventional imaging techniques such as magnetic resonance imaging (MRI) for boundary delineation. This method, based solely on macroscopic imaging characterization, presents problems in clinical practice. Gliomas exhibit the typical biological characteristic of microscopic infiltration along white matter fiber bundles, which is the fundamental cause of surgical residue and postoperative recurrence. Current analytical models often focus only on static morphological features, failing to effectively simulate the adaptive growth process of tumor cells interacting with the complex brain microenvironment, and lacking the ability to deeply model the dynamic evolutionary mechanisms of tumor cells under evolutionary selection pressure.

[0005] Because existing technologies cannot accurately reconstruct the physical laws and biological evolutionary logic of microscopic infiltration, they have limitations in predicting the true boundaries of tumors, resulting in a lack of biologically in-depth evidence to support the determination of surgical boundaries. Therefore, there is an urgent need for an optimized intelligent data analysis system for pre-operative resection of gliomas. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent data analysis system for glioma resection, in order to solve the problems mentioned in the background art, that existing glioma analysis methods rely too much on macroscopic static images, cannot accurately simulate the microscopic infiltration patterns and biological dynamic evolution logic of tumor cells, and result in insufficient accuracy in predicting surgical boundaries.

[0007] This invention provides an intelligent data analysis system for glioma resection, comprising: A multimodal image data fusion unit is used to acquire magnetic resonance imaging, diffusion tensor imaging, and perfusion-weighted imaging data of the target patient; The microenvironment topology reconstruction unit is used to determine the orientation of white matter fiber bundles and construct a dominant diffusion vector field to guide the movement of tumor cells by analyzing diffusion tensor imaging data based on a 5-channel feature data array output by the multimodal image data fusion unit. Furthermore, it constructs a resource abundance map that characterizes the intensity of local nutrient supply by performing deconvolution processing on perfusion-weighted imaging data, thereby generating a structural scaffold model that characterizes the physical barrier and nutritional support capacity of brain parenchyma. The evolutionary dynamics simulation unit is used to divide the tumor cell population into migratory clonal subpopulations and proliferating clonal subpopulations under the constraints of the structural scaffold model, and to construct a fitness-based payoff function for each subpopulation. It uses evolutionary game logic to model the spatiotemporal evolution of the migration, proliferation and death behavior of tumor cells. By simulating the competitive process of cell subpopulations under the influence of local resource abundance and selection pressure, it generates a tumor micro-invasion probability distribution field. The invasion boundary determination unit is used to perform three-dimensional Gaussian filtering smoothing on the invasion probability distribution field, delineate the tumor core area, high-risk invasion area and potential affected warning area through adaptive clustering algorithm, and calculate the rate of change of probability gradient to identify the invasion front boundary. The surgical plan optimization unit combines the analysis results of the invasion boundary determination unit with the patient's functional magnetic resonance imaging data to construct a comprehensive objective function that includes resection rate score and functional damage risk score. Using a multi-objective optimization algorithm, it outputs the surgical resection boundary path that maximizes the resection rate score while ensuring that the functional damage risk is less than a second preset threshold.

[0008] Preferably, the multimodal image data fusion unit uses fluid attenuation inversion recovery sequence images as the reference space, performs spatial coordinate registration using the mutual information maximization criterion, and performs bias field correction and intensity normalization processing to construct a 5-channel feature data array containing anatomical structure, white matter fiber distribution and blood perfusion information. The process by which the multimodal image data fusion unit constructs a 5-channel feature data array is as follows: Acquire longitudinal relaxation-weighted sequences, transverse relaxation-weighted sequences, and fluid attenuation inversion recovery sequences from magnetic resonance imaging, and extract fractional anisotropy maps from diffusion tensor imaging and cerebral blood flow maps from perfusion-weighted imaging. A method combining affine transformation and nonlinear registration was used to uniformly map the image data of other modalities to the voxel coordinate system corresponding to the fluid attenuation inversion recovery sequence image; Normalized convolution algorithm is used to eliminate brightness non-uniformity and map the signal intensity of different sequences to a uniform 0 to 1 range; By splicing multi-dimensional tensors, feature vectors carrying information on tissue anatomy, water diffusion direction, diffusion degree, local blood supply level, and lesion contrast are constructed for each voxel point in the brain space.

[0009] Preferably, the process by which the microenvironment topology reconstruction unit generates the structural support model is as follows: The main diffusion direction is determined by solving the eigenvectors and eigenvalues ​​of the diffusion tensor matrix, and the dominant diffusion vector field is constructed by combining fractional anisotropy values. The tracer concentration-time curve in perfusion-weighted imaging was processed using a deconvolution algorithm to obtain the average transport time and local blood volume parameters of microvessels in order to construct a resource abundance map. The cerebrospinal fluid space and intracranial major blood vessels are defined as impenetrable physical boundaries, and different migration resistance coefficients are assigned to different anatomical regions.

[0010] Preferably, the microenvironment topology reconstruction unit is further used for: By analyzing the attenuation coefficient of the fluid attenuation inversion recovery sequence signal, we can distinguish between vasogenic edema and tumor-infiltrative edema. The diffusion coefficient was increased in the vasogenic edema region, and a higher evolutionary pressure factor was assigned in the infiltrative edema region to correct the parameter inputs in the evolutionary dynamics simulation unit.

[0011] Preferably, the evolutionary dynamics simulation unit divides the analysis region into voxel grids during modeling; The gains of proliferating clonal populations are positively regulated by the local resource abundance map and negatively regulated by the local total cell density. The gains of migratory clone populations are positively correlated with the direction of the dominance diffusion vector field; when the local resource abundance is less than the first preset threshold, the phenotypic switching from proliferating cells to migratory cells is triggered.

