21 results about "Fiber tract" patented technology

The application discloses a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease, and belongs to the technical field of medical image processing. The application solves the problem that the existing technology relies on manual delineation or traditional machine learning methods for white matter abnormality detection, and has the problems of low efficiency and strong subjectivity. By setting a first threshold value and a second threshold value, not only can the abnormal fiber bundle be identified, but also the required threshold range can be selected according to different research purposes and clinical needs, so that reliable judgment results can be provided in both diagnosis and early screening. If both threshold values are selected, the method can comprehensively judge each parameter of each fiber bundle, so as to more accurately identify the white matter abnormal fiber bundle of the Parkinson's disease patient, improve the accuracy of the fiber bundle labeling result of the white matter abnormalities of Parkinson's disease, realize the function of high-precision positioning of the white matter abnormal fiber bundle of Parkinson's disease, and provide stronger support for clinical diagnosis.

The invention discloses an accurate positioning system based on multi-modal image fusion, and the system comprises a multi-modal image collection module which synchronously collects an MRI structure image, an fMRI function image and a DTI white matter fiber bundle image of the brain of a patient; the image preprocessing module is connected with the acquisition module and is used for de-noising, registering and standardizing the multi-modal image data to generate fused image data under a unified coordinate system; the target analysis module is connected with the preprocessing module and calculates initial coordinates of the target based on the fused data; the dynamic correction module is connected with the analysis module, monitors the head movement in real time and dynamically corrects the target position; and the target spot guiding terminal is connected with the correction module and controls the mechanical arm to accurately position according to the corrected coordinates. According to the system, the transcranial magnetic stimulation target spot positioning accuracy and the personalized adaptive capacity can be remarkably improved, the problems that a traditional method depends on experience and the single-mode image accuracy is insufficient are solved, and meanwhile the positioning stability and the effect evaluability in treatment are ensured.

This invention provides a parameter conversion method and system for multimodal brain network maps. The method includes: acquiring and comparing multiple source brain regions and multiple target brain regions of a single subject; constructing a set of overlapping brain regions between each target brain region and the source brain region; and statistically analyzing the number of white matter fiber tracts and brain functional connectivity coefficients between each set of overlapping brain regions. Based on this, the remapping coefficients of the white matter fiber brain network and the brain functional connectivity coefficient brain network are calculated respectively. The method also includes: acquiring and statistically analyzing the set of overlapping brain regions between the target brain regions and the source brain regions of multiple subjects, as well as the corresponding number of white matter fiber tracts and brain functional connectivity coefficients; calculating the variance of the brain connectivity strength of the first experimental group and the first control group under the source map, and the second experimental group and the second control group under the target map; and weighting and summing the source brain connectivity statistics between the overlapping brain region sets using influence weights to obtain the target brain connectivity statistics between the target brain regions.

A computer-implemented method for performing diffusion tractography of a subject's brain, includes generating, by a computer system that includes at least one processor in communication with at least one memory system, for each canonical fiber tract bundle in a canonical fiber tract model, subject specific regions of interest (ROIs), detecting, by the computer system, a canonical set of fiber tracts in magnetic resonance (MR) data of the subject's brain based on the canonical fiber tract model and the subject specific ROIs, and generating, by the computing system, a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject's brain.

Provided is a technique capable of evaluating cross fibers in a fiber bundle. The method of the present invention comprises the following steps: (a) acquiring a brightness image of a fiber bundle; (b) generating a two-dimensional power spectrum image by performing a two-dimensional FFT analysis on the luminance image data; (c) generating a corrected power spectrum image by performing mask processing on the two-dimensional power spectrum image to remove a mask region corresponding to a luminance image component in which the angle of intersection with the horizontal direction of the luminance image is equal to or less than an angle threshold; and (d) generating a modified luminance image by performing an inverse FFT analysis on the modified power spectrum image.

1. The name of the design product: surgical planning graphical user interface for electronic device. 2. The use of the design product: an electronic device, such as a medical instrument device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface can be used in a medical instrument device, such as with laser treatment, to design a surgical plan on a sequence of images. In the design 5 front view, move the controller (such as a mouse, touch pen, etc.) into the box in the lower right to display design 5 change state figure 1. In the design 6 front view, click the "surgical view" button in the lower right to display design 6 change state figure. In the design 7 front view, click the "sequence registration" button in the upper right to display design 7 change state figure. In the design 8 front view, click the "fusion" button on the right to display design 8 change state figure. In the design 9 front view, click the "fusion" button on the right to display design 9 change state figure 1, and in design 9 change state figure 1, click the "3D model display" button on the right to display design 9 change state figure 2. In the design 10 front view, click the "fusion" button on the right to display design 10 change state figure 1, and in design 10 change state figure 1, click the "3D model display" button on the right to display design 10 change state figure 2, and in design 10 change state figure 2, click the "fiber tract reconstruction" button on the right to display design 10 change state figure 3.

