148 results about "Mr images" patented technology

The invention discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

The present application provides a cardiac fibrosis diagnosis model based on multi-task attentional feature fusion. The cardiac fibrosis diagnosis model is established by the following steps: S01: image collection and labeling: obtaining cardiac magnetic resonance (MR) images as sample data, and performing manual labeling to obtain heart labels corresponding to the MR images; S02: image preprocessing, including normalization processing, data enhancement, and data clipping; S03: model establishment, including establishment of an image recovery network and establishment of an image segmentation and classification network, and executing an image recovery task; S04: model pre-training: training the image recovery network such that the encoder of the image recovery network fully learns the feature of the cardiac fibrosis image; and S05: model training. An objective of the present application is to improve the segmentation precision and diagnosis accuracy of a network model for a cardiac fibrosis image.

The invention provides a multi-mode nuclear magnetic resonance image glioma segmentation method and application thereof, and belongs to the field of medical image processing. The invention provides an M2ES-UNet network aiming at the problems of insufficient utilization of spatial features, single multi-modal fusion mechanism and cross-level semantic loss of an existing segmentation method. According to the method, multi-view anatomical information is extracted through a multi-plane feature collaboration module; utilizing an orthogonal dimension fusion convolution and modal introspection-collaboration module to respectively realize differential fusion of shallow and deep features; and the progressive jump transmission of the features is realized through a coding information smooth transmission module. According to the method, multi-plane and multi-mode complementary information can be effectively mined, the boundary precision and robustness of brain glioma segmentation are remarkably improved, and clinical diagnosis is assisted.

A system and method for synthesis of PET images from multimodal MR images comprising a user (21), input / output device(s) (23) and processing unit (24). The method comprises three stages namely, model training stage, validation stage and model deployment stage. The model training stage comprises the steps of creation of the dataset in the form of pairs of images comprising a single or plurality of multimodal MR images (2) and corresponding single or plurality of PET images (16) and model training using a DCGAN workflow (1) that consists of a generator network (3) and a discriminator network (17). The validation stage is performed to assess similarity between synthetic (15) and real PET images (16) and clinical utility of synthesized PET images. In the model deployment stage, the trained model is loaded and deployed to obtain synthetic PET images (15) from multimodal MR images (2) as input (22).

The invention discloses a pericardium adipose tissue segmentation method, device and equipment based on an MR image and a medium. The method comprises the following steps: acquiring a target heart magnetic resonance image containing pericardium adipose tissue of a target object; segmenting pericardium adipose tissue in the target heart magnetic resonance image based on a tissue segmentation model to obtain target pericardium adipose tissue of the target object; the tissue segmentation model comprises a three-branch cross-domain feature collaborative encoder, a double attention feature fusion module and a dynamic boundary perception decoder, and the three-branch cross-domain feature collaborative encoder performs feature extraction from global, local and frequency domains based on a three-branch structure; the double attention feature fusion module is established based on a space attention mechanism and a parallel channel attention mechanism, and the dynamic boundary perception decoder is used for enhancing the global and boundary perception capability. According to the scheme, the pericardium adipose tissue in the heart MR image can be accurately and efficiently segmented by using the tissue segmentation model based on multi-feature collaboration.

The invention provides a multi-modal weak supervision medical image segmentation method, and belongs to the field of medical image segmentation, and the method comprises the steps: carrying out the feature extraction of CT and MR images, and obtaining CT and MR modal feature representations; fusing same-layer features represented by CT and MR modal features under each scale; performing down-sampling and multi-layer convolution processing on the fusion feature representation; respectively decoding the enhanced feature representation, the CT modal feature representation and the MR modal feature representation to obtain a multi-modal prediction map, a CT prediction map and an MR prediction map, calculating cross entropy loss, respectively calculating multi-view CRF loss of the CT image and the MR image, obtaining intra-modal regular loss, calculating inter-modal consistency loss, adding the cross entropy, the intra-modal regular loss and the inter-modal consistency loss, and when the total loss is minimum, determining that the total loss is minimum. Obtaining a trained image segmentation model; inputting CT and MR images to be segmented into the trained image segmentation model, and outputting segmented images; the invention also provides an image segmentation system. And multi-modal image information is effectively fused and over-fitting is inhibited under a weak supervision condition.

