371 results about "Medical imaging technology" patented technology

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention provides a double-path medical image segmentation method and device based on multistage comprehensive attention, and relates to the technical field of medical images. The method comprises the following steps: acquiring to-be-segmented medical image data, and inputting the to-be-segmented medical image data into a constructed comprehensive attention converter to obtain a medical image segmentation result; the comprehensive attention converter comprises a dual-path encoder, a first mixed attention converter module and a decoder; the encoder comprises a plurality of layers; the decoder comprises a multi-scale feature fusion decoder and a multi-scale aggregation module; the multi-scale feature fusion decoder comprises a channel-based efficient feature fusion module and a comprehensive attention module; the channel-based efficient feature fusion module comprises a multi-channel connectivity extraction module and a channel-level feature fusion module; the comprehensive attention module comprises an enhanced converter module, a channel attention module, a space attention module and a convolution block; the multi-scale aggregation module includes a second hybrid attention converter module and a multi-layer prediction link. And the feature extraction performance is improved.

The invention provides a three-dimensional super-resolution method suitable for a CT image sequence, and relates to the technical field of medical images. According to the method, image high-frequency information is extracted more accurately through gradual combination of multi-scale deep convolution and an attention mechanism, a medical CT image interlayer super-resolution network which is iteratively refined from coarse to fine is designed, and a cross section slice of a high-resolution middle frame is synthesized by using two continuous slices at spatial positions. The method comprises the following steps of: generating a multi-scale context feature by using a feature pyramid structure, gradually fusing a two-way deformation estimation graph through an IIR (Infinite Impulse Response) module by combining a global two-way distortion function (GBDF) with two-way optical flow information and the multi-scale context feature, complementing detail information for an intermediate frame by combining an STIFOF (Spatial-Temporal Interval-Frame Optimization Function) function to realize motion compensation, and finally obtaining a high-resolution intermediate frame prediction graph.

The present invention relates to the technical field of medical images. Disclosed are a deep learning-based CT artifact removal method and system. The method comprises: acquiring a CT image, and separately performing sine transform and wavelet transform processing on the CT image; constructing an image enhancement model, and performing image optimization on the processed image separately by means of the image enhancement model and a random inversion layer which are connected in sequence; coupling the optimized image and the original image and then inputting the coupled image into the image enhancement model for reprocessing; and performing element-wise addition on the reprocessed image and the optimized image to obtain an artifact-removed CT image. In the present invention, wavelet transform is introduced to process the CT image to extract the context and spatial information of the CT image, effectively extracting feature information in an artifact removal process and improving the performance of image enhancement; and a CT image resolution enhancement model based on a VMamba model is established, enhancing the long-term dependencies in network training, effectively recognizing and removing radioactive artifacts, and improving the network training efficiency.

The invention relates to the technical field of medical images, in particular to a neural CT map segmentation method based on high-precision registration. The method comprises the following steps: acquiring a nerve CT image; performing coarse registration based on mutual information according to the CT image, and performing multi-scale fusion based on wavelet to obtain a fused image; performing standard map preprocessing on a preset standard nerve CT map database according to the fused image to obtain a preprocessed standard map set; adding a multi-scale feature fusion module to a pre-established basic U-Net architecture, and embedding a space attention module to obtain a to-be-trained U-Net network; and performing deep registration network training on the to-be-trained U-Net network by using the fused image and the preprocessed standard atlas set to obtain a trained U-Net network. According to the invention, through technologies of multi-modal image fusion, deep learning registration and conditional random field optimization, a segmentation effect with higher precision and higher robustness is realized.

The invention discloses a self-adaptive adjustment method and system for operating parameters of color ultrasound equipment, and relates to the technical field of medical imagines.The method comprises the steps that in the cardiac ultrasound examination process, image feature state data information is associated with echo signal state data information, and a motion artifact evaluation coefficient Xwy is obtained; after comparison, an artifact image frame set is marked, and a gain adjustment analysis instruction is sent out; the method comprises the following steps: acquiring vascular rigidity state data information according to interference of vascular instantaneous relaxation on echo signals, and associating the vascular rigidity state data information with a corresponding motion artifact evaluation coefficient Xwy to obtain an echo signal gain degree index Zzy; through comparative analysis, whether each frame of echo signal corresponding to the current artifact image frame set needs gain compensation is judged so as to generate and execute a gain adjustment instruction of a corresponding grade, and the process optimizes the gain adjustment of the echo signal, improves the image quality, ensures the definition of ultrasonic imaging, and improves the accuracy of ultrasonic imaging. Therefore, the accuracy and the diagnosis reliability of cardiac ultrasonic examination are improved.

