A medical image processing apparatus comprises processing circuitry configured to acquire a first blood vessel image based on X-rays that are irradiated from a first direction and a second blood vessel image based on X-rays that are irradiated from a second direction; determine a corresponding point on the second blood vessel image, which is a point corresponding to a subject point on the first blood vessel image, by using an epipolar line corresponding to the subject point and blood-flow information based on a change of a density of a contrast agent over time at the subject point; and reconstruct a three-dimensional blood vessel image by using information about the subject point and the corresponding point.

The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

A computer-implemented method for automatically generating a fully quantitative myocardial blood flow map, comprising: receiving myocardial perfusion magnetic resonance imaging (MRI) images and arterial input function (AIF) MRI images; correcting a motion of a heart in the myocardial perfusion MRI images and the AIF MRI images, thereby obtaining motion corrected myocardial perfusion MRI images and motion corrected AIF images; correcting an intensity of the motion corrected myocardial perfusion MRI images and an intensity of the motion corrected AIF images, thereby obtaining surface coil intensity corrected MRI images and surface coil intensity corrected AIF images; using the surface coil intensity corrected MRI images and the surface coil intensity corrected AIF images, determining time-signal intensity characteristics and segmenting a left ventricle myocardial tissue region; and generating the myocardial blood flow map using the motion corrected myocardial perfusion MRI images, the left ventricle myocardial tissue region segmentation and the time-signal intensity characteristics.

One or more devices, systems, methods and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for viewing, controlling, updating, and emphasizing one or more imaging modalities and/or for constructing or reconstructing 2D and/or 3D structure(s) are provided herein. One or more embodiments provide at least one intuitive Graphical User Interface (GUI), method, device, apparatus, system, or storage medium to comprehend information, including, but not limited to, molecular structure of a vessel, and to provide an ability to manipulate the vessel information and/or to construct or reconstruct 2D and/or 3D structure(s) of the vessel to improve or maximize accuracy in one or more images. In addition to controlling one or more imaging modalities, the GUI may operate for one or more applications, including, but not limited to, expansion/underexpansion (e.g., for a stent) and/or apposition/malapposition (e.g., for a stent), co-registration, and imaging.

The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

A method comprises: inputting contrast enhancement data for at least one organ of a patient, at least one patient attribute of the patient, and a test bolus data or bolus tracking data to a regressor; receiving a contrast agent administration protocol from the regressor, based on the contrast enhancement data, the at least one patient attribute and the test bolus or bolus tracking data; and injecting a contrast agent into the patient according to the received contrast agent administration protocol.

Systems and methods for determining modified fractional flow reserve values of vascular lesions are provided. Patient physiologic data, including coronary vascular information, is measured. According to the physiologic data, a coronary vascular model is generated. Lesions of interest within the coronary vascular system of the patient are identified for modified fractional flow reserve value determination. The coronary vascular model is modified to generate modified blood flow information for determining the modified fractional flow reserve value.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

This image processing apparatus is provided with an image acquisition unit for generating a concentration change image and a control unit for performing control for displaying a blood vessel image and a concentration change image, and the control unit is configured to perform control for accepting a selection of a target region on the blood vessel image displayed on the display unit and for displaying the concentration change image corresponding to the selected target region.