A method for determining a tissue parameter of tissue that may be determined from passage of a contrast agent through the tissue based on a series of temporally consecutive two-dimensional digital subtraction angiography x-ray images showing propagation of the contrast agent in the tissue over time and a vascular system present in a region of the tissue includes locating at least some of the vessels of the vascular system by segmentation in the x-ray images. The method also includes assigning pixels showing segmented vessels an interpolation intensity determined by interpolation from intensities of at least some of the pixels bordering the segmented vessel, so that x-ray images from which vessels have been eliminated result. The method includes determining tissue parameters for at least some of the pixels of the series of x-ray images from which the vessels have been eliminated.

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.

Systems and methods for generating a medical image of a subject that includes functional information. First, two medical images are acquired. One is weighted based on functional information reflecting physiological functions of the subject and the other weighted based on anatomic information of the subject. A difference image between the two images are generated. By subjecting the difference image and the second image to a localized kernel, a local similarity image is generated. Using the local similarity image, an improved difference image is generated. Lastly, by subtracting the improved difference image from the first image, an enhanced medical image that retains the functional information reflecting physiological functions of the subject is generated.

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.

An ophthalmic imaging apparatus of an embodiment includes a data acquisition unit, a blood vessel enhanced image forming unit, and a blood vessel gradient distribution determination unit. The data acquisition unit is configured to acquire a three dimensional data set of a fundus of a subject's eye using optical coherence tomography (OCT). The blood vessel enhanced image forming unit is configured to form a blood vessel enhanced image based on the three dimensional data set. The blood vessel gradient distribution determination unit is configured to determine a blood vessel gradient distribution that shows gradients of blood vessels at a plurality of locations in the fundus, based on the blood vessel enhanced image.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

A method for determining a clinical characteristic of a body vessel segment including providing, to a computing device, a three-dimensional reconstruction of a body vessel containing the body vessel segment. A segmented angiography recording of the body vessel segment is provided to the computing device. The computing device extracts at least one global feature of the body vessel from the three-dimensional reconstruction and extracts at least one local feature of the body vessel segment from the angiography recording. The clinical characteristic is determined for the body vessel segment as a function of the at least one extracted local feature and the at least one extracted global feature.

A method and device is proposed for automatic detection of an event in which a device is leaving a stable position relative to and within an anatomy. The method comprises the steps of receiving a sequence of fluoroscopic images, detecting a device in at least two of the fluoroscopic images, determining a motion field of the detected device in the sequence of fluoroscopic images, generating a sequence of integrated images by integrating the sequence of fluoroscopic images taking into consideration the motion field, determining a saliency metric based on the integrated images, identifying a landmark in the integrated images based on the saliency metric, and determining as to whether the landmark is moving relative to the device, based on a variation of the saliency metric.

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.