245 results about "Mr images" patented technology

The invention discloses an image detection method based on an improved YOLO algorithm. The method comprises the following steps: collecting a nuclear magnetic resonance image with a focus; a target detection network model is constructed: basic framework improvement is carried out based on YOLOv8, an improved backbone network comprises a C2f module, a convolution module and an improved multichannel spatial pyramid pooling module SPPMC, and the image feature extraction capability is enhanced; the improved neck network comprises an up-sampling layer, a splicing layer, a C2f module, a convolution layer and an embedded attention mechanism UECA module, multi-scale feature map fusion is carried out, and the feature fusion efficiency is improved; the improved header network adopts an improved bounding box regression loss function L (iS-IoU), the convergence speed of the model is increased, and the positioning precision is improved; inputting a training image set into the target detection network model for training; and performing image focus detection by using the trained target detection network model. The method has the characteristic of effectively improving the robustness and the detection performance of the target detection model.

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 invention discloses a method for identifying an autism brain image through multi-mode brain image information fusion, is applied to the field of image identification, and aims to solve the problems that multi-mode nuclear magnetic resonance image data are not fully utilized and the model interpretability is insufficient in the prior art. The method comprises the following steps: respectively extracting brain region features from three modal data of a structural magnetic resonance image, a functional magnetic resonance image and a phenotype, then carrying out multi-modal fusion, and constructing a brain region map structure; designing a drawing convolution module and a loss function to learn how to extract brain map depth features; on the basis of brain map depth features, a multi-layer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; the trained multi-mode brain image fusion identification autism model can be used for assisting autism diagnosis and identification.

The invention discloses a method and a device for testing moisture migration characteristics of frozen soil under low-temperature nuclear magnetic triaxial seepage, and relates to the technical field of frozen soil testing. In order to solve the problems that in the prior art, compatibility control of freezing and seepage cannot be achieved, and whole-process evolution of frozen soil moisture migration characteristics is difficult to dynamically capture, the scheme comprises the following steps that the real confining pressure, axial pressure and seepage speed of the environment where a soil sample is located are simulated; collecting a nuclear magnetic resonance image and an overall and layered T2 spectrum of the soil sample before freezing under the action of seepage; gradually freezing the soil sample based on different temperature gradients, and collecting a nuclear magnetic resonance image and an overall and layered T2 spectrum of the soil sample under the seepage and freezing coupling action in real time; data are collected according to the whole soil sample freezing process, and the evolution law of water migration characteristics in the soil body freezing process is analyzed. According to the invention, the whole-process, visual and nondestructive detection is realized, and the whole-process evolution rule of water migration characteristics such as unfrozen water content and pore cracks in the frozen soil under the seepage and freezing coupling action can be accurately captured.

The invention relates to a method of MR imaging of an object positioned in the examination volume of an MR system (1). It is an object of the invention to provide a deep learning-based denoising approach that overcomes the Rician bias problem. As a solution, the invention proposes a method comprising the following steps: a) subjecting the object to an imaging sequence comprising RF pulses and switched magnetic field gradients, whereby MR signals are generated, b) acquiring the MR signals, c) reconstructing a complex-valued MR image from the acquired MR signals, d) denoising the MR image using a deep learning algorithm that operates on the real and the imaginary parts of the MR image, and c) computing a magnitude MR image from the denoised complex-valued MR image. According to an aspect of the invention, the deep learning algorithm uses a set of denoising models that are trained using different loss functions. In this way, a trade-off between noise removal and preservation of small image details can be controlled. Moreover, the invention relates to an MR system (1) and to a computer program for an MR system (1).

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 relates to the technical field of medical image processing, in particular to a high-definition diffusion MR image breast cancer detection method and system, and the method comprises the steps: dividing an obtained historical mammary gland high-definition diffusion MR image data set into a training set and a verification set according to a preset distribution proportion; for each divided image, carrying out breast region segmentation, and carrying out standardization processing on the segmented image to obtain a pre-processed image; the preprocessed training set and verification set are input into a residual error classification network model based on multi-scale multi-head attention for model training, and the residual error classification network model comprises a front convolutional layer, an MSAA-ResNet composite module fusing a multi-scale attention mechanism and residual error learning, and a global feature association module introducing the multi-head attention mechanism; inputting the obtained real-time mammary gland high-definition diffusion MR image into the trained residual error classification network model to obtain a focus classification result; and based on a focus classification result, inputting the real-time mammary gland high-definition dispersion MR image corresponding to the focus into the YOLOv8 target detection model to obtain focus region position information.

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.

