An ultrasound diagnosis apparatus includes: transmission and reception circuitry that generates reception signals corresponding to channels, from reflected waves arranged to be received at mutually the same time by transducer elements that transmitted an ultrasound wave, by controlling transducer elements included in an ultrasound probe; extracting circuitry that extracts, prior to a beam forming process, first signals corresponding to the channels from the reception signals corresponding to the channels while suppressing signals originating from a tissue and further extracts a second signal by performing the beam forming process after suppressing, of the extracted first signals corresponding to the channels, a component in a predetermined direction; calculating circuitry that calculates blood flow information from the second signal; and controlling circuitry that generates a blood flow image from the blood flow information and causes display to display the generated blood flow image.

Visualization of a region of interest and planning a location of at least one injection point for a medical procedure is provided. At least one volume of imaging data for a region of interest is received. At least one virtual injection point is obtained. The at least one injection point indicates a location in a network of blood vessels for at least one injection. First and second rendering modules are controlled to construct a combined volume presentation including a first volume region rendered by a first rendering module at a relatively low level of detail and a second volume region is rendered at a higher level of detail by a second rendering module. The first and second volume regions are designated based on the at least one virtual injection point.

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.

According to the present invention, accurate T2* and vascular images are concurrently acquired by acquiring a T2* image without a flow compensation and a T2* image with a flow compensation and subtracting the two images to reconstitute an image showing the flow phenomenon. Furthermore, an accurate T2* image can be acquired by using the readout gradient without the flow compensation and also the accurate T2* and vascular images can be concurrently acquired. The clinical judgment for blood flow rate of the blood vessel and the clinical judgment for acute stroke can be concurrently made, and so the present invention can be widely utilized in clinical practice.

The present disclosure provides systems and methods for the determining a rate of change of one or more analyte concentrations in a target using non invasive non contact imaging techniques such as OCT. Generally, OCT data is acquired and optical information is extracted from OCT scans to quantitatively determine a flow rate of fluid in the target; angiography is also performed using one or more fast scanning methods to determine a concentration of one or more analytes. Both calculations can provide a means to determine a change in rate of an analyte over time. Example methods and systems of the disclosure may be used in assessing metabolism of a tissue, where oxygen is the analyte detected, or other functional states, and be generally used for the diagnosis, monitoring and treatment of disease.

Embodiments include methods and systems for determining a sensitivity of a patient's blood flow characteristic to anatomical or geometrical uncertainty. For each of one or more of individuals, a sensitivity of a blood flow characteristic may be obtained for one or more uncertain parameters. An algorithm may be trained based on the sensitivities of the blood flow characteristic and one or more of the uncertain parameters for each of the plurality of individuals. A geometric model, a blood flow characteristic, and one or more of the uncertain parameters of at least part of the patient's vascular system may be obtained for a patient. The sensitivity of the patient's blood flow characteristic to one or more of the uncertain parameters may be calculated by executing the algorithm on the blood flow characteristic of at least part of the patient's vascular system, and one or more of the uncertain parameters.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to acquire pieces of change information indicating temporal changes in computed tomography (CT) values of a myocardium and a right ventricular of a subject based on a plurality of chronologically consecutive images that are generated by an X-ray CT apparatus by scanning the subject to which a contrast agent is administered. The processing circuitry is configured to correct the piece of change information on the myocardium based on the piece of change information on the right ventricular.

This disclosure describes an electric-field imaging system and method of use. In accordance with implementations of the electric-field imaging system, a fluid sample can be placed on top of a pixel-based impedance sensor. An image of the target analytes can be created immediately afterwards. From this image, computer imaging algorithms can determine attributes (e.g., size, type, morphology, volume, distribution, number, concentration, or motility, etc.) of the target analytes.

The uncertainty, sensitivity, and/or standard deviation for a patient-specific hemodynamic quantification is determined. The contribution of different information, such as the fit of the geometry at different locations, to the uncertainty or sensitivity is determined. Alternatively or additionally, the amount of contribution of information at one location (e.g., geometric fit at the one location) to uncertainty or sensitivity at other locations is determined. Rather than relying on time consuming statistical analysis for each patient, a machine-learnt classifier is trained to determine the uncertainty, sensitivity, and/or standard deviation for the patient.

The systems and methods can accurately and efficiently determine boundary conditions for an arterial segment and thereby efficiently determine hemodynamic information for that segment. The method may include receiving medical image data of a patient. The method may further include generating a geometrical representation of the one or more arterial segments from the medical image data. The method may further include determining boundaries and geometry data for each arterial segment. The method may further include determining boundary conditions for the inflow boundary and each outflow boundary. The boundary conditions for each outflow boundary may be determined using an outflow distribution parameter. The outflow distribution parameter may be determined using the geometry data for one or more of the one or more outflow boundaries, stored hemodynamic data, or a combination thereof. The method may further include determining flow field for each arterial segment and determining hemodynamic information.