303 results about "Cerebral part" patented technology

A histotripsy therapy system configured for the treatment of brain tissue is provided, which may include any number of features. In one embodiment, the system includes an ultrasound therapy transducer, a drainage catheter, and a plurality of piezoelectric sensors disposed in the drainage catheter. The ultrasound therapy is configured to transmit ultrasound pulses into the brain to generate cavitation that liquefies a target tissue in the brain. The drainage catheter is configured to detect the ultrasound pulses. An aberration correction algorithm can be executed by the system based on the ultrasound pulses measured by the drainage catheter to automatically correct for an aberration effect caused by the ultrasound pulses passing through a skullcap of the patient.

The invention discloses a multi-modal iconography evaluation method suitable for atrial fibrillation cardiac stroke. The method comprises the following steps: acquiring and preprocessing heart and brain multi-modal image data; carrying out multi-modal image registration on the preprocessed data; feature extraction and fusion are carried out on the registered heart and brain multi-mode image data; performing cross-modal feature alignment and fusion on the heart and brain image data; constructing a heart and cerebral vessel integrated evaluation interaction model based on a graph neural network; predicting the risk of the end-to-end cardiac stroke; according to the multi-modal iconography evaluation method, multi-modal heart image data and multi-modal brain image data are combined, a heart and cerebral vessel integrated evaluation framework is established, image features and clinical data are fused through an artificial intelligence method, and end-to-end stroke risk prediction and evaluation are achieved. According to the method, artificial intelligence and medical technologies are comprehensively utilized, and the accurate cardiac stroke assessment method is provided.

The invention discloses a multi-scale brain tumor segmentation method and system based on adaptive KAN, and a storage medium. The method comprises the following steps: preprocessing three-dimensional brain magnetic resonance imaging data; the preprocessed data are input into an encoder, the encoder comprises a plurality of levels, and each level executes convolution operation to extract local features, executes spatial KAN processing to extract spatial features and downsamples a feature map; the output of the encoder is input into a bottleneck layer, and the bottleneck layer captures a multi-scale global context by using a plurality of parallel expansion convolution branches; the output of the bottleneck layer is input into a decoder, the decoder comprises a plurality of stages, and each stage executes transposing a convolution up-sampling feature map, executes cross-scale gating processing to fuse encoder jump connection features and decoder features, and executes spatial KAN processing to optimize features; and the output of the decoder is input into the output module. According to the method, the problems of low calculation efficiency, poor tumor heterogeneity adaptation, insufficient multi-scale context capture and the like in the existing brain tumor segmentation can be effectively solved.

The invention discloses a hidden multi-mode brain state real-time monitoring and closed-loop intervention system, and relates to the technical field of intelligent wearable equipment. The system comprises a cap body, and further comprises a multi-point electrode array used for collecting EEG (electroencephalogram) signals, EOG (eye movement) signals and EMG (electromyography) signals of a human body; the brain blood oxygen monitoring module is used for monitoring brain blood oxygen saturation; the signal receiving and processing module is used for preprocessing and analyzing data acquired by the multi-point electrode array and the brain blood oxygen monitoring module; the multi-mode stimulation module is used for implementing sensory and nerve stimulation on the user according to a judgment result of the state judgment module so as to intervene in a drowsiness or attention decline state; and the power supply module and the communication unit are used for providing a working power supply for the modules needing power supply in the system and carrying out data communication with external terminal equipment. The system can monitor the waking degree of the user with high precision and carry out closed-loop feedback intervention by various stimulation means.

The invention relates to the technical field of internet big data, in particular to a cerebral hemorrhage space positioning and prognosis prediction network system based on three-dimensional point cloud. The cerebral hemorrhage space positioning system comprises an image acquisition module used for acquiring a brain CT image to be processed; the offset correction module is used for performing offset correction on the brain CT image to obtain a corrected brain CT image; the hematoma region segmentation model is used for segmenting the corrected brain CT image to obtain a hematoma region; the point cloud calculation module is used for calculating three-dimensional space coordinates of hematoma voxels in a hematoma area in a brain coordinate system based on the corrected brain CT image and taking the three-dimensional space coordinates as point cloud data of the hematoma area; and the point cloud data output module is used for performing normalization processing on the point cloud data of the hematoma area to obtain hematoma three-dimensional point cloud data representing the hematoma three-dimensional space position. According to the method, an accurate and comprehensive data basis is provided for cerebral hemorrhage prognosis prediction, and the accuracy and comprehensiveness of subsequent cerebral hemorrhage prognosis prediction are improved.