The invention discloses a lightweight 3D heart MRI segmentation algorithm based on 3D efficient partial attention and three-dimensional sparse MLP, and the algorithm obtains an MRI pixel block with a detailed target region feature through a 3D efficient deformable attention module EDA, and fits the edge distribution of a segmented region with lower calculation complexity through reference point translation. Efficient attention calculation ensures feature information extraction and greatly reduces resource consumption; the method comprises the following steps: constructing a cross-shaped receptive field at extremely low cost through information in transverse, longitudinal and depth directions of a three-dimensional sparse MLP module (triSMLP) mixed voxel block, and outputting a fine characteristic spectrum with a global receptive field through three-time superposition; the feature maps output by the EDA and the triSMLP recover down-sampling lost detail features at an encoder through a partial convolution feature fusion decoder. Compared with most of the leading edge 3D segmentation algorithms at present, the method has the advantage that a better segmentation effect is obtained with extremely small computing resources.

A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

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).

Methods and systems for automatic segmentation of structures of interest of an organ in an MR image. The method includes creating a weighted active shape model (wASM); registering model points of the structures in an MR atlas image to a target image that is the MR image to be segmented, as initial model points of the structures in the target image; and iteratively fitting the wASM to the target image, starting from the initial model points, until the shape converges, wherein the final shape is the segmentation of the structures of interest.

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 discloses a reference driving undersampling magnetic resonance (MR) image reconstruction method based on denoising regularization (RED) and depth image prior. The method does not depend on a large-scale clinical data set, a constraint reconstruction model based on denoising regularization and depth image prior is constructed, and the reconstruction of the target MR image under the under-sampling data can be realized only through the driving of a high-resolution reference image similar to the structure of the target image to be reconstructed. The method comprises the specific steps of selection of a single magnetic resonance reference image, denoising engine training of reference driving, depth image prior network construction based on U-Net, construction of a constraint optimization model combining k-space data fidelity and RED regularization, sub-problem decomposition and iterative updating based on an ADMM algorithm, and data correction and reconstruction output. According to the method, the structure prior is introduced into the deep network by using the reference image, the learning efficiency is improved, the dependence on the training data is reduced, the high-quality reconstruction of the MR image under the under-sampling data can be realized, the detail structure and the texture feature are effectively reserved, and the reconstruction precision and the visual quality are remarkably improved. The method is suitable for clinical MRI rapid reconstruction and other imaging applications, and has high practical value.

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 present invention is applicable to the field of computers and provides a method for registering MR images and CT images, including: obtaining the MR image and the CT image to be registered; generating a simulated CT image according to the MR image; determining a current displacement field according to the simulated CT image and the CT image to be registered; processing the current displacement field and the previous displacement field based on a diffeomorphic algorithm to generate a superposed displacement field; performing regularization processing on the superposed displacement field to generate a smooth deformation field; generating a registration pending confirmation image according to the smooth deformation field and the simulated CT image; until a preset condition is satisfied, confirming the registration pending confirmation image as the registered image, otherwise returning to the step of determining the current displacement field according to the simulated CT image and the CT image to be registered for iterative processing. The registration method provided by the embodiments of the present invention has a good registration effect on images with large deformations by processing the MR image as a simulated CT image and then performing iterative optimization registration.

The invention discloses a radioactive temporal lobe injury risk image classification method based on multi-time-sequence MRI. The method comprises the following steps: acquiring a nasopharynx head and neck multi-sequence nuclear magnetic resonance image and clinical characteristic parameters of a patient; performing time sequence division on the nasopharynx head and neck multi-sequence nuclear magnetic resonance image to obtain a plurality of original sequences; extracting image features of the nasopharynx head and neck multi-sequence nuclear magnetic resonance image in the original sequence; a dynamic MRI radiomics label based on image features is constructed by adopting a long-short time neural network; fusing the dynamic MRI radiomics tag and the clinical characteristic parameters, and establishing a radioactive temporal lobe injury image classification model; and obtaining an image classification result based on the radioactive temporal lobe injury image classification model. According to the method, high-throughput and time-sequence dynamic image space omics information and clinical characteristic indexes are fused, and a more comprehensive radioactive temporal lobe injury image classification model is established by applying an artificial intelligence algorithm.

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.

The invention discloses an Alzheimer's disease diagnosis and prediction method based on multi-source knowledge guidance, and belongs to the technical field of medical artificial intelligence assistance. Comprising the following steps: acquiring a nuclear magnetic resonance image, brain statistical data and clinical data of a participant; processing the nuclear magnetic resonance image by using a dual-path feature extraction strategy, obtaining global brain features and local brain features, carrying out collaborative learning, and generating fused visual features; the brain statistical data and the fused visual features are fused and then input into a trained diagnosis module for processing, and a diagnosis result at the current moment is obtained; and taking the clinical data as guidance, combining the diagnosis result at the current moment, and utilizing the trained prediction module to obtain the disease progress at the future moment. The multi-modal data can be integrated, the accuracy and comprehensiveness of Alzheimer's disease diagnosis and prediction are improved, and the problems that the accuracy of existing Alzheimer's disease diagnosis is not high, and clinical requirements are difficult to meet are solved.

