Synthetic Multi-Exposure Speckle Imaging (syMESI), in which with the use of conventional, conventionally substantially incapable of quantitative assessment of a motion (at a scene being imaged) LSCI apparatus, such quantitative results are obtained with the use of empirical imaging at only one, fixed-duration exposure time and the following transformation of the so-obtained raw speckle image(s) with the use of various spatial averaging to obtain speckle images representing multiple different synthetic exposure times and, optionallyspeckle contrast images.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

There is provided a method for reconstructing a 3D image, comprising: obtaining Doppler ultrasound images depicting a blood vessel and measurement of blood flow in the blood vessel, and 2D camera images captured by a camera depicting fiducial objects randomly distributed within a viscous material and spaced apart by random distances on a surface of the individual, computing 3D coordinates within a world coordinate system for pixels of the Doppler images using an external reference of an ultrasound transducer pose computed by analysis of relative changes in locations of the fiducial objects within sequential camera images, and computing a respective estimated blood flow for pixels of the Doppler images at locations within the blood vessel, reconstructing a 3D image from 3D voxels computed from the 3D coordinates of pixels of the Doppler images, including respective estimated blood flow, wherein the 3D image depicts the blood vessel and blood flow.

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.

An aspect of the present disclosure provides an apparatus for extracting a vascular function including an information reception unit configured to extract an original CT image from brain-related information received from the outside; an NIFTI image transformation unit configured to transform the original CT image into an NIFTI file format image to acquire time sequence data; a time interpolation unit configured to apply time interpolation to the original CT image through the time sequence data to transform the original CT image into each time-specific 3D CT image; a vessel segmentation unit configured to predict a vessel segmentation mask by passing the each time-specific 3D CT image through a deep learning-based vessel segmentation deep-learning model 141 and generate a 4D vessel mask image by stacking the 3D CT images based on a time axis; and a vascular function extraction unit configured to extract a vascular function from a vessel region of the 4D vessel mask image and calculate a blood flow parameter using an artery function which is one of the vascular functions.

Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.

Minimizing image sensor input/output pads in a pulsed laser mapping imaging system is disclosed. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation. The system includes a plurality of bidirectional pads comprising an output state for issuing data and an input state for receiving data. The system includes a controller configured to synchronize timing of the emitter and the image sensor. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises a laser mapping pattern.

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.

Techniques for processing multiple cardiac images are disclosed. The processing may take place either during or after an angiography exam of a coronary artery of interest. The multiple cardiac images are obtained either during or after the angiography exam. Each of the multiple cardiac images depicts a respective segment of the coronary artery of interest. A geometric structure of the coronary artery of interest is determined based on the multiple cardiac images. A lumped parameter model of the coronary artery of interest is determined based on the geometric structure, and respective values of at least one hemodynamic index at a position of the coronary artery of interest is determined based on the lumped parameter model of the coronary artery of interest.

A method for providing supply data relating to supply to a parenchyma, comprises: receiving first imaging data, wherein the first imaging data relates to the parenchyma and/or a vascular structure that serves to supply the parenchyma; receiving reference data for the supply to the parenchyma; calculating the supply data based on the first imaging data and the reference data, wherein the supply data relates to the supply to the parenchyma; and providing the supply data.