The invention belongs to the technical field of medical images, and particularly relates to an anterior mediastinal surgery three-dimensional imaging method and system based on big data. According to the method, parallel preprocessing is carried out on the multi-modal medical image data through a distributed computing framework, the data processing speed and efficiency are effectively improved, through comparative analysis of different reference image data, an overlapping area and an independent area can be determined, corresponding partition processing is carried out on the overlapping area, and in the three-dimensional reconstruction stage, the three-dimensional reconstruction efficiency is improved. Three-dimensional reconstruction is respectively carried out on the independent region and the overlapped sub-regions, a three-dimensional image model of the independent region and a fused overlapped sub-region three-dimensional image model are generated, the spatial topological structure and the surface geometric characteristics of image data are fully considered, the accuracy of the reconstructed image is ensured, and in the image reconstruction stage, the image reconstruction efficiency is improved. And the three-dimensional image model of the independent region and the three-dimensional image model of the overlapped sub-regions are combined to generate a complete anterior mediastinal surgery three-dimensional image, so that a powerful guarantee is provided for accurate implementation of the surgery.

The invention discloses a robot-assisted real-time three-dimensional Doppler OCT (optical coherence tomography) angiography method and system, relates to the technical field of medical imaging, and can be used for carrying out real-time and large-view-field three-dimensional Doppler optical coherence imaging evaluation during a microvascular anastomosis operation and improving the anastomosis operation evaluation accuracy and efficiency. According to the system, a spectral domain optical coherence tomography system SD-OCT is used for performing spectral domain optical coherence tomography on a sample to obtain a sample cross-sectional image, and the sample cross-sectional image is sent to a display to be displayed. The OCT imaging probe and the binocular depth camera are integrally installed at the tail end of the robot arm, and the robot arm operates the OCT probe and the binocular depth camera to move to different positions and angles to image a sample. The OCT imaging probe collects optical signals reflected by a sample, and the signal processing host and the displayer receive spectral signals transmitted by the OCT imaging probe and display the three-dimensional structural characteristics of the microvessel and the blood flow velocity condition in the blood vessel.

The invention relates to the technical field of medical images, and discloses a medical image recognition method and system based on deep learning, electronic equipment and a medium, and the method comprises the steps: carrying out the split-mode adaptive normalization of an initial medical image, carrying out the preprocessing through elastic deformation, random zooming and Gaussian noise, and obtaining a preprocessed medical image, constructing a Swinin-ResNeSt hybrid model based on the preprocessed medical image, adding an adaptive multi-scale attention module in the Swinin-ResNeSt hybrid model, designing double task heads including classification and positioning, adopting a hybrid loss function combining a Focal Loss function and a GIoU Loss function, and outputting a focus category and a bounding box; according to the invention, the identification accuracy is improved.

According to the multi-domain perceptual contrast enhanced computed tomography image synthesis method and system and the electronic equipment, efficient synthesis from non-contrast enhanced images to high-quality contrast enhanced images is successfully achieved by adopting the 3D auto-encoder and the de-noising diffusion probability model, the calculation process is optimized, and the image quality is improved. Due to the introduction of multi-domain perceptual regularization, fine-grained enhancement is carried out on the image at different frequency bands, and the detail expressive force and texture consistency of the synthesized image are remarkably improved. Besides, automation of an image synthesis process is realized through a deep learning framework, manual operation is reduced, processing time is shortened, and a rapid, accurate and reliable technical solution is provided for the field of medical image processing. The technical innovation provides a new path for the generation of the CECT image, and promotes the development of the medical imaging technology.