[0012] Preferably, the calculation logic for the benefit score of the proliferating clonal population is as follows: ; The overall benefit score representing a proliferating clonal population. The local resource abundance value, representing the spatial location, is obtained by mapping from the perfusion-weighted imaging data; This represents the total density of tumor cells within the current voxel. This is a preset environmental capacity constant used to limit the maximum number of cells that a single voxel can hold; This represents the baseline value of metabolic consumption that cells use to maintain basic life activities.

[0013] Preferably, the evolutionary dynamics simulation unit is further used for: Multiple rounds of iterative calculations were performed to simulate the infiltration process from the tumor core to the surrounding brain parenchyma; Record the state of each voxel point invaded by tumor cells in each iteration, and statistically analyze the frequency of invasion of each voxel based on the simulated sequence to form a probability distribution field reflecting the dynamic invasion depth of the tumor.

[0014] Preferably, the hierarchical region division logic executed by the infiltration boundary determination unit is as follows: Regions with a probability greater than 0.8 are defined as the tumor solid core region; regions with a probability between 0.3 and 0.8 are defined as high-risk infiltration regions. The region with a probability between 0.05 and 0.3 is defined as the potential affected warning zone; and The closed surface formed by the set of maxima of the probability gradient magnitude is the boundary of the biological infiltration front.

[0015] Preferably, the decision logic executed by the surgical plan optimization unit is as follows: Identify key neural functional nodes in functional magnetic resonance imaging data and calculate the spatial Euclidean distance between each functional node and high-risk infiltration areas; The resection rate score in the comprehensive objective function is proportional to the integral of the infiltration probability covered by the preset resection boundary, and the functional impairment risk score is proportional to the probability that the resection path touches the key neural functional area; the optimal closed surface that maximizes the resection rate score is found using a multi-objective optimization algorithm.

[0016] Preferably, the system further includes: The real-time calibration feedback module is used to receive intraoperative image data during the operation, calculate the non-rigid transformation matrix between the preoperative and intraoperative images, and compensate and calibrate the output surgical resection boundary path. A high-performance parallel computing platform, which includes a graphics processor, for parallel evolutionary game state updates of voxel grid points. The user interface unit is used to display the infiltration envelope under different probability thresholds in the form of isosurfaces, and to update the display of the surgical resection boundary path in real time in response to the adjustment command of the risk preference coefficient.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates information from macroscopic anatomy to microscopic diffusion and metabolic perfusion through a multimodal image data fusion unit, breaking the limitations of a single image modality in terms of information richness; by establishing a 5-channel feature tensor, the system can collaboratively characterize the brain tissue environment from the perspective of multiple physical quantities, laying a solid data foundation for subsequent accurate analysis.

[0018] 2. This invention innovatively introduces an evolutionary dynamics simulation unit, treating tumor growth as an evolutionary game process constrained by the microenvironment and possessing adaptive characteristics. The design not only considers the biological characteristics of tumor cells microscopically infiltrating along white matter fiber bundles, but also deeply models the trade-off mechanism between migration and proliferation of tumor clones under selection pressure. This dynamic simulation scheme can reveal the microscopic infiltration patterns hidden beneath conventional imaging, significantly improving the scientific rigor and foresight of predicting the true boundaries of tumors.

[0019] 3. This invention achieves quantitative balance in medical decision-making through a surgical planning optimization unit, transforming traditional experience-based surgical planning into scientific calculation based on resection utility and risk cost modeling. By identifying the spatial topological relationship between the infiltration boundary and key functional areas, the system can tailor the optimal boundary path for each patient that balances complete resection and functional preservation, greatly reducing the risk of postoperative recurrence and disability rate, and has extremely high clinical application value and social benefits.

[0020] 4. This invention fills the gap in existing technology for simulating tumor microscopic behavior by applying a complex evolutionary game model and topological reconstruction logic to glioma analysis. The system’s high degree of certainty and objectivity eliminates subjective bias from human interpretation and provides decision-making references with in-depth biological evidence to support precision neurosurgery. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the evolution dynamics simulation unit in this invention; Figure 3 This is a logical flowchart of the multimodal image data fusion and microenvironment topology reconstruction in this invention; Figure 4 This is a flowchart illustrating the risk level classification logic of the infiltration boundary determination unit in this invention. Figure 5 This is a schematic diagram of the surgical plan optimization unit's decision-making framework for weighing the resection efficacy and risks in this invention. Detailed Implementation

[0022] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 This embodiment discloses a data intelligent analysis system for glioma resection, whose overall architecture is based on the deep fusion of multi-source heterogeneous data and the simulation of biological evolution logic. The system uses a multimodal image data fusion unit as the aggregation center of the underlying data to transform medical image information with different physical properties into a unified digital representation, and then the micro-environment topology reconstruction unit constructs the microstructural scaffold of brain tissue.

[0023] Under the physical and physiological constraints of this scaffold, the evolutionary dynamics simulation unit is responsible for simulating the dynamic expansion behavior of tumor cells in a complex microenvironment, generating a predictive invasion probability distribution field. The invasion boundary determination unit determines the fine spatial extent of the tumor through quantitative analysis, and finally, the surgical planning optimization unit outputs the optimal surgical resection boundary path that is clinically feasible.

