788results about "Mammography" patented technology

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

Disclosed are approaches to data acquisition, analysis, and computational forecasting that employs quantitative MRI data to predict the response of cancer to therapy. Example protocols detail how to acquire needed images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The response of individual cancer patients to therapy is forecast by application of a biophysical, reaction-diffusion model to these data. Application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. A modified therapy can be determined based on predicted response.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

For a biopsy guided by contrast-enhanced x-ray imaging, a breast imaging system may image a patient's breast subsequent to injection of contrast agent to locate a site for biopsy and / or position a biopsy device relative to the site. Once a tissue specimen is removed from the site for diagnostic evaluation, a confirmation that the specimen was removed from an intended area of interest for biopsy is performed using the systems and methods described herein. For example, the breast imaging system and / or a separate specimen imaging system may capture high and low energy images of the specimen, subtract the low energy image from the high energy image to generate a subtracted image of the specimen, and determine, based on the subtracted image, that the contrast agent is present within the specimen to confirm the site from which the specimen was removed is the intended site for biopsy.

In an ultrasound system and a control method of the ultrasound system, a second region-of-interest specifying unit specifies a second region of interest included in an ultrasound image, and an identity determination unit determines whether or not a first region of interest included in a radiation image and the second region of interest are identical to each other.

Exemplary artificial intelligence (AI) systems, methods and computer-accessible medium can be provided for detecting a breast cancer using mammography. For example, using at least one computer processor, it is possible to receive a mammography image, apply a neural network employing, e.g., a You Only Look Once X (YOLOX) architecture to predict one or more locations and probabilities of lesions. Further, with such exemplary systems, methods and computer-accessible medium, it is possible to make an overall image-level prediction for the received mammography image so as to provide a particular breast cancer prediction. It is also possible to generate breast-level predictions by averaging all predictions from FFDM, C-View, DBT modalities for each breast. For example, when used in clinical decision support, the exemplary model can reduce radiologist workload by, e.g., about 45% and unnecessary recalls by, e.g., about 32.4%, without missing malignancies.

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.

The invention relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

An ultrasound-metabolic synergistic system for diagnosing SLN metastasis of breast cancer. A deep learning model based on grayscale ultrasound is constructed to predict SLN metastasis of breast cancer, and 9 grayscale deep features and 15 grayscale manual features associated with SLN metastasis are obtained. A matrix material based on two-dimensional material MXene / multi-walled carbon nanotubes improves the detection of small metabolic molecules in blood, and metabolomics analysis results show that this has certain potential in detecting SLN metastasis of breast cancer, while revealing metabolic pathways closely associated with metastasis, such as the glucose-alanine cycle and glycolysis, suggesting that alterations in relevant molecular pathways may underlie heterogeneous features in ultrasound radiomics. The combination of ultrasound omics and metabolomics further improves the diagnostic performance for SLN metastasis of breast cancer. Therefore, organic integration of the two serves the diagnosis of SLN metastasis of breast cancer, providing a new strategy and theoretical basis for the accurate diagnosis of SLN metastasis of breast cancer, and possesses practical clinical application values.

A method for non-invasively identifying a location of a mass or tumor including: providing a patient with a possible mass or tumor; providing a detector including one or more resonant sensors, each sensor including at least one loop resonator, at least one cavity resonator, or at least one waveguide resonator; detecting resonant frequencies at different locations on the patient's skin near locations of the mass or possible tumor; and locating the mass or tumor by noting a location of an atypical resonant frequency.

An ultrasonic diagnostic apparatus includes: a lesion detection unit (26) that detects a suspected lesion region in a mammary gland region of a subject based on an ultrasonic image in which the mammary gland region is imaged; a mask data creation unit (27) that creates mask data of the detected suspected lesion region; an exclusion region setting unit (28) that sets an exclusion region to be excluded from a target of a glandular tissue component evaluation based on the mask data; and an evaluation unit (29) that performs the glandular tissue component evaluation on an evaluation target region obtained by excluding the exclusion region from the mammary gland region.

This invention relates to the field of optical imaging and detection technology, specifically providing a broadband snapshot-type polarization interferometry imaging detection system and method for breast tumors. A broadband light source illuminates a breast tumor tissue sample. The reflected signal light is collected and collimated by a pre-optical system, then sequentially passes through a first and a second ray shaping unit for two shearing and two polarization state transformations, resulting in two beams of linearly polarized light and two beams of circularly polarized light. These beams are imaged by an imaging lens to an area array detector to obtain a polarization interferogram, which is then demodulated to obtain all Stokes parameters of the breast tumor tissue sample. This invention achieves broadband snapshot imaging of breast tumors with all Stokes parameters without using an analyzer, doubling the light throughput compared to traditional polarization imaging instruments using analyzers. Furthermore, this invention has advantages such as compact structure, no moving parts, easy assembly and adjustment, and no need for image registration.

One or more example embodiments relates to a medical imaging device for examination of a body part of a patient, in particular a mammography device for examination of the female breast, comprising an imaging unit configured to perform an X-ray-based imaging concerning the body part positioned in a positioning area of the medical imaging device; and a biopsy device comprising a biopsy needle configured to obtain a tissue sample from the body part positioned in the positioning area and a light generating device configured to generate light to reach the positioning area.

