82 results about "Tissue characterization" patented technology

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.

The invention relates to the technical field of ultrasonic image processing systems, and provides an ultrasonic image processing system based on three-dimensional reconstruction, which comprises an ultrasonic data acquisition terminal, an image processing terminal, a three-dimensional reconstruction terminal, a quality evaluation terminal and a doctor interaction terminal, the ultrasonic data acquisition terminal is used for acquiring ultrasonic original radio frequency data and a B mode image and synchronously acquiring probe position information of the ultrasonic probe; the image processing terminal is used for performing image denoising, edge enhancement and tissue feature labeling according to the ultrasonic original radio frequency data and the B mode image, and extracting key section information for subsequent reconstruction; the three-dimensional reconstruction terminal is used for performing spatial reconstruction based on the key section data and the probe position information to form a three-dimensional ultrasonic image body; the quality evaluation terminal is used for scoring the three-dimensional reconstruction effect of the three-dimensional ultrasonic image body and generating three-dimensional reconstruction quality evaluation information; and the doctor interaction terminal is used for a doctor to check the three-dimensional ultrasonic image body and the three-dimensional reconstruction quality evaluation information in real time and generate and display optimization suggestion information. The method has the effect of improving the accuracy of three-dimensional reconstruction quality evaluation.

The invention discloses a postpartum pelvic floor function evaluation system based on three-dimensional image reconstruction, which belongs to the technical field of medical image processing and biomechanical analysis and comprises an ultrasonic image acquisition module, a three-dimensional reconstruction module, a tissue elasticity analysis module, a rehabilitation scheme generation module and a visual display module. Performing three-dimensional space registration and feature point extraction through a multi-angle ultrasonic image sequence, and realizing three-dimensional reconstruction of a pelvic floor anatomical structure with the precision of 0.2 mm by using a multi-scale fusion network; a deep learning neural network is adopted to extract tissue features, and elastic modulus distribution is calculated through a strain analysis model; an individualized rehabilitation training plan is automatically generated according to an evaluation result, and the rehabilitation effect is improved by 75%; according to the postpartum pelvic floor function evaluation system and method, the three-dimensional visual display and rehabilitation process tracking functions are provided, accurate evaluation and scientific rehabilitation guidance of the postpartum pelvic floor function are achieved, the screening efficiency is improved by 320%, and the clinical application value is remarkable.

The invention relates to the technical field of medical instruments, and discloses an interventional ultrasound method and system for minimally invasive surgery, and the method comprises the steps: obtaining ultrasound image data and operation state parameters; a three-dimensional ultrasonic image model is constructed through multi-layer scanning, and an initial tissue feature evaluation model is analyzed and generated; dynamic blood vessel data and operation constraint conditions are loaded, a comprehensive evaluation model is generated, and puncture point dynamic simulation is carried out; constructing a dynamic blood vessel prediction model, training to obtain a puncture point adjustment model, and generating initial adjustment parameters; obtaining dynamic adjustment simulation information based on the parameters and the model; constructing a multi-path optimization model, and carrying out iterative optimization to obtain an optimal path planning parameter; the safety puncture probability is inferred in combination with real-time images, and a real-time puncture strategy is adjusted to achieve dynamic matching. The system comprises a data acquisition module, an image modeling and analysis module and the like. The safety and accuracy of minimally invasive surgery puncture are improved, and the method is suitable for interventional ultrasound guided minimally invasive surgery scenes.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention relates to the technical field of image recognition, in particular to a gynecological operation navigation method and system based on the minimally invasive technology, and the method comprises the steps: carrying out the feature recognition operation of a three-dimensional focus region structure diagram, and obtaining a focus surrounding tissue feature set; performing minimally invasive surgery scheme identification operation on the focus surrounding tissue feature set to obtain a predicted channel number; performing minimally invasive surgery path recognition operation based on the predicted channel number on the tissue feature set around the focus to obtain a minimally invasive path set, and acquiring necessary avoidance tissue sequences in a preset range region on each minimally invasive path in the minimally invasive path set to obtain a necessary avoidance tissue sequence set; according to the necessary avoiding tissue sequence set, the real-time instrument position information, the minimally invasive path set and the path distribution purpose type set, cutting operation is conducted on the target treatment area, and a minimally invasive channel set is obtained. The navigation accuracy of the minimally invasive surgery can be improved, and the safety of the minimally invasive surgery is improved.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

