Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

Systems and methods for automatically selecting an optimal inversion time for Flow-Independent Dark-blood Delayed Enhancement (FIDDLE). Deep learning is used to train a neural network to perform myocardium segmentation on REF images associated with FIDDLE images. A separate neural network is trained to find the intersection of the recovery curves of normal myocardium and blood pool. The intersection is used to determine the optimal inversion time.

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.

Method for estimating a movement of particles in a bone, including the following steps: obtaining data associated with ultrasonic waves emitted at a temporal frequency; obtaining ultrasonic wave phase velocities in the bone in various directions followed by the ultrasonic waves; obtaining videos showing the bone at a point, the videos issuing from ultrasonic waves; computing phase variations at the point between two images of the videos; and computing a particle movement at the point based on the temporal frequency, the data associated with the ultrasonic waves, the phase variations, and the phase velocities in the various directions.

A method and apparatus for calculating the blood flow rate in a coronary artery, an electronic device and a storage medium. The method for calculating the blood flow rate in a coronary artery comprises the following steps: S1, acquiring an angiography image of the coronary artery, segmenting the angiography image of the coronary artery by using deep learning, and obtaining segmented images of a main vessel (S1); S2, calculating the length of the main vessel in each segmented image frame on the basis of the segmented images of the main vessel (S2); and S3, obtaining the blood flow rate in the main vessel on the basis of the calculated change of the lengths of the main vessel with time (S3). By using the method and apparatus for calculating the blood flow rate in a coronary artery and the electronic device, the automation of the calculation of the blood flow rate in a coronary artery is achieved, the calculated blood flow rate in the coronary artery is more accurate, and the calculation method is simple.

A shape of a cell is favorably observed in a non-invasive manner. One aspect of a cell analysis device according to the present invention includes: holographic microscopy, an image generation unit configured to generate a phase image of a cell based on hologram data acquired by observing the cell with the holographic microscopy, a first machine learning model storage unit configured to store a cell area machine learning model generated by performing machine learning using training data in which a phase image of a cell generated based on the hologram data is used as an input image and a pseudo cell area image based on a stained image acquired by staining a cytoskeleton corresponding to the phase image is regarded as a ground truth image, and a cell area estimation unit configured to output a cell area estimation image indicating a cell area using a machine learning model stored in the first machine learning model storage unit, wherein the phase image generated by the image generation unit for the analysis target cell is used as an input image.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of virtual revascularization of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

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 PPG imaging system and method is provided for processing 3D images to derive remote PPG signals. A surface mesh of a region of interest is created from each of a set of the 3D images. The surface meshes are matched to each other using mesh transformations thereby providing motion compensation. A remote PPG signal is then obtained from each of a set of mesh locations of the matched surface meshes, thereby to derive a set of PPG signals. A perfusion map and/or a PPG delay map may be obtained from the set of PPG signals.