The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

Several methods of automatically measuring blood pressure are disclosed, which may include applying a sphygmometer cuff coupled to a transducer or detector capable of detecting an imaging signal to an extremity of a patient. The detector may include an ultrasonic or photoacoustic means of determining blood pressure. A device for automatically measuring blood pressure utilizing several methods and having a variety of configurations for obtaining blood pressure data from a patient is also disclosed.

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.

Elicited MRI signals are processed into MR image data in conjunction (a) with use of an initial spatially-selective RF tag pulse (tag-on) and (b) without use of an initial spatially-selective NMR RF tag pulse (tag-off) in respectively corresponding data acquisition subsequences. Multi-dimensional tag-on and tag-off data acquisition subsequences are used for each of plural time-to-inversion (TI) intervals without using an injected contrast agent. Acquired image data sets are subtracted for each TI interval to produce difference values as a function of time representing blood perfusion for the ROI that differentiates between normal, ischemic and infarct tissues.

A computer-implemented method for providing a scene with synthetic contrast includes receiving preoperative image data of an examination region containing a hollow organ, wherein the medical image data images a contrast agent flow in the hollow organ; receiving intraoperative image data of the examination region of the examination subject, wherein the intraoperative image data images a medical object at least partially disposed in the hollow organ, generating the scene with synthetic contrast by applying a trained function to input data, wherein the input data is based on the preoperative image data and the intraoperative image data, wherein the scene with synthetic contrast images a virtual contrast agent flow in the hollow organ taking into account the medical object disposed therein, wherein at least one parameter of the trained function is based on a comparison between a training scene and a comparison scene; and providing the scene with synthetic contrast.

Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

The brain appears to have organized cardiac frequency angiographic phenomena with such coherence as to qualify as vascular pulse waves. Separate arterial and venous vascular pulse waves may be resolved. This disclosure states the method of extracting a spatiotemporal reconstruction of the cardiac frequency phenomena present in an angiogram obtained at faster than cardiac frequency. A wavelet transform is applied to each of the pixel-wise time signals of the angiogram. If there is motion alias then instead a high frequency resolution wavelet transform of the overall angiographic time intensity curve is cross-correlated to high temporal resolution wavelet transforms of the pixel-wise time signals. The result is filtered for cardiac wavelet scale then pixel-wise inverse wavelet transformed. This gives a complex-valued spatiotemporal grid of cardiac frequency angiographic phenomena. It may be rendered with a brightness-hue color model or subjected to further analysis.

In spectral Doppler imaging, a high PRF is used independent of the velocity scale. The adjustment is then of the velocity scale. By optimizing the velocity scale independent of the high PRF in an on-going or automated basis, user activation may be avoided and/or interruption to reconfigure for an altered PRF may be avoided. The acquired data may be stored, allowing for past data to be processed again when a new velocity scale or other setting is selected. The resulting spectral Doppler image may continue to display spectra over time without a gap or without premature loss of spectra due to reconfiguring.

A method for generating three-dimensional blood vessel profile data comprises acquiring a plurality of images including blood vessels, generating three-dimensional first blood vessel profile data by using a first image and a second image of the plurality of images, generating three-dimensional second blood vessel profile data by using the second image and a third image of the plurality of images, identifying a first coordinate value corresponding to a first point in the blood vessels in the second blood vessel profile data, identifying a second coordinate value in the second image based on the first coordinate value, identifying a third coordinate value corresponding to the first point in the first blood vessel profile data based on the second coordinate value, and acquiring three-dimensional third blood vessel profile data by merging the first blood vessel profile data and the second blood vessel profile data based on the third coordinate value.