The invention relates to the technical field of medical imaging, and discloses a method for automatically discriminating and enhancing phases in CT scanning by an attention mechanism based on multi-instance learning, and the method comprises the steps: extracting a CT scanning image and DICOM information from a data set, and unifying the image size after HU conversion, truncation and linear zooming; obtaining a fixed number of slices by using direct or indirect random sampling and a cubic interpolation strategy; then, a pre-trained model is adopted to extract slice features, scanning level feature vectors are formed through attention aggregation, and finally prediction probabilities of four types of phases are output through a full connection layer and a Softmax module; the model is trained by adopting cross entropy loss and an Adam optimizer, the performance is evaluated through indexes such as precision, recall rate and F1-score, and a thermodynamic diagram is generated by combining gradient back propagation, so that visual visualization of a result is realized, and clinical judgment is assisted. According to the method, the CT scanning multi-phase can be accurately, rapidly and interpretably classified in a real clinical environment.

The invention relates to the technical field of medical imaging, and discloses an image processing method and system for a dual-energy X-ray bone mineral densitometer, and the method comprises the steps: collecting a dual-energy X-ray image, and carrying out the light and dark field correction of original projection data; based on the corrected projection data, using an aluminum sheet to simulate bones and glass to simulate tissues, carrying out data fitting to obtain a bone mineral density value, and fitting a bone-aluminum conversion coefficient; preprocessing the image, wherein the preprocessing comprises Gaussian smooth filtering, threshold processing and corrosion operation; segmenting the image by using a region growing algorithm, and carrying out edge detection by using a Canny operator; extracting an ROI (Region of Interest), and determining the boundary of the ROI by searching coordinates meeting a preset threshold condition within a preset proportion range in the vertical direction of the image; and calculating the area of the ROI, the bone mineral density BMD and the bone mineral salt content. The accuracy of bone mineral density measurement is improved, the influence of noise and artifacts can be effectively reduced, and the target area is accurately extracted.

The invention provides an imaging data analysis method of X-ray system equipment, which relates to the technical field of medical imaging and comprises the following steps: receiving an imaging frame selection area and interested demand information; performing scanning coverage analysis on the imaging frame selection area, and outputting a non-equiangular scanning path; dynamically scanning the imaging target by taking the non-equiangular scanning path as a constraint to obtain a two-dimensional slice sequence; constructing an ROI identification network to analyze and identify the two-dimensional slice sequence to obtain a two-dimensional identification sequence; and cooperatively operating a three-dimensional reconstruction module and a space-time fusion module to respectively carry out space-time reconstruction fusion on the two-dimensional identification sequence and the two-dimensional slice sequence to obtain a dynamic VOI and a dynamic three-dimensional model, and carrying out imaging fusion to obtain dynamic label body data. The technical problem that the dynamic change of the target cannot be comprehensively captured due to redundant scanning paths and lack of accurate data fusion processing in the prior art is solved, and the technical effect of improving the imaging accuracy and the dynamic expressive force is achieved.

The invention provides a tumor cell assisted positioning system and method based on a composite image, and relates to the technical field of medical image.The system comprises a data acquisition module used for synchronously acquiring radio frequency echo signals, ultrasonic images and computed tomography data of target biological tissue to generate an original fusion data set; the feature processing module is used for performing spatial registration and feature extraction on the original fusion data set to obtain a multi-dimensional feature set; and the identification imaging module is used for extracting a multi-band radio frequency echo signal intensity level value of the target area based on the multi-dimensional feature set, generating a tissue radio frequency characteristic diagram through logarithmic compression, and performing pixel-level fusion on the tissue radio frequency characteristic diagram and the synchronous ultrasonic image to generate a composite image. According to the method, the three-dimensional coordinates of the tumor boundary are determined through multi-modal data fusion and precise calibration in combination with anatomical structure constraints, and the accuracy of tumor cell positioning is improved.

