312 results about "Imaging modalities" patented technology

The invention belongs to the technical field of image processing, and particularly relates to a motion artifact adaptive correction method based on multi-modal MRI fusion, which comprises the following steps: acquiring sMRI and fMRI of a patient, preprocessing the sMRI and fMRI, creating a cross-modal feature fusion model, and inputting the preprocessed sMRI and fMRI into the cross-modal feature fusion model for double-branch feature extraction; the cross-modal feature fusion model carries out cross-modal feature interaction fusion on the extracted double-branch features and carries out multi-scale feature reconstruction; detecting image artifacts based on the reconstructed multi-scale features, and performing dynamic space-time attention correction on the detected image artifacts; and establishing a three-level loss function system to carry out multi-task joint optimization. By integrating sMRI and fMRI information and based on image processing and deep learning, the defect that various complex head movement conditions cannot be completely handled in the prior art is overcome, and the technical problems of information loss between image modalities and insufficient feature utilization are solved.

The present disclosure relates to diagnosis of ocular diseases by leveraging an attention-based multimodal graphical neural network (AM-GNN) based on multimodal data that includes one or more image-text pairs. Each image of the image-text pair may depict a retinal scan of a subject that may correspond to one or more imaging modalities. A multimodal dataset may be collected from different imaging modalities by data crawling to automatically collect relevant data for the targeted diseases from identified sources. In some aspects, a concept bank, comprising concepts or terminologies associated with the targeted diseases, may be provided to the AM-GNN to generate a multimodal graph. The AM-GNN may be configured to find an association between a set of nodes including image-text pairs, a set of diseases, and the concepts using an attention mechanism. Based on the multimodal graph, an output may be generated, including a predicted disease and related concept(s).

The invention relates to the technical field of image detection and quality assessment, and discloses an image-based cable stranded wire quality assessment method and system, and the method comprises the steps: obtaining an illumination reflection image set of a cable stranded wire through employing a multi-angle annular illumination imaging mode; constructing a multi-angle reflection image sequence; generating a defect suppression highlight image; carrying out image enhancement and three-dimensional normal estimation; and outputting a quality grade evaluation result. In the prior art, fine scratches, burrs and distortion defects are difficult to accurately identify under the condition of strong reflection interference, and particularly, high-resolution quality evaluation cannot be realized under the conditions of complex surface structure of a cable stranded wire and non-uniform illumination response. According to the method, the multi-angle illumination model and the angle domain sparse unmixing mechanism are constructed, and the defect enhancement driven by the normal disturbance and the multi-modal fusion classification are combined, so that the precise positioning and grade evaluation of the tiny defect area are realized, and the precision of the quality detection of the stranded cable is improved.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

A method is provided for optimizing workflow for a vascular intervention based on a three-dimensional dataset of a vessel, particularly a coronary artery, acquired from a 3D imaging modality, which involves:determining vessel centerline;performing segmentation in the dataset to identify the vessel lumen;performing plaque segmentation in the dataset to identifying the plaque in the vessel wall;defining a parameter related to the plaque severity in the vessel wall; andcreating a two-dimensional image spanning the length of the segmented vessel to illustrate both the plaque severity parameter and its spatial distribution in relation to the vessel wall circumference using the position of points along the centerline in the segmented vessel as primary axis.Other aspects are described and claimed.

The invention discloses a high-speed high-resolution compressed sensing imaging system and an imaging method. Light of an object is emitted to the polarization splitting prism to be transmitted and reflected after passing through the first-stage imaging module, the transmitted light is reflected back to the polarization splitting prism after being coded and modulated by the spatial light modulator, and then the transmitted light enters the target surface of the camera through the second-stage imaging module to form a coded imaging image; the reflected light is transmitted to the hollow ridge reflecting prism through the quarter-wave plate, is reflected back to the quarter-wave plate through the hollow ridge reflecting prism, then sequentially passes through the quarter-wave plate, the polarization splitting prism and the secondary imaging module and then enters the target surface of the camera to form a common imaging image, and then coded imaging data is matched with common imaging data; and decoding reconstruction is carried out by adopting an alternating direction multiplier method of a point spread function integrated with the first-stage imaging module and the second-stage imaging module. Compared with a traditional imaging mode, the method can break through the imaging frame rate limit of a camera, and has the advantages of high imaging resolution and small data volume.

