69 results about "Spatially adaptive" patented technology

The invention relates to the technical field of image compression, in particular to a hierarchical multi-context space adaptive image compression method. The method comprises the steps of obtaining a training set and a test set; constructing a spatial adaptive feature modulation network, wherein the spatial adaptive feature modulation network comprises an encoder, a decoder, a super-prior encoder and a super-prior decoder; constructing a multi-context joint entropy estimation model, wherein the multi-context joint entropy estimation model comprises a local context model, a channel context model, an in-layer anchor point context model and a cross-layer global context model; obtaining an image compression network in combination with the spatial adaptive feature modulation network and the multi-context joint entropy estimation model; respectively training and testing an image compression network by using the training set and the test set to obtain a trained image compression network; and inputting a to-be-processed image into the trained image compression network to obtain a reconstructed image. According to the invention, the image compression effect is improved.

The invention provides an exhibition hall dynamic layout planning method and system based on three-dimensional modeling, and relates to the technical field of simulation modeling, and the method comprises the steps: building a layout simulation model of exhibition hall layout planning; performing association behavior response constraint on a layout evolution topological structure in the layout simulation model according to a cross correlation coefficient of display response characteristics among different display units to obtain a layout constraint condition among the display units; determining the spatial adaptability loss of each display unit under the condition of displaying different contents according to the change characteristics of the space utilization rate of each display unit under the condition of displaying different contents and the behavior feedback characteristics of the audience when each display unit displays different contents; and target optimization is carried out on the spatial streamline of each display unit in the target exhibition hall based on the layout constraint condition and all the spatial adaptability losses, and an optimal structure path for dynamic layout planning of the exhibition hall is obtained. According to the method, cost optimization of the exhibition hall layout structure can be realized based on the simulation model, so that the robustness of dynamic layout planning of the exhibition hall is improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an aerial work six-degree-of-freedom mechanical arm and a self-adaptive anti-interference control method and system.The self-adaptive anti-interference control method comprises the steps that a mechanical arm structure is manufactured through an arm body designed in a carbon fiber topological optimization mode and a joint shell provided with a flow guide groove; constructing a dynamic disturbance observer based on a DDPG algorithm, outputting a dynamic compensation amount through the dynamic disturbance observer, calculating a wind power disturbance value in combination with a disturbance compensation algorithm, and integrating the wind power disturbance value to a control law of the unmanned aerial vehicle; adjusting impedance parameters on line based on a TD3 algorithm; and generating a real-time obstacle avoidance path based on an optimized multi-target PPO path planning algorithm, and executing a path planning task of the corresponding real-time obstacle avoidance path. The unmanned aerial vehicle has the advantages that the wind disturbance resistance is remarkably enhanced, the load sudden change stability is remarkably optimized, the task energy consumption is remarkably reduced, the narrow space adaptability is remarkably improved, and the light weight is achieved.

The invention discloses a code rate adaptive image compression method and system, and the method comprises the steps: obtaining an input image, and calculating the local information entropy of the input image to generate a spatial adaptive mask; obtaining target code rate and actual code rate information to generate a code rate control signal; and dynamically selecting a calculation path by using a mask through a dynamic reversible neural network, and executing reversible quantization transformation according to the control signal to obtain a compressed bit stream and actual code rate information for closed-loop feedback. According to the method, a symmetric reversible framework with shared parameters is constructed, so that the model storage requirement is reduced; the calculation depth of the network is dynamically adjusted through entropy sensing gating, and the overall calculation amount is reduced; and a composite control and PID optimization mechanism is adopted, so that continuous and smooth code rate control is realized. The deployment and application efficiency of the compression system in a resource limited scene can be effectively improved.

The invention relates to the field of image processing, in particular to a circuit board microsection image defect detection method, which comprises the following steps: acquiring a circuit board microsection image, calculating local direction energy of each pixel point in the image in a preset scratch direction, and generating a direction energy diagram; calculating a direction continuity index of each pixel point by analyzing neighborhood energy distribution of each pixel point in the preset scratch direction; according to the direction continuity index, constructing a spatially adaptive scratch similarity for each pixel point; and on the basis of the scratch similarity, executing spatial adaptive anisotropic filtering processing on the circuit board microsection image so as to realize accurate detection of circuit board defects. According to the method, by intelligently recognizing the structural difference between the scratches and the defects, the background scratches are accurately removed, meanwhile, the contour integrity of the real defects is kept, and the accuracy and reliability of microsection defect detection are remarkably improved.

