A human detection device according to an aspect of the present disclosure includes at least one light source that, in operation, projects, onto a target, at least one dot formed by first light, the target including a person and an object other than the person; an image capturing system including photodetector cells that detect second light from the target on which the at least one dot is projected, the image capturing system, in operation, generating and outputting an image signal denoting an image of the target on which the at least one dot is projected; and an arithmetic circuit that is connected to the image capturing system and that, in operation, generates and outputs information indicating whether the person is located at a position corresponding to each pixel included in the image denoted by the image signal.

A method for processing CT imaging data includes providing CT imaging data obtained at two x-ray energy levels in a first respiratory phase, preferably in an inhalation phase, of the subject and providing second CT imaging data obtained at two x-ray energy levels in a second respiratory phase, preferably in an exhalation phase, of the subject. The method may include reconstructing first regional perfusion blood volume (PBV) imaging data from the provided first CT imaging data, reconstructing second regional PBV imaging data from the provided second CT imaging data, reconstructing first virtual non-contrast (VNC) imaging data from the provided first CT imaging data, reconstructing second VNC imaging data from the provided second CT imaging data, determining a transformation function for registering the first and second reconstructed VNC imaging data, and registering the first and second reconstructed VNC imaging data by applying the transformation function.

The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

An apparatus for assessing a coronary vasculature and a corresponding method are provided which allow to globally assess a coronary artery disease directly from the contrast agent dynamics as derived from diagnostic images acquired using an invasive medical imaging modality by following the time course of the area occupied by the vessels in the diagnostic images.

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Disclosed is a system and method for a system and method for an image processing-based approach has been developed for in vivo quantification of tissue and bodyfluid kinematics when certain human movements, physical loads and physiological stresses are experienced. Due to the absence of artificial or physical markers in those tissues or fluids during typical imaging (ultrasound, CT-scan or MRI), a virtual marker displacement and deformation scheme has been developed to measure movement and strain of both tissues and body fluids.

The present disclosure relates to an image processing apparatus and method and an image processing system that enable highly accurate observation. An intra-frame operation unit performs, as online processing, image processing of speckles generated by irradiation with laser light on a captured image in accordance with a relationship between an image output frame rate and a sampling rate. A high-precision operation unit performs, as offline processing, the image processing of speckles on the captured image in accordance with the relationship between an image output frame rate and a sampling rate. The present disclosure may be applied to an image processing system including, for example, a speckle imaging apparatus.

A method, device and system for calculating a microcirculation indicator are provided. A method includes: injecting a vasodilator and subjecting a blood vessel to be measured to coronary angiography; selecting at least one angiographic image of a first body position when the blood vessel to be measured is in a resting state and an angiographic image of a second body position when the blood vessel to be measured is in a dilated state; obtaining a three-dimensional coronary artery vascular model by three-dimensional modeling; injecting a contrast agent, and obtaining time T.sub.1 taken for the contrast agent flowing from an inlet to an outlet of the segment of blood vessel and time T.sub.2 taken for the contrast agent flowing from an inlet to an outlet of the segment of blood vessel; obtaining the microcirculation indicator.

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.

According to one embodiment, a depth map used for a reflection model is generated based on a power image as a blood flow image. A reflection image is generated from the depth map according to the reflection model. By synthesizing the reflection image 70 with the power image, a weighted power image is generated. Using the same method as described above, a weighted velocity image may be generated.