A system and method are provided for generating time resolved series of angiographic volume data having flow information. The system and method are configured to receive angiographic volume data acquired from a subject having received a dose of a contrast agent using an imaging system and process the angiographic volume data to generate angiographic volume images. The angiographic volume data is processed to derive flow information by determining a distance between two points along a vessel in the angiographic volume images and determining a phase at each of the two points along the vessel in the angiographic volume images. A flow direction or a velocity of flow within the vessel is determined using the distance between the two points along the vessel and the phase at each of the two points along the vessel.

A method and system for non-invasive hemodynamic assessment of coronary artery stenosis based on medical image data is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient. Patient-specific boundary conditions of a computational model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements of the coronary arteries. Blood flow and pressure in the coronary arteries are simulated using the computational model of coronary circulation and the patient-specific boundary conditions and coronary autoregulation is modeled during the simulation of blood flow and pressure in the coronary arteries. A wave-free period is identified in a simulated cardiac cycle, and an instantaneous wave-Free Ratio (iFR) value is calculated for a stenosis region based on simulated pressure values in the wave-free period.

In one implementation, an apparatus estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point is described, estimates temperature from a digital infrared sensor and determines vital signs from a solid-state image transducer, or determines vital signs from a solid-state image transducer and estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point; after which the estimated and/or determined information is transmitted to an external database.

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.

Systems and methods for accelerated arterial spin labeling (ASL) using compressed sensing are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject, wherein the area of interest corresponds to one or more physiological activities of the subject. The method also includes performing image reconstruction using temporally constrained compressed sensing reconstruction on at least a portion of the acquired magnetic resonance data, wherein acquiring the magnetic resonance data includes receiving data associated with ASL of the area of interest of the subject.

Disclosed herein are an ultrasound imaging apparatus and a method for controlling the same. An occluded region generated in a 2D image may be removed by performing frame interpolation on a surface region of an object by extracting the surface region of the object from 3D ultrasonic volume data and calculating a motion vector in the extracted surface region, and an amount of calculation may be reduced by calculating a motion vector of the surface region in 3D volume data. The ultrasound imaging apparatus includes a volume data generator configured to acquire volume data which relates to the object, a surface region extractor configured to extract the surface region of the object based on the acquired volume data, and a frame interpolator configured to perform frame interpolation on the extracted surface region of the object.

Elicited MRI signals are processed into MR image data in conjunction (a) with use of an initial spatially-selective RF tag pulse (tag-on) and (b) without use of an initial spatially-selective NMR RF tag pulse (tag-off) in respectively corresponding data acquisition subsequences. Multi-dimensional tag-on and tag-off data acquisition subsequences are used for each of plural time-to-inversion (TI) intervals without using an injected contrast agent. Acquired image data sets are subtracted for each TI interval to produce difference values as a function of time representing blood perfusion for the ROI that differentiates between normal, ischemic and infarct tissues.

A computing device determines a contrast medium uptake time using magnetic resonance imaging data. Image data constructed from data generated by a magnetic resonance imaging (MRI) machine of a subject is read. A representation computed from the read image data is presented on a display device. Baseline artery locations identified within the presented representation that are associated with a baseline artery are received. A first time-of-arrival (TOA) of contrast medium into the baseline artery is determined using the received baseline artery locations and the read image data. For a plurality of locations within the read image data excluding the baseline artery locations, a second TOA of the contrast medium into a respective location relative to the determined first TOA is determined using the read image data, and the determined second TOA is stored in association with the respective location to assist in lesion identification for the subject.

An information processing unit acquires spatial frequency distribution data in a depth direction for one frame in two-dimensional ultrasound image data of a plurality of frames constituting three-dimensional ultrasound image data, and performs filter processing, based on filter characteristics determined in accordance with the spatial frequency distribution data and characteristics of transmitted ultrasound in which each two-dimensional ultrasound image data is acquired. The information processing unit searches for a spatial frequency corresponding to a maximal value of a distribution indicated by the spatial frequency distribution data in a search range determined in accordance with a pulse width of the transmitted ultrasound, and obtains characteristics for suppressing a level of the spatial frequency distribution in a spatial frequency band including the spatial frequency that is searched for.

The application discloses a method, a device, a system and a computer storage medium for obtaining the CT perfusion imaging parameter maps of brain. The method includes: obtaining CT perfusion images, pre-processing the CT perfusion images, and obtaining discrete contrast agent concentration curve C(n) of each pixel point in the brain tissue; reading the acquisition time information of the CT perfusion images to obtain the acquisition time array T(n); intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n); combining the discrete contrast agent concentration curve C(n) with the corresponding relative acquisition time array t(n) to obtain the discrete time-concentration curve C(t.sub.n) of each pixel point in the brain tissue; after fitting or interpolating the discrete time-concentration curve C(t.sub.n), re-discretizing at the same time interval, and obtaining the discrete time-concentration curve C(n) of each pixel point in brain tissue. The same processing is performed on the arterial input change curve AIF(n) and the venous output change curve VOF(n). The application improves the usability of the tissue density time curve, which helps to reduce the difficulty of solving and improve the accuracy of solving.