298 results about "Imaging diagnosis" patented technology

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

The invention discloses a brain tumor image diagnosis large model pre-training method, diagnosis method and system, and the method comprises the steps: obtaining the image data of a brain tumor patient and a corresponding diagnosis text, and the image data comprises a plurality of sequences; constructing a visual unified model, carrying out complete sequence standard training and missing sequence distillation training on the visual unified model by utilizing the image data, learning unified visual representation of the image data of any sequence combination, and taking characteristics of the complete sequence image data as teacher characteristics in the missing sequence distillation training; the features of the missing sequence image data serve as student features, and distribution alignment of the student features and the teacher features in the feature space is restrained; constructing a visual language model, taking unified visual representation output by the trained visual unified model as input based on a multi-task target, and training the visual language model in combination with the diagnosis text; the trained model can effectively process the sequence missing condition, and the accuracy and robustness of diagnosis are improved.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The embodiment of the invention discloses an oral and maxillofacial surgical image recognition and diagnosis method and system based on deep learning, and the method comprises the steps: firstly obtaining an oral and maxillofacial three-dimensional image data set of a target patient, then carrying out the image feature extraction processing of the three-dimensional image data set, and obtaining a hierarchical image feature set; comprising local anatomical structure features and global spatial distribution features, and then calling a pre-trained multi-scale feature fusion network to perform multi-scale feature fusion on the hierarchical image feature set to generate a fusion feature map. And performing focus area identification processing based on the fusion characteristic spectrum, determining position information and form description information of an oral and maxillofacial abnormal area of the target patient, generating a diagnosis report according to the position information and form description information of the oral and maxillofacial abnormal area, and transmitting the diagnosis report to medical terminal equipment for display. Therefore, the accuracy and efficiency of oral and maxillofacial surgery image diagnosis are improved.

The invention discloses a multi-stage loss optimization OCTA (Optical Coherence Tomography Angiography) image diagnosis method based on semi-supervised learning and knowledge distillation. The method comprises the following steps: (1) constructing a teacher-student model framework in combination with semi-supervised learning; (2) data set processing: randomly removing part of sample labels of the training set as label-free data; (3) training a teacher model by using the labeled data, then generating a pseudo label of non-labeled data through the teacher model, and only retaining a high-quality pseudo label meeting a confidence threshold; and (4) training a student model by using a multi-stage loss function, wherein the student model comprises supervision loss of label data, supervision loss of pseudo-label data and knowledge distillation loss. Dependence on annotation data is reduced through semi-supervised learning, the performance of the lightweight student model is improved through knowledge distillation, and the method is suitable for scenes with limited clinical resources.

An AI image diagnosis apparatus into which three-dimensional tooth image data captured by a dental OCT device is inputted and which analyzes the inputted three-dimensional tooth image data, comprising: a model executor that sequentially inputs two-dimensional tomographic image data constituting the diagnostic target three-dimensional tooth image data into a trained model, to thereby obtain, as an execution processing result of the trained model, lesion information data, which is data related to a part identified as a characteristic part such as a lesion in the inputted tomographic image data, for each piece of tomographic image data in the three-dimensional tooth image data, and detect lesions from the inputted three-dimensional tooth image data using the obtained lesion information data, wherein the trained model is constructed by training of three-dimensional tooth image data of multiple examinees captured by a dental OCT device in the past.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention discloses a medical analysis method and system based on a multi-modal large language model and chain reasoning, and belongs to the crossing field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, carrying out preprocessing and modal alignment on medical multi-modal data to obtain image visual features and text embedding vectors of the same vector spatial dimension; capturing visual evidence in a structured pathological feature form through a self-excitation mechanism; on the premise of visual evidence, a thinking chain text containing causal logic is generated through evidence anchoring chain type reasoning; and finally, performing vision and text dual consistency verification on the thinking chain text, and outputting an analysis conclusion according to a result, or triggering a negative feedback correction mechanism to regenerate the thinking chain text. According to the method, medical reasoning full-process logic visualization is realized, medical illusion is eliminated, the diagnosis accuracy of complex cases is improved, and the method is suitable for medical scenes such as medical visual questions and answers and image diagnosis report generation.

