Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry acquires correspondence information, based on 3D medical image data of an object, that corresponds a blood vessel to information on a dominant area of the blood vessel in a region of the object. The processing circuitry acquires a plurality of X-ray images each including the blood vessel that are collected at different time phases on the object. The processing circuitry identifies, based on the plurality of X-ray images, a flow changed vessel in which blood flow has changed between the different time phases. The processing circuitry performs registration between the flow changed vessel and the 3D medical image data. The processing circuitry estimates information on the dominant area corresponding to the flow changed vessel based on registration results and the acquired correspondence information.

Apparatus and methods are described including preparing a blood sample for analysis by depositing the blood sample within a sample chamber (52), and placing the sample chamber, with the blood sample deposited therein, within a microscopy unit (24). One or more microscopic images of the sample chamber (52) with the blood sample deposited therein are acquired, using a microscope of the microscopy unit. Based upon the one or more images, an amount of one or more cell types within the sample chamber that had already settled within the sample chamber, prior to acquisition of the one or more microscopic images is determined. A characteristic of the sample is determined, at least partially in response thereto. Other applications are also described.

A method for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). Additionally, a radial blood velocity profile is computed indicative of blood velocity as a function of radial position across a plane cut perpendicularly across the vessel lumen. This gives an indication of how blood velocity varies for the individual patient across the vessel diameter. This information supplements the standard blood-velocity and arterial diameter measures as inputs to a transfer function which maps the inputs to hemodynamic parameters.

A method for determining blood vessel parameters is provided. The method may include obtaining a blood vessel image of a target blood vessel. The method may also include generating a blood vessel model of the target blood vessel based on the blood vessel image. The blood vessel model is a grid model. The method may further include determining at least one blood vessel parameter of the target blood vessel based at least on the blood vessel model.

A method for providing a classified data set includes capturing an image data set of an examination object by a medical imaging device. The image data set has a plurality of image points in each case with a time-intensity curve. The image points map an examination area of the examination object with at least one contrast-enhanced vascular section. The method further includes identifying first image points in the image data set whose time-intensity curves have a predefined variability as image points that map the at least one contrast-enhanced vascular section, and providing the classified data set based on the image data set and the first image points, wherein the classified data set has a classification between the first image points and further image points of the image data set.

Systems and methods for displaying flow index values on a user interface. An example method may include receiving medical images imaging a portion of a vasculature of a subject, with the portion of the vasculature including vessels; producing, by automatic processing of the medical images, a three-dimensional vascular model of the portion of the vasculature comprising the one or more vessels based on the medical images; calculating flow index values quantifying vascular function along each of the one or more vessels based on the three-dimensional vascular model; displaying a representation of the three-dimensional vascular model comprising the one or more vessels; and for a designated vessel of the one or more vessels, simultaneously displaying the flow value index for a designated location of the designated vessel along with the flow value index for a predetermined distal location along a length of the designated vessel.

An ultrasonic imaging method includes: generating and displaying a first contrast enhanced image in real time under a normal contrast enhanced mode; switching from the normal contrast enhanced mode to a super-resolution contrast enhanced imaging mode, obtaining second ultrasonic echo signals to generate and display a second contrast enhanced image, and performing super-resolution data processing on the second ultrasonic echo signals to obtain a super-resolution image when displaying the second contrast enhanced image; and displaying the super-resolution image. The present disclosure can generate and display the second contrast enhanced image when collecting the ultrasonic echo signals used for super-resolution data processing, so that it is convenient for users to observe the current state of microbubble perfusion to enable the users to observe and compare the super-resolution image and the second contrast enhanced image, obtaining more diagnostic information.

A method of quantifying a 3D fractional moving blood volume (3D-FMBV) in a tissue volume of a subject uses an ultrasound system. The method includes acquiring images of the tissue volume from a power Doppler scan of the tissue volume, applying image enhancement settings to the images, segmenting an organ, tissue or region thereof from the image data, determining geometric partitions of the segments based on distance from the transducer head of the ultrasound system, and computing a 3D-FMBV using a 3D-FMBV analysis algorithm from the partitions.

A system for generating a normative reference model is described. The system constructs spatial basis sets from medical scans, each basis set characterizing spatial property across the body part. A normative basis model is then generated for each spatial basis set, and a normative cross-basis model is generated from statistical relationships between the spatial basis sets. Thereafter, a normative reference model is generated from the normative basis models and the normative cross-basis model.