An image is created to visualize the efficacy of treatment to a fundus.

An image processing method includes extracting a first frame from a first moving image of an examined eye and extracting a second frame from a second moving image of the examined eye, and comparing the first frame against the second frame.

Data is computed in order to visualize the velocity of blood fluid flowing through a blood vessel at a fundus. An image processing method includes a step of performing registration of each frame in a moving image configured by plural frames of an imaged fundus, and a step of computing visualization data enabling visualization of a state of blood fluid flowing through a blood vessel at the fundus based on a pixel value in each of the registered frames.

A feature value related to a positional relationship between a vortex vein position and a characteristic point on a fundus image is computed.

The image processing method provided includes a step of analyzing a choroidal vascular image and estimating a vortex vein position, and a step of computing a feature value indicating a positional relationship between the vortex vein position and a position of a particular site on a fundus.

An ultrasonic system for detecting a fluid flow in an environment includes a probe configured for ultrasonic insonification of the environment and reception of an echo signal. The system also includes a control device configured to construct a series of images based on the signal received from the echoes. The images are filtered by a temporal high-pass filter. A local displacement of the flow between two successive images is determined by maximizing the similarity between blocks extracted from the two images.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.

Methods for calculating a MetaKG signal are provided. The method including illuminating a region of interest in a sample with a near-infrared (NIR) light source and/or a visible light source. The region of interest includes a sample portion and background portion, each having a different set of optical characteristics. Images of the region of interest are acquired and processed to obtain metadata associated with the acquired images. MetaKG signals are calculated for the region of interest and for the background. The MetaKG signal for the background is used to adjust the MetaKG signal for the region of interest to provide a final adjusted MetaKG signal for the region of interest.

In one embodiment, an MRI apparatus includes a memory storing a predetermined program and processing circuitry. The processing circuitry is configured, by executing the predetermined program, to generate a first image having a first phase affected by susceptibility, generate a second image having a second phase affected by both of the susceptibility and flow, and distinguish difference in susceptibility or flow for a pixel of a third image by using the first phase and the second phase or by using a value calculated from the first phase and a value calculated from the first phase, the third image having regions which are substantially same in contrast.

There are provided a vascular information acquisition device, an endoscope system, and a vascular information acquisition method that can accurately acquire vascular information on a blood vessel of a target layer that is an object to be measured of a subject. A first blood vessel extraction unit (82) analyzes the image of a target layer to be measured and extracts a blood vessel (first blood vessel) from the image of a target layer. A blood vessel specification unit (84) specifies a blood vessel (second blood vessel) extending to a non-target layer from the target layer. In a case in which the second blood vessel is specified, a second blood vessel extraction unit (83) analyzes the image of the non-target layer in which the second blood vessel is present and extracts the specified second blood vessel from the image of the non-target layer.

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.