2294 results about "Multi modality" patented technology

An instrument for ablation of tissue. The instrument including: a body having at least one surface for contacting a tissue surface, the at least one surface being substantially planar; an ultrasonic transducer disposed in the body for generating ultrasonic energy and directing at least a portion of the ultrasonic energy to the tissue surface, the ultrasonic transducer being operatively connected to an ultrasonic generator; at least one radio-frequency electrode disposed on the at least one surface for directing radio frequency energy to the tissue surface, the at least one radio-frequency electrode being operatively connected to a power source; and one or more switches for selectively coupling at least one of the ultrasonic transducer to the ultrasonic generator and the at least one radio-frequency electrode to the power source.

A classification apparatus, method, and computer program product for multi-modality classification are disclosed. For each of a plurality of modalities, the method includes extracting features from objects in a set of objects. The objects include electronic mail messages. A representation of each object for that modality is generated, based on its extracted features. At least one of the plurality of modalities is a social network modality in which social network features are extracted from a social network implicit in the set of electronic mail messages. A classifier system is trained based on class labels of a subset of the set of objects and on the representations generated for each of the modalities. With the trained classifier system, labels are predicted for unlabeled objects in the set of objects.

The present invention provides markers and methods of using markers to identify or treat anatomical sites in a variety of medical processes, procedures and treatments. The markers of embodiments of the present invention are permanently implantable, and are detectable in and compatible with images formed by at least two imaging modalities, wherein one of the imaging modalities is a magnetic field imaging modality. Images of anatomical sites marked according to embodiments of the present invention may be formed using various imaging modalities to provide information about the anatomical sites.

Herein are described methods and tools for acquiring accurately co-registered PET and TRUS images, as well as the construction and use of PET-TRUS prostate phantoms. Ultrasound imaging with a transrectal probe provides anatomical detail in the prostate region that can be accurately co-registered with the sensitive functional information from the PET imaging. Imaging the prostate with both PET and transrectal ultrasound (TRUS) will help determine the location of any cancer within the prostate region. This dual-modality imaging should help provide better detection and treatment of prostate cancer. Multi-modality phantoms are also described.

For diagnosis and treatment of cardiac disease, images of the heart muscle and coronary vessels are captured using different medical imaging modalities; e.g., single photon emission computed tomography (SPECT), positron emission tomography (PET), electron-beam X-ray computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound (US). For visualizing the multi-modal image data, the data is presented using a technique of volume rendering, which allows users to visually analyze both functional and anatomical cardiac data simultaneously. The display is also capable of showing additional information related to the heart muscle, such as coronary vessels. Users can interactively control the viewing angle based on the spatial distribution of the quantified cardiac phenomena or atherosclerotic lesions.

Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.

The invention relates to highly fluorescent metal oxide nanoparticles to which biomolecules and other compounds can be chemically linked to form biocompatible, stable optical imaging agents for in vitro and in vivo applications. The fluorescent metal oxide nanoparticles may also be used for magnetic resonance imaging (MRI), thus providing a multi modality imaging agent.

An ultrasound scan probe and support mechanism are provided for use in a multi-modality mammography imaging system, such as a combined tomosynthesis and ultrasound imaging system. In one embodiment, the ultrasound components may be positioned and configured so as not to interfere with the tomosynthesis imaging operation, such as to remain out of the X-ray beam path. Further, the ultrasound probe and associated components may be configured to as to move and scan the breast tissue under compression, such as under the compression provided by one or more paddles used in the tomosynthesis imaging operation.

The invention provides a multi-modality medical image identification method based on deep learning. The method includes the steps that on the basis of multi-modality medical images of a patient's part to be detected, a registration method is adopted to display the multi-modality medical images in the same three-dimensional space; on the basis of the multi-modality medical images, R-CNN is adopted to identify lesion areas in the multi-modality medical images; according to coordinates of the lesion areas in the multi-modality medical images, lesion bodies are displayed in the same three-dimensional space, and according to image blocks corresponding to diagnosed lesion areas, a dense sampling method and a CNN are adopted to obtain occurrence probabilities of various preset disease types. The invention provides a multi-modality medical image identification device based on the deep learning. The device includes a multi-modality medical image display module, a lesion area detection module, a lesion body display module and a preset disease-type occurrence probability module. According to the multi-modality medical image identification method and device, the automatic identification of the lesion areas in the medical images is achieved, and effective reference data is provided for further diagnosis by doctors.

The present invention relates to compositions for a multi-modality center-filled chewing gum. The individual gum pieces, which include the compositions of the present invention, may include a center-fill region surrounded by a gum region. The gum region may include a gum base. The individual gum pieces optionally may be further coated with an external coating layer. At least two components that create a duality, such as two flavors, may be incorporated into different regions of the gum.

Multiple modality contrast can be used to produce images that can be combined and rendered to produce images similar to those produced with wavelength absorbing stains viewed under transmitted white light illumination. Images obtained with other complementary contrast modalities can be presented using engineered color schemes based on classical contrast methods used to reveal the same anatomical structures and histochemistry, thereby providing relevance to medical training and experience. Dark-field contrast images derived from refractive index and fluorescent DAPI counterstain images are combined to produce images similar to those obtained with conventional H&E staining for pathology interpretation. Such multi-modal image data can be streamed for live navigation of histological samples, and can be combined with molecular localizations of genetic DNA probes (FISH), sites of mRNA expression (mRNA-ISH), and immunohistochemical (IHC) probes localized on the same tissue sections, used to evaluate and map tissue sections prepared for imaging mass spectrometry.