The invention relates to a medical image artifact classification method based on a CN-CLIP model, and the method comprises the steps: obtaining a medical image data set containing a plurality of CT images and MR images, and constructing a text label data set in a multi-segment cue word format based on the image type, examination part and artifact type of each image; performing fine tuning on the CN-CLIP pre-training model by using the medical image data set and the text label data set to obtain an artifact classification model; and inputting a medical image to be classified into the artifact classification model, and outputting an artifact classification result of the medical image. According to the medical image artifact classification method based on the CN-CLIP model, the pre-trained Chinese image-text matching model CN-CLIP is utilized, so that the defect that models need to be trained respectively for different image types and examination parts in the prior art is overcome; efficient and accurate artifact classification of CT and MR medical images of different parts is realized through a unified model.

The invention discloses a low-temperature nuclear magnetism-based method and device for testing pressure-thaw evolution of frozen soil under triaxial stress, and relates to the technical field of frozen soil testing. Aiming at the technical bottlenecks that low-temperature freezing-seepage-triaxial stress coupling application is difficult to realize and pore cracks and ice content changes cannot be dynamically captured in the prior art, a low-temperature nuclear magnetic resonance test technology and a soil triaxial test technology are organically combined, low-temperature online accurate freezing is realized in the whole process, and test data is acquired in real time; and analyzing the frozen soil pressure-melting characteristic evolution law. The testing method comprises the following steps: simulating initial confining pressure, axial pressure and seepage velocity of a real environment of a soil sample, freezing the soil sample to a target temperature and keeping the temperature constant; dynamically acquiring a nuclear magnetic resonance image and an overall and layered T2 spectrum in an initial environment; changing the confining pressure and / or the axial pressure, and dynamically acquiring a nuclear magnetic resonance image and an overall and layered T2 spectrum in the change process of the confining pressure and / or the axial pressure; and analyzing the whole-process evolution of the pressure-thaw characteristics of the frozen soil under the action of different stresses.

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.

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 invention relates to the technical field of lumbar vertebra segmentation, and particularly discloses a small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, comprising the following steps: obtaining a lumbar vertebra MR image sample set comprising a plurality of sample lumbar vertebra MR images; constructing a small sample lumbar vertebra labeling model, and training the small sample lumbar vertebra labeling model by using the lumbar vertebra MR image sample set to obtain a trained small sample lumbar vertebra labeling model; and inputting a to-be-labeled lumbar vertebra MR image into the trained small sample lumbar vertebra labeling model for labeling so as to obtain a lumbar vertebra labeling prediction result of the to-be-labeled lumbar vertebra MR image. According to the small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, rapid, accurate and high-throughput lumbar vertebra labeling can be realized.

The present disclosure relates to systems, methods, and apparatuses associated with reconstructing MR images based on undersampled magnetic resonance (MR) data. The MR data includes 2D or 3D information and may include multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which can accelerate the scanning and / or image generation process. By reconstructing the MR images based on respective subsets of contrasts, coils, and / or readout segments and then combining the reconstructed MR images to obtain a desired multi-contrast result, the challenges imposed by large amounts of MR data and hardware limitations are overcome.

The invention discloses a liver cancer TACE postoperative active focus automatic detection and segmentation model and a construction method thereof, and the construction method comprises the steps: obtaining a TACE postoperative MR image of a liver cancer patient as a training sample, and dividing the training sample into a training set and a verification set; performing digital subtraction processing on the MR images in the training set to generate digital subtraction images corresponding to the advanced artery period, the portal vein period and the delay period; based on a deep learning network, performing deep learning network training by jointly training each stage image of the MR image in the set and the corresponding digital subtraction image to obtain a liver cancer TACE postoperative active focus automatic detection and segmentation model; and verifying the detection and segmentation efficiency of the model through a verification set. The model provided by the invention can realize automatic detection and segmentation of the liver cancer TACE postoperative active lesion, can automatically obtain the TACE postoperative active lesion information of the liver cancer patient, has high accuracy, and effectively assists a clinician in accurately diagnosing the treatment effect of the liver cancer patient after intervention.

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.

The invention relates to a magnetic resonance imaging system and a method for suppressing electromagnetic interference in magnetic resonance data obtained by the magnetic resonance imaging system. Systems and methods for suppressing electromagnetic interference (EMI) in magnetic resonance (MR) data are also provided. These systems and methods include identifying a first subset of MR data that is affected by EMI and suppressing EMI in the first subset to obtain a second subset of MR data. Suppression of EMI in the first subset is performed by applying filtering to the first subset of MR data to suppress contributions of MR spin echo signals in the first subset of MR data, thereby obtaining signal suppressed MR data; suppressing EMI in the signal suppressed MR data to obtain EMI suppressed MR data; and applying the filtered inverse to the EMI suppressed MR data to obtain a second subset of MR data. These systems and methods include generating MR images using the second subset of MR data and outputting the generated MR images.

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.