The invention discloses a pig brain deep brain stimulation DBS electrode planning and reconstruction system and method, and belongs to the technical field of medical image processing and neurosurgery path planning. An automatic registration module based on a magnetic resonance (MR) image and a pig brain standard map is used for accurately identifying a target brain region; the in-target optimal electrode trajectory planning module based on principal component analysis (PCA) and the electric field simulation module based on a Hodgkin-Huxley model are used for optimizing the treatment effect and predicting the tissue activation volume (VTA); and a multi-modal hybrid registration module based on postoperative computed tomography (CT) and preoperative MR images, which is used for accurately reconstructing the actual position of the electrode. According to the invention, through combination of automatic and data-driven planning and accurate postoperative verification, the accuracy, reliability and efficiency of electrode implantation in pig brain DBS research are significantly improved.

Systems and methods for image reconstruction and quantitative MRI. Generative models such as diffusion models are used to reconstruct MR images and generative models and constrained mathematical models fit to estimate quantitative maps from the reconstructed MR images.

The application discloses a kind of fast magnetic resonance multi-sequence joint imaging method, system, equipment and medium, it is related to deep learning technical field, including the following steps: acquisition different sequence multiple undersampling MR images, wherein multiple undersampling MR images share the same public feature, each undersampling MR image enjoys respective corresponding specific feature;The mapping relationship between multiple undersampling MR images and latent variable is modeled based on latent variable model, and the optimization objective of reconstruction task in fast magnetic resonance multi-sequence joint imaging is constructed based on mapping relationship;Optimization objective is solved by depth development network.The features of multiple undersampling MR images are subdivided into intermodal shared public features and modality unique specific features, the fine decoupling of the shared and specific features of MR image, solve the problem that intermodal correlation modeling is weak, and redundant information can be effectively avoided or sequence-specific detail information is lost.

A first artificial intelligence (Al) engine receives a plurality of incomplete magnetic resonance (MR) K-space data matrices of an object scanned by an MR device. Each of the incomplete MR K-space data matrices comprises complex values and is the result of a corresponding san of the object by the MR device using a sparse-sampled MR scan acquisition sequence. Each sparse- sample MR scan acquisition sequence employs a unique sampling pattern. The first Al engine reconstructs a complete MR K-space data matrix of the scanned object, corresponding to a complete MR K-space acquisition. The reconstruction is based on the data in the plurality of incomplete MR K-space data matrices.

This application provides a method, system, device, and computer-readable storage medium for planning the healing of spinal compression fractures based on multimodal three-dimensional medical images. The method includes: acquiring spinal CT and MR images of the patient; identifying and segmenting the spinal CT and MR images to obtain CT and MR images of the fractured vertebral body at the site of the compression fracture; inputting the CT and MR images of the fractured vertebral body into a preset bone cement anti-leakage planning model to output a bone cement injection anti-leakage planning scheme; wherein the bone cement injection anti-leakage planning scheme includes at least: the viscosity of the bone cement, the injection volume, the injection location, the injection timing, and the injection rate. According to the embodiments of this application, bone cement injection anti-leakage planning can be performed quickly and accurately.

The application belongs to the technical field of image processing, and provides a fusion processing system for spine MR images and CT images, a segmentation model constructed through a Mamba structure, multi-scale feature extraction realized by using a three-way Mamba module, and the segmentation accuracy of the bony structure of the spine, intervertebral discs and soft tissues is significantly improved by combining gated spatial convolution and feature-level uncertainty estimation; at the same time, the GPU memory consumption is effectively reduced by the cascade training flow design, and under the premise of ensuring the segmentation quality, the generation of high-resolution fusion images is realized; on this basis, the fusion network based on a double-input Mamba structure has global perception ability and linear extraction ability of directional features, can efficiently fuse the high spatial resolution bone structure information of CT and the soft tissue contrast advantage of MRI, ensures the consistency of anatomical structures, fully retains the complementary features between modalities, and significantly improves the comprehensive analysis accuracy of spine images.

