1175results 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.

The invention belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

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.

A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.

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.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

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.

An ultrasound diagnostic apparatus includes a debris specifying unit 25 that specifies debris in the cyst from each of a first ultrasound image acquired by the image acquisition unit 41, and a second ultrasound image acquired by the image acquisition unit 41 by imaging the cyst from the same direction as a direction in a case of capturing the first ultrasound image with respect to the cyst after an external force different from an external force in a case of capturing the first ultrasound image has acted on the cyst or in a state where the external force is acting on the cyst, and a debris movement information providing unit 26 that provides information regarding movement of the debris, which is specified by the debris specifying unit 25, in the cyst between the first ultrasound image and the second ultrasound image.

The invention discloses intelligent wearable equipment for breast health monitoring and a data analysis system and method. The equipment comprises a plurality of wearable ultrasonic patches, a wireless communication module, a signal processing and control unit and a power management module. The data analysis system comprises a data acquisition system, a data transmission system, a data processing and analysis system and a user interaction system, and the data acquisition system is used for acquiring ultrasonic images of mammary tissue and physiological parameters of a mammary area; the data processing and analysis system comprises a data preprocessing module, a feature extraction module and a multi-modal deep learning algorithm module, data preprocessing, feature extraction and data fusion are achieved, and the user interaction system comprises a data visualization system and an early warning notification module. According to the invention, ultrasonic images and various physiological parameters of the mammary gland can be collected in real time, long-term, repeatable and non-invasive monitoring can be carried out on the mammary gland tissue, and early warning and risk assessment of mammary gland diseases can be realized.

The embodiment of the invention provides a method for performing ultrasonic scanning on breasts and an ultrasonic imaging system. The ultrasonic scanning is at least partially carried out through a scanning assembly, the scanning assembly comprises a frame, a scanning probe and a driving device are contained in the frame, and the driving device drives the scanning probe to move in the frame to carry out the ultrasonic scanning. The method comprises the following steps: pre-scanning a breast to generate a pre-scanning ultrasonic image of the breast; performing image recognition on the pre-scanning ultrasonic image to obtain the breast size of the breast; and performing scanning planning on the scanning assembly based on the breast size, and performing scanning imaging on the breast by using the scanning assembly based on the scanning planning. According to the embodiment of the invention, the scanning time can be saved, and the scanning success rate is improved.

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.

To reduce the labor required for collection of ultrasonic images.SOLUTION: A medical image processing device according to an embodiment comprises: a conversion unit; and a determination unit. The conversion unit performs position conversion such that a position of an area of interest set in a mammography image obtained by imaging the breast of a subject and an inspection position on the body of the subject in an ultrasonic inspection are expressed on the plane in the same scale. The determination unit determines whether or not the position of the area of interest matches the inspection position on the plane. The determination unit stores or outputs the ultrasonic image corresponding to the inspection position when determining that the position of the area of interest matches the inspection position.SELECTED DRAWING: Figure 1

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.

Examples of the present disclosure describe systems and methods for predicting the reading time and / or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict / estimate an expected reading time and / or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and / or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

The invention relates to the technical field of human body intercostal model trajectory construction, and discloses an intercostal trajectory planning method and system based on an AI reinforcement learning robot. The inter-costal trajectory planning method is applied to an AI robot, and specifically comprises the following steps: S101, receiving an inter-costal region three-dimensional anatomical model construction instruction sent by a user terminal, obtaining anatomical image data of a thoracic cavity from CT or MRI scanning of a patient, and segmenting the anatomical image data by using a deep convolutional image segmentation network to obtain an anatomical image target region; the CT anatomical model and the reinforcement learning technology are fully combined, the completely observable Markov decision model is constructed, the robot path planning strategy is trained in the virtual environment, the robot can autonomously adjust the posture and position of the ultrasonic probe, it is ensured that the probe achieves accurate movement in the complex intercostal region, and the accuracy of the ultrasonic probe is improved. Therefore, limitation of rib sound shadow is broken through, and imaging quality of a target area is remarkably improved.

