10567results about "Computerised tomographs" patented technology

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

The invention relates to the technical field of region segmentation, in particular to a tumor medical image processing method and system of a tumor ablation treatment system. According to the method, a CT image of a disease site is primarily segmented to obtain a tissue region, an initial abnormal region is screened out, and a final abnormal region is screened out according to the difference between the shape change degrees of the initial abnormal region and an analysis region thereof and the difference between the image numbers of the CT image of the disease site where the initial abnormal region and the analysis region thereof are located. Then, mistakenly segmented areas are screened; and according to the position distribution difference of the edge pixel points of the mistakenly segmented region and the analysis region thereof, adjusting the gradient value of the edge pixel points of the mistakenly segmented region, and then performing region segmentation on the CT image of the diseased part again by using the adjusted gradient value. According to the method, the accuracy of region segmentation of the CT image of the diseased part is effectively improved.

Systems and methods are provided for improving soft tissue contrast, characterizing tissue, classifying phenotypes, stratifying risk, and performing multi-scale modeling aided by multi-energy or multi-contrast excitation and evaluation, including using single and multi-phase acquisitions, as well as broad and localized spectral imaging to assess atherosclerotic plaque tissue, for example, in vessel walls and perivascular spaces.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A method of anonymizing medical images includes acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

Methods and corresponding systems of identifying coronary artery disease. The methods comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The methods also comprise producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of assessment and early warning systems, and particularly discloses a rectal cancer operation risk assessment and early warning system based on multi-modal data fusion, which comprises a multi-modal data acquisition module, a data preprocessing module, a cross-modal fusion module, a dynamic risk assessment module, an intelligent early warning module and a system self-checking module. By integrating multi-modal data and dynamically allocating weights, real-time risk updating in the whole operation period is achieved, the comprehensiveness, timeliness and precision of risk assessment are comprehensively improved by combining the grading early warning function, the autonomous detection calibration function and the risk tracing and countermeasure matching function, long-term reliable operation of the system is guaranteed, powerful support is provided for clinical decision making, and the risk assessment method is suitable for clinical decision making. The surgical risk and the complication occurrence rate are effectively reduced.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.