The disclosed system provides an integrated neuroimaging analytics platform for comprehensive preoperative neurosurgical planning, intraoperative neurosurgical guidance and post-operative assessment by processing multimodal MRI (structural, diffusion, functional, and angiography) and CT data. The platform performs detailed anatomical characterization by delineating tumor subregions (peritumoral edema, enhancing tumor, and necrotic core), segmenting brain tissues (gray matter, white matter, and CSF), and executing lobe, cortical / subcortical parcellation. Advanced 3D rendering visualizes tumors alongside critical white matter fibre tracts derived from diffusion MRI, while MRA data is used to segment cerebrovascular structures, and functional MRI analysis identifies eloquent cortices associated with motor, speech, and visual functions. All results are integrated within a user-friendly GUI featuring advanced multiplanar slicing and a smart brush for interactive mask editing, complemented by a speech-to-text engine for streamlined analytical reporting. This comprehensive approach facilitates precise and efficient surgical planning, thereby enhancing patient safety and improving clinical outcomes.

The application relates to the field of stereotactic surgery technology, and discloses a neurosurgical stereotactic surgery positioning system based on multi-modal image fusion, which comprises a multi-modal image acquisition module, an intelligent registration and fusion module, a three-dimensional modeling module, a surgery navigation engine, an augmented reality interface and a dynamic calibration module; through the setting of the cross-modal elastic registration module, when the multi-modal image fusion is carried out, the spatial matching degree of functional images and structural images is detected in real time through a nonlinear deformation compensation algorithm; when there is a registration deviation between the white matter fiber bundle and the tumor boundary, the system dynamically generates an elastic deformation compensation field, the positioning accuracy of the functional area is improved, and the risk of functional area cutting is reduced; through the setting of the dynamic drift correction module, when deep target point navigation is carried out, the brain tissue displacement change is sensed in real time based on laser surface scanning, the accurate correction of the position and posture of a surgical instrument is realized, and the reliability of deep target point positioning is ensured.

Abnormal activity in the brain can be detected by identifying changes in tissue excitability measured in response to stimulation of fiber tracts such as the corpus callosum. A stimulating electrode contact can apply a stimulation to the fiber tract, which can have one or more terminals to a cortex of a patient's brain, and a recording electrode contact positioned in the fiber tract and / or the cortex to record a tissue excitability response to the stimulation. A controller can receive the tissue excitability response from the at least one recording electrode, identify a parameter of the tissue excitability response, and predict the abnormal activity in the patient's brain based on the parameter being different from a baseline for the parameter. When an abnormal activity is predicted the patient can be alerted to start a therapy and / or a therapy can be applied in closed loop manner by a connected therapy device.

This invention discloses a method and system for neural fiber tract clustering and segmentation based on diffuse hypervoxels, relating to the fields of medical image analysis and artificial intelligence. The method includes: receiving and preprocessing three-dimensional brain magnetic resonance imaging (MRI) data; establishing a population-matched personalized MRI brain template using an iterative registration method; constructing a diffusion-weighted undirected graph and segmenting diffuse hypervoxels based on diffusion geodesic distance; clustering fiber tracts based on the maximum clusters of the neural fiber connectivity graph of the diffuse hypervoxels; registering the diffuse hypervoxels to an individual space and using the maximum clusters to achieve personalized fiber tract segmentation; constructing a center curve for the fiber tract clusters and mapping diffusion features to this curve; establishing a statistical analysis method for the center curve based on permutation tests and building a machine learning classification model.

The invention belongs to the technical field of medical image data processing, and particularly relates to a cerebral hemorrhage patient white matter fiber bundle reconstruction method based on a CT image. According to the method, paired CT and dMRI data are utilized to make training data, then a segmentation model and a generative model are adopted to construct a reconstruction network, a trained reconstruction network is obtained after training, and white matter fiber bundle reconstruction is performed by utilizing the trained reconstruction network. According to the cerebral hemorrhage patient white matter fiber bundle reconstruction method based on the CT image, white matter fiber bundle reconstruction can be directly carried out on the CT image without depending on dMRI, and a certain guiding effect on clinical cerebral hemorrhage diagnosis and treatment schemes is achieved.