A method for aligning a pair of images includes providing a pair of images, identifying salient feature regions in both a first image and a second image, wherein each region is associated with a spatial scale, representing feature regions by a center point of each region, registering the feature points of one image with the feature points of the other image based on local intensities, ordering said feature pairs by a similarity measure, and optimizing a joint correspondence set of feature pairs by refining the center points to sub-pixel accuracy.

Systems and Methods for multi-modal or multimedia image retrieval are provided. Automatic image annotation is achieved based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer comprising the semantic concepts to be discovered, to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework to provide confidence of the association. A hidden concept layer which connects the visual feature(s) and the words is discovered by fitting a generative model to the training image and annotation words. An Expectation-Maximization (EM) based iterative learning procedure determines the conditional probabilities of the visual features and the textual words given a hidden concept class. Based on the discovered hidden concept layer and the corresponding conditional probabilities, the image annotation and the text-to-image retrieval are performed using the Bayesian framework.

An integrated, multi-modality imaging technique is disclosed. The embodiment described combines a split-magnet MRI system with a digital x-ray system. The two systems are employed together to generate images of a subject in accordance with their individual physics and imaging characteristics. The images may be displaced in real time, such as during a surgical intervention. The images may be registered with one another and combined to form a composite image in which tissues or objects difficult to image in one modality are visible. By appropriately selecting the position of an x-ray source and detector, and by programming a desired corresponding slice for MRI imaging, useful combined images may be obtained and displayed.

An multi-modality imaging system for imaging of an object under study that includes a magnetic resonance imaging (MRI) apparatus and an MRI-compatible single-photon nuclear imaging apparatus imbedded within the RF coil of the MRI system such that sequential or simultaneous imaging can be done with the two modalities using the same support bed of the object under study during the imaging session.

A multi-modality diagnostic imaging system includes a first imaging subsystem, such as a computed tomographic (CT) system, for performing a first imaging procedure on a subject. The first imaging system has a gantry that slides along rails. A second imaging subsystem, such as a nuclear medicine system (NUC), performs a second imaging procedure on a subject. The second imaging subsystem is separate from the first imaging system, but in the same room. The second imaging subsystem has a gantry or is gantryless. A patient couch supports a subject. The patient couch is supported on a couch support having vertical adjustment and horizontally fixed. In this manner, the patient is maintained in the same position for multiple types of scans using the same couch support without horizontal translation of the patient couch and patient.

The invention aims to provide an intelligent glasses device and method used for performing interaction based on multimodal input and capable of enabling the interaction to be closer to natural interaction of users. The method comprises the steps of obtaining multiple pieces of input information from at least one of multiple input modules; performing comprehensive logic analysis on the input information to generate an operation command, wherein the operation command has operation elements, and the operation elements at least include an operation object, an operation action and an operation parameter; and executing corresponding operation on the operation object based on the operation command. According to the intelligent glasses device and method, the input information of multiple channels is obtained through the input modules and is subjected to the comprehensive logic analysis, the operation object, the operation action and the operation element of the operation action are determined to generate the operation command, and the corresponding operation is executed based on the operation command, so that the information is subjected to fusion processing in real time, the interaction of the users is closer to an interactive mode of a natural language, and the interactive experience of the users is improved.

The invention discloses a multi-modal knowledge graph construction method, and relates to the knowledge engineering technology in the field of big data. The method is realized through the following technical scheme: firstly, extracting multi-modal data semantic features based on a multi-modal data feature representation model, constructing a pre-training model-based data feature extraction model for texts, images, audios, videos and the like, and respectively finishing single-modal data semantic feature extraction; secondly, projecting different types of data into the same vector space for representation on the basis of unsupervised graph, attribute graph, heterogeneous graph embedding and other modes, so as to realize cross-modal multi-modal knowledge representation; on the basis of the above work, two maps needing to be fused and aligned are converted into vector representation forms respectively, then based on the obtained multi-modal knowledge representation, the mapping relation of entity pairs between knowledge maps is learned according to priori alignment data, multi-modal knowledge fusion disambiguation is completed, decoding and mapping to corresponding nodes in the knowledge maps are completed, and a fused new atlas, entities and attributes thereof are generated.

The invention involves a multi-mode medical body data three-dimensional video-method. Most the existing technologies are based on two-two-dimensional lay or two-three dimensional lay, which do not realize the integrated display course of medical body data aligning and amalgamation. The said method includes standardization mutual information-way aligning based on multi resolution ratio and single drop multi-mode straight body protracting-way amalgamation display. The previous one includes three approaches, such as coordinate transform, orientation criterion and multi resolution ratio optimize; the latter includes five approaches, such as impress function definition, the calculation of illumination model, image composition, single drop multi-mode show of the multi-mode data and amalgamation display. The invention can realize the integrated display course of medical body data aligning and amalgamation, and is a real two-three dimensional multi-mode video-method.

The invention discloses a personal identity identification method based on hand shapes, which adopts an imaging device to obtain human hand images, and obtaining personal character description through computer analyzing and processing, and achieving personal identity identification. The concentrate implementation step comprises the following steps: firstly, adopting an imaging device to obtain hand shape images, secondly, extracting hand shape outlines on the hand shape images, thirdly, extracting hand shape characteristic points according to the hand shape outlines, fourthly, forming hand shape characteristic vectors according to the hand shape characteristic points, fifthly, matching hand shape characteristics according to the hand shape characteristic vectors. The personal identity identification method is mainly characterized in of simple algorithm, high received degree of a tester and stable performance, which is easy to achieve multi-modality identification. The personal identity identification method is particularly suitable for the occasions of a gate inhibition and the like.