As described herein, an unknown FFR is classified based on certain extracted features. In addition, an estimation of the unknown FFR can be determined based on certain extracted features. Furthermore, a confidence interval can be determined for the estimated FFR. In another instance, boundary conditions for determining an FFR via simulation are determined. The boundary conditions can be used to classify the unknown FFR.

A system and method adapted for at least one health-related application selected from physiological monitoring, defibrillation, and pacing in the presence of electromagnetic interference (EMI) using the time-domain features of EMI patterns and physiological waveforms. The invention enables EMI detection and identification in a plurality of signals, including various physiological signals, which may contain both physiological information and EMI-generated artifacts. The system utilizes adaptive and versatile modular architecture with a set of modules for various filtering, conditioning, processing, and wireless transmission functions, which can be assembled in different configurations for different settings. In some preferred embodiments, the method and system of this invention are incorporated into (or attached to) an external cardiac defibrillator/monitor or cardiac pacing device. Other preferred embodiments include a wireless monitoring system that provides reliable wireless data transmission during patient table (bed) movement.

According to an embodiment of the present disclosure, there is provided a method of predicting a brain tissue lesion distribution, the method including: a model learning operation of learning a prediction model for predicting a brain tissue lesion distribution of a subject by using brain image data of a plurality of previous patients; an input obtaining operation of obtaining input data from brain image data of the subject; and an output operation of generating output image data including information on the lesion distribution after recanalization treatment for the subject, by using the prediction model. The prediction model includes a success prediction model that is learned by using data of patients in which recanalization treatment is successful among the plurality of previous patients, and a failure prediction model that is learned by using data of patients in which recanalization treatment fails among the plurality of previous patients.

Systems and methods are described herein for modeling neural architecture. Regions of interest of a brain of a subject can be identified based on image data characterizing the brain of the subject. the identified regions of interest can be mapped to a connectivity matrix. The connectivity matrix can be a weighted and undirected network. A multivariate transformation can be applied to the connectivity matrix to transform the connectivity matrix into a partial correlation matrix. The multivariate transformation can maintain a positive definite constraint for the connectivity matrix. The partial correlation matrix can be transformed into a neural model indicative of the connectivity matrix.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processes the images to produce a stenotic model over the vasculature. The stenotic model has measurements of the vasculature at one or more locations along vessels of the vasculature. The apparatus, in some embodiments, determines a flow characteristic of the stenotic model and calculates an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

Systems and methods are provided to incorporate an off-resonance correction into the pulse labeling train of PCASL/VEPCASL. In one or more aspects, the systems and methods are based on a method for generating an encoding scheme for any number and arrangement of blood vessels. The off-resonance correction can be incorporated into the generation of optimized encodings to acquire arterial spin labeling (ASL) data, such as PCASL and VEPCASL data.

The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

An implantable device includes an outer tubular member defining a longitudinal axis and a lumen. The outer tubular member includes: an outer wall portion having a plurality of first strands defining a plurality of first openings therebetween, the outer wall portion having a first porosity; and an inner baffle portion disposed within the lumen, the inner baffle portion including a plurality of second strands defining a plurality of second openings therebetween, the inner baffle portion having a second porosity that is lower than the first porosity of the outer wall portion.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

Conventionally, the arterial input function is determined by administering a contrast agent and measuring the responsive magnetic signal in a reference voxel located in a large artery such as the middle cerebral artery. By instead measuring the signal in a voxel of the choroid plexus, a more accurate profile for the arterial input function may be obtained. The metabolic activity in the choroid plexus is negligible, which provides greater certainty for signal sampling.