[0024] The core task of the multimodal image data fusion unit during execution is to eliminate spatial deformation and intensity differences between different scan sequences. Please refer to the appendix. Figure 3 The multimodal image data fusion unit first acquires the original image sequences of the target patient through an external data interface, specifically including longitudinal relaxation-weighted sequences, transverse relaxation-weighted sequences, and fluid attenuation inversion recovery sequences from magnetic resonance imaging. These sequences provide the anatomical contours of the tumor entity, differences in tissue water content, and edema signals at the lesion margins, respectively.

[0025] Subsequently, the system further extracts the fractional anisotropy map from diffusion tensor imaging. This fractional anisotropy map carries information about the spatial orientation of water molecule diffusion and can directly reflect the integrity and trend of white matter fiber bundles. Simultaneously, perfusion-weighted imaging of cerebral blood flow is acquired to characterize the blood supply richness of the tumor and its surrounding areas.

[0026] To achieve spatial consistency of multimodal data, the multimodal image data fusion unit sets the fluid attenuation inversion recovery sequence image as the reference space, because the fluid attenuation inversion recovery sequence can clearly delineate tissue changes caused by tumor infiltration. The system uses the mutual information maximization criterion as the evaluation function for registration and uses the gradient ascent iterative algorithm to find the optimal affine transformation matrix, thereby correcting the rigid displacement caused by slight changes in patient position.

[0027] Building upon this foundation, the system further introduces a nonlinear registration strategy based on a free deformation model. By controlling the local deformation of the control point mesh, the nonlinear distortion caused by tumor compression of brain tissue is eliminated. The sampling step size during the registration process is set to 1 mm, and the iteration termination condition is that the mutual information increment between two adjacent iterations is less than 0.0001, ensuring that the final registration error is strictly controlled within 0.5 mm.

[0028] After spatial alignment, the multimodal image data fusion unit performs bias field correction on all voxels and uses a normalized convolution algorithm to eliminate brightness artifacts caused by magnetic field inhomogeneity. Intensity normalization is performed using statistical histogram matching technology to map the signal intensities of different sequences to a unified 0-1 interval.

[0029] Finally, the system performs multi-dimensional tensor concatenation on the processed data from the five modalities. Specifically, for each voxel in the brain space, a vector containing five feature channels is constructed, carrying the longitudinal relaxation-weighted signal, the transverse relaxation-weighted signal, the fluid decay inversion recovery signal, the fractional anisotropy value, and the cerebral blood flow value, respectively. The resulting five-channel feature data array provides complete information input for subsequent microenvironment modeling.

[0030] The microenvironment topology reconstruction unit, based on the data array output by the multimodal image data fusion unit, begins to construct a structural scaffold model characterizing the physical barriers and nutritional support capabilities of the brain parenchyma. Please refer to the appendix. Figure 3 The system first calculates the diffusion tensor eigenvalues ​​of each voxel by analyzing the diffusion tensor imaging data.

[0031] Specifically, the dominant diffusion direction is determined by solving for the eigenvectors and eigenvalues ​​of the tensor matrix. This dominant diffusion direction represents the trajectory of the white matter fiber bundles in space, and the system uses this to construct a dominant diffusion vector field that guides tumor cells to move along the fiber bundles. The dominant diffusion vector field not only contains directional information but also a quantified weighting factor, which is proportional to the fractional anisotropy value and reflects the guiding strength of the fiber bundles for cell migration.

[0032] Simultaneously, the microenvironment topology reconstruction unit utilizes perfusion-weighted imaging data and processes the tracer concentration-time curve using a deconvolution algorithm to obtain the average transport time and local blood volume parameters of microvessels. Based on these parameters, the system constructs a resource abundance map to simulate the oxygen and glucose supply intensity required for tumor cell growth. In the resource abundance map, higher values ​​represent richer blood supply, which can support higher density tumor cell proliferation.

[0033] Building upon this foundation, the microenvironment topology reconstruction unit further defines the cerebrospinal fluid space and intracranial major blood vessels as impenetrable physical boundaries. When constructing the structural scaffold model, the system assigns differentiated migration resistance coefficients to different spatial regions based on tissue anatomy information. The white matter region, due to its parallel fiber structure, has a migration resistance coefficient set to 0.2, while the gray matter region, with its densely packed neuronal cell bodies and lack of directional guidance structures, has a migration resistance coefficient set to 0.8.

[0034] This non-uniform resistance distribution accurately reflects the biological characteristics of tumor cells as they spread within the brain, avoiding dense structures and preferentially infiltrating along fiber bundles. Furthermore, the system subdivides the edema region surrounding the tumor by analyzing the attenuation coefficient of the fluid attenuation inversion recovery sequence signal. The diffusion coefficient of vasogenic edema is increased, while invasive edema regions suspected of tumor infiltration are assigned a higher evolutionary pressure factor, thereby correcting the parameter inputs in the evolutionary dynamics simulation.

[0035] The evolutionary dynamics simulation unit is the core computational engine of this system. Please refer to the appendix. Figure 2 Under the constraints of a structural scaffold model, the system abstracts the tumor cell population into subpopulations exhibiting game-like behavior. Specifically, this includes migratory clonal subpopulations and proliferating clonal subpopulations. The system divides the entire analysis region into millions of 1 mm × 1 mm × 1 mm voxel units, each serving as a basic grid point for the evolutionary game. Within each simulation step, the system calculates the fitness score of each subpopulation in the current voxel environment.

[0036] The payoff function of proliferating clonal populations is linearly positively correlated with the nutrient levels provided by the resource abundance map, while being moderated by nonlinear negative feedback from the total cell density within local voxels. This regulatory mechanism simulates biological contact inhibition effects and spatial competition. The payoff function of migratory clonal populations, on the other hand, depends primarily on the degree of overlap between their movement direction and the dominant diffusion vector field.