This application relates to the field of medical image processing technology, and discloses a multimodal image recognition method and system for dynamic assessment of diabetic wound healing. The method includes acquiring infrared thermal imaging images, hyperspectral images, and visible light color images of a diabetic wound collected at the same time of visit; identifying the wound boundary between the diabetic wound and surrounding tissues in the visible light color image, and determining the central reference point of the visible light color image based on the wound boundary; establishing a virtual polar coordinate system based on the central reference point and preset anatomical reference markers; extracting tissue feature information characterizing the pathological state of the wound from the infrared thermal imaging image, hyperspectral image, and visible light color image, and mapping the spatial position of the tissue feature information in its corresponding image to the virtual polar coordinate system; normalizing the mapped tissue feature information, and calculating wound healing assessment indicators based on the normalized tissue feature information in the virtual polar coordinate system.

The utility model provides a laser diagnosis device comprising a first laser light source used for emitting diagnosis laser; the light deflector is connected with the output end of the first laser light source and is used for carrying out light beam deflection at a specific angle on the diagnosis laser; the optical fiber coupler is arranged at the output end of the optical deflector and is used for coupling the diagnosis laser with the deflected light beam into one optical fiber branch of the optical fiber array; the optical fiber array is connected with the optical fiber coupler and is used for transmitting the diagnosis laser coupled by the optical fiber coupler to a focusing microlens of the focusing microlens array; wherein the optical fiber branches in the optical fiber array are mutually independent; the focusing micro-lens array is connected with the optical fiber array and is used for focusing the diagnosis laser on the diseased tissue through a focusing micro-lens to generate a laser signal with tissue characteristic information; wherein each focusing microlens in the focusing microlens array is correspondingly connected with one optical fiber branch of the optical fiber array; the device is compact in structure, easy and convenient to operate and capable of conducting refined diagnosis on diseased tissue.

Changes in tissue stiffness have long been associated with disease. Systems and methods for determining the stiffness of tissues using ultrasonography may include a device for inducing a propagating shear wave in tissue and tracking the speed of propagation, which is directly related to tissue stiffness and density. The speed of a propagating shear wave may be detected by imaging a tissue at a high frame rate and detecting the propagating wave as a perturbance in successive image frames relative to a baseline image of the tissue in an undisturbed state. In some embodiments, sufficiently high frame rates may be achieved by using a ping-based ultrasound imaging technique in which unfocused omni-directional pings are transmitted (in an imaging plane or in a hemisphere) into a region of interest. Receiving echoes of the omnidirectional pings with multiple receive apertures allows for substantially improved lateral resolution.

Systems and techniques are disclosed for improving real-time tissue characterizations during endovascular procedures. In some implementations, a method involves characterizing biological tissue for an endoluminal procedure. A first electrode is positioned within an endoluminal space adjacent to a region of biological tissue. A second electrode is positioned spatially separated from the first electrode. A variable-frequency input electrical signal is applied across the first electrode and the second electrode to generate an electrical field corresponding to the region. A voltage response is measured between the first electrode and the second electrode based on the electric field. A voltage phasor is determined based on the voltage response relative to the variable-frequency input electrical signal. A characteristic of the region is determined based on the voltage phasor.

A computer-implemented method and system are described. The method comprises the steps of: identifying a region having an electrical activation sequence within a chamber of the heart from an electrograph, the electrical activation sequence characterizing the region as a potential drive of an abnormal heart rhythm; for each sensing location at or substantially around the region, determining an earliest activation electrode site from a primary activation; for each determined earliest activated electrode site: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers determined from electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site; determining a sorting factor calculated according to the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; and outputting data identifying the region, the data changing a significance of each of the determined earliest activated electrode sites depending on the ranking.