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

The invention provides an eye control interaction method, device and system based on eye movement tracking, and relates to the technical field of data processing. The method comprises the following steps: collecting eye movement data and user feature data of a testee; the user feature data comprises age, pupil color and eye disease type; extracting eye movement feature data according to the eye movement data and the user feature data; generating brain activity state information of the testee according to the eye movement feature data, and determining an eye control instruction of the testee according to the brain activity state information, the eye movement feature data and the user feature data; and performing eye control interaction based on the eye control instruction. According to the method, the characteristics of brain activity states, eye diseases and the like possibly existing in different types of users can be considered, the user feature data are collected, feature extraction is carried out in combination with the eye movement data, and the eye movement feature data accurately reflecting the real eye movement intention of the user is obtained, so that the obtained eye control instruction is adaptive to different users; while the eye control interaction accuracy is improved, smoother and more natural interaction experience is brought to the user.

The invention relates to the technical field of medical instruments, in particular to a sleep monitoring system based on a non-invasive brain oxygen saturation monitor, which comprises a monitoring system and is used for synchronously and dynamically monitoring local brain oxygen in real time, and the monitoring system comprises a non-invasive brain oxygen dynamic monitoring unit used for monitoring the brain blood oxygen saturation of a patient in real time by adopting a near infrared spectrum technology; the multi-parameter synchronous analysis unit is used for integrating the brain oxygen data, the electroencephalogram signals, the cerebral blood flow data and the respiratory event indexes; the intelligent risk assessment engine analyzes a brain oxygen change rule based on a deep learning algorithm, and predicts a brain function impairment risk caused by sleep breathing disorder; in the intelligent risk assessment engine, on the basis of a space-time convolutional neural network, brain oxygen time sequence data and spatial distribution characteristics are combined, and a brain hypoxia mode of the OSA patient is recognized; the method has the characteristic of realizing early warning of the brain injury related to sleep breathing disorder through noninvasive brain oxygen monitoring, multi-parameter fusion analysis and intelligent risk assessment.

The invention discloses a blood volume detection device and method based on a brain angiography image, and relates to the technical field of blood volume detection of the brain angiography image, the blood volume detection device comprises a detection device body and a substrate, the top surface of the substrate is fixedly provided with a lifting platform, and the surface of the top end of the lifting platform is fixedly provided with a workbench; and the linear motor is installed in the middle of the top face of the workbench, and a movable bed plate is horizontally and fixedly installed on the movable end face of the top of the linear motor. According to the blood volume detection device and method based on the brain angiography image, displacement compensation closed-loop control is achieved through the fine adjustment feedback module of the adjusting mechanism, and the gear meshing clearance compensation error is reduced; the LSTM network and the hybrid FEM-LBM algorithm are cooperatively optimized, the blood flow calculation speed is multiplied, an isolation telescopic sleeve shell and a shielding plate are arranged to form a composite shielding layer, the radiation leakage amount is reduced, the radiation monitoring module achieves real-time alarm of dose exceeding, and the protection level is improved.

The invention discloses a cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The invention discloses a method for performing micro-hemorrhage focus recognition on brain images by using a deep learning algorithm, which comprises the following steps of: performing brain skull removal operation on magnetic sensitive weighted imaging images and phase images of a plurality of patients by using an HD-BET tool to obtain an intracerebral region map in a 2D image format; the method comprises the following steps of: firstly, firstly, carrying out brain detection on a brain region map, then, carrying out mesh division on each brain region map, dividing the brain region maps into mesh region maps with the same size, and then, training a YOLOv8 target detection model and a 3D-CNN network model by utilizing the mesh region maps, so that micro-hemorrhagic spots can be quickly predicted through the YOLOv8 target detection model, and then false positive micro-hemorrhagic spots are eliminated through the 3D-CNN network model. And a final micro-bleeding point detection result is obtained.