Systems and methods are provided herein for developing and deploying active-prompt-tuned, domain-specific image diagnosis and categorization applications. Processes of the present disclosure may control and provide for human-confirmable diagnostics from images, based on controlled instructions provided to vision-language models. The controlled instructions may be developed by systems provided herein, which generate system prompts and example prompt sets using active prompt tuning approaches.

The invention relates to the technical field of medical image intelligent diagnosis, and provides an interpretable lung CT image intelligent diagnosis method and device, and the device comprises an image calling module, an image processing module, an image analysis module and a result display module. According to the method, identity information of a patient is read to automatically verify and authorize, and an electronic CT image is called and stored in a data storage card through an encryption transmission channel. And the image processing module performs denoising, enhancement and interpretability analysis on the original image to generate a thermodynamic diagram to assist in decision making. The image analysis module completes lesion detection and classification, verifies the consistency between a model decision and doctor experience through a thermodynamic diagram, and optimizes network parameters in combination with feedback data. And the result display module displays a diagnosis result and a thermodynamic diagram in real time, and supports multi-terminal cooperation and data archiving. The device supports data sharing and remote consultation, the efficiency and convenience of diagnosis are improved, and the wide application prospect of artificial intelligence in the field of medical diagnosis is shown.

The invention relates to the field of biomedicine, particularly provides a copper-based nano-enzyme as well as a preparation method and application thereof, and aims to solve the problems that in the prior art, clinical treatment on primary sclerosing cholangitis (PSC) is difficult in diagnosis and lacks of effective treatment drugs. The copper-based nano-enzyme comprises a nano-enzyme carrier and a copper-based nano-enzyme, wherein the nano-enzyme carrier is a two-dimensional nanosheet formed by copper ions, gallic acid and ursodesoxycholic acid through coordinate bonds; the probe molecule is a cyanine dye molecule connected to the surface of the nano-enzyme carrier through a covalent bond; wherein the fluorescence intensity of the copper-based nano enzyme is enhanced after the action of the alkaline phosphatase. The three functions of alkaline phosphatase responsive diagnosis, nano-enzyme catalytic treatment and ursodesoxycholic acid hepatic targeting are innovatively and synergistically integrated into one nano platform, the treatment function can be executed while specific imaging diagnosis is performed on diseases, and a new strategy is provided for diagnosis and treatment of diseases such as PSC.

The invention discloses a brain tumor diagnosis method and system based on multi-modal supervised contrast learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting an MRI image of a patient into a trained brain tumor diagnosis model, so as to output a brain tumor diagnosis result; the training process of the brain tumor diagnosis model is as follows: acquiring and preprocessing MRI images of a patient to construct an original MRI image set, and fusing MRI images of different modals of the same patient in the original MRI image set to obtain a fused MRI image set; combining the original MRI image set and the fused MRI image set to obtain a combined image set, and extracting image diagnosis features from the combined image set based on an image encoder; constructing cross entropy loss and multi-modal supervised comparison loss, and performing supervised learning on an image encoder to train a brain tumor diagnosis model; according to the brain tumor diagnosis method and system, high-precision brain tumor diagnosis is realized.

The invention provides a comprehensive solution for a medical ultrasonic image diagnosis task, which comprises two innovative parts, namely a CAPT parameter efficient fine tuning method based on a convolution adapter and embedded prompt tuning and a model lightweight method based on evolution pruning. Aiming at double challenges of scarcity of data and difficulty in model adaptation in the field of medical ultrasonic images, on one hand, the CAPT method introduces a convolution module into a traditional adapter to enhance the fine-grained feature extraction capability by fusing adapter tuning and prompt tuning mechanisms, and dynamically generates a prompt vector embedded with attention calculation by using adapter learning context information; the adaptability and the small sample generalization ability of the Transform model in an ultrasonic image classification task are obviously improved; on the other hand, aiming at a model edge deployment requirement, designing an evolution pruning algorithm, taking DenseNet-121 as a basic framework, inheriting parent strong neuron connection by adopting an asexual reproduction mechanism, and through a channel importance evaluation method for coupling a BN layer scaling factor and a weight L1 norm and in combination with a sparse evolution strategy guided by environmental constraint, constructing a model edge deployment model. And the classification precision is maintained, and meanwhile, the model parameters and the calculated amount are greatly compressed. The two technologies respectively break through the technical bottlenecks of medical image diagnosis from two dimensions of parameter efficient fine tuning and model structured pruning, the former improves the adaptability of the model field through feature extraction optimization, and the latter balances network lightweight and performance stability by means of an evolutionary algorithm. And an innovative technical path is provided for efficient deployment of an ultrasonic image auxiliary diagnosis system in a data limited scene and an edge computing environment.