A method for molecular image acquisition includes beginning acquisition of a molecular image via a molecular imaging system, performing one or more analyses of the molecular image during acquisition of the molecular image to determine whether at least one hot spot is present in the image, and if it is determined that it is statistically significant that there is either at least one hot spot present or there is not at least one hot spot present in the image in any one of the one or more analyses of the molecular image, performing at least one of providing a recommendation that the acquisition be stopped or stopping the acquisition automatically.

A first display unit that is provided at a position of an arm part in front of an examinee, and a second display unit that is provided at a side surface of the arm part are provided. Then, control of switching between the display units that display an ultrasound image is performed according to a rotational position of the arm part.

The invention discloses a multi-modal ultrasonic prediction method based on hierarchical attention fusion, and belongs to the field of medical information processing.The prediction method comprises the following steps that firstly, ultrasonic image features of each modal are extracted through Swin Transform V2; secondly, splicing the ultrasonic image features of the four modes, and inputting the spliced ultrasonic image features into a constructed ultrasonic self-attention and convolution fusion module to realize coarse fusion of multi-mode-multi-scale features; a designed multi-modal cross attention fusion module is adopted to realize hierarchical fusion of global-local information, and fine fusion of multi-modal information is realized; and finally, inputting the final cross attention fusion feature into a classifier, and further outputting a tumor prediction result. According to the method, the application of tumor prediction on the ultrasonic image based on the hierarchical attention fusion model is realized, the accuracy and robustness of tumor diagnosis are remarkably improved, and the method has clinical popularization and application values.

The invention relates to an NAC curative effect prediction method and device based on multi-modal feature fusion and Heman optimization, and the method comprises the steps: firstly, extracting radiomics features, including texture, morphology, first-order statistics, wavelet and other features, from ultrasonic images of three modalities of two-dimensional gray scale, elasticity and color Doppler blood flow imaging, splicing the feature vectors, and carrying out the fusion of the feature vectors; a high-dimensional feature vector is formed, and lesion information is comprehensively represented. Then, an improved Hemma optimization algorithm is adopted to carry out dimension reduction on the features, and penalty factors and kernel parameters of a support vector machine are optimized. According to the algorithm, chaotic mapping is introduced, so that the global search capability is enhanced, local optimum is avoided, and the optimal feature subset and SVM parameters are screened out. Finally, an SVM model is trained based on the optimized features and parameters, an independent test set is used for model evaluation, and compared with various existing optimization algorithms, the advantages of the improved HO algorithm in the aspect of improving breast cancer NAC curative effect prediction accuracy are verified.

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.

A method and apparatus for preparing tissue of interest in a patient for possible excision by surgery. In one embodiment, the method comprises the steps of: removing a biopsy sample from the tissue of interest; placing a magnetic marker at the biopsy site; performing a pathology analysis of the biopsy sample; and if the pathology analysis indicates that the tissue of interest should be removed, locating the tissue for surgery using a magnetic detection probe. In one embodiment, the marker comprises magnetic nanoparticles in a bioabsorbable matrix. A system for preparing tissue of interest in a patient for possible excision by surgery. In one embodiment, the system includes a magnetic marker and magnetic detection probe system.

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.

An apparatus has an upright gantry. A tube arm assembly and a compression arm assembly are rotatably coupled to the gantry. The tube arm assembly independently rotates relative to the compression arm assembly. A controller disposed on the gantry is operably connected to at least one of the tube arm assembly and the compression arm assembly.

A method of imaging a breast of a patient using an imaging system includes applying, with a first component of the imaging system, a compressive force to the breast. A second component of the imaging system is positioned in a start position. The imaging system sends a first guidance signal to the patient. An imaging procedure of the breast is performed with the second component of the imaging system. Subsequent to performing the imaging procedure, a second guidance signal is sent to the patient.