The invention discloses an open vocabulary target detection method and device, equipment and a storage medium. Comprising the following steps: inputting image data into a visual feature extraction module to obtain multi-scale visual features; obtaining a text embedding vector generated by precoding, wherein the text embedding vector is generated by a text encoder based on offset fine tuning; inputting the multi-scale visual features and the text embedding vectors into a sparse hybrid expert detection head module, and outputting sparse hybrid features; and obtaining a target detection result based on the sparse mixed features. In the reasoning stage, all types of texts can be pre-coded into static embedded vectors, the static embedded vectors are directly input to the detection process, complete text branches do not need to be operated, and the reasoning speed and the resource efficiency are greatly improved. And a sparse hybrid expert detection head is provided, which can dynamically select and activate the most adaptive expert sub-network according to the characteristics of the image region, thereby improving the spatial adaptability and modeling ability of the model, and maintaining the high efficiency and low delay of reasoning.

The invention relates to the technical field of image rain and snow removal, in particular to a feature-driven GAN-based self-adaptive snow and rain removal method, which comprises the following steps: S1, constructing a feature extraction network architecture oriented to complex degradation features; s2, a spatial self-adaptive layered constraint loss system is established; s3, establishing a double-path reasoning framework of multi-stage collaborative optimization; and S4, constructing an industrial-grade credible evaluation system. The rain / snow removal frame based on the generative adversarial network is innovated in the aspects of generator architecture, loss function design and cooperative training of the generator and the discriminator, and by introducing a double-generator structure, an attention mechanism and space guide loss, the recovery performance of the rain and snow degraded image is remarkably improved; an effective solution is provided for practical application, and through the innovations, the model shows an excellent image recovery effect in a complex rain and snow scene.

The invention discloses an echocardiogram myocardial motion estimation method and system based on a structure perception diffusion model, and relates to the technical field of ultrasonic video data processing. The invention aims to solve the problem of myocardial motion estimation in an ultrasonic video. According to the technical key points, the method comprises the following steps: (1) preprocessing collected echocardiogram video data; (2) establishing an echocardiogram myocardial motion estimation model; (3) constructing a diffusion model motion estimation module of structure perception; (4) constructing a spatial adaptive up-sampling module; (5) training the network by using the existing data to obtain a network model; and (6) estimating the myocardial movement of the echocardiogram video by using the trained model. According to the method, the cardiac structure information and the multi-scale correlation contained in the echocardiogram can be fully utilized to automatically estimate the cardiac muscle movement of the echocardiogram, and the accuracy and the robustness of the algorithm are effectively improved.

The invention provides a global vegetation canopy moisture content inversion method, which belongs to the field of remote sensing geosciences, and comprises the following steps: acquiring vegetation character data in a global vegetation character database, and counting the distribution condition and correlation of each character in different vegetation types; training samples of different vegetation types are generated through forward simulation; performing convolution conversion on the simulated spectrum data by adopting a spectrum response function to obtain an equivalent spectrum; evaluating feature combinations of different spectrums and vegetation indexes, and determining an optimal feature combination and a machine learning algorithm suitable for different vegetation types; a corresponding inversion model is adaptively called according to the vegetation type and the canopy structure, and remote sensing estimation of the global vegetation canopy moisture content is achieved. According to the method, the canopy moisture content of different vegetation types can be efficiently inverted to generate a global vegetation canopy moisture content remote sensing product with space adaptability and relatively high precision, and data support is provided for applications such as ecological environment monitoring, drought evaluation and carbon-water flux simulation.

The invention discloses a data management system based on unmanned aerial vehicle surveying and mapping, and particularly relates to the technical field of data management. The method comprises the steps of collecting unmanned aerial vehicle surveying and mapping data, and performing space coordinate registration and unified coding; performing spatial region division according to the terrain elevation variable coefficient and the obstacle projection coverage rate to obtain a high-density verification region and a low-density verification region; for the high-density verification area, calculating the feature matching degree of an overlapping area of adjacent frames through a local feature matching algorithm, and outputting first correction data; performing data compensation on the low-density verification region by adopting spatial interpolation and cubic spline interpolation to generate second correction data; generating a full-scene reconstruction data frame based on the first correction data and the second correction data; based on full-scene data frame reconstruction, region weight factor distribution, differential error correction coding execution and unmanned aerial vehicle surveying and mapping result data output, the spatial adaptability of data verification is effectively improved, and the overall accuracy and integrity of unmanned aerial vehicle surveying and mapping data are improved.