The invention discloses a dynamic path planning method and system for anterior cruciate ligament tendon dissection, and relates to the technical field of ligament tendon dissection, and the method comprises the following steps: obtaining anatomical structure data of an anterior cruciate ligament tendon and tissue characteristic parameters of a to-be-dissected region; marking key monitoring points in the to-be-stripped area to obtain stripping monitoring points; counting the space distance and the tissue density difference value between the adjacent stripping monitoring points to obtain a to-be-measured space parameter; historical tissue stress distribution characteristics of different anatomical structure types are extracted from historical anterior cruciate ligament tendon dissection operation data, and according to the historical tissue stress distribution characteristics, a dissection path deviation value in the historical operation data and a path correction correlation factor between the to-be-measured spatial parameters are counted; according to the anatomical structure data and the tissue characteristic parameters of the current tendon to be stripped, the effects are that operation errors caused by path deviation are reduced, and the accuracy of operation is improved.

This invention discloses a deep learning-based method for predicting orthodontic outcomes, belonging to the field of artificial intelligence-assisted technology. The method includes acquiring multimodal initial data of the target patient, comprising craniofacial CBCT 3D images, intraoral 3D scan models, and a digital design scheme for orthodontic appliances containing target tooth displacement data. Based on the CBCT 3D images, macroscopic geometric features of the jawbone are extracted using a macroscopic morphological encoder, and microscopic tissue features of the periodontal region are extracted using a microscopic tissue feature encoder. The macroscopic morphological encoder is a 3D convolutional neural network, and the microscopic tissue feature encoder is a local window attention network based on a visual Transformer. This invention can predict the dynamic effects of the interaction between tooth movement and adaptive skeletal remodeling, and is particularly suitable for predicting orthodontic outcomes in adolescents during their growth and development period, addressing the industry pain point of large systematic biases in static models in such scenarios.

Provided herein is an intravascular endoscope with an improved balloon design. The endoscope comprises an anterior balloon with a camera, light source, and working channel port for visualization and therapeutic access. One or more posterior balloons are positioned inferior to, at the same level as, or superior to the anterior balloon. Inflation of the one or more posterior balloons pushes the anterior balloon against the blood vessel wall while maintaining a channel for blood flow past the anterior balloon. The endoscope enables real-time, direct visualization of the interior of blood vessels for diagnosis and treatment of vascular diseases while preserving critical blood flow. Additional imaging modalities, such as ultraviolet and infrared, can be incorporated to enhance tissue characterization. Visual markers on devices inserted through the working channel improve visibility and guidance under endoscopic imaging. The endoscope is compatible with arteries and veins throughout the body and provides advantages over traditional imaging techniques for managing vascular disease.

The invention discloses a cardiac magnetic resonance report generation method and system based on a multi-dimensional tissue feature knowledge graph. The method comprises the following steps: performing motion correction and multi-view feature extraction on a cardiac magnetic resonance multi-sequence image to obtain an original numerical attribute of an instance node; constructing a dynamic knowledge graph containing tissue characteristics and medical priori, and generating a target reasoning chain with a clinical basis in combination with neural symbol reasoning; then, in a controlled generation stage, an inference chain is injected into a prefix tree, in a decoding step, mask physical shielding logic conflict description is utilized, and a pointer network is synchronously started to directly copy original numerical value attributes into a text sequence; and finally, executing consistency logic verification through a text restoration technology, and identifying and intercepting factual errors. Through the atlas constraint and numerical unvarnished transmission mechanism, the factual illusion problem of a generative model is effectively solved, and the accuracy and reliability of a heart diagnosis report are remarkably improved.

Provided herein are methods of analyzing tissue using a magnetic resonance imaging (MRI) compatible tissue analysis device that includes radio-frequency (RF) tracking and imaging elements. Related kits, systems, and computer program products are also provided.

To detect early changes in the characteristics of disease-related tissues within the subject's body. [Solution] The medical information processing device according to this embodiment includes an acquisition unit, a comparison unit, a tissue change estimation unit, and a display control unit. The acquisition unit acquires tissue information indicating the state of tissues related to diseases in the body of a subject, and surface information indicating the state of the body surface of the subject. The comparison unit compares the tissue information and the surface information and calculates the comparison result between the tissue information and the surface information. The tissue change estimation unit estimates the change in the characteristics of the tissue based on the change in the comparison result over time. The display control unit displays the estimated result of the change in the characteristics of the tissue on a terminal.

