In a first aspect, the current invention concerns a computer-implemented method, system and computer program product for identifying a zone in a blood vessel system with a risk level for stasis, comprising the following steps: a) providing an angiogram of a blood vessel system, said angiogram comprising a time series of images of said blood vessel system, at least some of said images showing a contrast agent present in said blood vessel system; b) identifying at least one zone within said blood vessel system on a multitude of images of said time series; c) characterising variations in greyscale intensity in said zone across at least part of said time series of images; d) characterising an outflow of said contrast agent from said zone on the basis of said variations in intensity in said zone; and e) providing a local risk level for stasis in said zone. In further aspects, the current invention also concerns a computer-implemented method, system and computer program product for identifying a zone in a blood vessel with a risk level for stenosis and stent malpositioning, and for measuring the coronary flow reserve (CFR).

An apparatus and a method for contrast-enhanced ultrasound (CEUS) including use of a fluid dynamics model for the analysis of dynamic contrast-enhanced ultrasound (DCEUS).

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 framework for quantitative evaluation of time-varying data. In accordance with one aspect, the framework delineates a volume of interest in a four-dimensional (4D) Digital Subtraction Angiography (DSA) dataset (204). The framework then extracts a centerline of the volume of interest (206). In response to receiving one or more user-selected points along the centerline (208), the framework determines at least one blood dynamics measure associated with the one or more user-selected points (210), and generates a visualization based on the blood dynamics measure (212).

A dynamic analysis system includes a diagnostic console which calculates at least one index value representing variation in a target portion of a human body from at least one dynamic image acquired by performing radiographic imaging to a subject containing the target portion, and evaluates flexibility of the target portion based on the calculated index value.

A system and method for enhancing visualization of color flow ultrasound is provided. The method includes generating estimated parameter values from filtered ultrasound image data. The method includes applying filter thresholds to the estimated parameter values, wherein the filter thresholds comprise first and second high power rejection thresholds and first and second low power and low velocity rejection thresholds. The method includes applying a transparency map to the estimated parameter values between the first and second high power rejection thresholds and between the first and second low power and low velocity rejection thresholds. The method includes generating a color flow image based at least in part on the estimated parameter values. The method includes presenting the color flow image at a display system.

The invention relates to a registration apparatus (14) for registering images comprising a unit (11) for providing a first and a second image of an object, such that an image element of the first image at a respective position has been reconstructed by multiplying projection data values of rays traversing the image element with weights and by backprojecting the weighted projection data values, a unit (12) for providing a confidence map comprising for different positions in the first image confidence values being indicative of a likelihood that an image feature is caused by a structure of the object, the confidence value being calculated as a sum of a function, which depends on the respective weight, over the rays traversing the respective image element, and a unit (13) for determining a transformation for registering the first and second image to each other under consideration of the confidence map.

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 patients 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.

Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.

Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.