183 results about "Medical imaging technology" patented technology

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, 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.

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 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 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 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.

The invention discloses a default mode network construction method, system and device and a storage medium, and relates to the technical field of medical imaging, and the method comprises the following steps: selecting a plurality of seed points in an fMRI image, constructing an original default mode network based on the seed points, and obtaining a corresponding function connection diagram; overlapping the function connection diagrams generated by the seed points to obtain an overlapping graph; screening the functional connection strength of each voxel in the overlapped graph based on a correlation threshold, and obtaining a plurality of new ROIs according to a screening result; and extracting a new ROI time sequence, calculating Pearson correlation coefficients of the time sequence, and generating a function connection matrix based on a plurality of Pearson correlation coefficients, namely generating a new default mode network. According to the method, the DMN network structure can be reflected more comprehensively through the seed points at different positions, the one-sidedness of a single seed point is made up, and a more accurate DMN network can be constructed.

The invention provides a liver cancer onset risk prediction method and system, and belongs to the technical field of liver cancer onset risk prediction. The method comprises the following steps: carrying out longitudinal multi-omics data acquisition on a target individual to generate an individualized multi-omics time sequence data set; constructing an individualized liver cancer evolution graph based on the data set; according to the individualized liver cancer evolution graph, quantifying a liver microenvironment pressure field in combination with a medical imaging technology, and performing space-time coupling on a pressure field quantification result and a multi-omics time sequence data set to generate a liver cancer dynamic risk evolution trajectory; through longitudinal multi-omics data acquisition and individualized liver cancer evolution graph construction, a liver microenvironment pressure field is quantified in combination with a medical imaging technology, dynamic and accurate prediction of the liver cancer occurrence risk is realized, and the prediction accuracy is remarkably improved.

The invention is suitable for the technical field of medical imaging, and provides an ultrasonic signal processing system and method and a brain-like calculation module.The system comprises an ultrasonic transducer, an ultrasonic transmitting module and the brain-like calculation module.The ultrasonic transducer is coupled with the ultrasonic transmitting module and the brain-like calculation module; the ultrasonic transmitting module is used for controlling the ultrasonic transducer to transmit ultrasonic waves; and the brain-like calculation module is used for processing the echo signal received by the ultrasonic transducer based on brain-like calculation.

The invention discloses a quality control method and system for an intra-frame weighted medical image with similar specific attention, relates to the technical field of medical images, and solves the technical problems that when an existing medical image processing method is used for processing a 3D medical image, the discrimination capability is weak, and the sensitivity to subtle artifacts or body position deviation is low, so that the recognition capability is weak. Comprising the steps that medical image data are acquired, and the medical image data comprise a 3D medical image and an evaluation system corresponding to the 3D medical image; performing data labeling on the 3D medical image based on an evaluation system to obtain a plurality of sequence data; dividing the plurality of sequence data into a training set, a verification set and a test set; training the medical image quality control model based on the training set, the verification set and the test set; the training process comprises data preprocessing, spatial feature extraction, time sequence enhancement feature extraction, class-guided intra-frame weighted score calculation, model optimization and class activation graph generation; and the model identification capability is enhanced.

The invention relates to the technical field of medical images, and discloses a medical image zero-watermark protection method and device based on depth frequency domain fusion features, and the method comprises the steps: obtaining carrier image data; preprocessing the carrier image data to obtain a standardized carrier image with a preset size; performing depth frequency domain fusion feature extraction on the standardized carrier image to obtain a binary feature vector; carrying out encryption processing on the original watermark by adopting a logic mapping chaotic system with an optimized control parameter range to obtain an encrypted watermark; performing bit-by-bit XOR operation on the encrypted watermark and the binary feature vector to obtain a zero-watermark key, and uploading the zero-watermark key to a third-party authentication center; when copyright authentication or tracing needs to be carried out on the medical image, a binary feature vector is extracted from the medical image to be verified, and a zero-watermark key stored in a third-party authentication center is downloaded; performing bit-by-bit XOR operation on the binary feature vector of the to-be-verified medical image and the zero watermark key to obtain a to-be-decrypted watermark; and decrypting the watermark to be decrypted through the logic mapping chaotic system to obtain an original watermark.

