Systems and methods are disclosed for assessing the severity of plaque and/or stenotic lesions using contrast distribution predictions and measurements. One method includes: receiving patient-specific images of a patient's vasculature and a measured distribution of a contrast agent delivered through the patient's vasculature; associating the measured distribution of the contrast agent with a patient-specific anatomic model of the patient's vasculature; defining physiological and boundary conditions of a blood flow model of the patient's blood flow and pressure; simulating the distribution of the contrast agent through the patient-specific anatomic model; comparing the measured distribution of the contrast agent and the simulated distribution of the contrast agent through the patient-specific anatomic model to determine whether a similarity condition is satisfied; and updating the defined physiological and boundary conditions and re-simulating distribution of the contrast agent through the one or more points of the patient-specific anatomic model until the similarity condition is satisfied.

For decision support based on perfusion in medical imaging, a machine-learned model, such as a model trained with deep learning, generates perfusion examination information from CT scans of the patient. Other information, such as patient-specific information, may be used with the CT scans to generate the perfusion examination information. Since a machine-learned model is used, the perfusion examination information may be estimated from a spatial and/or temporally sparse number of scan shots or amount of CT dose. The results of perfusion imaging may be provided with less than the current, standard, or typical radiation dose.

An image acquisition unit acquires a phase contrast image consisting of a plurality of phases, in which a pixel value of each pixel represents a velocity of fluid, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a phase contrast magnetic resonance method. An identification unit identifies a region of the structure in the phase contrast image on the basis of a maximum value of the velocity of the fluid between corresponding pixels in each of the phases of the phase contrast image.

A computer-implemented method includes obtaining, via a processor, clinical images including vessels and generating, via the processor, straightened-out images for each coronary tree path within respective clinical images, The method also includes extracting, via the processor, segmented 3D image patches, determining, via the processor, overlapping binary mask volumes for each segment, and predicting, via the processor, pressure drops across the segmented image patches using a trained deep neural network.

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.

The embodiments of the present disclosure provide a method for vascular analysis. The method may include determining a peak phase in a plurality of phases based on perfusion scanning data of the plurality of phases; obtaining a reconstruction result of the peak phase by performing, based on the perfusion scanning data of the peak phase, image reconstruction; and performing vascular analysis based on the reconstruction result of peak phase.

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.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a graph of an index value related to a blood flow based on a CT image and a graph of an index value related to the blood flow based on an angiography image. On the basis of shapes of the graphs of the index values related to the blood flow, the processing circuitry is configured to determine a target position for performing a position alignment between the graph of the index value related to the blood flow based on the CT image and the graph of the index value related to the blood flow based on the angiography image. On the basis of the target position, the processing circuitry is configured to perform the position alignment between the graph of the index value related to the blood flow based on the CT image and the graph of the index value related to the blood flow based on the angiography image.

A system and method are provided that includes acquiring or accessing x-ray data of a subject experiencing a delivery of a contrast agent to the vessel of the subject and generating time-attenuation curves for the vessel using the x-ray data. The method also includes identifying a plurality of points within the vessel and extending along a lumen of the vessel and sampling the time-attenuation curves at the plurality of points to generate a time-attenuation map. The method further includes performing a Fourier transform on the time-attenuation map to generate a spatio-temporal map of spatial frequency versus temporal frequency and identifying a peak frequency in the spatio-temporal map corresponding to flow or velocity in the vessel. The method then includes quantifying the flow or velocity within the vessel of the subject using the peak frequency and generating a report including quantified flow or velocity through the vessel.

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.