The invention relates to a brain blood oxygen monitoring method and device, and relates to the technical field of biomedical engineering.The method comprises the steps that a light attenuation signal generated when near-infrared light penetrates through the brain and a motion signal of the brain are synchronously collected, the motion signal and the light attenuation signal are coupled, and an infrared light-acceleration coupling signal is formed; preprocessing the infrared light-acceleration coupling signal to obtain a pure coupling signal; filtering the pure coupling signal through a quality evaluation mechanism to obtain an effective coupling signal; the effective coupling signals are input into a dual-wavelength ratio model, and the percutaneous blood oxygen saturation is calculated by fusing an experience calibration equation. By improving the algorithm, the accuracy of near-infrared brain blood oxygen monitoring can be effectively improved, and the signal error caused by head shaking can be reduced in combination with the motion monitoring function of the three-axis acceleration sensor.

The invention relates to the field of small cerebral vessels, in particular to a medical image-based early feature analysis method and system for small cerebral vessels. The method comprises the following steps: acquiring a brain magnetic resonance image data set, preliminarily analyzing brain small vessel local features based on the brain magnetic resonance image data set, and generating a basic brain small vessel feature set; obtaining a clinical parameter set of the patient, analyzing the recessive influence factors according to the clinical parameter set of the patient, and generating a brain small vessel optimized feature set based on the basic brain small vessel feature set and in combination with the recessive influence factors; and performing visual fusion rendering according to the brain small vessel optimized feature set, and generating and outputting a record analysis log. In the process of early feature analysis of small cerebral vessels, doctors are assisted in accurately identifying the enlargement of the gaps around the vessels and simulators thereof, the misjudgment risk is effectively reduced, and the accuracy and reliability of early diagnosis of small cerebral vessel diseases are improved.

The invention provides a deep brain stimulation method and system based on controllable current vectors of high-channel-number electrodes, and the method comprises the following steps: implanting two electrodes into a target region, and enabling a nucleus of the target region to be located between the two electrodes; by selecting different contacts on the stimulating electrodes, multiple channels of induced currents in different directions are generated in a plane constructed by the two electrodes; vector current is formed through vector superposition of the induction current of each channel, and the vector current acts on the target nuclei. The high-channel-number electrodes are adopted, programming control is carried out between the two electrodes, accurate control over current vectors is achieved, control over the magnitude and direction of target region nuclei stimulation current is accurately achieved through current vector superposition, therefore, a brain lesion area can be stimulated more accurately, meanwhile, the current vectors can be accurately controlled, and the stimulation effect of the brain lesion area is improved. And unnecessary current is prevented from diffusing to surrounding normal brain tissues, so that side effects caused by stimulation of a non-target area are reduced.

The invention discloses a cerebral hemorrhage volume measurement method and related equipment. The method comprises the following steps: acquiring brain medical image data to be measured; inputting the brain medical image data into a three-dimensional segmentation network for cerebral hemorrhage region segmentation to obtain a cerebral hemorrhage region image; identifying a cerebral hemorrhage stage of the cerebral hemorrhage area image according to the cerebral hemorrhage area image; constructing a standard hemorrhage template based on the cerebral hemorrhage stage and the lesion anatomical position in the cerebral hemorrhage area image; performing gradient response analysis based on the feature map of the last convolutional layer in the three-dimensional segmentation network to generate an interpretation map; performing spatial similarity analysis based on the standard bleeding template and the interpretation graph to obtain a consistency score between the standard bleeding template and the interpretation graph; according to the cerebral hemorrhage area image, calculating an initial cerebral hemorrhage volume through a preset dual-path estimation model; and adjusting the fusion weight of the dual-path model according to the consistency score, and determining the target cerebral hemorrhage volume according to the adjusted fusion weight and the initial cerebral hemorrhage volume.