The invention discloses an in-situ high-temperature and high-pressure visible reaction kettle capable of considering the action of gravity and application of the in-situ high-temperature and high-pressure visible reaction kettle, and belongs to the technical field of multiphase fluid mechanics experimental equipment. According to the in-situ high-temperature and high-pressure visible reaction kettle provided by the invention, in-situ simulation of a gravity vector under a non-rotation condition is realized by utilizing linkage and inclination angle adjustment (0-90 degrees) of the optical fiber coupling laser light source and the model clamping device; the polygon prism reflection system corrects light path offset in real time to form an orthogonalization collimation light path, and the observation view field stability and the data accuracy in the processes of oil-water separation, CO2 diffusion and the like in the high-temperature and high-pressure environment are ensured. Besides, through a split type hoop body and a rigid locking sealing structure, the alternating stress effect of rotating torque on a sealing interface is eliminated, the high-pressure dynamic sealing failure risk is remarkably reduced, meanwhile, the device size is simplified, interference of peripheral equipment is avoided, and the experiment safety and the space adaptability are improved.

The invention relates to the technical field of medical image processing, in particular to a spatial self-adaption and thermodynamic diagram guided retina image segmentation method, which comprises the following steps of: acquiring retina image data containing three-dimensional information and at least one focus, optimizing an existing SAM model, and constructing a segmentation model, the architecture of which comprises the steps of taking a visual Transform as a backbone and taking a thermodynamic diagram as a template; designing and introducing an image encoder of a spatial adaptive feature enhanced Transform block, a dual-stage mask decoder and a thermodynamic diagram guided mask self-attention module, adopting a training process of a joint loss function supervision model based on cross entropy loss and Dice loss, and inputting an image to be segmented into the model to obtain a segmentation result; the scheme has the advantages that the three-dimensional structure information of the retina image can be fully mined, the segmentation precision of different types of lesions is remarkably improved, the problem of class imbalance is relieved, and high-precision and high-efficiency segmentation of the retina lesions is realized.

The invention relates to the technical field of sensing navigation, in particular to a patrol robot based on sensing obstacle recognition, which comprises a path dynamic priority module, a high-flow avoidance module, a bedside dynamic area module, an approaching risk monitoring module and a path coverage state module. According to the method, node pressure distribution is adjusted and optimized through dynamic priorities, path flexibility is improved, a self-adaptive avoidance area is defined synchronously based on a bed displacement trend and a safety redundancy range, the path congestion risk is reduced, direction deviation is controllable, detour redundancy is reduced, the dynamic avoidance area shrinks or expands along with environment changes, and space adaptability is enhanced. The approach risk level is combined with the angle change rate and the distance decay rate to quantify the potential risk, the coverage overlapping proportion threshold linkage turning freezing mechanism prevents path conflicts, the multi-dimensional decision basis improves the obstacle avoidance response speed and safety, and compared with a static rule, the adaptability to ICU high-frequency environment changes is enhanced, and the method has the advantages of being simple in structure and convenient to operate. A pressure redistribution and risk grading mechanism reduces obstacle avoidance delay.

The present invention discloses a rate-adaptive image compression method and system, which includes: obtaining an input image, calculating its local information entropy to generate a spatially adaptive mask; obtaining target bit rate and actual bit rate information to generate a bit rate control signal; using a dynamic reversible neural network, dynamically selecting a calculation path using a mask, and performing a reversible quantization transformation based on the control signal to obtain a compressed bit stream and actual bit rate information for closed-loop feedback. The present invention reduces model storage requirements by constructing a symmetric reversible architecture with parameter sharing; dynamically adjusts the network calculation depth using entropy-aware gating to reduce the overall computational complexity; and adopts a composite control and PID optimization mechanism to achieve continuous and smooth bit rate control. It can effectively improve the deployment and application efficiency of compression systems in resource-constrained scenarios.

The application relates to the field of image data processing and discloses a texture preservation type image denoising and enhancing method based on a generative adversarial network, which comprises the following steps: collecting an original image signal, calculating low-frequency subband data and high-frequency subband data by using discrete wavelet transform; calculating the gradient amplitude of the low-frequency subband data to generate a structure saliency gradient map; establishing an inverse mapping relationship based on the structure saliency gradient map to generate a spatially adaptive dynamic gating threshold; performing statistical gating on the high-frequency subband data by using the dynamic gating threshold to generate a high-pass gain coefficient and a low-pass suppression coefficient; and weighting and filtering the high-frequency subband data by using the coefficient and reconstructing a target output image. The application solves the problem that weak texture signals are prone to be lost under non-uniform illumination by establishing a cross-band modulation logic of structure flow to texture flow, and realizes non-structured noise filtering and structured micro-texture recovery without relying on semantic labels.

