56 results about "Radiodensity" patented technology

Systems, methods and computer instructions for displaying multi-energy data are provided. Certain methods include: receiving multi-energy data from an operably connected data source; displaying the multi-energy data as an image via a graphical user interface; selecting a region of interest in the displayed image; and displaying information regarding the region of interest via the graphical user interface. Certain methods include: (1) displaying a graph that includes an attenuation line depicting radiodensity versus energy level; (2) displaying a material density graph of a region of interest and / or of results of segmenting a region of interest; and / or (3) displaying information in a window configured to be movable about an interface. Certain methods include: receiving multi-energy data from an operably connected data source; and displaying the multi-energy data as a fused image via a graphical user interface.

The invention discloses a method and device for detecting mass flow rate of pulverized coals in a process of conveying the pulverized coals. Sound waves generated by frictions and collision of fluid particles in a non-invasive pick-up pipe with a pipe wall are used to detect medium flow rate in the pipe, thereby solving the problem of large errors in a metering method of the pulverized coals, and improving the measuring accuracy. Sound wave detection is a non-invasive detection method, and a detection device is simple, safe, environmental-friendly, and is suitable for online detection in an industrial production process. A sound wave sensor array is used, and influences of unstable conveyance of the pulverized coals on the measuring accuracy can be effectively eliminated by fusing data of a plurality of sensors. Compared with the prior art, flow velocity, humidity ratio, particle size and mass flow-rate detection technologies based on the sound wave detection are more sensitive, and higher in the detection accuracy, and can be used for replacing a radioactive density meter harmful to a human body.

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.

Devices and methods are disclosed for achieving control and stabilization of bone fractures in mammals, most specifically humans. Stabilization and traction is often required to support fractured bones of the arms or legs. The devices and methods disclosed herein are especially useful in the emergency or military setting. The devices utilize a collapsible frame that may be expanded and locked into position. The frame is fabricated primarily from polymeric materials with low radiodensity. The limb contact regions are adjustable to fit a wide variety of limb sizes and fracture locations. The traction applied by the splint is adjustable, controllable and measurable. The traction splint of the current invention is sufficiently compact that it will fit in a compartment of most ambulances and emergency rescue vehicles, thus making it more available for use than standard traction splints in use today. The traction splint is either a separate device or integrated into a backboard.

Devices and methods are disclosed for achieving control and stabilization of bone fractures in mammals, most specifically humans. Stabilization and traction is often required to support fractured bones of the arms or legs. The devices and methods disclosed herein are especially useful in the emergency or military setting. The devices utilize a collapsible frame or inflatable member that may be expanded and locked into position. The frame or inflatable member is fabricated primarily from polymeric materials with low radiodensity. The limb contact regions are adjustable to fit a wide variety of limb sizes and fracture locations. The traction applied by the splint is adjustable, controllable and measurable. The traction splint is sufficiently compact that it will fit in a compartment of most ambulances and emergency rescue vehicles, thus making it more available for use than standard traction splints in use today. The traction splint is either a separate device or integrated into a backboard.

Devices and methods are disclosed for achieving control and stabilization of bone fractures in mammals, most specifically humans. Stabilization and traction is often required to support fractured bones of the arms or legs. The devices and methods disclosed herein are especially useful in the emergency or military setting. The devices utilize a collapsible frame or inflatable member that may be expanded and locked into position. The frame or inflatable member is fabricated primarily from polymeric materials with low radiodensity. The limb contact regions are adjustable to fit a wide variety of limb sizes and fracture locations. The traction applied by the splint is adjustable, controllable and measurable. The traction splint is sufficiently compact that it will fit in a compartment of most ambulances and emergency rescue vehicles, thus making it more available for use than standard traction splints in use today. The traction splint is either a separate device or integrated into a backboard.

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, 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.

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, 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, 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, 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, methods and computer instructions for displaying multi-energy data are provided. Certain methods include: receiving multi-energy data from an operably connected data source; displaying the multi-energy data as an image via a graphical user interface; selecting a region of interest in the displayed image; and displaying information regarding the region of interest via the graphical user interface. Certain methods include: (1) displaying a graph that includes an attenuation line depicting radiodensity versus energy level; (2) displaying a material density graph of a region of interest and / or of results of segmenting a region of interest; and / or (3) displaying information in a window configured to be movable about an interface. Certain methods include: receiving multi-energy data from an operably connected data source; and displaying the multi-energy data as a fused image via a graphical user interface.

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, 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, 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.

X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order / multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).

The invention provides a non-radioactive density measurement device. The non-radioactive density measurement device comprises a sand mixing tank, a sand mixing truck data acquisition instrument and a Coriolis force mass flow meter; the Coriolis force mass flow meter is electrically connected with the sand mixing truck data acquisition instrument; an outlet of the sand mixing tank is connected with a discharging main manifold; a bypass pipeline is connected between the discharging main manifold and an inlet of the sand mixing tank; a flow pipe is connected onto the bypass pipeline in series; the flow pipe is vertically mounted by adopting a whole straight pipe and liquid flows from bottom to top. Cesium-137 is not used as a measuring medium so that the device has the radiation-free advantage and also meets the strict requirements on density measurement in existing fracturing construction when being compared with a traditional radioactive densimeter; the device adopts a Coriolis force principle and a previous Coriolis force mass flow meter is improved and is applied to a fracturing construction process, so that accurate measurement of density and concentration of a proppant can be guaranteed and the problems of pipeline abrasion, liquid corrosion and pressure bearing, caused by the proppant, are avoided through the utilization of a flexible hose and a titanium material.

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.

X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order / multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).

The invention discloses a preparation method of a sealing type gamma ray source in a radioactive density meter, which belongs to the technical field of radioactive well logging. The method comprises the following steps: transferring isotopic elements into a source storage container in a state of a water solution of compounds thereof; controlling the total activity of the water solution of the isotopic element compounds in the source storage container between 30 and 70 mCi; adding water-soluble precipitating agents for precipitating compounds containing the isotopic elements; and sealing the source storage container in a sealed way after heating and drying to obtain the sealed type gamma ray source, wherein the half life of the isotopic elements is shorter than 60 days, in addition, a gamma ray with the energy between 0.1 and 1.4 Mev can be released through the disintegration, and at the same time, the disintegration mode accounts for 40 percent of the total disintegration of the isotopic elements. The gamma ray energy of the sealing type gamma ray source prepared by the invention is in the radioactive density meter sensing range in common use in the well logging, the half life is short, the environment pollution is little, the sealing type gamma ray source can directly replace the original 137 Cs density source on the radioactive density meter, and at the same time, the invention has the advantages of simplicity and easy implementation of production process, easy acquisition of raw materials and low cost, and has wide market potential.

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.