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

The invention discloses a cerebral hemorrhage heterogeneity quantitative evaluation system based on cyclic cross-attention clustering, and relates to the field of cerebral hemorrhage evaluation, and the system comprises an image segmentation module which is used for carrying out the three-dimensional segmentation of a brain plain-scan CT image through an nnU-NetV2 model, and outputting the binary masks of a hematoma region and an edema region; the feature extraction module is used for extracting high-dimensional pixel features from the mask region and generating an initial feature embedding matrix; the cyclic cross-attention clustering module is used for dynamically updating a clustering center through a multi-scale cyclic EM cross-attention mechanism and executing clustering analysis on the feature embedding matrix; the heterogeneity scoring module is used for calculating information entropy and generating hematoma and edema heterogeneity scores; and the prognosis prediction module is used for predicting the hematoma expansion risk and the neural function outcome through a machine learning model. According to the scheme, objective quantification of cerebral hemorrhage heterogeneity can be realized, and the prediction accuracy of the hematoma expansion risk and the neural function outcome is improved.

The embodiment of the invention discloses a brain operation path determination method and device, equipment, a medium and a medical system. The brain operation path determination method comprises the steps that a brain structure model and at least one target spot of a target user are obtained; based on the brain structure model, determining an information transfer relationship between each nuclear group sub-model and each nerve fiber sub-model; determining a plurality of candidate operation schemes based on the plurality of preset path starting points, the target group sub-model to which the at least one target point belongs and the information transfer relationship; for each candidate operation plan, performing quality evaluation on the candidate operation plan based on a quantitative evaluation model under at least one evaluation dimension, and determining a path quality attribute corresponding to each candidate operation plan; according to the method, at least one target recommended operation scheme is determined based on each path quality attribute, a plurality of candidate paths conforming to nerve conduction characteristics are automatically generated, and an optimal scheme is screened through a quantitative evaluation model, so that the brain tissue injury risk and the operation complexity are effectively reduced while the operation precision is improved.

Methods and systems confirm the treatment focus location of ultrasound energy to be delivered to a patient from a plurality of transducers. The location of focus of treatment ultrasound may be confirmed prior to the start of ultrasound therapy. In some embodiments, ultrasound therapy may comprise delivering treatment ultrasound energy from the treatment ultrasound transducers through the skull to the brain of a patient. Methods for confirming the treatment ultrasound focus location comprise determining receive focus profiles ϕx,v<sub2>i < / sub2>for particular candidate three-dimensional (3D) treatment focus locations and particular assumed ultrasound propagation velocity profiles; performing a pre-treatment (e.g. low-power) ultrasound transmission into the brain of the patient; receiving return signals; and determining the location of treatment focus based at least in part on the received return signals and the receive focus profiles ϕx,v<sub2>i< / sub2>.

The present invention discloses a digital twin brain projection method with individual intrinsic structural characteristics, which is applied to the field of biomedical engineering. Aiming at the problem that the existing digital brain cannot reflect the individual intrinsic characteristics of the user to be analyzed, the present invention uses a fresh corpse with a death time less than 2 hours and no lesions or injuries in the head tissues, which is closest to a normal human body; constructs a head brain voxel multimodal image dataset and a human head tissue projection dataset; then collects the head brain voxel multimodal image data of a normal human body, and based on the established projection data index dataset, automatically establishes the brain tissue mapping function of this normal human body to achieve individual intrinsic brain voxel segmentation and definition; then uses the cloud point reconstruction technology to realize the reconstruction of the individual intrinsic brain, so as to systematically complete the digital twin brain projection with individual intrinsic characteristics.

Systems and methods for non-invasively measuring intracranial in accordance with embodiments of the invention are illustrated. One embodiment includes a method for estimating an intracranial pressure of a patient, including introducing a tracer into the blood of a patient, recording, using an imaging device, the traversal of the tracer through a brain of the patient as a kinetic curve, estimating a mean transit time (MTT) of the tracer through the brain of the patient based on the kinetic curve, measuring a mean arterial pressure (MAP) of the patient, calculating an intracranial pressure index as MAP−Ck / MTT, where C is a constant to scale proportional relationships based on healthy patient population characteristics, and k is a constant in pressure / time, and providing the intracranial pressure index. In various embodiments, regions of the brain unaffected by transient vascular abnormalities are identified and are used for data collection.