The invention discloses a vehicle re-identification method and device, equipment and a medium, relates to the technical field of computer vision, and aims to solve the problem that attribute information of a vehicle cannot be utilized due to lack of data annotation by extracting a semantic feature vector related to the color of the vehicle and a semantic feature vector related to the type of the vehicle. The vehicle color features and the vehicle type features are aligned to the spatial dimension of the vehicle global features, the two aligned features are spliced into attribute features, and the attribute features pass through a convolutional layer and a sigmoid activation function to generate a spatial adaptive color space attention map and a spatial adaptive type space attention map respectively; in the process, the weight of the vehicle color feature and the weight of the vehicle type feature are adaptively and dynamically allocated to improve the feature representation capability, and finally, the global feature capable of representing the appearance feature and the visual feature is spliced with the two weighted features after weight adjustment to perform vehicle re-identification, so that the accuracy of vehicle re-identification is improved.

This invention relates to the field of three-dimensional cloud field forecasting technology, and specifically to a spatially scalable forecasting method, system, device, and medium for three-dimensional cloud fields. The method includes: constructing a first basic training set based on preprocessed first historical radar network data; inputting the first basic training set into a model to obtain an initial forecasting model; inputting various specialized historical data into the initial forecasting model to train specialized identification capabilities, obtaining a specialized enhancement model; constructing a second basic training set based on preprocessed second historical radar network data, and processing the second basic training set based on the specialized enhancement model to select and construct multiple specialized training sets; inputting the second basic training set and the multiple specialized training sets into the specialized enhancement model for multi-resolution joint training to obtain a target forecasting model. This method solves the problems of insufficient spatial adaptability and specialized identification capabilities of three-dimensional cloud field forecasting models under complex weather conditions.

The invention provides a modular assembly type reaction wall capable of intelligently regulating and controlling electromagnetic adsorption, and belongs to the field of civil engineering test facilities, a reaction wall main body is formed by combining a plurality of standardized modules, multi-degree-of-freedom reconstruction between the standardized force bearing modules is realized through electromagnetic chucks, and a built-in load sensor feeds back a connection state in real time; and in cooperation with the matrix distribution design of the loading holes, a multi-form reaction wall structure can be quickly built according to test requirements. Each module unit comprises a concrete block, an embedded steel plate, an electromagnetic chuck, a loading hole and a load sensor. The device is characterized by comprising an electromagnetic adsorption interface with adjustable magnetic flux; a modular assembly error self-compensation mechanism is adopted; the invention discloses a distributed sensor network and an intelligent control system based on a PID algorithm. Compared with a traditional cast-in-place structure, the construction efficiency can be effectively improved, repeated disassembly and assembly of the modules are supported, the space adaptability is high, and the multi-scene test requirements of earthquake simulation, structure loading and the like are met.

Belonging to the field of computer vision technology, this invention provides a method and apparatus for teacher-assisted atomic motion recognition based on an improved end-to-end network. [Solution] The method determines time-dimensional and spatial-dimensional video features from the target teacher's current lesson video based on an improved target end-to-end network, generates multi-window feature groups, merges the multi-window feature groups using a frame selection network, and recognizes the target teacher's atomic behavior based on the fusion result. This method embeds spatially adaptive units and time-series adaptive units within the end-to-end network, performs feature extraction on each, and reduces video memory consumption by training only the parameters of these units during network training, thereby effectively improving the accuracy and efficiency of atomic behavior recognition, reducing memory requirements, and lowering deployment difficulty.

The application provides a compression method based on information density driving and adaptive quadtree partitioning, comprising the following steps: S1, obtaining an original high-dimensional visual token set and screening to obtain a visual token sub-set meeting the requirements; S2, using a spatially adaptive recursive quadtree partitioning mechanism, reconstructing the visual token sub-set into a token grid according to the two-dimensional spatial position, and partitioning an information-rich region and a low semantic density region, performing fine-grained partitioning and token retention strategy on the information-rich region to obtain a first compressed token sequence, and performing token merging and down-sampling strategy on the low semantic density region to obtain a second compressed token sequence; S3, splicing the first compressed token sequence, the second compressed token sequence and the text token sequence to obtain a final token sequence, which is input into a multi-modal large model for cross-modal reasoning. The beneficial effect is that the application can maintain the understanding performance and spatial structure integrity of multi-modal generalization under extremely low token budget.