The invention relates to the technical field of medical imaging, in particular to a pelvic stability dynamic detection method and stress analysis system based on dynamic DR. The method comprises the steps that on the basis of sign information of a user, parameters of a three-dimensional infrared motion capture system, a force measuring table and a pressure sensor are adapted to obtain static CT or MRI image data of a pelvis of a patient, and then the pelvic stability dynamic detection method and stress analysis system are obtained; establishing a pelvis coordinate system and defining geometric features of key points; collecting to-be-detected data of a user in the load bearing state; preprocessing the collected user data, and reconstructing a three-dimensional pelvis model of the user; performing biomechanical analysis based on the reconstructed three-dimensional pelvis model of the user to generate a pelvis stability index; and according to the pelvic stability index, automatically generating a pelvic stability risk index, and outputting a clinical decision suggestion. The problems that a traditional pelvis detection method still depends on a static image, dynamic stress distribution and stability evaluation cannot be effectively quantified, and an evaluation result is not accurate enough are solved.

The invention relates to the technical field of medical imaging, in particular to a breast cancer tissue HER2 automatic scoring method and device based on a characteristic curve, and the method comprises the steps: carrying out the reading operation of a full-slice image of immunohistochemical staining; extracting a target region of interest of the full slice image; slicing and screening the target region of interest to obtain a plurality of image slices; calculating a percentage-saturation characteristic curve of each image slice, and extracting characteristic information of the percentage-saturation characteristic curve; based on the feature information of the percentage-saturation feature curve, performing preliminary HER2 scoring on each image slice in combination with a rule base and a logistic regression classification algorithm; and integrating the scoring results of the image slices, and outputting a final HER2 score and a characteristic curve visualization graph. According to the method, the feature curve is combined with the rule base and the logistic regression classification algorithm, so that the objectivity of HER2 scoring is improved, and the problems of subjectivity and low efficiency of manual evaluation are reduced.

The application provides a pathological section image analysis method and device based on an image coding large model, and belongs to the technical field of medical images. The method comprises the following steps: cutting an original pathological section image to obtain a pathological section sub-image set; sequentially inputting the pathological section sub-images in the set into a target region segmentation large model to obtain corresponding local target region distribution maps, and then obtaining an overall target region distribution map and generating a set of positive local target region distribution maps; based on the set of positive local target region distribution maps, using an abnormal lesion detection network to generate an abnormal lesion distribution map; and based on the target region distribution map and the abnormal lesion distribution map, finally realizing statistical analysis on the original pathological section image. The application can analyze various pathological section images, automatically identify and classify abnormal lesion regions in the images, and automatically statistically analyze to obtain key clinical indicators such as lesion rates, thereby improving the accuracy and efficiency of diagnosis.

The utility model provides a suspension type multifunctional dynamic digital X-ray imaging system, which relates to the technical field of medical imaging, and comprises a fixed seat and a cross beam which are fixedly arranged on a wall body; the transverse mounting guide rail is fixedly mounted on the fixed seat and the cross beam; the longitudinal moving guide rail is mounted on the transverse mounting guide rail in a sliding manner and slides left and right along the transverse mounting guide rail; the suspension frame is mounted on the longitudinal moving guide rail, the fixed seat and the cross beam; the X-ray generating device is connected with the suspension frame in a sliding manner and is used for generating X-rays; and the X-ray photography bed is connected with the suspension frame in a sliding manner and is used for the user to lie down so as to carry out X-ray photography. According to the utility model, multiple functions are realized, the diagnosis efficiency and accuracy are improved, the space utilization and equipment management are efficient, and a more comprehensive and efficient solution is provided for clinical medical treatment.

