19879results about "Tomography" patented technology

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving one or more vascular models associated with either the patient or with a plurality of individuals; receiving observed perfusion information associated with the patient; and estimating, using one or more computer processors, one or more blood flow characteristics or one or more pathological characteristics of the patient based on the observed perfusion information and the one or more vascular models.

A system comprises a processor and a memory having computer readable instructions stored thereon, which, when executed by the processor, cause the system to display an image of a surgical environment. The image includes an information icon corresponding to an instrument in the surgical environment. The instructions further cause the system to display an image of a body part of a user. The image of the body part is used to interact with the information icon. The instructions further cause the system to receive a gesture of the body part, via a gesture-based input device registering movement of the body part, to cause the image of the body part to interact with the information icon. The instructions further cause the system to display an information menu in the image of the surgical environment based on the interaction between the image of the body part and the information icon.

A computer-implemented method for designing a patient-specific orthopedic implant can include creating a user account associated with a patient. A patient-specific orthopedic implant can be designed based on patient data and imaging data. A healthcare provider can provide feedback for a design of the patient-specific orthopedic implant, treatment protocol, or other aspects of treatment. The patient can provide data and feedback.

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.

A computer-implemented method of processing patient image data comprises: receiving a patient-specific vascular model of an individual, wherein the patient-specific vascular model is based at least in part on medical images acquired of the individual; receiving a patient-specific tissue model of a tissue of the individual; observing an actual perfusion value of the tissue; determining at least one vessel pathology that causes the actual perfusion value; and selecting a candidate updated vascular model of the plurality of candidate updated vascular models that results in a minimum difference between the hypothetical perfusion value and the actual perfusion value; updating the patient-specific vascular model to include the at least one vessel pathology of the selected candidate updated vascular model; and outputting an estimate of a location of the at least one vessel pathology or an estimate of a severity of the at least one vessel pathology.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

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 for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

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.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

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.

A method comprises receiving a user input to select a first point and a second point from a medical image, extracting a plurality of blood vessel regions from a plurality of frame images of the medical image, determining a plurality of first regions associated with the first point and a plurality of second regions associated with the second point, determining a first frame image and a second frame image with the contrast agent arriving at the first point and the second point, based on a change in pixel intensity for each of the plurality of first regions and the plurality of second regions, and computing the blood flow velocity from the first point to the second point based on a time interval between the first frame image and the second frame image and on a distance between the first point and the second point.

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.

Disclosed herein is a method of assisting finding region matching in medical images, the method comprising: receiving a second medical image generated at a different time and containing anatomical information about a body part of a patient that is the same as that of a first medical image; receiving a user input for a first finding region, unmatched with second finding regions identified in the second medical image, among first finding regions identified in the first medical image; and for the unmatched first finding region, providing a user with one or more matching finding region candidates among the second finding regions for matching with the unmatched first finding region.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A method of visualizing structural information of pulmonary vessels in medical images includes obtaining structural information of pulmonary vessels based on anatomical structure features of the pulmonary vessels from a medical image based on segmentation results of a pulmonary vascular region of the medical image; obtaining pulmonary embolism finding information in the pulmonary vessels of the medical image; visualizing the structural information of the pulmonary vessels in accordance with hierarchical information; and visualizing the pulmonary embolism finding information in association with individual sub-regions of the structural information of the pulmonary vessels visualized in accordance with the hierarchical information.

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.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

A system (100) for providing a normalised microcirculatory resistance value for a vessel (110), is provided. The system includes one or more processors (120) configured to: compute (S110) a microcirculatory resistance value for the vessel (110) based on a transit time (TT) taken for an injected bolus to travel between a proximal position (Posa) in the vessel, and a distal position (Posd) in the vessel; and divide (S120) the computed microcirculatory resistance value by a transit length (dT) representing a length of the vessel between the proximal position (Posa) and the distal position (Posd), to provide the normalised microcirculatory resistance value.

Provided is a method for computing a SYNTAX score using a cardio angiogram, which is performed by at least one processor of a computing device, and includes acquiring an angiogram of a cardiovascular system, providing a cardiovascular segment map associated with the angiogram, in response to a user input that selects one of one or more frames included in the acquired angiogram and associates the selected one frame with a cardiovascular segment included in the cardiovascular segment map, associating the selected one frame with the cardiovascular segment, generating, using the selected one frame, an analysis result for a lesion in the associated cardiovascular segment, determining, based on the analysis result, a SYNTAX score for at least a part of the cardiovascular system, and generating an indication of the SYNTAX score.

Disclosed are a method of anonymizing medical images, the method comprising: 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.

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.