Exemplary system, method and computer arrangement for determining rotational invariants, fiber orientations, and scalar parameters of fiber tracts (e.g., compartment fractions, which can relate to intra / extra-cellular space volumes; compartment diffusivities; relaxation rates; exchange rates between compartments; characteristics of structural disorder such as axonal beading) from a general diffusion MRI acquisition is described. For example, gradient directions may not necessarily be arranged in so-called shells, and an acquisition may vary spatially. Furthermore, each acquisition can be undersampled in the k-space. A procedure can also be included for receiving information related to the at least one image. Another procedure can be provided for decoupling tissue and protocol parameters based on a singular value decomposition. A further procedure can be provided for grouping singular vectors into multiplets based on symmetries. Still further procedures can be provided for forming rotational invariants and / or for a parameter estimation.

The invention relates to a nerve fiber bundle reconstruction method and system based on streamline tracking. The method comprises the following steps: extracting a diffusion weighting factor and a diffusion gradient vector of a diffusion weighted image and carrying out preprocessing; constructing a linear equation set for diffusion tensor matrix calculation; solving a diffusion tensor matrix through a non-negatively constrained iterative reweighted least square method; carrying out diagonalization processing on the diffusion tensor matrix, and solving a characteristic value and a characteristic vector; according to the solved characteristic value, calculating the fractional anisotropy of each voxel in the diffusion weighted image, and according to the calculated fractional anisotropy, determining an initial threshold value and an end threshold value of streamline tracking; according to the solved feature vector, the initial threshold value and the termination threshold value, streamline tracking is carried out through an adaptive step length Runge-Kutta integration method; and reconstructing the nerve fiber bundle according to a tracking result. According to the invention, more accurate and efficient nerve fiber bundle reconstruction can be realized.

The invention discloses a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormality of Parkinson's disease, and belongs to the technical field of medical image processing. The problems that in the prior art, white matter anomaly detection depends on manual sketching or a traditional machine learning method, efficiency is low, and subjectivity is high are solved, by setting the first threshold value and the second threshold value, the abnormal fiber bundles can be recognized, the needed threshold value range can be selected according to different research purposes and clinical requirements, and the detection accuracy is improved. Therefore, reliable judgment results can be provided after definite diagnosis and in early screening; if the two thresholds are selected to be used at the same time, the method can comprehensively judge each parameter of each fiber bundle, so that the white matter abnormal fiber bundle of the Parkinson's disease patient can be more accurately identified, the accuracy of the marking result of the white matter abnormal fiber bundle of the Parkinson's disease is improved, and the accuracy of the marking result of the Parkinson's disease white matter abnormal fiber bundle is improved. The high-precision positioning function of the fiber bundle for white matter abnormality of the Parkinson's disease is achieved, and more powerful support is provided for clinical diagnosis.

The invention belongs to the cross technical field of biomedical engineering and brain science, and particularly relates to a method for constructing a heterogeneous whole-brain model based on a fusion connection-non-connection effect of a linearization theory, which comprises the following steps of: constructing a heterogeneous whole-brain model, and using structural connection obtained by real fiber bundle tracking as model input; the heterogeneous parameters are compressed based on the real nerve image data, the influence of the non-connection effect on the whole brain model is introduced after parameter optimization is completed, and the non-connection effect is solved in a mathematical simplification mode; therefore, a specific method is provided for constructing an individual-level heterogeneity whole-brain model. According to the method, by introducing real nerve image data, the difficulty of high-dimensional parameter optimization is overcome, and the biological rationality of heterogeneity parameters is enhanced; meanwhile, the speed and the stability of parameter optimization are remarkably improved by adopting a linearization method.

This disclosure relates to an artificial intelligence-driven method, apparatus, device, and medium for simulating and generating white matter fiber tracts based on CT images. The method includes: acquiring a training dataset, which includes CT images of patients with cerebral hemorrhage and diffusion tensor imaging data paired with the CT images; establishing a fiber tract model and generating CST anatomical labels in the diffusion tensor imaging space; mapping the CST anatomical labels in the diffusion tensor imaging space to the corresponding CT space using a linear registration algorithm; annotating the CT images with CST fiber tracking masks based on the CST anatomical labels in the CT space; training a CST prediction model based on the training dataset; and inputting the CT image to be predicted into the CST prediction model to generate a CST prediction mask for the CT image to be predicted. According to the technical solution of this disclosure, damage assessment of key white matter tracts in patients with cerebral hemorrhage can be achieved under resource-constrained conditions.