For autonomous MR scanning of a given medical test, a simplified MR scanner can be used without or with little input or control by a technical expert, e.g., by a physician, radiologist, or person trained in MR scanner operation. The MR scanner autonomously positions, scans, checks quality, analyzes, and / or outputs answers to diagnostic questions with or without MR images. Artificial intelligence-based scan analysis allows continuous or instant changes to the scan configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both the positioning of the patient relative to the MR scanner and the localization of the scan by the MR scanner are jointly solved. Sensors can sense the patient at the scan position, with reduced radio frequency requirements allowing a more open bore.

The present invention refers to an apparatus 130 for reconstructing MR images and / or parameter maps based on MR time-domain signals. A providing unit 133 provides time-domain signals acquired in time-domain MRI using a k-space sampling pattern and a magnetic resonance acquisition sequence. A providing unit 132 provides a subspace sampling operator that provides given k-space locations of the sampling pattern time subsamples of basis vectors of a compressed subspace. The compressed subspace can be used to compress the time-domain signals. A processing unit 133 processes the time-domain signals using the subspace sampling operator. A reconstruction unit 134 reconstructs MR images and / or parameter maps based on the processed time-domain signals. This allows for more accurate and less computationally resource-intensive reconstruction of MR images and / or parameter maps in the context of time-domain magnetic resonance imaging.

The present disclosure relates to a lumbar vertebra lesion identification method, device, medium and electronic equipment, wherein the method comprises: determining a lumbar vertebra image of a user, the lumbar vertebra image being a T2 weighted magnetic resonance image; inputting the lumbar vertebra image into a target detection model to obtain a lesion category of the lumbar vertebra of the user, wherein the target detection model comprises a down-sampling layer and a pooling layer, the down-sampling layer is used to extract feature data of the lumbar vertebra image, and the pooling layer is used to classify the feature data to obtain the lumbar vertebra lesion category of the user. Compared with the way of judging the lumbar vertebra lesion by artificial judgment and identifying the lumbar vertebra lesion by machine learning method in the related art, the error of identifying the lumbar vertebra lesion type can be reduced, and thus the accuracy of identifying the lumbar vertebra lesion can be improved.

An artificial intelligence (Al) engine is trained on a plurality of annotated magnetic resonance (MR) images of a patient's brain. A plurality of MR images of a patient's head is provided. For each MR image in the plurality of provided MR images, the Al engine detects a plurality of edges of the brain and determine a biparietal diameter (BP) value, detects a plurality of frontal horn edges, and detects a plurality of occipital horn edges. A correction module determines that at least one detected edge is associated with a non -ventricular body and updates the edge to correspond to the applicable horn. The Al engine determines a frontal horn diameter (F) value, and a occipital horn diameter (O) value. An indication module provides an indication on abnormal dilation of the patient's brain ventricles based on a maximum F value, a maximum O value, and the maximum BP value.

The application provides a method and system for removing electro-physiological signal gradient artifacts in a magnetic resonance environment, and relates to the technical field of magnetic resonance image processing. The method comprises: collecting an electro-physiological signal of a magnetic resonance scan and a scan synchronization signal; aligning and windowing the electro-physiological signal based on the scan synchronization signal to obtain a windowed sequence; constructing an artifact template based on the windowed sequence; combining the windowed sequence and the artifact template to calculate a first residual; performing principal component analysis on the artifact template and the first residual to obtain a second residual; performing logarithmic domain conversion and polarity extraction operations on the second residual to obtain a logarithmic domain sequence and a polarity symbol layer; inputting the logarithmic domain sequence into a deep learning denoising network to output a de-artifact logarithmic domain sequence; and performing inverse logarithmic transformation and polarity recovery operations on the de-artifact logarithmic domain sequence and the polarity symbol layer, respectively, to determine a de-artifact signal.