The invention belongs to the technical field of medical imaging, and discloses an angiography high-pressure injection system which comprises the following steps: acquiring physiological data information of a patient, and calculating an individualized contrast agent injection parameter coefficient based on the physiological data information; acquiring anatomical structure information of a blood vessel region of the target patient, and performing pressure evaluation based on hemodynamic characteristics to obtain blood vessel pressure bearing capacity and injection pressure regulation factors; performing physiological status assessment based on the physiological data information of the patient to obtain a physiological status influence factor and generate a risk marker value; performing multi-source data fusion according to the injection pressure regulation factor, the physiological status risk marker value and the historical radiography record, and constructing the radiography safety degree of the blood vessel sub-region; an optimal injection parameter path is obtained based on the radiography safety degree in combination with a deep reinforcement learning algorithm, and high-pressure injection equipment is controlled to execute injection operation; according to the invention, accurate and personalized control of contrast agent injection is realized, and the safety and imaging effect of contrast examination are remarkably improved.

The invention discloses a multi-label medical image classification method, belongs to the technical field of medical images, and solves the problem that an existing CNN-based multi-label medical image classification method is low in classification accuracy. According to the method, the image data in the training image set and the clinical risk data in the feature set are combined, the features of which the importance ranks in the front are selected from the single labels through the univariate decision tree model to form the top-N feature set, and then the top-N feature set is input into the MedFusionNet model, so that the multi-mode learning of the MedFusionNet model is expanded, and the accuracy of the multi-mode learning of the MedFusionNet model is improved. According to the method, input features can be represented more abundant, challenges of label dependency, data imbalance and modal integration are effectively solved, meanwhile, interpretability and predictability are enhanced, and classification accuracy is improved.

The invention discloses a CT image change-based lung cancer curative effect dynamic prediction method and system, and relates to the technical field of medical imaging. Clinical information, a baseline CT image and a follow-up CT image of a patient are acquired; performing feature extraction on the baseline CT image and the follow-up CT image to obtain baseline features and follow-up features; obtaining an enhanced CT dynamic feature matrix and a flat-scanning CT dynamic feature matrix based on the baseline features and the follow-up visit features; inputting the enhanced CT dynamic feature matrix, the plain-scan CT dynamic feature matrix and clinical information into a curative effect prediction model, outputting logic risk scores at different time points, and obtaining progress or death risk probabilities of the patient at different time points; according to the method, image feature changes in a baseline and follow-up visit are effectively represented through a dynamic feature matrix, correlation between the interior of the dynamic feature matrix and between different matrixes is identified through a Transform attention network to predict the curative effect, and the relationship between accumulated data at each follow-up visit time point and the curative effect is learned through a time-sharing training strategy.

The embodiment of the invention is suitable for the technical field of medical imaging and image calibration, and provides an ultrasonic probe calibration method and system and electronic equipment, and the method comprises the steps: receiving an ultrasonic probe pose sequence and a calibration model pose sequence; receiving an ultrasonic image sequence; performing mark point identification and straight line identification on the ultrasonic image sequence to determine a first noise image and a second noise image in the ultrasonic image sequence; after the second noise image is deleted from the ultrasonic image sequence, time calibration is carried out on the ultrasonic probe, and a rate difference value between an ultrasonic probe displacement signal and an ultrasonic image displacement signal is obtained; and after deleting the first noise image from the ultrasonic image sequence, performing spatial pose calibration on the ultrasonic probe based on the rate difference value to obtain a spatial pose calibration matrix of the ultrasonic probe. By adopting the method, the calibration precision of the ultrasonic probe and the stability of the calibration result can be improved.

The invention discloses a tumor angiogenesis detection and analysis method based on multi-modal image fusion, and relates to the technical field of medical imaging, and the method comprises the steps: collecting multi-modal image data of a target tumor; performing feature extraction on the acquired multi-modal image data according to a neural network model; fusing the extracted features through a fusion network to obtain a comprehensive feature vector; analyzing the tumor angiogenesis state according to the comprehensive feature vector to obtain an analysis result; according to the analysis result, the angiogenesis state of the tumor is evaluated, and a report is generated. More comprehensive tumor angiogenesis information can be provided, and the accuracy of detection and analysis is improved. Through multi-modal image data fusion, the in-vivo tumor microenvironment can be simulated more truly. The method can be used as a preclinical research platform of anti-tumor and anti-tumor angiogenesis drugs, and provides support for drug screening and evaluation.