The invention discloses a human brain specific fluid flow direction identification method comprising the following steps: S1, multi-input network model construction: using a decoder to segment a task, and introducing a boundary detection task; high-density lesions and surrounding edema in the medical image are accurately segmented according to the Vi < T > and an attention mechanism; s2, fluid mechanics model construction and parameter setting, focus classification and risk quantification: optimizing time windows of two times of CT scanning; constructing a three-dimensional geometric model of high-density lesions, peripheral edema and normal brain tissue RO I; constructing initial physical field distribution; setting boundary conditions corresponding to a vascular rupture source and a focus drainage area; and dynamically adjusting the time step length based on the flow velocity and the diffusion coefficient. The invention provides a human brain specific fluid flow direction identification method, which combines image high-dimensional features with a multi-physics field coupling model, and reveals the heterogeneity of human brain specific fluid-blood conversion and contrast agent leakage in absorption and peripheral edema expansion modes.

The utility model provides a head, neck, chest and waist fixer, including front paste fixing piece, back paste fixing piece and binding piece, the front paste fixing piece includes paste chest part, first side waist part, first neck part and face part, the first side waist part extends from the both sides of paste chest part and is used for fitting waist part, and the back paste fixing piece is used for binding the first neck part and the face part. The first neck part extends upwards from the top of the chest fitting part and is used for fitting the neck, the face part extends upwards from the first neck part and is used for fitting the outline of the face, an opening is formed in the front face of the face part, the eyes, the nose and the mouth are exposed through the opening, and the opening is in a T shape. The head, neck, chest and waist fixator has the advantages that the front patch fixing part and the rear patch fixing part are integrally formed, cheekbones and eyebrow bones are used as supporting points on the face, the head, the neck, the chest and the waist are fixed together under the support of the back brain, and the head, neck, chest and waist fixator is better in fitting performance, more stable in supporting point position and higher in comfort.

The invention relates to the technical field of neurosurgery navigation, in particular to a neurosurgery brain puncture path accurate planning method and system.The neurosurgery brain puncture path accurate planning method comprises the following steps that by obtaining a preoperative brain function area image and nerve fiber bundle data, a function area range and sensitivity are extracted, a focus and cranial entry point path set is constructed, and a path set is established; the method comprises the steps of analyzing the distance between a path and a functional area and a fiber bundle included angle, calculating a functional interaction risk, dividing path sections and calculating the distances to a blood vessel, a ventricle and the functional area, generating obstacle field intensity and calculating a gradient difference, constructing a risk map, scoring candidate paths, and screening an optimal path to generate a puncture result. The method comprises the following steps: extracting a brain function region range and a fiber bundle direction, introducing a function sensitivity parameter, improving tiny damage identification, calculating obstacle field intensity in a path segmentation manner, forming a gradient, dynamically identifying a high-risk region, grading and screening candidate paths by integrating risk factors, performing function protection and risk reduction, and improving path planning refinement and safety.

The invention discloses a brain lesion detection method and system based on multi-modal data, and relates to the technical field of brain disease detection. The invention discloses a brain lesion detection system based on multi-modal data. The brain lesion detection system comprises a brain lesion detection triggering module, a brain lesion detection primary screening module and a brain lesion detection secondary screening module. According to the method, screening is performed in combination with current and historical treatment records, so that potential brain lesion clues can be found earlier, and the possibility of early diagnosis is improved; a brain variation feature extraction model is combined with a disease-medication-parameter element path and three attention channel feature extraction method, so that lesion features can be analyzed more comprehensively and deeply, and the accuracy of preliminary screening is improved; a multi-stage elimination mechanism is introduced through a brain transformer accurate re-screening model, and parallel mixed feature analysis and time-space data are combined, so that misjudgment can be effectively reduced, and the accuracy of final diagnosis is improved.

The invention relates to a magnetoencephalography registration method, a magnetoencephalography registration device, magnetoencephalography registration equipment, a storage medium and a program product. Generating a helmet face model including the first facial feature of the testee and the first marker feature corresponding to the marker arranged on the brain magnetic helmet; according to the brain nuclear magnetic data of the testee, generating a scalp face model including a second facial feature of the testee; according to the design parameters of the brain magnetic helmet, acquiring a helmet device model comprising second marker features corresponding to the plurality of markers; registering the helmet face model with the scalp face model according to the first facial feature and the second facial feature to obtain a registered helmet face model including the registered first marker feature; and according to the registered first marker feature and the second marker feature, registering the helmet device model with the registered helmet face model to obtain a registered helmet device model.