[0037] If a cell attempts to migrate in the same direction as the white matter fiber bundle, its survival probability reward reaches its maximum. The system introduces selection pressure parameters generated by oxygen consumption and metabolic waste accumulation. When the local resource abundance is less than a first preset threshold, the system triggers a phenotypic switching logic from proliferating cells to migrating cells, simulating the survival strategy of tumor cells escaping to surrounding areas in extreme environments.

[0038] In the specific calculation logic, the payoff value of the proliferating subgroup is calculated as follows: ; The overall benefit score representing a proliferating clonal population. The local resource abundance value, representing the spatial location, is obtained by mapping from the perfusion-weighted imaging data. This represents the total density of tumor cells within the current voxel. This is a preset environmental capacity constant used to limit the maximum number of cells that a single voxel can hold. This represents the baseline value of metabolic consumption for cells to maintain basic life activities. The formula describes the proliferation dynamics of tumor cells under resource constraints and spatial competition pressures.

[0039] The system simulates the infiltration and evolution process from the initial tumor core to the surrounding brain parenchyma through multiple rounds of iterative calculations. Each iteration corresponds to a 24-hour period in real time, with a total of 200 iterations to cover the tumor's evolution over a timescale of several months. The system records the state of tumor cell invasion at each voxel point in each iteration and statistically analyzes the frequency of invasion for each voxel based on a large number of simulated sequences, ultimately forming a probability distribution field reflecting the dynamic depth of tumor invasion.

[0040] The infiltration boundary determination unit performs in-depth analysis of the infiltration probability distribution field generated by the evolutionary dynamics simulation unit. Please refer to the appendix. Figure 4 The system first employs a three-dimensional Gaussian filter to spatially smooth the probability distribution field, eliminating numerical steps and noise caused by gridded calculations. Subsequently, an adaptive clustering algorithm is used to divide the entire brain tissue space into three clinically significant risk regions.

[0041] Voxels with a probability greater than 0.8 are designated as the core area of ​​the tumor solid, which typically corresponds to enhancing lesions and their close periphery on imaging.

[0042] Voxels with a probability between 0.3 and 0.8 are defined as high-risk infiltration areas. These high-risk infiltration areas may appear normal on conventional images, but show an extremely high frequency of cell invasion in evolutionary simulations.

[0043] Areas with a probability between 0.05 and 0.3 are defined as potential affected warning zones to alert physicians to possible distal microinvasive lesions.

[0044] To accurately pinpoint the biological boundary, the invasion boundary determination unit further calculates the spatial gradient of the probability distribution field. The system searches for the locations where the probability decreases most rapidly, i.e., the set of points with maximum gradient magnitudes. The closed surface formed by these points is identified as the biological invasion front boundary. The invasion front boundary can penetrate visual edema artifacts, revealing the critical region that has been actually damaged by tumor cells but has not yet produced macroscopic imaging changes.

[0045] The surgical plan optimization unit is responsible for translating the above analysis results into surgical decisions. Please refer to the appendix. Figure 5 The system first imports the patient's functional magnetic resonance imaging (fMRI) data to identify key neural functional nodes such as the motor cortex and language center. The system automatically calculates the spatial Euclidean distance between each functional node and high-risk infiltration areas and potential affected warning areas.

[0046] In order to strike a balance between thorough resection and preservation of neurological function, the surgical planning optimization unit constructed a comprehensive objective function.

[0047] The comprehensive objective function includes two core components: the resection rate score and the functional impairment risk score. The resection rate score is directly proportional to the integral of the infiltration probability covered by the recommended resection area. The functional impairment risk score is positively correlated with the likelihood that the surgical path and resection surface will touch functional areas or important subcortical conduction tracts.

[0048] The system employs a multi-objective optimization algorithm to find the optimal closed surface that maximizes the resection rate score, provided that the probability of functional impairment risk is less than a second preset threshold (usually set to 5%). The optimization process is implemented through the following logic: ; It serves as an overall indicator for evaluating surgical efficacy. The integral of the cumulative tumor invasion probability within the pre-defined resection boundary reflects the thoroughness of the resection. This represents the probability of the resection boundary touching or damaging key neural functional areas. This is the risk preference coefficient, which ranges from 0 to 1. When... When the value approaches 1, the system tends to adopt a more aggressive resection strategy to pursue a lower recurrence rate. When the value approaches 0, the system prioritizes protecting functional areas, even if this may result in some residual tumor tissue.

[0049] The decision instructions generated by the surgical planning optimization unit are ultimately converted into a 3D model file, specifically mesh data containing vertices and faces. This mesh data is transmitted to the neurosurgical navigation system via a standard medical communication protocol. During the surgery, the system works in conjunction with a real-time calibration feedback module. This module receives intraoperative MRI or ultrasound imaging data and monitors in real time brain tissue displacement and deformation caused by cerebrospinal fluid loss and lesion resection. By calculating the non-rigid transformation matrix between preoperative and intraoperative images, the module dynamically compensates for the preset resection boundary path, ensuring that the virtual predicted boundary under augmented reality navigation always precisely coincides with the actual anatomical location of the brain tissue.

[0050] The system's user interface displays the infiltration envelope in real-time as isosurfaces under different probability thresholds. Doctors can manually adjust the risk preference coefficient and observe the dynamic process of the resection range changing with the risk threshold by dragging a slider. This interactive design transforms surgical planning from a static image interpretation into a data-driven simulation process.