The invention provides a three-dimensional organ reconstruction registration fusion method based on a multi-modal medical image, and belongs to the technical field of medical images. Comprising the following steps: S1, collecting a multi-modal medical image of an ith organ of a target patient in real time, and constructing a preoperative data set; s2, after the first data set is constructed, continuously constructing an intra-operative data set through the intra-operative ultrasonic image; s3, constructing an AI model by using a convolutional neural network; s4, based on the preoperative data set and the intraoperative data set, constructing a stability coefficient and a crowding degree coefficient of the ith organ; and S5, associating the stability coefficient of the ith organ with the crowding degree coefficient of the ith organ, and constructing a comprehensive dynamic risk coefficient. According to the method, CT, MRI and intraoperative ultrasonic images are collected at the same time, the preoperative data set is constructed, the organ volume change rate and the body position adjustment displacement are updated in real time in the operation, the optimal operation path can be automatically predicted, and the subjectivity of depending on doctor experience in traditional operation path planning is reduced.

This invention discloses a low-pass filtered non-breath-hold saline angiography combined with cardiac mirror contrast imaging for lung perfusion imaging, belonging to the field of medical imaging technology. The method includes the following steps: S1: determining whether the subject's tidal impedance variation meets the imaging criteria; S2: injecting contrast agent into the subject to perform angiography; S3: obtaining an impedance decrease signal; S4: combining cardiac mirror contrast imaging with lung perfusion images. This invention solves the problem of low success rate of saline angiography in existing technologies, achieves separation of thoracic impedance caused by tidal breathing and thoracic impedance caused by saline angiography, reduces the influence of the cardiac region on lung perfusion images, further improves the accuracy of lung perfusion imaging, greatly reduces the interference of breath-holding on respiratory circulation, improves the simplicity of clinical application, and expands clinical application scenarios.

The application discloses a method for constructing a 4D FLOW-based hemodynamic evaluation pulmonary embolism prognosis prediction model and application, relates to the medical image technology field, and comprises the following steps: collecting 4D-Flow images of patients who have been diagnosed with pulmonary embolism from medical institutions to obtain image data A; pre-processing the image data A, and dividing the pre-processed image data A into a training set and a verification set according to a proportion; respectively performing imageomic feature extraction, clinical feature extraction and 4D FLOW hemodynamic feature extraction on the training set and the verification set; screening the imageomic features and the clinical features and 4D FLOW hemodynamic detection indexes to obtain key features; and based on the key features, establishing a multi-omics prediction model for pulmonary embolism prognosis prediction, thereby providing a new imaging reference for pulmonary embolism treatment.

The application relates to the field of medical imaging technology and provides an ultrahigh-speed laser transmission system and a CT detection device, wherein the photoelectric emission system is suitable for emitting light signals of at least two different wavelengths; the optical array assembly comprises an even number of emitting lenses and receiving lenses; the emitting lenses are connected with the photoelectric emission system; the emitting lenses are arranged in a circumferential array on a rotor structure; the wavelengths of the light signals passing through two adjacent emitting lenses are different; the receiving lenses are arranged on a stator structure; the field of view of the receiving lenses covers the exit regions of the at least two emitting lenses; the receiving lenses are used for receiving the spatial light beams emitted by the two adjacent emitting lenses into a single optical fiber for transmission; the photoelectric receiving system is connected with the receiving lenses; the photoelectric receiving system is used for separating the signals of different wavelengths for corresponding signal processing; and the signal synchronous processing system is used for synchronously processing the received light signals. The system is simple in structure, convenient to install, can optimize the bandwidth, and can improve the transmission efficiency.

The invention is suitable for the technical field of medical imaging, and provides a robot remote guidance method, device and system, and the method comprises the steps: obtaining a left and right eye image group sequence collected by a binocular camera through first electronic equipment of an operation end, and determining a target disparity map of each left and right eye image group, taking the integer part and the decimal part of the pixel value of each pixel in the target disparity map as a first channel value and a second channel value respectively, taking a preset placeholder as a third pixel value of the pixel to obtain a three-channel disparity map, and compressing the three-channel disparity map and the reference image into a target compressed file, sending the target compressed file and the parameter information of the binocular camera to the head-mounted device of the guidance end; and the head-mounted device restores the target three-dimensional coordinates and the color information of each spatial point from the target compressed file according to the parameter information, and renders and displays all spatial points according to the target three-dimensional coordinates and the color information, so that the data transmission efficiency and the data quality can be improved on the premise of ensuring the accuracy of remote operation guidance.