The invention relates to the technical field of three-dimensional cloud field forecasting, in particular to a three-dimensional cloud field space expandability forecasting method, system and device and a medium. The method comprises the following steps: constructing a first basic training set based on preprocessed first historical radar networking data; inputting the first basic training set into the model to obtain an initial forecasting model; inputting various special historical data into the initial forecasting model for special identification ability training to obtain a special improvement model; constructing a second basic training set based on the preprocessed second historical radar networking data, processing the second basic training set based on a special lifting model, and screening and constructing a plurality of special training sets; and inputting the second basic training set and the plurality of special training sets into a special lifting model for multi-resolution joint training to obtain a target forecasting model. The problem that a three-dimensional cloud field forecasting model is insufficient in space adaptability and special identification capability under complex weather conditions is solved.

This invention discloses a data management system based on UAV mapping, specifically relating to the field of data management technology. It involves collecting UAV mapping data and performing spatial coordinate registration and unified encoding; dividing spatial regions based on terrain elevation variation coefficient and obstacle projection coverage to obtain high-density and low-density verification zones; calculating the feature matching degree of overlapping areas in adjacent frames using a local feature matching algorithm for the high-density verification zone, and outputting first correction data; performing data compensation using spatial interpolation and cubic spline interpolation for the low-density verification zone to generate second correction data; generating a full-scene reconstruction data frame based on the first and second correction data; allocating regional weight factors based on the full-scene reconstruction data frame, performing differential error correction encoding, and outputting UAV mapping results data, effectively improving the spatial adaptability of data verification and enhancing the overall accuracy and completeness of UAV mapping data.

The present application relates to the technical field of medical image processing, in particular to a kind of space adaptive and heat map guided retinal image segmentation method, comprising: obtaining the retinal image data containing three-dimensional information and at least one focus, optimizing existing SAM model, construct a segmentation model, its architecture contains with visual Transformer as backbone, and design is introduced space adaptive feature enhancement Transformer block image encoder, two-stage mask decoder and heat map guided mask self-attention module, using the combined loss function based on cross-entropy loss and Dice loss composition supervises the training process of model, the image to be segmented is input into model to obtain segmentation result;The advantages of the scheme are that the three-dimensional structure information of retinal image can be fully tapped, the segmentation accuracy of different types of lesions can be significantly improved, and the problem of class imbalance can be alleviated, and high-precision and efficient segmentation of retinal lesions is realized.

The invention provides a self-adaptive phase field sea ice image segmentation method. The method comprises the following steps: S1, initializing a phase field function based on the intensity information of an input sea ice image; s2, calculating an edge feature map of the input sea ice image, and extracting multi-scale texture features of the input sea ice image to form texture feature items; s3, determining a spatially adaptive evolution speed parameter and a spatially adaptive coupling parameter according to the edge feature map; s4, iteratively updating the phase-field function based on the evolution equation according to the evolution speed parameter of the space self-adaption and the coupling parameter of the space self-adaption; and S5, after the phase-field function reaches a convergence condition, obtaining a final phase-field function, and performing threshold processing on the final phase-field function to generate a binarized sea ice segmentation result image. According to the method, accurate and robust segmentation of the complex sea ice scene is realized by fusing the low-level features of the image and the internal constraints of the physical model, and the method has good generalization ability and relatively low sample dependence.

This invention discloses a multi-branch parallel weighted and multi-scale spatially adaptive infrared image processing method and system. It reconstructs the output data of an infrared detector into a two-dimensional grayscale image matrix; obtains three branch output images through parallel processing of three-branch complementary feature extraction operators; obtains a base image through pixel-level weighted fusion using local spatial information entropy mapping; normalizes and inverts the base image to obtain an inverted image; and processes the inverted image based on a spatially adaptive exponential enhancement model using a multi-scale Gaussian ratio factor to obtain the final enhanced image. This invention not only achieves heterogeneous branch complementary fusion with adaptive parameter determination, enabling the extraction of subtle target details, and low computational complexity, but also possesses potential for dataset generation and migration to embedded real-time systems. Furthermore, it addresses the challenge of existing infrared image enhancement techniques in balancing noise suppression, target detail preservation, and real-time processing.