[0051] Example 2: Based on Example 1, this example further refines and expands the cell interaction mechanism in the evolutionary dynamics simulation unit to address the analysis of highly heterogeneous multi-level gliomas.

[0052] In Example 2, the evolutionary dynamics simulation unit not only considers the game between cells and the environment but also introduces the logic of co-evolution between cells. The system further refines tumor cells into subpopulations with different metabolic characteristics, such as antioxidant subpopulations and high-energy-consuming subpopulations. Antioxidant subpopulations have higher survival benefits in areas with low resource abundance but slower proliferation rates; high-energy-consuming subpopulations exhibit extremely strong expansion capabilities in areas with abundant blood supply. The system describes the competitive relationship between different cell types when they coexist at the same voxel point by constructing an evolutionary game benefit matrix.

[0053] This refined simulation process is also based on millions of individual pixel grid points. The 5-channel feature tensor output by the multimodal image data fusion unit is expanded to 7 channels, adding an assessment factor reflecting tissue oxygen content. The microenvironment topology reconstruction unit extracts vascular permeability parameters by analyzing the contrast agent elution rate in perfusion-weighted imaging, thereby more accurately characterizing the degree of damage to the blood-brain barrier and using it as a correction term for resource delivery efficiency.

[0054] In each iteration, the evolutionary dynamics simulation unit not only calculates the spatial migration of cell density but also simulates the frequency of cell phenotype transitions induced by the environment. When the accumulation of metabolic waste in a certain region exceeds a critical value, the system triggers a large-scale apoptosis simulation and calculates the attraction of the resulting spatial vacancies to surrounding active tumor cells. This dynamic simulation can capture the alternating evolution of necrotic areas within the tumor and active areas at the periphery.

[0055] In the invasion boundary determination unit, a hierarchical clustering algorithm is employed to address this multi-subgroup evolution result. First, the overall tumor envelope is identified at a macroscopic scale, followed by the identification of discrete invasive foci driven by specific subgroups at a microscopic scale. These discrete invasive foci are often located at the ends of white matter fiber bundles or in areas with dense perforating vessels, representing high-risk points for postoperative recurrence. By marking these high-risk points, the invasion boundary determination unit guides the surgical planning optimization unit to proactively expand the resection boundary in these specific directions.

[0056] The surgical planning optimization unit further integrates a three-dimensional model of subcortical conduction tracts generated by diffusion tensor beam imaging technology during decision-making calculations. The system not only calculates the distance between the resection surface and functional nuclei, but also accurately assesses the risk of physical transection of key fiber tracts such as the pyramidal tract and optic radiation. A fiber tract integrity protection weight is introduced into the comprehensive objective function. If the predicted resection boundary path would sever more than 20% of a key conduction tract, the system automatically triggers an early warning and forces path replanning to find an alternative path, preserving nerve conduction function to the greatest extent possible while removing as much tumor as possible.

[0057] The real-time calibration feedback module in this embodiment enhances the predictive ability for intraoperative brain collapse effects. The module utilizes a biomechanical finite element model, combined with intraoperatively acquired intracranial pressure parameters and brain tissue emptying volume, to simulate the physical process of brain tissue collapsing into the resection cavity. By calculating the displacement vector of each anatomical node, the preoperatively generated resection boundary is corrected point-to-point. This compensation mechanism based on a mechanical model enables the system to still provide millimeter-level boundary positioning accuracy even in the later stages of surgery and under conditions of severe brain tissue deformation.

[0058] Furthermore, the system in this embodiment runs on a high-performance graphics processor parallel computing platform, utilizing unified computing device architecture technology to achieve synchronous state updates of massive grid data. In a brain model containing 4 million voxels, the time to complete 200 evolutionary iterations is compressed to less than 120 seconds, meeting the needs of rapid preoperative clinical planning.

[0059] Example 3: This example focuses on describing the special implementation of the system when dealing with tumors with complex morphology and recurrent cases, especially the deep collaborative logic between the microenvironment topology reconstruction unit and the invasion boundary determination unit.

[0060] In some cases, gliomas may exhibit multicentric growth or infiltrate covertly along specific anatomical sutures. To address these issues, the microenvironment topology reconstruction unit introduces a topology embedding algorithm based on Laplace eigenmaps. This algorithm maps anatomical structures in three-dimensional space to a high-dimensional manifold space, thereby identifying anatomical connectivity that is difficult to detect using conventional Euclidean distance. The system extracts impedance features from structures such as the meninges and ependyma, defining them as low-probability passage zones, while simultaneously defining their surfaces as two-dimensional manifold interfaces along which cells can easily glide.

[0061] In this implementation, the evolutionary dynamics simulation unit employs an algorithm based on a hybrid of cellular automata and dynamic game theory. The system not only focuses on cell density within voxels but also introduces the concept of "probe" particles. These particles simulate highly migratory tumor stem cells, performing non-local random jumps within the simulated field to predict potential satellite lesions. When a probe particle lands in a nutrient-rich area with low defensive resistance, the system automatically initializes a new growth center, simulating the clinically common phenomenon of skip metastasis.

[0062] Based on the simulation results, the infiltration boundary determination unit adopted an active contour model based on shape prior. This active contour model is driven by a probability distribution field generated by evolutionary dynamics, combined with a preset smoothness constraint, enabling the boundary surface to automatically contract or expand. When encountering complex finger-like infiltration morphologies, the active contour model can accurately embed itself into these slender infiltration grooves, thus defining edges that are more biologically logical than traditional spherical or elliptical boundaries.