The invention relates to the technical field of medical imaging, and discloses an image reconstruction method and device for a Compton camera, computer equipment and a storage medium. Comprising the following steps: acquiring current weight distribution and reference weight distribution corresponding to an imaging plane; real radioactive source simulation is carried out based on the current weight distribution, and simulation weight distribution corresponding to the imaging plane is obtained; updating elements in the current source set according to the simulation weight distribution and the reference weight distribution to obtain an updated source set so as to perform image reconstruction on the Compton camera; wherein elements in the current source set are used for representing radioactive source coordinates corresponding to Compton events occurring in the Compton camera. Therefore, the image reconstruction method capable of improving the imaging quality of the Compton camera is realized, and the reconstructed image can reflect the actual distribution condition of the radioactive source more accurately, so that a reliable basis is provided for real-time monitoring of the boron concentration in the BNCT treatment process.

The invention discloses a dynamic DR lung field area diagnosis system based on deep learning, and relates to the technical field of medical imaging, and the system comprises an image collection optimization module which is used for capturing a low-dose dynamic DR sequence of a complete period of calm respiration and best effort respiration of a patient, generating a dynamic DR image, and optimizing the dynamic DR image through employing a multi-mode transfer learning strategy, outputting a DR time sequence tensor; and the intelligent diagnosis module is used for carrying out disease cause classification through a gating chart convolutional network in combination with the lung field dilation function ratio and the space structure complexity index, outputting a classification result, and generating a structured report in combination with the lung field dilation function ratio, the space structure complexity index and the continuous lung field area change curve. The differential homeomorphic transformation field is generated through the Lie group exponential mapping algorithm, and the lung lobe boundary connectivity and the bronchial tree topological structure are strictly kept in the three-dimensional deformation process.

The invention relates to the technical field of medical images, in particular to a nervous system medical image case database construction system based on artificial intelligence, which comprises a nervous system medical image database module, a teaching interactive learning platform module, a user self-test module and a user online examination module. The teaching system truly restores a diagnosis report written in a clinical working environment of a doctor, organically combines book knowledge with clinical practice, can arouse the learning enthusiasm and initiative of imaging medical students and living training users aiming at complex nervous system medical imaging teaching, improves the teaching quality and practical skills, and improves the teaching efficiency. The learning effect is fed back in real time through the computer, rapid fusion of theoretical knowledge and clinical practice is promoted, accurate lesson preparation of teachers is facilitated, a flexible and convenient education mode and an accurate and individualized training mode are achieved, the training process of image resident physicians is accelerated, the teaching efficiency and quality are improved, and the system adapts to a modern teaching mode.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention relates to the technical field of medical images, in particular to a multi-modal medical image registration and fusion analysis method, which comprises the following steps of: eliminating image noise and artifacts based on a modal adaptive filtering strategy; constructing a pyramid type feature extraction network to realize multi-scale feature extraction, calculating feature matching degrees among different modal images, and dynamically adjusting matching weights by combining feature differences among modals; a focus area attention mask is constructed, targeted enhancement of registration image features is realized, and a hierarchical fusion strategy is adopted to evaluate the quality of a fused image; and constructing a multi-task deep learning model to complete focus automatic detection, segmentation and benign and malignant preliminary judgment on the fused image. According to the multi-modal medical image registration and fusion analysis method, a three-layer feature pyramid is constructed, a multi-feature fusion matching cost function is introduced, and cross-modal feature matching is optimized through an adaptive weight iteration nearest point ICP algorithm, so that the information richness, marginal definition and focus discrimination of a fused image reach the standard.

The invention relates to the technical field of medical imaging, and discloses an artificial intelligence-based temporomandibular joint disorder prediction method, which comprises the following steps: acquiring an MRI image of a patient; performing data resampling according to the average spatial resolution of the patient image; cutting the patient data three-dimensional image layer by layer to obtain cut patient three-dimensional image data; introducing the cut three-dimensional image data of the patient into an artificial intelligence algorithm; and grouping verification is carried out on the cut three-dimensional image data of the patient, so that a temporomandibular joint disorder classification prediction model is established for prediction. The application proposes that MRI iconomics examination has significant advantages in diagnosis of temporomandibular joint disorder, data are collected to construct an MRI iconomics diagnosis model, clinical factors of a patient suffering from temporomandibular joint disorder are combined with an iconomics score calculation formula (Radio-score, Rad-score), an iconomics-clinical combined prediction model of temporomandibular joint disorder cases is established, and the prediction accuracy of the temporomandibular joint disorder cases is improved. And a powerful tool is provided for accurate diagnosis of temporomandibular joint disorder.