The invention discloses a sonar data processing and image fusion method, which belongs to the technical field of image processing, and comprises the following steps of: performing geometric correction and registration processing on forward-looking sonar data by using respective advantages of side-scan sonar data and forward-looking sonar data and taking the side-scan sonar data as a benchmark reference source, so that the imaging mode of the forward-looking sonar data is matched with that of the side-scan sonar; multiple frames of images of the forward-looking sonar are integrated through an image splicing algorithm, and a water bottom image of a complete area is constructed; and finally, the blind area of the side-scan sonar is compensated by using a splicing result, so that complete reconstruction of underwater topography information is realized. The method is easy and convenient to operate and wide in application prospect, blind area compensation of the side-scan sonar image blind area can be achieved, and a complete underwater image is obtained.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

The invention discloses a human body peripheral blood vessel photoacoustic three-dimensional imaging device and an imaging method thereof, and belongs to the field of blood vessel imaging. Through high resolution and functional monitoring capability of photoacoustic imaging, detailed blood vessel structure and blood flow dynamic information are provided, and early recognition and treatment effect evaluation of common lesions such as diabetic foot and peripheral artery diseases are promoted; meanwhile, a mechanical system is adopted, so that the detection front end with relatively high quality can be borne, and stable operation is realized, and a relatively good photoacoustic imaging result is obtained; the system has a highly stable accurate scanning imaging mode, and is convenient for multiple imaging modes of clinical application; through the arrangement of the imaging water tank and the lifting structure of the imaging water tank, switching of imaging modes in a limited space can be ensured, so that the device can be suitable for the layout of a portal type three-dimensional moving device. The system is expected to promote the portability of equipment, reduce the research and development cost of the equipment, be widely applied to clinical practice and community medical treatment, and improve the disease management level.

A method of virtual staining of a tissue sample includes obtaining multiple sets of imaging data. The imaging data depicts a tissue sample and has been acquired using multiple imaging modalities. Further, the method includes fusing and processing the multiple sets of imaging data in a machine-learning logic. The machine-learning logic is configured to provide at least one output image. Each one of the at least one output image depicts the tissue sample comprising a respective virtual stain.

The invention relates to the technical field of multi-modal ophthalmology image analysis, and particularly discloses a multi-modal ophthalmology image analysis method, model and system based on optimal transmission image diffusion and a medium thereof.The method at least comprises the steps that S100, the distance between fundus color photo and OCT image modals is explicitly calculated through an optimal transmission algorithm, and the distance between fundus color photo and OCT image modals is calculated; carrying out collaborative evolution on the features in a continuous time domain in combination with a graph neural network and a Sheng differential equation model; and step S200, applying self-attention and cross attention in parallel, reserving modal specificity with low-layer fine granularity, and progressively fusing the color photo texture and the OCT depth structure in a high layer to realize cross-layer long-range dependent depth semantic fusion. The method not only promotes the leading-edge development of multi-modal medical image analysis, but also provides important scientific basis and technical support for constructing a high-precision and high-robustness intelligent ophthalmology diagnosis system.

The disclosure generally relates to systems, devices and methods to facilitate image-guided medical treatment and / or diagnostic procedures (e.g., surgery or other intervention among other considered medical usages), and to the generation of current and / or accurate anatomical images for facilitating image-guided medical treatment and / or diagnostic procedures (e.g., surgery or other intervention) and calibration and registration of imaging modalities (e.g., tomographic, volume-imaging and / or fluoroscopic modalities) used in such medical treatment and / or diagnostic procedures.

An example semiconductor workpiece imaging system includes an imaging device. The example semiconductor workpiece imaging system includes a workpiece holder operable to receive a semiconductor workpiece. The example semiconductor workpiece imaging system includes a fiducial structure having one or more fiducial markers. The one or more fiducial markers and at least a portion of the semiconductor workpiece are detectable in a plurality of different image modalities.

Methods, systems, and devices for evaluating function of a heart are provided. The methods can include use of one or more imaging modalities to assess diastolic function. The methods, systems, and devices can employ various features of a cardiac volume curve to assess cardiac function. The methods, systems, and devices can be used to guide diagnosis, therapy, and / or training for sick or well patients. Methods, systems, and devices for assessing likelihood of hospital readmission for a heart failure patient are also provided.

The present application provides a multi-modality based building image retrieval method, device and apparatus. The method includes the steps of: acquiring a building example image set, a positive prompt word set, and a negative prompt word set inputted by a user; extracting features from the building example image set, the positive prompt word set and the negative prompt word set, and calculating a mean image feature, a mean positive feature, and a mean negative feature; and calculating correlations between the building example image set and various building images in a building image library according to the mean image feature, the mean positive feature, and the mean negative feature. In the present application, image retrieval is performed using data of text and image modalities, and the obtained target images have a high correlation with building example images, requiring no manual screening, and further enhancing work efficiency.