The invention discloses a double-arm ultrasonic robot breast curved surface self-adaptive scanning method and related equipment. The method comprises the following steps: by taking a central axis of a human body as a starting point, performing equal-interval segmentation on a mammary gland three-dimensional point cloud to obtain an autonomous scanning track; according to the autonomous scanning tracks, one mechanical arm of the double-arm ultrasonic robot is controlled to move up and down in sequence from one side to the direction where the midmost segmentation line is located, and meanwhile, the other mechanical arm is controlled to move up and down in sequence from the midmost segmentation line to the other side until the probe passes through the autonomous scanning tracks corresponding to all the segmentation lines; for each probe, when the probe goes downhill, the probe is controlled to rotate around the Y axis of the tool coordinate system according to the contact force of the probe; and when the probe goes uphill, determining a Z-axis direction vector of the position of the target probe according to the position information of the probe, and controlling the probe to rotate around the Y axis of the tool coordinate system. According to the method, it can be ensured that the probe is attached to the surface of the human body, and therefore scanning accuracy and scanning efficiency are improved.

The present disclosure provides methods and systems directed to performing real-time and / or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

An ultrasound diagnostic apparatus includes: an image acquisition unit (36) that performs a scan using an ultrasound probe (1) to acquire ultrasound images of a breast; a mammary gland region extraction unit (25) that extracts a mammary gland region from the ultrasound images; a thickness calculation unit (26) that calculates a thickness of the mammary gland region in a depth direction; a lesion detection unit (27) that detects a suspected lesion region for the ultrasound images; a frame selection unit (29) that selects, as an evaluation target frame group, at least frames which exclude a frame in which the suspected lesion region is detected, and in which the thickness of the mammary gland region is equal to or greater than a thickness threshold value, among a plurality of frames; and an evaluation unit (32) that performs a glandular tissue component evaluation on the ultrasound image of the evaluation target frame group.

The invention relates to the technical field of ultrasonic and molybdenum target detection, in particular to an intelligent breast lump diagnosis system based on ultrasonic-molybdenum target multi-modal image and serum P16 protein detection, and the intelligent breast lump diagnosis system is characterized in that an ultrasonic image, a molybdenum target image and multi-modal data of serum P16 protein detection are fused; and optimal integration of cross-modal features is realized through a dynamic weight strategy. The ultrasonic image provides morphological and hemodynamic information of the lumps; the molybdenum target image reveals the characteristics of calcification density, calcification cluster heterogeneity, structure distortion depth and the like; serum P16 protein detection prompts malignant risks from the molecular level, and especially has important value in early recognition of triple negative breast cancer. A feature association graph is constructed through a graph neural network (GNN), the weight is dynamically adjusted in combination with a Bayesian probability integration formula, and optimal fusion of different modal features is ensured. The limitation of a single mode is made up, and the sensitivity and the specificity of diagnosis are also remarkably improved.

A whole breast 3D ultrasound imaging system involves a wearable adapter subassembly, a compression plate subassembly releasably mountable on the wearable adapter subassembly and a motor plate subassembly releasably mountable on the compression plate subassembly so that the compression plate subassembly is between the wearable adapter subassembly and the motor plate subassembly. The wearable adapter subassembly acts as a dam to contain ultrasound fluid around a subject's breast. The compression plate subassembly has an ultrasound compression plate in a frame, whereby the ultrasound compression plate is height adjustable. The motor plate subassembly has an ultrasound transducer and an actuator operatively coupled to the ultrasound transducer to translate the ultrasound transducer in a plane parallel to the ultrasound compression plate. The imaging system is cost-effective, portable and hands-free and can be used for bedside point-of-care imaging to acquire clear, sharp 3D ultrasound images of dense and / or small breasts.

The invention relates to the technical field of deep learning, and provides a mammary gland blood oxygen dynamic imaging method and system based on multispectral fusion, and the method comprises the steps: irradiating mammary gland tissues, and collecting mammary gland tissue reflected light image sequences under different wavelengths to obtain multispectral time sequence data; constructing an adaptive weight prediction network model, and dynamically predicting the fusion weight of each spectral wavelength at different moments in the future according to the blood oxygen change intensity and historical time sequence information at the current moment; performing multi-resolution time sequence modeling on the multi-spectral time sequence data, respectively extracting short-term, medium-term and long-term time sequence features, and performing weighted fusion on the features of different spectral wavelengths through a fusion weight to obtain fusion feature data; and performing inversion processing on the fused feature data, and calculating oxyhemoglobin saturation distribution of a continuous time sequence in real time to obtain a dynamic blood oxygen image sequence. According to the invention, the blood oxygen detection precision and the depth resolution are improved, and high-precision dynamic imaging of the blood oxygen saturation of the breast tissue is realized.