The utility model discloses an auxiliary device for achieving simultaneous single-photon and double-photon imaging of the brain and the spinal cord of a mouse, and relates to the technical field of experimental devices.The auxiliary device comprises a brain imaging auxiliary plate, a spine clamping piece and a spinal cord imaging auxiliary plate, a first water containing groove is formed in the middle of the brain imaging auxiliary plate, and a brain imaging auxiliary piece is fixed to the brain of the mouse in an adhesive mode; the spine clamping piece comprises a clamping bottom plate and a pair of clamping plate assemblies, and the clamping plate assemblies can get close to each other in the same direction and get away from each other in opposite directions to clamp and loosen the spine of the mouse; the spinal cord imaging auxiliary plate is detachably arranged on the clamping plate assembly, and a second water containing groove is formed in the middle of the spinal cord imaging auxiliary plate. The brain imaging plate is attached and fixed to the brain of the mouse, the spine clamping piece clamps the spine of the mouse, the spinal cord imaging auxiliary plate is fixed to the spine clamping piece, the spinal cord imaging auxiliary plate is attached to the spine of the mouse, the first water containing groove and the second water containing groove are used for containing water, and therefore groove milling in the skull and the spine of the mouse can be avoided; and the operation difficulty and the operation risk are greatly reduced.

The utility model relates to the technical field of headbands, in particular to a brain protection headband, which is used for surrounding and fixing along the head and comprises a headband body, an application band and an outer band, the application band and the outer band are fixedly connected, and the band surface of the application band is provided with a convex ball used for being applied to the head; the rubber inner pad is used for providing shaping support and is arranged in the headband body, and a third embedding cavity is formed in the rubber inner pad; the sweat-absorbing clamping pad is mounted at the third embedding cavity; according to the brain protection headband, a strip-shaped structure is adopted to replace an existing annular headband, so that the binding force of the headband is convenient to adjust; the shaping capability of the head band can be improved through the rubber inner pad arranged in the head band; in order to improve the wearing comfort, the sweat-absorbing clamping pad is arranged for absorbing sweat; in addition, a convex ball used for being applied to the head is arranged on the belt face of the application belt and can be matched with a strip-shaped structure to adjust the brain pressing force, the adjustability is high, and the application range is wider.

A therapeutic brain stimulation system comprises several stimulation signal emitters (I) generating stimulation signals from different positions towards a common target region in a patient's brain. A kit and a method are provided for mounting the system on a patient's head. The kit comprises a support structure (6) to be temporarily mounted on the patient's head as an aid for fixing the signal emitters (I) in the cranial bone structure. The support structure (6) has support legs (13a - 13c) for temporarily fixing the support structure (B) on the patient's head and guide holes (7) for aiding in guiding an orientation pin (0), a driver (3), a drill guide (B), a drill (D), a thread cutter (T) and the signal emitters (I) towards and against the patient's head.

The invention discloses a perfusion imaging method and device for low-rotating-speed imaging equipment, a medium and a product, and relates to the field of medical image processing. The method comprises the following steps: acquiring plain-scan projection data and time sequence projection data of a brain interested body layer; determining a target time decay curve associated with the interested cerebral artery according to the time sequence projection data; determining a difference value between projection data corresponding to each scanning moment in the time sequence projection data and the plain scanning projection data, and taking the difference value as target projection data of the corresponding scanning moment; determining a target independent variable capable of enabling the target projection data and the forward model projection data to meet a difference minimization condition, and determining a perfusion parameter diagram combination based on the target independent variable; the forward model projection data is determined based on a target time decay curve and an independent variable, and the independent variable comprises a cerebral blood flow chart to be optimized and an average passing time chart. According to the invention, the accuracy of the perfusion parameter diagram combination determined based on the projection data acquired by the low-rotating-speed imaging equipment can be improved.