The invention relates to an oxidase response type molecular probe as well as a preparation method and application thereof, and belongs to the technical field of medical imaging. Aiming at the problem that the inflammation activity is difficult to accurately evaluate in the prior art, the invention provides a molecular imaging probe capable of responding to the activity of in-vivo myeloperoxidase (MPO), and the molecular imaging probe can be used for real-time and non-invasive imaging monitoring of the activity of the in-vivo MPO, so that the activity of the inflammatory reaction of a focus is accurately evaluated. The invention provides a chelating agent with structural characteristics of a chelating unit and an enzyme response unit, and is characterized in that the enzyme response molecular probe is constructed by taking rigid framework cyclohexanediamine (CDTA) as a transition metal chelating unit and taking electron-rich functional groups (such as phenol, catechol or 5-hydroxytryptamine) as the enzyme response unit. The probe is chelated with paramagnetic metal (such as Mn < 2 + >) for magnetic resonance imaging (MRI) or radioactive metal (such as 68Ga) for positron emission tomography (PET), so that specific response to MPO activity is realized. The dynamic stability of the probe is remarkably improved; after the MPO is responded, the relaxation efficiency is improved by 2-3 times, and the sensitivity is optimized; the cytotoxicity is low; in an acute pancreatitis model, the MRI contrast noise ratio (delta CNR) reaches 77.39 + / -7.04 and is 4.2 times that of a control group, the PET uptake rate is remarkably improved, bimodal imaging is supported, and accurate positioning and quantitative evaluation of the inflammation activity are achieved.

Implantable transponders comprising no ferromagnetic parts for use in medical implants are disclosed herein. Such transponders may assist in preventing interference of transponders with medical imaging technologies. Such transponders may optionally be of a small size, and may assist in collecting and transmitting data and information regarding implanted medical devices. Methods of using such transponders, readers for detecting such transponders, and methods for using such readers are also described.

The invention discloses an intelligent auxiliary system for microsurgery in the field of medical auxiliary systems, which is characterized in that a sensor and an image recognition device are integrated to monitor the action posture of medical personnel, and the medical imaging technology and the sensor technology are utilized to monitor the information of the surgical site of a patient; based on a machine learning algorithm and a big data analysis technology, learning action posture data of the medical staff in a historical operation process, and establishing an operation action posture model of the medical staff; according to monitored medical staff action posture information and patient operation site information, combined with the established model, the operation action posture needed by medical staff is predicted, real-time response is achieved, and the supporting device and the operation instrument are automatically adjusted through the controller. The system has the advantages of improving operation precision and efficiency, relieving burden of medical staff, enhancing operation safety, improving operation collaboration, promoting medical technology innovation and development and the like, and intelligent auxiliary operation can be conveniently carried out during microsurgery.

The invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

The invention relates to the technical field of medical imaging, and discloses a peripheral lung cancer pleural invasion evaluation system based on ultrasonic targeted radiography, comprising: a double-frequency ultrasonic probe configured to independently work at different frequencies to realize depth imaging and high-resolution imaging of lung tissues and suitable for different types of lung lesions; the signal processing module can receive, analyze and process echo signals transmitted by the ultrasonic probe, generate images in real time and extract feature information; and the dynamic motion compensation module is used for automatically adjusting the emission time sequence and parameter setting of the ultrasonic signal based on the real-time breathing motion state of the patient. By adopting the design of the double-frequency ultrasonic probe and the core-shell structure, high-resolution imaging and depth detection of lung tissues are realized. By dynamically adjusting the thickness of the probe shell to be matched with the frequency, the system can adapt to complex tissue environments under different physiological conditions, and image definition and stability are ensured.