A console as a medical image acquisition guidance device includes a projection processing unit configured to, in a case in which a radiation image and an ultrasound image of a breast in a state of being compressed by a compression member are captured, perform processing of projecting a guide image for guiding at least one of a scanning region or a scanning direction of an ultrasound probe used for capturing the ultrasound image onto a side of the compression member opposite to a compression side of the breast according to an effective width, which is a region in which transmission and reception of an ultrasonic wave are possible, of the ultrasound probe, in capturing the ultrasound image.

Systems and methods are described that implement displaying grouped medical images for review. Current medical image management systems generally use thumbnail image grouping of images that have the same position and side. These groups of thumbnail images can be large, making it difficult for a clinician (e.g., a radiologist) to quickly find a particular view they are interested in reviewing. The disclosed systems and methods provide for user-selectable grouping and ungrouping of thumbnail images in a graphical user interface, which makes it easier for a clinician to quickly find a particular view that they are interested in and enables a more-efficient review of an imaging study.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

Presented herein are systems and methods of determining density values from mammograms. A computing system may identify a first mammogram of a first breast region of a first subject. The first mammogram may have a first region of interest (ROI) corresponding to a first dense area of the first breast region. The computing system may apply the first mammogram to a machine learning (ML) model to generate a first segmentation map identifying the first ROI within the first mammogram. The computing system may determine a density value for the first dense area of the first breast region based on the first segmentation map.

An ultrasonic diagnostic apparatus includes a first category determination unit (25) that determines, based on an ultrasonic image including a mammary gland region in a breast of a subject, a category of a glandular tissue component in the breast, and a category output unit (26) that outputs the category of the glandular tissue component determined by the first category determination unit (25).

According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.

The invention belongs to the technical field of respiratory therapy equipment control, and discloses a multi-band airway vibration sputum excretion adaptive optimization system, which obtains acoustic impedance distribution data of each micro-section of the inner wall of the airway of a patient through an acoustic impedance imaging technology, accurately recognizes an abnormal region of a tissue interface, and improves the accuracy of sputum excretion. And calculating a reflection enhancement coefficient and a transmission attenuation coefficient of the vibration wave in the areas. And based on the multi-path interference model and the sputum excretion demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated, and effective compensation of the energy transfer efficiency of the abnormal region of the tissue interface is realized. A closed-loop feedback control system is formed by monitoring spectrum characteristics of pressure pulsation signals in an airway in real time, identifying characteristic harmonic peaks and dynamically adjusting vibration wave parameters according to the characteristic harmonic peaks. The sputum excretion parameters can be optimized in real time according to individual differences of patients and sputum characteristics, the sputum excretion efficiency is remarkably improved, and discomfort of the patients is relieved.

An optical sensing system comprises a human interface structure to be applied to a user. The system comprises a time-of-flight sensor with an array of laser diodes with Bragg reflectors operating in the near-infrared, a detection system, and a processor generating a time-of-flight measurement and a first image. The system also includes a first camera system capturing a second image, and a second camera system illuminated by light emitting diodes to monitor one or more eyes of the user. In communication with or operating on the processor is an artificial intelligence assistant, which comprises a multi-modal generative artificial intelligence model. The system may be part of wearable eyewear such as smart glasses or extended reality goggles. The system may perform gesture analysis, eye tracking and speed recognition, and video and verbal outputs can be provided to the user. An artificial intelligence agent may have awareness of the environment.

A method and a system for generating a multimodal diagnostic output related to a pathological condition of a patient includes at least one processor and a memory storing instructions executable by the processor to receive medical image data and narrative data of the patient. The medical image data is preprocessed through normalization, while the narrative data is standardized to ensure consistency. The preprocessed image data is input into a trained deep residual network (ResNet) to extract quantitative imaging features indicative of the pathological condition. In parallel, the preprocessed narrative data is input into a thematic analysis module to derive thematic codes reflecting emotional, cognitive, or social factors of the patient. The extracted imaging features and thematic codes are fused into a combined feature vector, which is processed by an integrated diagnostic model to generate a multimodal diagnostic output that enhances clinical precision and contextual interpretation.

A wearable device configured to evaluate a breast area of a user and provide a prediction associated with the breast area of the user is provided. The wearable device includes a garment insert detachably coupled to a garment designed for at least partial contact with the breast area of the user and sensors configured to collect data associated with the breast area of the user. The sensors may collect first breast data at a first time and second breast data at a second time, different from the first time. A breast area prediction may be generated based on comparing the first breast data to the second breast data, and may define an estimation of breast area risk for the user. A feedback indication may be provided to a user interface based on the breast area prediction, indicating whether the user should receive a breast area evaluation from a medical professional.

An image processing apparatus includes spectral image acquisition means that acquires a spectral image that is an image generated using photoacoustic signals corresponding to a plurality of different wavelengths, based on photoacoustic waves generated by radiating lights with the plurality of different wavelengths to an object into which a contrast agent has been injected, contrast agent information acquisition means that acquires information about the contrast agent, region determination means that determines a region corresponding to the contrast agent in the spectral image on the basis of the information about the contrast agent, and display control means that displays the spectral image such that the region corresponding to the contrast agent is distinguishable from a region other than the region corresponding to the contrast agent.