[0063] For recurrent cases, the multimodal imaging data fusion unit adds a comparative dimension to postoperative historical images. The system performs temporal analysis of the tissue signal evolution at the edge of historical resection cavities, extracting subtle differences in signal characteristics between radiotherapy-induced tissue fibrosis and residual tumor infiltration. The microenvironment topology reconstruction unit marks the surgical scar area as a high-resistance zone, but simultaneously identifies the scar edge as a potential blood supply window based on abnormal enhancement shown in perfusion-weighted imaging.

[0064] When handling recurrent cases, the surgical planning optimization unit incorporates logic to avoid previous surgical pathways. By reading resection range data from historical surgical records, the system calculates the optimal approach for the current surgery, avoiding excessive skin and skull dissection at the same location and reducing surgical complications. Simultaneously, a parameter called "resection edge confidence" has been added to the system's decision-making logic. In areas with blurred image signals and high uncertainty in evolutionary prediction, the system displays the resection boundary with varying degrees of transparency and provides multiple candidate options for the surgeon to choose from.

[0065] In Example 3, the real-time calibration feedback module integrates a marker-based visual positioning system. After craniotomy, the system uses an optical camera to capture anatomical features on the dura mater surface or artificially placed sterile markers, achieving rapid alignment between the physical space and the virtual image space. When intraoperative ultrasound detects relative displacement of deep tissue structures, the module updates the displacement field of the entire three-dimensional space in real time through rapid radial basis function interpolation, ensuring that the guidance of the resection boundary remains at a high confidence level.

[0066] In summary, the intelligent data analysis system for glioma resection disclosed in this invention scientifically reveals the microscopic invasion patterns of tumors through in-depth mining of multimodal imaging data and precise simulation of biological evolutionary logic. The system not only transforms static images into dynamic evolutionary predictions at the theoretical level, but also provides quantitative and precise navigation support for complex surgical decisions at the clinical application level. Through the organic combination of core modules such as microenvironment topology reconstruction, evolutionary dynamics simulation, adaptive boundary determination, and multi-objective surgical optimization, it significantly improves the scientific rigor of glioma resection, effectively reduces the risk of recurrence due to tumor residue, and greatly protects the patient's neurological function, demonstrating significant engineering innovation and clinical practical value.

[0067] In the actual deployment of the system, all calculations are carried out by a backend distributed computing cluster, while the front-end interactive interface uses 3D rendering technology to ensure that doctors can intuitively observe the complex infiltration topology within brain tissue. The various functional units of the system communicate logically via a high-speed data bus, ensuring the continuity and integrity of the process from data input to solution output. This end-to-end, intelligent analysis solution provides solid technical support for precision surgery.

[0068] In the system's operation, the multimodal image data fusion unit first ensures the accurate overlay of information from various dimensions, eliminating deviations in spatial scale and signal dimensions for subsequent analysis. The microenvironment topology reconstruction unit constructs a computational sandbox with biophysical significance through the interpretation of physiological parameters. The evolutionary dynamics simulation unit simulates tumor growth competition on this sandbox, predicting the true boundaries of the tumor at a microscopic level. The invasion boundary determination unit transforms complex simulation results into easily understandable risk levels through gradient analysis of the probability field and region division. The surgical plan optimization unit, as the final decision-making checkpoint, maximizes treatment effectiveness while ensuring the patient's quality of life.

[0069] At the hardware level, the system achieves sub-second processing of massive amounts of medical image data through optimized use of high-performance processors. At the software architecture level, a modular design allows the system to flexibly adjust evolutionary model parameters or surgical optimization objective functions according to different clinical needs. Furthermore, the system possesses excellent scalability, enabling seamless integration with existing neurosurgical navigation systems and intraoperative image acquisition equipment. Through this deeply integrated analysis system, clinicians can gain profound insights beyond the limitations of vision, providing a new solution to the medical challenge of glioma.

[0070] When performing evolutionary dynamics simulations, the system also specifically considers the physical limitations imposed by different anatomical structures of brain tissue on tumor spread. For example, the system defines the ventricular system as a fluid environment to simulate the floating and implantation of tumor cells in cerebrospinal fluid. For tumor infiltration across the hemispheres via the corpus callosum, the system dynamically adjusts the drag coefficient in the evolutionary game based on the density of the corpus callosum fibers. This meticulous simulation of anatomical details results in a highly anatomically consistent infiltration probability distribution field.

[0071] In determining the invasion boundary, the system incorporates a dynamic threshold mechanism. For gliomas of different pathological grades, the system automatically adjusts the risk classification criteria. For low-grade gliomas, the system focuses more on long-distance invasion along fibrous bundles, while for high-grade gliomas, it strengthens monitoring of areas with dense neovascularization and the edema front. This classification-based analytical strategy ensures the system's broad applicability to various clinical cases.

[0072] When outputting decision instructions, the surgical planning optimization unit automatically generates a detailed risk assessment report. This report lists all key anatomical structures near the resection boundary and their probability of involvement, providing objective communication data for doctors during preoperative consultations. Simultaneously, the system supports virtual simulations of the surgical procedure. Doctors can simulate different resection approaches on a 3D model, observing the impact of different approaches on functional areas and the degree of exposure of deep infiltrating lesions. This virtual surgery function further enhances the scientific rigor and safety of surgical planning.

[0073] Through in-depth optimization of the real-time calibration feedback module, the system can handle complex surgical scenarios, such as sudden situations like midline shift caused by intraoperative tumor bleeding. By rapidly extracting features from real-time images, the module can recalculate the deformation field within seconds, correcting navigation errors. This powerful real-time feedback capability makes the system a reliable assistant for neurosurgeons in complex surgeries.

