A method includes obtaining, by a processor, a set of ultrasound frames showing a portion of a heart of a subject, identifying a subset of the frames, responsively to the subset having been acquired at one or more predefined phases of at least one physiological cycle of the subject, computing respective image-quality scores for at least the subset of the frames, each of the scores quantifying an image quality with which one or more anatomical portions of interest are shown in a respective one of the frames, and, based on the image-quality scores, selecting, for subsequent use, at least one frame from the subset of the frames. Other embodiments are also described.

Methods and systems are provided for cardiac triggering of an imaging system. a method for an imaging system comprises acquiring, during a scan of a subject, an electrical signal indicating a periodic physiological motion of an organ of the subject, inputting a sample of the electrical signal into a trained neural network to detect whether a peak is present in the sample, triggering acquisition of image data responsive to detecting the peak in the sample, and not triggering the acquisition of image data responsive to not detecting the peak in the sample. In this way, the timing of data acquisition may be optimally and robustly synchronized with a cardiac cycle.

A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.

A rapid chest pain risk assessment system includes an assessor, a computed tomography (CT) scanner, an electrocardiogram device for providing electrocardiogram related data, and an enzyme analyzer for analyzing the patient's blood. A computer enabled risk calculator categorizes the patients into low, intermediate, and high risk categories. The computer enabled risk calculator, using data from electrocardiogram, blood analyzer and patient's age, other risk factors and history, automatically generates orders for patients in low and intermediate risk categories to undergo a CT scan. A CAC analyzer using the computer file for analyzing the CT scan results then provides a CAC score based on those CT scan results. A risk score based on electrocardiogram, blood analyzer and patient's age, other risk factors and history of symptoms plus the CAC score is generated. Patients that are automatically assessed as being very low risk based on the risk score are recommended for discharge from the emergency room thereby lowering the unnecessary prolonged ER stay time.

Systems and methods are disclosed for an optical mapping device. The device emits different wavelengths of light from a plurality of light sources to a cardiac tissue and passes the light through a lens, a first filter cube in the path of the light with a first light filter, a second light filter, and a third light filter. Light passing through the filters is recorded by three cameras that each record an indicator of cardiac physiology, which are mapped simultaneously by the device.

A T1-weighted turbo-spin-echo magnetic resonance imaging system configured to capture data associated with a subject's heart during a time period and produce MR images has a dark-blood preparation module, a data capture module, and an image reconstruction module. The dark-blood preparation module performs dark-blood preparation through double inversion during some, but not all of the heartbeats within the time period. The data capture module configured performs data readouts to capture imaging data of an imaging slice during every heartbeat in which dark-blood preparation is performed. The data capture module also performs a steady state maintenance step during every heartbeat in which dark-blood preparation is not performed in order to maintain maximum T1-weighting. The image reconstruction module configured to reconstruct a T1-weighted image based on the imaging data.

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

A system for noninvasive extraction, identification, and marking of the heart valve signals to evaluate and monitor elevated left ventricular end-diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) using at rest assessment of hemodynamic performance, based on quantitative measurements of heart and lung related parameters and cardiac events for diagnostic and therapeutic purposes includes one or more signals from one or more noninvasive sensors or transducers that measure one or more physiological effects that are correlated with cardiopulmonary functions, transmission of the data to a computing device and analysis software where a trained algorithm processes the data to determine the state or condition of elevated LVEDP or PCWP and provides an output indicative of the state or condition of the analysis. The described noninvasive cardiopulmonary health assessment and monitoring systems and methods can provide effective at-home self-assessment or an integrated telehealth remote patient monitoring (RPM) system.

Exemplary embodiments of systems and methods can be provided which can generate data associated with at least one portion of a sample. For example, at least one first radiation can be forwarded to the portion through at least one optical arrangement. At least one second radiation can be received from the portion which is based on the first radiation. Based on an interaction between the optical arrangement and the first radiation and/or the second radiation, the optical arrangement can have a first transfer function. Further, it is possible to forward at least one third radiation to the portion through such optical arrangement (or through another optical arrangement), and receive at least one fourth radiation from the portion which is based on the third radiation. Based on an interaction between the optical arrangement (or the other optical arrangement) and the third radiation and/or the fourth radiation, the optical arrangement (or the other optical arrangement) can have a second transfer function. The first transfer function can be at least partially different from the second transfer function. The data can be generated based on the second and fourth radiations.

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.