The present disclosure relates to an apparatus for optoacoustic sensing of tissue features, such as tissue architecture, morphological information, function, physiology and / or molecular information about biomolecules, in particular metabolites, in a biological tissue (1) and a corresponding method. The apparatus comprises at least one illumination device (2) configured to illuminate the tissue (1) with first electromagnetic radiation (3) exhibiting one or more wavelengths or wavelength ranges in a first spectral range, at least one detection device (5) configured to detect, in response to illuminating the tissue (1) with the first electromagnetic radiation (3), at least one first optoacoustic signal corresponding to ultrasonic waves (6) emanating from a region of interest (ROI) in the tissue (1), and a processor (7) configured to determine, based on the first optoacoustic signal, at least one first parameter characterizing a first tissue feature of interest in the region of interest (ROI).

According to one embodiment, a medical diagnostic apparatus includes processing circuitry and display circuitry. The processing circuitry estimates a position of a structure in a subject based on data obtained by scanning with respect to the subject and analyzes tissue characterization in the subject. The display circuitry displays an analysis result of the tissue characterization obtained by the processing circuitry with respect to a plurality of positions in the subject except for the estimated structure position.

The invention provides a laparoscope data processing method and system, relates to the technical field of data processing, and aims at outputting and interacting laparoscope data based on forms such as natural language processing, interacting according to the requirements of doctors, realizing preoperative identification of operators, and improving the accuracy of laparoscope data processing. The system is automatically converted into image modes for habits of an operator, including brightness, saturation, focal length and the like. The blood vessel or the ureter can be recognized based on the fact that the special spectrum can be combined with typed cells or tracers in the blood vessel or the ureter, blood vessel damage can be avoided as much as possible by recognizing the blood vessel, intraoperative bleeding is reduced, and the ureter damage probability can be reduced by recognizing the ureter. The method can obtain the abdominal cavity image collected by the laparoscope, determines the tissue characteristics of each tissue catheter based on the abdominal cavity image, the tissue characteristics comprise the tissue trend and the tissue diameter, determines the organ information of the recognition object, can perform point location recommendation and determine the tissue type according to the data collected by the laparoscope, and improves the judgment efficiency and accuracy of the user.

Disclosed are devices, systems, and methods for characterizing tissue using light scattering spectroscopy. A tissue characterization probe includes an elongate member having a proximal end and a plurality of distal probe tips at a distal end. A plurality of illumination fibers extend through the elongate member to the distal probe tips such that each distal probe tip includes at least one illumination fiber. A plurality of detection fibers also extend through the elongate member such that each probe tip includes at least one detection fiber. The disclosed devices and systems beneficially enable characterization of tissues within depths greater than 100 μm. The disclosed devices and systems also enable effective characterization of anisotropic tissues, such as cardiac myocardium.

The present application relates to pathological analysis technical field, specifically to a kind of pathological section intelligent analysis method and system based on deep learning, including the following steps: based on tissue section image, utilize multi-channel analysis tissue structure and dye distribution, adjust collection parameter and optimize image definition and cell structure performance, combine spatial distribution and gray layering relationship, automatically distinguish mutation area and structure boundary, output demarcation structure positioning mark.The present application is through collection sequence optimization, parameter dynamic adjustment and spatial distribution automatic discrimination, microstructure change, dye distribution and cell arrangement relationship are mapped as structure response index, realizes the efficient linkage of collection parameter and tissue characteristics, supports the synchronous identification of spatial mutation, dye layering and demarcation relationship in complex section, greatly improves the objectivity and coherence of area identification, structure annotation and demarcation positioning, avoids feature omission, enhances the integrity of data processing link and the practical depth of medical image analysis.