A system and method automatically detect, in MR images, WM lesions exhibiting a central vein sign. A set of MR images of a brain lesion is acquired, using images of the set as input to different ML algorithms. A first ML algorithm classifies inputted image(s) into first or second classes. The first class includes CVS+ / − and the second class CVSe lesions. A second ML algorithm classifies inputted image(s) into third or fourth classes. The third class includes CVS+ lesions and the fourth central vein sign− lesions. For each set, probability values are used that the set belongs to classes as inputs to a final classifier performing a final classification of the set into second, third, or fourth classes. For each class, the final classifier outputs final probability that the set belongs to the class. The second, third or fourth class with highest probability value is provided through an interface.

The application discloses a CT image synthesis method and device based on MR images, equipment and medium, the application obtains an image synthesis model by inputting a first MR image and a first CT image into a network model for training; a global block embedding module is used for global feature extraction on the first MR image, so that the global feature extraction result is more global; an encoding module is used for local feature extraction on the first MR image, an RSC self-attention module is used for first feature processing according to the global feature extraction result and the local feature extraction result, which can expand the receptive field of the features, a decoding module is used for decoding processing on the first feature processing result to generate a second CT image; a discriminator is used for generating a discrimination result according to the first CT image and the second CT image for training, and the finally obtained target CT image is more detailed and clear, and the application can be widely applied to the field of image processing.

This invention relates to a two-stage super-resolution method for MRI images using high-quality codebook prior information. The method includes: a) data preprocessing, undersampling the fully sampled K-space data; b) designing a one-stage codebook network, pre-trained using ground truth images to obtain high-quality codebook information; d) designing a two-stage codebook-prior super-resolution network model that integrates prior information; e) supervised training of the model using ground truth images; and f) selecting the optimal model based on a validation set, inputting test data into the model to obtain the super-resolution MRI image. Compared with existing technologies, this invention introduces high-quality codebook prior information during the super-resolution process, effectively solving the problem of poor restoration of details and texture structure in super-resolution images. Better restoration of detailed texture information aids in clinical diagnosis and has promising application prospects.

There is provided a computer-implemented method for synthesising magnetic resonance angiography (MRA) images from other types of inputted magnetic resonance (MR) images, in a subject-specific manner, the method comprising providing a conditional generative adversarial network (cGAN) that learns a combined latent representation of the inputted magnetic resonance images for each subject and learns to transform this combined latent representation to a magnetic resonance angiography image corresponding to that subject, providing a plurality of magnetic resonance (MR) images as input into the cGAN, and outputting a plurality of MRA images from the cGAN based on the plurality of inputted MR images.

The application discloses a kind of based on N2V and EMI removal algorithm's electromagnetic imaging detection dual denoising method and device, the present application is by introducing N2V algorithm to carry out image noise to the image after initial denoising, and using the image after adding noise to carry out secondary denoising training;Thus, it can utilize secondary denoising model, to the initial denoising nuclear magnetic resonance image secondary denoising processing, to obtain the nuclear magnetic resonance image with better denoising effect, based on this, the present application can be in the case of lacking real image secondary denoising training, realizes the secondary denoising processing of nuclear magnetic resonance image, solves the restriction that image secondary denoising training cannot be carried out in traditional technology due to lack of clean image and noise image pair, whereby, the present application can further improve image quality, applicable in the field of ultra-low field MRI equipment imaging image denoising Large-scale application and popularization.

Neural network-based systems, methods, and apparatus can be used to remove motion artifacts from magnetic resonance (MR) images. Such neural network-based systems can be trained to perform motion artifact removal tasks without a reference (e.g., without using pairs of motion-contaminated and motion-free MR images). Various training techniques are described herein, including techniques that present pairs of MR images with varying levels of motion contamination to a neural network and force the neural network to learn to correct for the motion contamination by transforming the first image of the contaminated pair into the second image of the contaminated pair. Other neural network training techniques are also described that aim to reduce reliance on difficult-to-obtain training data.

This invention discloses a method and system for identifying brain tumors in brain MRI images. The method includes the following steps: S1, acquiring the original brain MRI image data of the object to be identified and performing preprocessing operations to obtain standardized multimodal MRI image data; S2, performing preliminary tumor region screening on the preprocessed image data using a tumor tissue probability decoupling identification method based on multi-echo relaxation differences. This invention relates to the field of medical image processing technology. This method and system for identifying brain tumors in brain MRI images employs a tumor tissue probability decoupling model based on multi-echo relaxation differences and cross-scale brain network topology perturbation analysis, which can accurately screen potential tumor regions. Furthermore, it improves identification accuracy through dual constraints of metabolism and morphology. The introduction of cross-scale topology analysis, combined with abnormal propagation detection of brain network structures, effectively identifies suspected tumor regions and improves identification accuracy.