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 present invention relates to the field of image processing technology, and in particular to a method for image diagnosis and positioning of a supplementary motor area based on a resting state, comprising: acquiring a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the acquired user's brain magnetic resonance image; acquiring the user's brain magnetic resonance image, and removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image; acquiring the supplementary motor area, and performing low-frequency pulsation amplitude analysis, local consistency analysis, and functional connection abnormality analysis; the present invention utilizes brain maps for preliminary positioning and functional connection mutual verification, can more accurately determine the position of the supplementary motor area, reduce positioning errors, and evaluate the state of the supplementary motor area from two aspects of local brain activity and functional connection abnormality analysis, and based on this method, reduces the interference of subjective factors, realizes the quantification of diagnostic indicators, and makes the results more objective and reliable.

The invention discloses a medical image privacy protection-oriented structure perception adaptive steganography method, which comprises the following steps of: in a training stage, generating a diagnosis related region mask based on a pre-trained IMIS-Net, extracting an embeddable suppression mask, and obtaining an initial embedding probability under the minimum distortion constraint; and the self-adaptive adjustment of structure perception is carried out on the pixel-level embedding probability in combination with a reinforcement learning normal form, so that the ROI is protected preferentially and a high-risk region is inhibited on the premise of meeting the set capacity, and the steganalysis resistance and perception quality are improved. And after the training is completed, in a reasoning stage, the masks and probabilities are firstly generated or quoted, then action probabilities are obtained through a trained strategy network, then probability consistency is mapped into STC cost, real embedding is completed by the STC, and a secret-containing medical image is obtained. According to the invention, on the premise that the availability of image diagnosis is not reduced, safe embedding and hidden transmission of sensitive information are realized.

The invention relates to the technical field of computer vision and industrial equipment fault diagnosis, in particular to a fault image diagnosis system for a remote controller of a bolter miner driven by a CMC chip. Comprising a data sensing module for synchronously acquiring a CMC chip internal logic state vector and a real-time observation image, and preprocessing to obtain target visual data; the generative reconstruction module maps the logic state vector to a high-dimensional space to generate an ideal feature map; the visual analysis module is used for extracting an observation feature map through a convolutional neural network, generating an interference distribution mask based on frequency domain texture statistics, and identifying an unstructured environmental noise region; the difference analysis module is used for calculating a semantic residual image by combining the ideal feature map, the observation feature map and the interference distribution mask, and generating an image difference analysis result; according to the method, ideal features are generated by using the internal logic state vector of the CMC, and the residual calculation of the coal dust noise area is shielded by the interference mask, so that the false alarm caused by the environmental noise is effectively eliminated, and the closed-loop verification that what you see is what you control is realized.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention provides a concrete structure integrity evaluation method and device based on wave velocity image diagnosis, and is applied to the technical field of geophysical exploration, and the method comprises the steps: obtaining a vibration signal, monitored by a vibration sensor in real time, of a concrete structure; when the vibration signal exceeds a preset vibration threshold value, determining the vibration signal as a target vibration signal; identifying the target vibration signal, and when the target vibration signal is an internal micro-seismic signal of the concrete structure, determining the arrival time of a first arrival wave of the target vibration signal; on the basis of a speed model generated by preorder iteration, arrival time inversion positioning is carried out on arrival time of the first arrival waves, and focus coordinates of the target vibration signals are obtained; constructing a dynamic speed model based on the arrival time of the first arrival wave and the focus coordinates; and performing real-time evaluation and damage trend analysis on the interior of the concrete structure based on the dynamic speed model. According to the invention, high-precision real-time monitoring of the wave velocity distribution of the concrete structure can be realized.