The invention discloses a duodenum mucosa remodeling system based on pulse field ablation and a control method, and the system comprises a multi-modal sensing ablation catheter which is used for collecting multi-modal data related to duodenum ablation through a multi-modal sensor; the multi-sensing data fusion unit is used for extracting key features after preprocessing the multi-modal data, and fusing the key features according to a preset algorithm to obtain a multi-modal feature vector; the real-time tissue feature analysis module is used for recognizing the tissue type of the current ablation point location in real time according to the multi-modal feature vector and analyzing the dynamic change of tissue features; the self-adaptive parameter adjustment engine is used for determining corresponding ablation parameters according to the tissue type of the current ablation point location and the dynamic change of the tissue characteristics; and the motion compensation and prediction system is used for predicting the motion trail of the duodenum in a future preset time period. The problem that in the prior art, a duodenum mucosa remodeling system is poor in remodeling effect is solved.

A medical system includes first and second opposing jaws each having an internal surface, at least one light emitter and at least one light sensor each disposed on the internal surface of one of the first and second jaws, and at least one of the first and second jaws having an external surface opposite the internal surface, the external surface having at least one marking disposed thereon aligned with the at least one light sensor. The medical system also includes at least one visual display, and a controller coupled to the light sensor and the visual display. The controller is configured to determine a position of a tissue relative to the light sensor, and control the visual display to display a graphical interface comprising at least one marking corresponding to the marking on the external surface in combination with an image corresponding to the tissue.

The present invention discloses a method, device, and storage medium for determining a marker clip. The method comprises obtaining a tissue image and tissue feature distribution information of a target tissue; extracting the edge contour shape and area size of the target tissue from the tissue image; extracting the hardness distribution information of the target tissue from the tissue feature distribution information; and inputting the edge contour shape, area size, and hardness distribution information into a marker clip calculation model, which outputs the number of marker clips required for marking the target tissue. The method achieves high efficiency and accuracy in marking the target tissue using the marker clip.

This invention discloses an AI-based medical image analysis and processing method, belonging to the field of image analysis technology. It involves collecting multimodal medical image data from patients using imaging equipment and preprocessing it; fusing the preprocessed multimodal medical image data and extracting biological tissue features to construct a biological tissue feature library, marking abnormal biological tissue features; training an AI visual detection model based on the marked biological tissue feature library according to the medical image analysis task to identify and classify different abnormal biological tissues; and automatically detecting abnormal biological tissue regions in the images using the AI ​​visual detection model. This invention enables high-precision analysis of medical images through AI technology, detecting minute abnormal changes. Furthermore, through quantitative analysis of image features, it can assess the severity of lesions and predict disease progression trends, allowing for timely detection before lesions cause obvious symptoms, thus reducing the risk of disease progression.

This invention discloses a deformable capsule endoscope probe and image fusion system based on multimodal imaging, belonging to the field of medical endoscopy technology. This invention solves the problems of existing capsule endoscope technology, which has relatively limited functionality and a fixed shape, making it difficult to adapt to the complex structure of the digestive tract and resulting in poor imaging effects. By using flexible silicone material to make the capsule endoscope probe's shell and deformable structure, it can automatically adjust its shape according to the shape of the digestive tract lumen, ensuring clear and accurate multimodal images are acquired from different locations. A neural network model accurately identifies potential lesion areas and adaptively adjusts the fusion weights based on the importance of the lesion and tissue characteristics to optimize image quality. Furthermore, a deep learning fusion network generates a comprehensive image, which, after contrast optimization and automatic labeling of lesion areas, provides doctors with clearer and more detailed diagnostic prompts, aiding in the early diagnosis and precise treatment of digestive tract diseases.

A system is provided that includes a processor circuit in communication with an intravascular imaging catheter. The processor circuit is operable to: receive an intravascular imaging signal; perform, tissue characterization to identify a plaque component of the blood vessel; generate an intravascular image; receive an extravascular image of a blood vessel; co-register the intravascular image and the extravascular image to associate the intravascular image with a location of the blood vessel in the extravascular image; determine, if a spatial distribution of the plaque component within the intravascular image satisfies a criterion associated with the spatial distribution; and when the spatial distribution satisfies the criterion, output a screen display comprising: the extravascular image; and a first indicator at the location of the blood vessel in extravascular image. The first indicator is representative of the spatial distribution of the plaque component. Associated methods and devices are also provided.

In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.