A conversion device is operable to perform a learning-based method of generating a synthetic electron density image (sCT) of an anatomical portion based on one or more magnetic resonance (MR) images. The method is processing-efficient and capable of producing highly accurate sCT images irrespective of misalignment in the underlying training set. The conversion device receives and installs a machine-learning model trained to predict coefficients of an image transfer function. The conversion device then receives a current set of MR images of the anatomical portion, computes current coefficients of the image transfer function by operating the machine-learning model on the current set of MR images, and computes a current sCT image of the anatomical portion by operating the current coefficients, in accordance with the image transfer function, on the current set of MR images.

The invention relates to the technical field of image processing, in particular to a synchronous acquisition and playback method and system for nuclear magnetic resonance images. The method comprises the steps that a high-resolution anatomical image is obtained, blood flow velocity information is coded for dynamic imaging, distortion data is corrected in real time, and analog signal data is output; the analog signal data are preprocessed, the preprocessed data are subjected to layered coding, a fixed-length abstract is generated, encryption processing is carried out, and encrypted data are output; decrypting the encrypted data, reconstructing a playback image sequence, and displaying the image sequence; the system comprises a data acquisition module, a fixed-length abstract generation module and a playback image sequence reconstruction module. By means of the mode, the change of blood flow along with time is dynamically displayed, and the assessment ability of lesion risks is improved.

The present invention discloses a diffusion magnetic resonance (MR) method for measuring arterial pulsation dependence of perivascular cerebrospinal fluid flow in glymphatic system: DTI acquisition: Brain MRI images were acquired using dynamic diffusion tensor imaging (DTI); Cardiac signal synchronization: Simultaneously collect heart rate fluctuation time-series signals; Peak coordinate determination: Identify the peak timing of cardiac pulsation signals from the time-series data; Image realignment: Reorganize brain MR images according to their temporal positions within the cardiac cycle; Temporal interpolation: Perform uniform time-sampling reconstruction to generate equidistant diffusion MRI datasets across the cardiac cycle; Parameter calculation: Compute axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) at each voxel level; Mask-based analysis: Generate characteristic curves of AD / RD / MD and spin density(S) dynamics using region-specific masks in individual space. This method enables non-invasive measurement of: (1) Cerebrospinal fluid (CSF) flow velocity / direction in large perivascular spaces during heartbeats; (2) Microvascular perivascular CSF dynamics through diffusion parameter analysis.

To provide a resource allocation system and method for managing the use of local and remote resources for medical image processing tasks.SOLUTION: A simplified medical imaging ecosystem 100 including various networked medical facilities includes a first medical facility 102 and a second medical facility 103 that are operated as part of a medical system 106. The medical facility 102 includes a CT scanner 110, which includes a LCI112 used to process acquired projections to reconstruct CT images, and an MR scanner 120, which includes a LCI122 and processes acquired projections to reconstruct MR images. Medical facility 103 includes a computed tomography (CT) scanner 130 that includes a LCI132 used to process acquired projections to reconstruct CT images and a positron emission tomography (PET) scanner 140 that includes a LCI142 used to process acquired projections to reconstruct PET images.SELECTED DRAWING: Figure 1

For a given MR image having an anatomical landmark and a frame of reference, a method includes automatically selecting one or more series of MR images from a plurality of series of MR images previously acquired and having the anatomical landmark and the frame of reference. The method includes automatically selecting one or more candidate groups from the one or more series of MR images that were selected, wherein selection of the one or more candidate groups is based on predetermined rules. The method includes automatically selecting a respective reference image from each candidate group, wherein selection of each respective reference image is based on the predetermined rules. The method further includes automatically displaying the respective reference image selected for each candidate group, wherein each respective reference image is displayed in reference viewports on a user interface on a display based on the predetermined rules.