According to the present disclosure, provided are a method for harmonizing a medical image which includes receiving a medical image for an object, and harmonizing the received medical image to acquire a filtered medical image by using a convolutional filter based on a predictive model trained to output a reconstructed image using the medical image as input, in which the convolutional filter corresponds to a first convolutional layer of the predictive model, and a device using the same and the present disclosure is a technology developed through the Seoul Business Agency of the Seoul Metropolitan Government (2023 Bio-Medical Technology Commercialization Support Project BT230080 Validation of Safety and Efficacy of Echocardiographic Imaging Diagnosis Solution through Myocardial Texture Analysis Based on Ultrasound Imaging).

The invention relates to the technical field of medical image artificial intelligence, in particular to a multi-prototype cross-modal contrast learning fetal heart ultrasound image intelligent diagnosis system which comprises the steps that a to-be-detected fetal heart ultrasound image is obtained through an obtaining module, and a processing module inputs the to-be-detected fetal heart ultrasound image into a pre-trained image diagnosis model; the method comprises the following steps: performing feature extraction on a to-be-detected fetal heart ultrasound image, performing cross-modal multi-prototype comparison according to a feature extraction result to obtain a diagnosis category and a most matched image-text prototype pair, outputting the diagnosis category and the most matched image-text prototype pair by an output module, and finally performing CHD classification and screening according to the diagnosis category and the most matched image-text prototype pair. The problem that the model generalization ability is insufficient due to the fact that a single prototype center is mainly relied on to represent each category and the significant difference of the same category under different conditions is neglected is solved, and multi-category CHD intelligent screening is achieved by utilizing unlabeled images and a small amount of text priori on the premise that a large number of graph-text pairs are not needed.

The invention relates to the technical field of medical image processing, and discloses a temporomandibular joint three-dimensional reconstruction system based on joint imaging diagnosis. The system utilizes CBCT equipment to collect temporal-mandibular joint tomographic image data, and extracts facial feature sites and maxillofacial joint standard model approval sites through a multi-scale residual network. And carrying out space registration by adopting an affine transformation and nonlinear optimization algorithm to generate a synchronous correction parameter, further carrying out deformation interpolation on the standard model of the maxillofacial joint to construct a maxillofacial model, and adjusting the position of a condylar process. And based on the deformed maxillofacial model, generating an occlusal plate structure model by using an isogeometric analysis method, and iteratively optimizing the degree of fit through an edge calculation frame. The system can accurately reconstruct the three-dimensional model of the temporomandibular joint, optimizes the adaptation of the biteplate, provides powerful support for the diagnosis and treatment of temporomandibular joint diseases, and facilitates the improvement of the diagnosis and treatment level.

The invention discloses a clinical automatic diagnosis and treatment platform for temporomandibular joint disorder, and relates to the field of medical instruments. The platform comprises a patient subjective symptom evaluation system, a clinical diagnosis system, an image diagnosis system and a treatment scheme making system. The patient subjective symptom evaluation system is used for automatically obtaining a subjective feeling diagnosis result of the patient according to a subjective feeling questionnaire filled by the patient; the clinical diagnosis system is used for automatically obtaining a clinical diagnosis result of the patient according to the past medical history and clinical examination of the patient; the image diagnosis system is used for automatically obtaining an image diagnosis result of the patient according to the CBCT image and the MRI image of the patient; the treatment scheme making system is used for automatically making a treatment scheme. According to the platform constructed by the invention, subjective evaluation information of a patient, a clinical automatic diagnosis result and an imaging automatic diagnosis result are fused, a treatment scheme is automatically formulated, temporomandibular joint disorder can be found and diagnosed in time, and influences on treatment such as orthodontic treatment, orthognathic treatment, repair and implantation are avoided.

This invention relates to aggregation-induced emission probes with specific responses to tumor markers and their applications, belonging to the field of biochemistry. This invention utilizes molecular structure modulation to prepare a series of molecules possessing large Stokes shifts, resistance to photobleaching, near-infrared II (NIR-II) emission, and aggregation-induced emission properties. Different self-eliminating groups are attached to the dimethylamino group of the molecule via quaternization or to the pyridine group via pyridinium salting. By controlling the structure of these self-eliminating groups, activatable luminescent probes responding to different tumor markers can be constructed. These probes integrate deep NIR-II imaging, aggregation-point luminescence, large Stokes shifts, and marker-specific responses, providing a new approach for precise imaging diagnosis of tumors.