This invention relates to the field of semiconductor technology, specifically to a method and system for online control of in-plane electrical performance uniformity on a wafer. The method includes: acquiring key process parameters at multiple locations within the wafer plane online during wafer fabrication; inputting these parameters into a pre-trained machine learning model, which models the spatial correlation between locations and predicts the estimated electrical performance at different locations within the wafer plane; subsequently, generating a spatially adaptive process recipe for differentiated process compensation in different regions of the wafer based on the difference between the estimated and target performance; and finally, adjusting at least one process parameter in subsequent process steps according to this recipe to proactively correct electrical performance deviations. This invention achieves a shift from offline feedback to online feedforward, and by integrating spatial modeling and intelligent decision-making, it can suppress wafer-level process fluctuations in real time, proactively, and accurately, significantly improving chip electrical performance uniformity and manufacturing yield.

A point cloud collision detection method and device for disordered robot grasping includes: randomly sampling an imported gripper model through patches to obtain point cloud data, scanning with a 3D camera to obtain point cloud data of stacked workpieces; calculating bounding boxes and performing spatially adaptive segmentation on the point cloud data to construct a hierarchical bounding box tree of the gripper model and the stacked workpieces; performing collision detection on the two hierarchical bounding box trees using a depth-first recursive search to obtain collision results under a single discrete posture; interpolating the robot's motion trajectory, dynamically updating the hierarchical bounding box tree of the gripper model based on each interpolated posture, and detecting collisions in the overall motion trajectory. The present invention performs spatially adaptive segmentation on point cloud data to adapt to the actual distribution of point clouds, improve space utilization, and enhance the balance of the tree structure; interpolating the robot's motion trajectory and dynamically updating the hierarchical bounding box tree avoids frequent reconstruction of the entire tree structure, thereby achieving efficient path-continuous collision detection.

The invention relates to the technical field of unmanned aerial vehicle target detection, and provides an unmanned aerial vehicle night target detection method based on a parallel fusion neural network, and the method comprises the steps: carrying out the parallel operation of a light enhancement module and a target detection network, and achieving the deep feature fusion through end-to-end joint optimization; the light enhancement module adopts a ZeroDCE + + unsupervised enhancement module to carry out adaptive brightness correction, and brightness distribution can be adaptively adjusted without pairing training data; a target detection network carries out detection based on an improved lightweight YOLOv5 architecture, the improved lightweight YOLOv5 architecture introduces a spatial adaptive feature modulation SAFM module and an adaptive feature enhancement AFE module, and the SAFM module strengthens the perception of a model on local-global features through a multi-scale feature fusion mechanism. And the AFE module retains target edge details through parallel context modeling and feature refinement. According to the invention, unmanned aerial vehicle night target detection can be well carried out.

The present invention discloses an in-situ high-temperature and high-pressure visual reactor that can take into account the effect of gravity and its application, which belongs to the technical field of multiphase fluid mechanics experimental equipment. The in-situ high-temperature and high-pressure visual reactor provided by the present invention utilizes the linkage and tilt adjustment (0~90°) of the fiber-coupled laser light source and the model clamping device to realize the in-situ simulation of the gravity vector under non-rotating conditions; the multi-prism reflection system corrects the optical path offset in real time to form an orthogonal collimated optical path, ensuring the observation field stability and data accuracy of processes such as oil-water separation and CO2 diffusion under high-temperature and high-pressure environments. In addition, the present invention eliminates the alternating stress effect of the rotational torque on the sealing interface through the split clamp body and the rigid locking sealing structure, significantly reducing the risk of high-pressure dynamic seal failure, while simplifying the device volume, avoiding interference from peripheral equipment, and improving experimental safety and spatial adaptability.

Disclosed herein are systems and associated methods of medical imaging. An embodiment of the method comprises receiving (200) measured k-space data (122), wherein the measured k-space data comprises blades of k-space data acquired according to a rotated magnetic resonance imaging protocol. For the blades of the k-space data, the method further comprises: reconstructing (202) a blade-specific image (124); and assigning (204) a spatially dependent weighting score (128) to individual voxels of the blade-specific image by comparing the blade-specific image to a reference image (126). The method further comprises reconstructing (208) a magnetic resonance image (134) by combining the blade-specific images, wherein the reconstruction of the magnetic resonance image is guided using the spatially dependent weighting score of the individual voxels of the blade specific image for the respective blades of k-space data.