[0074] The system's overall design philosophy revolves around "precision, dynamism, and balance." Precision is reflected in the micrometer-level registration and voxel-level analysis of multimodal data; dynamism is reflected in the transformation of static images into dynamic models of spatiotemporal evolution; and balance is reflected in the quantitative trade-off between resection efficacy and functional preservation. This interdisciplinary technological integration represents the future direction of precision medicine navigation systems, providing powerful tools to improve the success rate of tumor surgery and enhance patient prognosis.

[0075] This system is not only applicable to preoperative analysis of gliomas, but its core evolutionary simulation and topology reconstruction logic can also be extended to the analysis of other solid tumors with invasive growth characteristics. By adjusting the corresponding tissue characteristic parameters and cell behavior logic, the system has the potential for transfer and application in a wider range of clinical oncology fields. In future iterations, the system will further integrate large-scale pathological data and utilize deep learning technology to automatically calibrate the payoff function in evolutionary dynamics, aiming to achieve higher boundary prediction accuracy.

[0076] This in-depth and detailed description of the implementation method demonstrates that the invention not only exhibits significant methodological innovation but also comprehensive consideration of engineering implementation details. The functional definition of each unit, the parameter settings of each algorithm, and the logical connections between units are all centered around addressing existing clinical pain points. The system's high degree of determinism, objectivity, and scientific rigor gives it a strong technological advantage in the current field of precision surgery. This closed-loop design, from underlying data fusion to high-level decision-making and planning, opens up a new technological path for the intelligent diagnosis and treatment of gliomas.

[0077] The system's reliability is also rigorously guaranteed. During the data acquisition phase, the multimodal image data fusion unit incorporates an automatic quality control mechanism capable of identifying and eliminating sequences with severe motion artifacts or metallic interference. In the simulation phase, the evolutionary dynamics simulation unit employs multi-threaded redundant computation to ensure the numerical stability of complex game processes. In the output phase, the surgical plan optimization unit uses encrypted data links to ensure patient privacy and the security of medical data. These meticulous engineering designs collectively form the foundation for the system's stable, efficient, and secure clinical application.

[0078] In practice, this system significantly reduces doctors' preoperative planning time. Traditional manual interpretation and plan formulation often take several hours or even longer, and it is difficult to quantitatively assess occult infiltration. This system, through a fully automated calculation process, can provide multiple optimized plans based on different risk preferences in a very short time. This not only improves the efficiency of medical resource utilization but also demonstrates a significant decision-making advantage in the handling of emergency and complex cases.

[0079] In summary, the intelligent data analysis system for glioma resection disclosed in this embodiment not only achieves a deep integration of medical imaging, computational science, and tumor biology in its technical architecture, but also provides extremely high convenience and accuracy in practical operation. Through in-depth reconstruction of the tumor microenvironment and scientific prediction of its dynamic behavior, the system truly achieves a leap from "seeing the lesion" to "understanding the lesion," contributing a key technical solution to the refinement and intelligent development of neurosurgery.

[0080] The unit names and terms used in this invention, such as "multimodal image data fusion unit," "microenvironment topology reconstruction unit," "evolutionary dynamics simulation unit," "infiltration boundary determination unit," and "surgical plan optimization unit," all refer to computational modules or hardware processing logic that perform specific functions. Their combination and parameter configuration in specific implementations can be optimized and adjusted according to specific clinical needs and computational environments without departing from the core design principles of this invention. All numerical settings, time steps, physical dimensions, and algorithm thresholds are optimal values ​​based on current biological understanding and engineering practice, aiming to achieve the best balance between prediction accuracy and computational efficiency.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data intelligent analysis system for glioma resection, characterized in that, include: A multimodal image data fusion unit is used to acquire magnetic resonance imaging, diffusion tensor imaging, and perfusion-weighted imaging data of the target patient; The microenvironment topology reconstruction unit is used to determine the orientation of white matter fiber bundles and construct a dominant diffusion vector field to guide the movement of tumor cells by analyzing diffusion tensor imaging data based on a 5-channel feature data array output by the multimodal image data fusion unit. Furthermore, it constructs a resource abundance map that characterizes the intensity of local nutrient supply by performing deconvolution processing on perfusion-weighted imaging data, thereby generating a structural scaffold model that characterizes the physical barrier and nutritional support capacity of brain parenchyma. The evolutionary dynamics simulation unit is used to divide the tumor cell population into migratory clonal subpopulations and proliferating clonal subpopulations under the constraints of the structural scaffold model, and to construct a fitness-based payoff function for each subpopulation. It uses evolutionary game logic to model the spatiotemporal evolution of the migration, proliferation and death behavior of tumor cells. By simulating the competitive process of cell subpopulations under the influence of local resource abundance and selection pressure, it generates a tumor micro-invasion probability distribution field. The invasion boundary determination unit is used to perform three-dimensional Gaussian filtering smoothing on the invasion probability distribution field, delineate the tumor core area, high-risk invasion area and potential affected warning area through adaptive clustering algorithm, and calculate the rate of change of probability gradient to identify the invasion front boundary. The surgical plan optimization unit combines the analysis results of the invasion boundary determination unit with the patient's functional magnetic resonance imaging data to construct a comprehensive objective function that includes resection rate score and functional damage risk score. Using a multi-objective optimization algorithm, it outputs the surgical resection boundary path that maximizes the resection rate score while ensuring that the functional damage risk is less than a second preset threshold.

