Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.

An information processing device including: a control unit configured to: acquire at least one frame image generated by using line data indicating intensity of a reflection wave with respect to an ultrasound radially transmitted from an ultrasound transducer moving inside a biological tissue, and extract a specific region from the at least one frame image based on a ratio between an average of luminance in a region of interest included in the at least one frame image and a dispersion of luminance in the region of interest.

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.

A method of generating a 3D ultrasound image includes acquiring a 3D volumetric data set corresponding to a 3D imaging volume of an ultrasound probe in a 3D detection volume; acquiring a position of the ultrasound probe with respect to the 3D detection volume; acquiring a position of an interventional medical device with respect to the 3D detection volume; determining a position of the interventional medical device relative to the 3D imaging volume of the ultrasound probe; determining an interventional medical device-aligned plane that intersects with a longitudinal axis of the interventional medical device; extracting a texture slice from the 3D imaging volume for a corresponding interventional medical device-aligned plane positional and rotational orientation; mapping the texture slice onto the interventional medical device-aligned plane; and rendering the interventional medical device-aligned plane as a 3D ultrasound image and displaying the rendered 3D ultrasound image on a display screen.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

One embodiment of the present invention includes a computer-implemented method that includes receiving spectral computed tomography (CT) volumetric image data. The spectral CT volumetric image data include data for at least two different energies and/or energy ranges. The spectral CT volumetric image data is processed with a machine learning engine configured to map spectrally enhanced features extracted from the spectral CT volumetric image data onto fractional flow reserve (FFR) values to determine a FFR value. The FFR value is then visually presented.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

One or more example embodiments of the present invention relates to a method for providing vessel wall-related data. The method includes receiving spectral computed tomography data of an examination region, the examination region having a vessel; calculating a representation of a vessel wall of the vessel and at least one parameter map of the examination region based on the spectral computed tomography data; calculating the vessel wall-related data based on the representation of the vessel wall and the at least one parameter map of the examination region; and providing the vessel wall-related data.

A biological information detection device includes: a video capture unit, a blood flow analysis unit, a local pulse wave detection unit, a pulse wave propagation velocity calculation unit, and a blood pressure estimation unit. The video capture unit obtains video information on a face of a living body. The blood flow analysis unit analyzes video data of at least three skin areas in the video information, as blood flow information. The local pulse wave detection unit is provided for each skin area to calculate pulse information based on the blood flow information sequenced chronologically. The pulse wave propagation velocity calculation unit calculates a pulse wave propagation velocity based on a phase difference between pieces of the pulse information at each skin area calculated by the local pulse wave detection unit. The blood pressure estimation unit estimates blood pressure based on the pulse wave propagation velocity.