The application discloses a brain disease classification method and system, which realizes accurate diagnosis through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic functional connection analysis on resting-state functional magnetic resonance timing signals to obtain a time-varying brain network characteristic matrix, and simultaneously performing white matter fiber bundle topological reconstruction on a structural connection matrix; constructing a four-dimensional correlation tensor through a neural dynamics model by using the time-varying network characteristics, the structural connection weight and the anatomical characteristics; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model to output a quantitative diagnosis result; and finally generating a clinical classification report integrating individualized brain network remodeling target points, disease progression risk stratification and treatment response prediction. Through dynamic fusion of structural and functional characteristics, the application realizes comprehensive characterization of the pathological mechanism of brain diseases, and provides a decision support with accuracy and interpretability for clinical diagnosis.

This invention discloses a distance function-based method for coupling the structure-function connectivity of white matter fiber tracts, comprising: acquiring brain imaging data and preprocessing it; extracting structural attributes and functional signals of white matter fiber tracts from the preprocessed brain imaging data; constructing a structural connectivity matrix and an individualized functional connectivity matrix based on the structural attributes and functional signals of white matter fiber tracts; measuring the similarity of connection weights between corresponding columns of the structural connectivity matrix and the functional connectivity matrix using a distance function; and coupling the structure-function connectivity of white matter fiber tracts based on the similarity of connection weights. This method can quantify the relationship between the structure and function of brain white matter fiber tracts and is applicable to research on brain structure and function analysis, brain development, and mental illnesses based on white matter fiber tracts.

The invention discloses an intelligent path planning system for epilepsy lesion resection based on multimode image fusion, and belongs to the field of medical instruments. According to the system, a structure image and a function image are integrated, submillimeter-level precise registration is achieved through a trans-modal feature fusion network based on Transform, and a focus is positioned in combination with intracranial electrophysiological data (the Dice coefficient is larger than or equal to 0.9). An improved A * algorithm is adopted to generate a multi-objective optimization path, path length, brain tissue damage and risk indexes (including 12 constraint conditions including white matter fiber bundle integrity, blood vessel distribution, functional area boundary and the like) are synthesized, and planning time is shortened to 15-20 minutes. A dissection-function two-dimensional risk assessment system is constructed, and the fiber bundle density, the blood vessel distance and the functional area risk are quantitatively analyzed. The surgical navigation module supports 5G real-time transmission and dynamic registration, a correction mechanism is automatically triggered when the deviation exceeds 2 mm, and hardware configuration meets the requirement that the three-dimensional image processing frame rate is larger than or equal to 15 fps.

The invention provides a brain state analysis method and device based on a fiber bundle guide graph auto-encoder, and the method comprises the steps: taking the dissection connection of white matter fiber bundles as a priori constraint, limiting an analysis range in a brain region connected with a specific fiber bundle in a functional connection dynamic graph construction stage, and employing a graph auto-encoder model to construct a functional connection dynamic graph. And the propagation path of the function information in the neural network is constrained by a binary matrix representing anatomical connection, so that deep fusion of the structure function information is realized in nonlinear dimensionality reduction, and finally, the fiber bundle related brain state with clear anatomical attribution and high specificity and the dynamic index thereof are identified. According to the method, the homogenization hypothesis of traditional whole brain state analysis is broken through, the macroscopic function is dynamically and accurately anchored on the microscopic anatomical pathway, and the anatomical specificity and biological interpretability of the recognized brain state are remarkably improved.

The present disclosure relates, inter alia, to a computer-implemented method for dividing a human brain grey matter of a human patient, comprising obtaining a fiber bundle map comprising fiber bundle map streamlines, each streamline having a first end located at a first portion of the grey matter and a second end located at a second portion of the grey matter. The partitioning method further includes using a predetermined clustering algorithm to obtain a plurality of fiber bundle diagram streamline clusters, and for each cluster of at least a portion of the clusters, identifying a respective first grey matter region and a respective second grey matter region, the respective first grey matter region including a first end of each streamline in the cluster, the respective second grey matter region including a second end of each streamline in the cluster. A respective second grey matter region includes a second end of each streamline in the cluster. The partitioning method further includes determining partitions based on the identified regions, including an iterative merging process that includes merging pairs of regions based on the quantized overlapping metrics.