383 results about "Imaging modalities" patented technology

An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

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 discloses an acoustic imaging positioning method for intelligent monitoring of electric power engineering regional faults, which relates to the technical field of fault monitoring, and comprises the following steps: arranging a microphone array to collect sound wave signals, and monitoring and recording sound data in real time; determining the spatial position of a sound source by using an ultrasonic sound source positioning technology; sound source distribution generated by the equipment fault is displayed; displaying the relationship between the three-dimensional position of the equipment fault point and the surrounding environment; fault sound sources are classified, and types and reasons are determined; a diagnosis result is fed back in real time, a fault position is displayed, and inspection and repair suggestions are provided; and detecting the health condition of the equipment in combination with a machine learning model, predicting potential faults and giving out early warning. According to the invention, a high-precision microphone array and an ultrasonic sound source positioning technology are utilized, the distribution state of a sound source in a space is displayed in real time through an imaging mode of superposing an acoustic image and a visible light image, a partial discharge point, a gas leakage point and an equipment abnormal sound site are rapidly determined, and the inspection efficiency and the maintenance speed are improved.

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 inventions provided herein relate to injection applicators, tissue markers and uses thereof, e.g., to mark a target tissue site (e.g., a biopsy site in a breast tissue) or to produce a cell scaffold. The tissue markers described herein are designed to be resistant to fast migration (e.g., immediate migration after implantation through a needle track) and slow migration (e.g., over an extended period of time) upon implantation at a target tissue site (e.g., a biopsy site in a breast tissue), without using an adhesive. Additionally or alternatively, the tissue markers described herein can be readily detectable by at least one imaging modality, e.g., but not limited to magnetic resonance imaging, X-ray imaging, ultrasound imaging, or a combination thereof.

The invention provides a cross-modal fundus image registration method and device, equipment and a medium, and the method comprises the steps: converting a to-be-matched multi-modal image into a blood vessel image through a wide-area blood vessel segmentation network, the multi-modal image being two fundus images of different modals obtained by the same fundus of the same patient at different times and in different imaging modes; performing feature matching on the vascular image of the to-be-matched multi-modal image through the key point detection model to obtain a matching set; and realizing coordinate transformation of the to-be-matched multi-modal image by using a double-fitting strategy based on the matching set, and completing image registration. Therefore, according to the registration strategy, the best registration performance at present is achieved, the registration performance of other existing models is greatly improved through the cutting alignment strategy and the double-fitting strategy, and the cross-modal fundus image registration method can be widely applied to cross-modal fundus image registration under the large-view-difference condition.

Methods and systems for providing a dataset of human in-bed poses include simultaneously gathered images of in-bed poses of humans from imaging modalities including red-green-blue (RGB) and one or more of long wavelength infrared (LWIR), depth imaging, and pressure mapping. The images are obtained under a lighting condition and a cover condition. The dataset can be used to train a model of estimating human in-bed poses and for methods of estimating human in-bed poses. Methods and systems of estimating three-dimensional human poses from two-dimensional input images are provided.

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.

The invention relates to the technical field of biological detection, and discloses an underwater organism detection method, device and equipment, a storage medium and a program product.A big language model is used for processing the category name of an underwater organism to be detected to obtain category description, and the name, a picture and the description are input into a target multi-modal detection model together for processing; the dependence on manual annotation is reduced, and the data processing efficiency is improved. Meanwhile, data information of two modes of text and image is fully utilized, the limitation of single-mode data is avoided, and the detection accuracy is improved. Furthermore, by calculating the similarity between the visual features of the target and the false detection clustering center and performing result judgment according to a similarity threshold value, the target possibly causing false detection can be effectively identified, so that the omission ratio can be better balanced, the detection performance reduction caused by improper false detection processing measures is avoided, and the detection accuracy is improved. The problem that the recall rate of the positive sample is reduced is solved, and the reliability of underwater biological detection is improved.

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.

Systems and methods of detecting and monitoring edema in a subject are described. The systems and methods include light sources, image detectors, filters, and processors that determine whether a subject has edema and the degree of severity. In some embodiments, the processor fuses data from more than one imaging modality.

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.

The invention belongs to the technical field of cross-modal pre-training, and discloses a multi-semantic granularity cross-modal pre-training method. The objective of the invention is to improve the accuracy and interpretability of medical image analysis by fusing a medical knowledge base, structured triple extraction, multi-task comparative learning and a cross-modal attention mechanism. The method is suitable for focus detection and disease typing and grading diagnosis, and is compatible with multiple medical image modalities such as CTA and IVUS. According to the method, firstly, a text processing module is constructed, redundant text content is cleaned, keywords are obtained, and feature extraction is carried out; and then a medical image processing module based on a 3D visual encoder is constructed. Then vision-text marking preliminary comparison is carried out, and finally fine comparison is carried out through mask language modeling and vision-text matching. And finally realizing cross-modal pre-training applied to the medical image.

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.

In part, the disclosure relates to determining a stent deployment location and other parameters using blood vessel data. Stent deployment can be planned such that the amount of blood flow restored from stenting relative to an unstented vessel increases one or more metrics. An end user can specify one or more stent lengths, including a range of stent lengths. In turn, diagnostic tools can generate candidate virtual stents having lengths within the specified range suitable for placement relative to a vessel representation. Blood vessel distance values such as blood vessel diameter, radius, area values, chord values, or other cross-sectional, etc. its length are used to identify stent landing zones. These tools can use or supplement angiography data and / or be co-registered therewith. Optical imaging, ultrasound, angiography or other imaging modalities are used to generate the blood vessel data.

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.