2. The intelligent data analysis system for glioma resection according to claim 1, characterized in that, The multimodal image data fusion unit uses fluid attenuation inversion recovery sequence images as the reference space, adopts the mutual information maximization criterion for spatial coordinate registration, and performs bias field correction and intensity normalization processing to construct a 5-channel feature data array containing anatomical structure, white matter fiber distribution and blood perfusion information. The process by which the multimodal image data fusion unit constructs a 5-channel feature data array is as follows: Acquire longitudinal relaxation-weighted sequences, transverse relaxation-weighted sequences, and fluid attenuation inversion recovery sequences from magnetic resonance imaging, and extract fractional anisotropy maps from diffusion tensor imaging and cerebral blood flow maps from perfusion-weighted imaging. A method combining affine transformation and nonlinear registration was used to uniformly map the image data of other modalities to the voxel coordinate system corresponding to the fluid attenuation inversion recovery sequence image; Normalized convolution algorithm is used to eliminate brightness non-uniformity and map the signal intensity of different sequences to a uniform 0 to 1 range; By splicing multi-dimensional tensors, feature vectors carrying information on tissue anatomy, water diffusion direction, diffusion degree, local blood supply level, and lesion contrast are constructed for each voxel point in the brain space.

3. The intelligent data analysis system for glioma resection according to claim 2, characterized in that, The process by which the microenvironment topology reconstruction unit generates the structural support model is as follows: The main diffusion direction is determined by solving the eigenvectors and eigenvalues ​​of the diffusion tensor matrix, and the dominant diffusion vector field is constructed by combining fractional anisotropy values. The tracer concentration-time curve in perfusion-weighted imaging was processed using a deconvolution algorithm to obtain the average transport time and local blood volume parameters of microvessels in order to construct a resource abundance map. The cerebrospinal fluid space and intracranial major blood vessels are defined as impenetrable physical boundaries, and different migration resistance coefficients are assigned to different anatomical regions.

4. The intelligent data analysis system for glioma resection according to claim 3, characterized in that, The microenvironment topology reconstruction unit is also used for: By analyzing the attenuation coefficient of the fluid attenuation inversion recovery sequence signal, we can distinguish between vasogenic edema and tumor-infiltrative edema. The diffusion coefficient was increased in the vasogenic edema region, and a higher evolutionary pressure factor was assigned in the infiltrative edema region to correct the parameter inputs in the evolutionary dynamics simulation unit.

5. The intelligent data analysis system for glioma resection according to claim 4, characterized in that, When modeling, the evolutionary dynamics simulation unit divides the analysis region into voxel grid points; The gains of proliferating clonal populations are positively regulated by the local resource abundance map and negatively regulated by the local total cell density. The gains of migratory clone populations are positively correlated with the direction of the dominance diffusion vector field; when the local resource abundance is less than the first preset threshold, the phenotypic switching from proliferating cells to migratory cells is triggered.

6. The intelligent data analysis system for glioma resection according to claim 5, characterized in that, The calculation logic for the profit score of the proliferating clonal subpopulation is as follows: ; The overall benefit score representing a proliferating clonal population. The local resource abundance value, representing the spatial location, is obtained by mapping from the perfusion-weighted imaging data; This represents the total density of tumor cells within the current voxel. This is a preset environmental capacity constant used to limit the maximum number of cells that a single voxel can hold; This represents the baseline value of metabolic consumption that cells use to maintain basic life activities.

7. The intelligent data analysis system for glioma resection according to claim 6, characterized in that, The evolutionary dynamics simulation unit is also used for: Multiple rounds of iterative calculations were performed to simulate the infiltration process from the tumor core to the surrounding brain parenchyma; Record the state of each voxel point invaded by tumor cells in each iteration, and statistically analyze the frequency of invasion of each voxel based on the simulated sequence to form a probability distribution field reflecting the dynamic invasion depth of the tumor.

8. The intelligent data analysis system for glioma resection according to claim 7, characterized in that, The hierarchical region division logic executed by the infiltration boundary determination unit is as follows: Regions with a probability greater than 0.8 are defined as the tumor solid core region; regions with a probability between 0.3 and 0.8 are defined as high-risk infiltration regions. The region with a probability between 0.05 and 0.3 is defined as the potential affected warning zone; and The closed surface formed by the set of maxima of the probability gradient magnitude is the boundary of the biological infiltration front.

9. The intelligent data analysis system for glioma resection according to claim 8, characterized in that, The decision-making logic executed by the surgical plan optimization unit is as follows: Identify key neural functional nodes in functional magnetic resonance imaging data and calculate the spatial Euclidean distance between each functional node and high-risk infiltration areas; The resection rate score in the comprehensive objective function is proportional to the integral of the infiltration probability covered by the preset resection boundary, and the functional impairment risk score is proportional to the probability that the resection path touches the key neural functional area; the optimal closed surface that maximizes the resection rate score is found using a multi-objective optimization algorithm.

10. The intelligent data analysis system for glioma resection according to claim 9, characterized in that, The system also includes: The real-time calibration feedback module is used to receive intraoperative image data during the operation, calculate the non-rigid transformation matrix between the preoperative and intraoperative images, and compensate and calibrate the output surgical resection boundary path. A high-performance parallel computing platform, which includes a graphics processor, for parallel evolutionary game state updates of voxel grid points. The user interface unit is used to display the infiltration envelope under different probability thresholds in the form of isosurfaces, and to update the display of the surgical resection boundary path in real time in response to the adjustment command of the risk preference coefficient.

Images