The invention provides a muscle mass evaluation method and system, and relates to the technical field of medical imaging, and the method comprises the steps: carrying out the ultrasonic scanning of human limbs, collecting an ultrasonic total scattering signal, and obtaining an ultrasonic image; acquiring a muscle structure image according to the ultrasonic image; performing segmentation processing on the muscle structure image to obtain contour information of a target muscle; and calculating the cross-sectional area and volume of the target muscle based on the contour information. According to the invention, the annular probe performs three-dimensional spiral scanning in the scanning process, and simultaneously obtains spatial information in the horizontal and vertical directions; echo signals are processed by adopting a delay superposition beam forming algorithm, and image segmentation is carried out in combination with a deep learning technology. According to the invention, radiation-free, high-precision, low-cost and portable muscle mass measurement is realized, and the method is especially suitable for evaluation, screening and monitoring of sarcopenia.

The invention discloses an ultrasonic self-adaptive scanning method and system for EIT dynamic path planning, and belongs to the technical field of medical imaging. Reconstructing an impedance distribution image in real time based on EIT and detecting an abnormal region; the coordinates of the abnormal area are mapped to an ultrasonic coordinate system, and breathing phase compensation is carried out; planning an ultrasonic beam scanning path according to clinical constraint conditions; controlling the ultrasonic beam to scan along the planned path; performing image analysis on the scanning area; and updating system parameters according to an analysis result. According to the method, abnormal coordinates detected by EIT are accurately converted into an ultrasonic beam deflection angle, clinical constraint path planning of a high-cost area of a rib shielding area and breathing phase dynamic compensation taking end expiratory as a benchmark are combined, so that the system realizes full-closed-loop control from abnormal sensing, path planning to beam execution, and the accuracy of the system is improved. The problems of scanning failure caused by rib shielding and coordinate drifting caused by respiratory movement in clinic are effectively solved, and the reliability of dynamic monitoring of the thoracic cavity is remarkably improved.

The application discloses a CT dose self-adaptive optimization system and method thereof, and belongs to the technical field of medical images, which comprises a CT scanner, an image data processing workstation, a high-risk population analysis device, a dose parameter self-adaptive optimization device, a dose parameter automatic adjustment device and an intelligent learning unit, and based on the theory of information geometry, the dose parameter optimization problem is promoted from the Euclidean space to the Riemannian manifold space, the dose parameter manifold and the image quality manifold are constructed, the geodesic line is calculated on the manifold through the Fisher information matrix as the Riemannian metric, and the optimal dose path is found. The system adopts a double-manifold collaborative optimization mechanism, establishes a mapping relationship between the two manifolds, and realizes accurate optimization of parameters in combination with a geometric-guided genetic algorithm, and through pre-scanning, high-risk population analysis, information geometry optimization and automatic parameter adjustment, individualized dose control is realized, the radiation dose is additionally reduced by 30-45% compared with a traditional method, the image quality is maintained or improved, and the safety of CT examination is remarkably improved.

This application relates to the field of medical imaging technology, and in particular to a method, apparatus, device, system, and medium for geometric calibration of a multi-source multi-detector system. The method includes: determining the coupling relationship between the X-ray source and detector of the multi-source multi-detector system based on the irradiation relationship between the X-ray source and detector; dividing the X-ray source and detector of the multi-source multi-detector system into coupled X-ray source subsets, coupled detector subsets, and uncoupled subsets based on the coupling relationship; designing a calibration phantom based on the design parameters of the multi-source multi-detector system and constructing a calibration dataset; and iteratively solving the geometric parameters of the multi-source multi-detector system sequentially based on the coupled X-ray source subset, coupled detector subset, uncoupled subset, and an objective function constructed using the calibration dataset, to complete the calibration of the overall geometric parameters of the multi-source multi-detector system. This solves the problem of high computational complexity in the geometric parameter calibration of multi-source multi-detector systems in related technologies.