The invention discloses a cross-modal conversion method of a medical image. The method comprises the steps of obtaining existing multi-modal medical image data and performing preprocessing to construct a training data set; for each type of medical image modality, adopting the training data set to train a corresponding auto-encoder; constructing a cross-modal conversion initial model of the medical image based on a variational diffusion model by combining an auto-encoder, and training to obtain a trained cross-modal conversion model of the medical image; and carrying out actual cross-modal conversion of the medical image by adopting the cross-modal conversion model of the medical image. The invention also discloses a system for realizing the cross-modal conversion method of the medical image. According to the method, cross-modal conversion of the medical image is realized, data sets do not need to be paired, and meanwhile, the reliability is higher and the accuracy is better.

Disclosed systems, apparatuses, and methods enable remotely controlling robotic-assisted medical systems, such as robotic-assisted surgery systems. Disclosed approaches facilitate deployment and improve the safety of robotic-assisted procedures.

The present disclosure provides to process a series of intravascular images, such as intravascular ultrasound (IVUS) images, to align the frames of the series of images longitudinally and angularly with respect to an extravascular image. In some examples, vessel fiducials are identified in each image modality and longitudinal and angular offsets are identified based on the location and angular orientation of the vessel fiducials. An aligned series of images can be generated by shifting and rotating frames of the series of images based on the identified offsets.

Systems and methods are disclosed that facilitate object pose determination via keypoint detection. A machine learning algorithm is employed to facilitate voting-based estimation of the locations of at least three keypoints. In some example embodiments, the machine learning algorithm is trained, based on reference intensity-depth images of an object, to determine a radial distance between a keypoint and a 3D scene location associated with each pixel. At inference, the algorithm is employed to process an intensity-depth image to determine radial distance estimates for each pixel, and these radial distances are employed to increment an accumulator space, such that a sphere is generated in the accumulator space for each pixel, centered on the 3D scene location, and a keypoint location is determined by a peak in the accumulator space, with multiple keypoints enabling pose determination. The methods are adapted to other imaging modalities, including non-depth images and point cloud datasets.

The present application provides a multi-modality based building image retrieval method, device and apparatus. The method includes the steps of: acquiring a building example image set, a positive prompt word set, and a negative prompt word set inputted by a user; extracting features from the building example image set, the positive prompt word set and the negative prompt word set, and calculating a mean image feature, a mean positive feature, and a mean negative feature; and calculating correlations between the building example image set and various building images in a building image library according to the mean image feature, the mean positive feature, and the mean negative feature. In the present application, image retrieval is performed using data of text and image modalities, and the obtained target images have a high correlation with building example images, requiring no manual screening, and further enhancing work efficiency.

The invention discloses an unsupervised multi-modal image registration method and system based on dual-path training, and the method comprises the following steps: obtaining a plurality of multi-modal image data, so as to construct a multi-modal image pair data set; constructing a multi-modal registration model; based on a registration network and a translation network, performing dual-path collaborative registration on the floating image to obtain a dual-path registration image; inputting the reference image and the dual-path registration image into a discriminator network for discrimination; a composite loss function is constructed, and a target function is established by using a minimum-maximum game method; training a multi-modal registration model based on the target function and the multi-modal image pair data set; according to the method, the dual-path collaborative registration framework is constructed through the registration network and the translation network, and the image alignment problem of different imaging modes is converted into the registration problem in the same mode, so that the problems of semantic gaps and measurement misalignment in cross-modal feature matching are solved, and the problem of cross-modal similarity measurement is avoided.

The invention belongs to the field of laparoscopes, and particularly relates to an integrated multi-modal image fusion laparoscope system which comprises a laparoscope body, a multi-modal image acquisition module, an image fusion processing module, a display control module and an energy supply module. The laparoscope body is integrally designed, and a plurality of image acquisition probes are arranged at the front end of the laparoscope body and used for acquiring image information in different modes; corresponding optical conduction and signal transmission lines are integrated in the lens body, so that acquired image information can be stably and efficiently transmitted to a rear-end module; the multi-mode image acquisition module comprises a white light imaging unit, a fluorescence imaging unit, an ultrasonic imaging unit and a photoacoustic imaging unit which are respectively used for acquiring image data of different modes. The system is reasonable in design, provides comprehensive operation area information for doctors by integrating various image modalities and realizing efficient fusion, improves the operation precision and operation efficiency, and is high in expandability.

