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.

Provided are methods for estimating a Reserve Strength Ratio in a segment of a blood vessel or a lymphatic vessel. In some embodiments, the methods include providing a multiphase Digital Imaging and Communications in Medicine (DICOM) stack of computed tomography (CT) or magnetic resonance (MR) images of a blood vessel or a lymphatic vessel to software, wherein the stack of DICOM images is organized by phase; providing the output from the software to a Model Segmentation procedure in which the first phase of the DICOM stack (1st phase) is segmented to create the Geometric Model and finite element mesh of the 1st phase and a map of Local Thickness Measure; uploading a mesh created for the first phase onto the DICOM image volume; mapping each voxel position of the mesh for the first phase to all the subsequent meshes using an optical flow (OF) algorithm; creating deformed meshes at all phases from the maps of displaced nodes; estimating local curvature at each node location for all the phases using a finite difference method; evaluating the local deformation at each phase from the meshes corresponding to all the phases using an element approach; calculating local thickness at each node for all the phases using the deformation calculation at each phase and the thickness measured at the first phase and using the assumption of incompressibility for the aortic wall; and calculating the local principal stresses for each element from an extension of Laplace's equation applied to the local principal directions of curvatures, whereby the Reserve Strength Ratio in a segment of a blood vessel or a lymphatic vessel is estimated. Also provided are methods for predicting an increased risk of rupture of a blood vessel or a lymphatic vessel, methods for identifying subjects as being at risk for rupture of a blood vessel or a lymphatic vessel, and computer program products with computer executable instructions embodied in computer readable medium for performing the methods disclosed herein.

A device for automatically imaging the capillary blood vessels of a living tissue likely to move, configured for selecting images of the sequence, called ‘sharp images’, arranged in chronological order of acquisition, shuffling the sharp images, for decorrelating temporally the sharp images, by arranging them in a shuffled order different from the chronological order, realigning spatially the sharp images arranged in the shuffled order, generating a projected image by projection of the pixels of the realigned sharp images, in a stack, the projected values of the pixels forming the projected image being extremal intensity values of the pixels of all the sharp images, the projection of the extremal of intensity values of the pixels rendering all the positions of all erythrocytes of all the sharp images in the projected image.

An ultrasound diagnostic apparatus according to one embodiment includes processing circuitry. The processing circuitry acquires local function index values related to a right ventricle. The processing circuitry generates a functional image of the right ventricle representing a distribution of the local function index values, using a medical model diagram of the right ventricle, the medical model diagram being a model diagram in which the right ventricle is developed onto a plane, and in which a blood inlet portion leading into the right ventricle and a blood outflow portion leading out from the right ventricle are plotted to positions that are separated from each other on the external circumference side of the model diagram. The processing circuitry then causes a display to display the functional image of the right ventricle.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method includes determining at least one characteristic about a stenosis in a vessel of a patient from image data of the stenosis, mapping the characteristic to a predefined stenosis characteristic to fractional flow reserve value look up table, identifying the fractional flow reserve value in the look up table corresponding to the characteristic, and visually presenting the image data and the identified fractional flow reserve value. A system includes memory storing a pre-defined stenosis characteristic to fractional flow reserve value look up table, a metric determiner (118) that maps at least one characteristic about a stenosis in a vessel of a patient, which is determined from image data of the stenosis, to a characteristic in the look up table and identifies a fractional flow reserve value corresponding to the characteristic, and a display (116) that visually presents the image data and the identified fractional flow reserve value.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.