The invention relates to the technical field of medical images, and discloses a coronary artery phase analysis method and device and a storage medium. The heart comprises a plurality of coronary arteries, for at least one coronary artery, first a plurality of phase reconstructed image sequences of the heart are acquired. Then, coronary artery motion data of the coronary artery and heart motion data corresponding to the position of the coronary artery are acquired based on the plurality of phase reconstruction image sequences. Then, based on the heart motion data, motion differences of different coronary artery partitions are analyzed, and a target interval corresponding to a target period of the coronary artery is obtained; and finally, based on the target interval, obtaining a target phase corresponding to the coronary artery on the coronary artery motion data, improving the accuracy of the optimal phase in the heart reconstruction image, reducing motion artifact interference, and generating a clearer coronary artery image.

The invention discloses a spectrum correction method and system for a photon counting CT detector, and relates to the technical field of medical images.The method comprises the steps that actually-measured film watching data of the detector under a target energy section is obtained; determining an energy section type of a target energy section, and distributing adaptive weights for a pre-constructed detector spectral response model and a pulse accumulation model based on the energy section type; fusing the detector spectral response model and the pulse accumulation model through the adaptive weight to realize coupling of the detector spectral response model and the pulse accumulation model; and carrying out spectrum correction on the photon counting CT detector based on the coupled detector spectrum response model and the pulse accumulation model. The method is helpful for solving the problem that the spectrum of the photon counting CT detector cannot be accurately corrected in the prior art.

The application relates to the technical field of medical imaging, and discloses a medical image recognition method and system based on deep learning, an electronic device and a medium, which perform self-adaptive normalization on an initial medical image, and perform preprocessing on the initial medical image by using elastic deformation, random scaling and Gaussian noise to obtain a preprocessed medical image, construct a Swin-ResNeSt hybrid model based on the preprocessed medical image, add an adaptive multi-scale attention module to the Swin-ResNeSt hybrid model, design a double-task head including classification and positioning, adopt a hybrid loss function combined by a Focal Loss function and a GIoU Loss function, and output a lesion category and a bounding box; and the application improves recognition accuracy.

The embodiment of the invention is suitable for the technical field of medical imaging and photoacoustic imaging, and provides a photoacoustic microscopic imaging system, an imaging method and imaging equipment, and the system comprises a light source excitation assembly which is used for transmitting pulse laser and irradiating a scanning galvanometer after the pulse laser is collimated; the scanning and focusing assembly is used for performing collimation and focusing treatment on the pulse laser reflected by the scanning galvanometer to obtain parallel light, and the parallel light passes through a light-transmitting and sound-reflecting device placed according to a preset angle and then irradiates the tissue surface of the to-be-scanned object; the signal acquisition control assembly is used for receiving an ultrasonic signal generated by the object to be scanned and generating imaging point data capable of being used for photoacoustic microscopic imaging based on the ultrasonic signal; the light-transmitting and sound-reflecting device is a sealed water filling device in which a plurality of hollow glass beads are arranged, and the sealing structure of the light-transmitting and sound-reflecting device is made of quartz materials. By adopting the system, photoacoustic microscopic imaging can be quickly, efficiently and accurately realized.

The invention discloses a pancreatic cancer CT positioning image adaptive artifact correction and target region sketching system, and relates to the technical field of medical imaging. The system comprises a user multi-dimensional data acquisition module, an artifact risk prediction analysis module, a radiography parameter intelligent optimization module and an image scanning and target area sketching module. A user multi-dimensional association data set is constructed by collecting multi-dimensional individual feature data of a patient, an artifact generation risk coefficient is predicted based on medical big data analysis, key influence factors are screened and contrast agent injection parameters are optimized accordingly, and finally personalized CT scanning and target area sketching are executed. According to the method, limitation of a fixed parameter scanning scheme is broken through, individualized adaptive optimization of contrast agent injection parameters is realized, generation of artifacts is effectively inhibited, pancreatic cancer CT positioning image quality and target region sketching precision are remarkably improved, and reliable technical guarantee is provided for precise radiotherapy.