A computer-implemented method for machine-learning a function configured to take as input a plurality of aligned images of a same patient and each of a different modality among a predetermined set of medical-imaging modalities, and to calculate a fused image. The method includes obtaining a dataset including, for each patient of a plurality of patients and for each modality of a respective at least part of the predetermined set, a respective image, the respective images for a patient being aligned; and training the function based on the dataset. This forms an improved solution for medical imaging.

The invention discloses a bioelectrical impedance tumor identification method based on multi-modal fusion. The method comprises the following steps: S1, acquiring electrical impedance signal data and tissue slice image data of a tumor sample; the tissue slice image data are pathological slice images corresponding to tumor tissues; s2, inputting the electrical impedance signal data and the tissue slice image data into a multi-modal feature coding module for asymmetric feature coding; s3, performing splicing and weighted fusion on the global semantic feature vector and the image semantic feature vector to form a multi-modal feature vector; and S4, inputting the multi-modal feature vector into a decoding network for classification prediction, and outputting a tumor identification result. According to the method, an asymmetric coding structure and a weighted fusion mechanism are constructed by fusing the electric signal modality and the image modality, so that the utilization efficiency and the feature discrimination capability of multi-modal information can be effectively improved, and the accuracy and the reliability of tumor recognition are remarkably improved.

The invention discloses a system for identifying fibers in antibacterial polyester-cotton composite fibers based on graphic data analysis, and the system comprises a composite fiber to-be-detected sample terminal, a dual-channel microscopic imaging terminal, a fiber image preprocessing and segmenting terminal, and a fiber classification and identification terminal. The problem that fiber morphology and texture features cannot be comprehensively obtained in a single imaging mode is solved, the weight of a regularization item is dynamically adjusted by using an adaptive distance regularization level set algorithm, and the problem that a traditional segmentation method is inaccurate in segmentation of a fiber adhesion region is solved. Meanwhile, accurate division and background separation of a fiber region are realized through a deep learning network model and an OTSU adaptive threshold algorithm, and finally, classification recognition training output is performed through multi-feature fusion recombination and by using a support vector machine, so that the problem of low recognition refinement degree in composite fiber recognition is solved, and the recognition precision of the composite fiber is improved. And the recognition precision of the system on the composite fiber is obviously improved.

The invention relates to the technical field of electric digital data processing, in particular to an ophthalmology department clinical nursing data preprocessing method and system. The method comprises the following steps: realizing stage clustering of pathological images through a K-Medoids algorithm based on predefined typical images; carrying out deformable image registration on the images in the same stage, and extracting key focus features through group difference analysis and structural pattern recognition; generating a weighted mask by using the lesion features, and realizing local enhancement processing of the original image; further extracting non-image modal data corresponding to the image and carrying out structured coding; and finally, constructing a time axis taking the image acquisition time as a reference, and aligning and fusing the image modality and the non-image modality to generate multi-modality comprehensive information. The system supports co-processing and feature enhancement of multi-source data, provides more accurate input for subsequent intelligent analysis and nursing intervention, and improves clinical aid decision-making efficiency and reliability.

The invention discloses a pluggable polarization imaging system based on a metasurface and traditional optics and a construction method, and relates to the technical field of polarization imaging. When the metasurface is not inserted or pulled out, the polarization imaging system has a light intensity imaging function; when the super-structure surface is inserted into the imaging system, the polarization imaging system has a polarization imaging function; after the polychromatic light source is incident to the refraction lens of the imaging system, the polychromatic light source is focused and irradiated to the surface of the metasurface, different polarization components in the polychromatic light source are split and deflected to corresponding positions by the surface of the metasurface, and different polarization imaging focuses are formed on the pixel surface of the detector, so that polarization imaging is realized. The imaging system constructed by the invention not only solves the problems of huge volume and difficulty in integration of a traditional polarization imaging mode, but also solves the problems of large aperture and dispersion of a super-structure surface imaging mode, and the whole optical system can adopt different imaging modes for different scenes due to the pluggable characteristic.

A method implemented by one or more computer devices includes receiving a plurality of images of a set of tissue cells, the plurality of images comprising a first image including a first visualization modality and a second image including a second visualization modality. The method includes identifying a first tissue cell of the set of tissue cells in the first image and the first tissue cell in the second image, and performing a cell-to-cell registration process based on the first tissue cell identified in the first image and the first tissue cell identified in the second image. The cell-to-cell registration process includes matching of the first tissue cell identified in the first image to the first tissue cell identified in the second image. The method includes classifying the first tissue cell into a phenotype based on the cell-to-cell registration process, the phenotype partially indicative of a disease pathology.