Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Embodiments include methods and systems and for determining a sensitivity of a patient's blood flow characteristic to anatomical or geometrical uncertainty. For each of one or more of individuals, a sensitivity of a blood flow characteristic may be obtained for one or more uncertain parameters. An algorithm may be trained based on the sensitivities of the blood flow characteristic and one or more of the uncertain parameters for each of the plurality of individuals. A geometric model, a blood flow characteristic, and one or more of the uncertain parameters of at least part of the patient's vascular system may be obtained for a patient. The sensitivity of the patient's blood flow characteristic to one or more of the uncertain parameters may be calculated by executing the algorithm on the blood flow characteristic of at least part of the patient's vascular system, and one or more of the uncertain parameters.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

A method, an apparatus, a device, and a storage medium for extracting a cardiovascular vessel from a CTA image, the method including the steps of: performing erosion operation and dilation operation on image data successively via a preset structural element to obtain a structure mask; performing a slice-by-slice transformation on the plane of section images of the structural mask to acquire the first ascending aortic structure in the structural mask, and acquiring an aortic center position and an aortic radius in the last slice of the plane of section image of the said structural mask; establishing a binarized sphere structure according to the aortic center position and the aortic radius, and synthesizing a second ascending aorta structure by combining the first ascending aorta structure with the structure mask and the binarized sphere structure.

The systems and methods can accurately and efficiently determine boundary conditions for an arterial segment and thereby efficiently determine hemodynamic information for that segment. The method may include receiving medical image data of a patient. The method may further include generating a geometrical representation of the one or more arterial segments from the medical image data. The method may further include determining boundaries and geometry data for each arterial segment. The method may further include determining boundary conditions for the inflow boundary and each outflow boundary. The boundary conditions for each outflow boundary may be determined using an outflow distribution parameter. The outflow distribution parameter may be determined using the geometry data for one or more of the one or more outflow boundaries, stored hemodynamic data, or a combination thereof. The method may further include determining flow field for each arterial segment and determining hemodynamic information.

Methods for calculating a MetaKG signal are provided. The method including illuminating a region of interest in a sample with a near-infrared (NIR) light source and/or a visible light source. The region of interest includes a sample portion and background portion, each having a different set of optical characteristics. Images of the region of interest are acquired and processed to obtain metadata associated with the acquired images. MetaKG signals are calculated for the region of interest and for the background. The MetaKG signal for the background is used to adjust the MetaKG signal for the region of interest to provide a final adjusted MetaKG signal for the region of interest.

Methods and systems for alignment of a subject for medical imaging are disclosed, and involve capturing an image of an anatomical region comprising a target tissue and determining, based on the image and based on a reference image, that a current alignment of the image capture device is aligned with a predefined alignment. The determination is performed before initiating capturing of a time series of fluorescence medical images from fluorescence emission. In response to the determining that the current alignment is aligned with the predefined alignment, capturing of the time series of fluorescence medical images from the fluorescence emission is initiated.

A method of diagnosing a condition of a subject, comprises: receiving image data of an anterior of an eye of the subject, and analyzing the image data to detect at least one of: flow of individual blood cells in libmal or conjunctival blood vessels of the eye, morphology of limbal or conjunctival blood vessels. The method also comprises determining the condition of the subject based on the detection(s).

An image processing apparatus comprises processing circuitry configured to: obtain first medical image data captured at a first time and second medical image data captured at a second time different from the first time, the first medical image data and the second medical image data including data representing a bolus of contrast material in a tubular anatomical structure, wherein the bolus of contrast material has moved between the first time and the second time; determine an expected motion of the bolus of contrast material through the tubular anatomical structure between the first time and the second time; and. perform a registration process to obtain a registration of the first medical image data and the second medical image data based at least in part the expected motion of the bolus of contrast material through the tubular anatomical structure.

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.