The invention discloses a preparation method and application of near-infrared fluorescent dye DOM-O for acute alcoholic liver injury detection. The specific chemical structural formula of the near-infrared fluorescent dye DOM-O is shown in the specification. The near-infrared fluorescent dye prepared by the invention has excellent fluorescence imaging performance, can realize specific fluorescence imaging diagnosis of the acute alcoholic liver injury, and provides an imaging dye with application prospect for early detection and clinical diagnosis of the acute alcoholic liver injury.

This invention provides a detachable, interchangeable imaging diagnostic device that allows for the use of various sizes of scopes, enabling easy image diagnosis of various affected areas. [Solution] The interchangeable imaging diagnostic device comprises a scope module including a fluoroscopy tube 110, a scope body 120 surrounding the rear of the fluoroscopy tube, an LED 130 providing light into the fluoroscopy tube, a heat sink 140, and an LED connection jack 150 protruding from the rear surface; a main body including a focus lens 210 for observing the condition of the affected area through the fluoroscopy tube, an adjustment lever 220 for magnifying or reducing the condition of the affected area as seen by the focus lens, a camera 230 for saving images of the body cavity, and an imaging trigger 240 for capturing images of the captured images. The main body has an LED power jack into which the LED connection jack is inserted and a coupling groove into which the rear end of the fluoroscopy tube is inserted, and is characterized by a structure that allows for the selection of scope modules with different fluoroscopy tube sizes and their detachable connection to the main body.

The application provides an ophthalmic image diagnosis method and system based on multi-modal imaging cooperation, and relates to the technical field of medical image diagnosis. First, the OCT, fundus camera and ultrasonic original image data of an ophthalmic examination object are acquired, and multi-modal dynamic correlation mapping results are obtained through dynamic correlation and trend correlation processing. Cross-modal lesion feature progressive mining and interactive verification are performed to obtain a cross-modal lesion correlation feature set. Multi-modal cooperative diagnosis reasoning and weight feedback optimization are used to generate an ophthalmic disease reasoning result containing disease types, lesion dynamic distribution and reasoning confidence. Finally, an ophthalmic diagnosis report with dynamic labeling and confidence explanation is generated. The application comprehensively utilizes the advantages of various image technologies to improve the accuracy of ophthalmic image diagnosis.

The application discloses a kind of based on CBCT image data evaluation implant stability classification model and the construction method and device thereof.First, through the Mobilenetv2-DeepLabV3+ network after training in cross-sectional image implant is segmented, then in combination with the knowledge of oral implantology and segmentation result corresponding implant around bone image is extracted on cross-sectional image, finally using the deep residual network Resnet-50 after training is completed classification, obtains implant stability evaluation result.Test evaluation, the model of the application has higher diagnostic performance, the time consumption of evaluation is short, accuracy is high (>90%), can be effectively used for implant stability evaluation.The classification model for evaluating implant stability of the application has important significance for better guiding further implant restoration treatment, and provides important reference for artificial intelligence used for image diagnosis.

The invention belongs to the technical field of radionuclide labeled radiochemistry and clinical nuclear medicine, and relates to a radionuclide labeled targeted CD44v6 antagonist as well as a preparation method and application thereof. The invention discloses a polypeptide radiopharmaceutical for CD44v6 targeting and a preparation method of the polypeptide radiopharmaceutical. The radiopharmaceutical is radionuclide-bifunctional chelating agent-CDA6, the radionuclide is labeled with polypeptide through the bifunctional chelating agent-CDA6, and the CDA6 polypeptide is of a straight chain structure composed of six amino acids. The radiopharmaceutical realizes specific nuclear medicine positron emission computed tomography (PET) or single photon emission computed tomography (SPECT) imaging diagnosis and / or radioactive therapy of CD44v6 positive tumors or CD44v6 positive lesions (autoimmune diseases, cardiovascular and cerebrovascular system diseases and the like) through specific recognition of the polypeptide CDA6 on CD44V6 in a living body.