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.

This invention stores necessary OCT data and reduces unnecessary OCT data. The OCT device includes: an OCT optical system that captures an image of a subject eye in accordance with a measurement action; a data obtaining section that obtains OCT data; a subject data determining section that determines non-reduction-subject data or reducing process section; a reducing process section that reduces a data amount of the reduction subject data or delete the reduction subject data; and a storage executing section that stores the non-reduction-subject data and after-reduction data in a storage section.

According to a first aspect, the present disclosure relates to a digital holography device (100) for full-field blood flow imaging of ocular vessels of a field of view of a layer (11) of the eye (10). The device comprises an optical source (101) configured for the generation of an illuminating beam (Eobj) and a reference beam (E.sub.LO), and a detector (135) configured to acquire a plurality of interferograms (I(x,y,t)) wherein an interferogram is defined as the signal resulting from the interference between the said reference beam (E.sub.LO) and a part of said illuminating beam (Eobj) that is backscattered from said layer (11). The device further comprises a processing unit (150) configured for processing said plurality of interferograms, (I(x,y,t)), wherein said processing comprises: the calculation (202), for each interferogram, of a hologram (H(x,y,t)), resulting in a first plurality of holograms; the selection (203), in sequential time windows, (tw), of second pluralities of holograms; the calculation (204), for each said second plurality of holograms, of a Doppler power spectrum (S(x,y,f)); the calculation (205), based on said Doppler power spectrum, of at least a first Doppler image thus generating at least a first plurality of Doppler images; the processing of each first Doppler image, wherein said processing comprises the devignetting (206) of said first Doppler image, resulting in a devignetted first Doppler image; the normalization (207) of said devignetted first Doppler image based on a spatial average of an intensity of said first Doppler image, resulting in a normalized first Doppler image; and the subtraction (208), from said normalized first Doppler image, of said spatial average of said intensity of said first Doppler image, resulting in a corrected first Doppler image.

A computed tomography (CT) system and method is provided. The CT system is used to carry out an image improvement method in which a prior or previously-acquired patient image can be used to supplement or otherwise improve an acquired CT image, wherein the acquired projection data representative of the acquired CT image might be truncated or otherwise incomplete/insufficient to accurately and stably recover the scanned object/patient.

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 apparatus for vascular modeling is disclosed. The apparatus receives medical images from an imaging device that include representations of a coronary vessel tree of a subject recorded at a different viewing angles. The apparatus determines, from a first of the medical images, a first centerline set and first vessel diameters for sample points along the first centerline set, and determines, from a second of the medical images, a second centerline set and second vessel diameters for sample points along the second centerline set. The apparatus determines a correspondence between the first centerline set and the second centerline set, and determines diameters for a combined centerline set based on the correspondence of sample points along the first and second centerline sets. The apparatus provides the combined centerline set for estimating blood flow resistance values of the coronary vessel tree of the subject to determine at least one potential stenosis.

Various methods and systems are described for obtaining at least one CTA perfusion functional map from Time Resolved Helical CTA (TRH-CTA) image data. At least one processor may be configured to preprocess the TRH-CTA helical image data to generate preprocessed TRH-CTA helical image data; generate time density curve data for a plurality of voxels from the preprocessed TRH-CTA helical image data for an axial imaging slice, where the time density curve data comprise intensity values for different phases of the preprocessed TRH-CTA helical image data arranged sequentially in time; generate at least one perfusion functional map for the axial imaging slice by at least one of: (1) applying at least one mapping function to different phases of the time density curve data corresponding to the axial imaging slice; (2) applying a deconvolution method to the time density curve data; and (3) applying a non-deconvolution method to the time density curve data; and perform spatial filtering on the perfusion functional map. A display may be used to display at least one filtered perfusion functional map.

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

Angiographic data is obtained by injecting a chemical contrast agent intravascularly, and imaging passage of the contrast as a function of time, thereby generating a sequence of images. To correct error from uncalibrated timestamps embedded in the image metadata, radio-opaque markers are used to generate a watermark embedding timestamp data in obtained images. The radio-opaque markers cause opacification on the x-ray images in the form of dynamic watermarks that encode timestamps. The positions of the markers in the watermark (cast from the radio-opaque markers) are then processed and analyzed to generate an accurate timestamp for the image. By generating an accurate timestamp, synchronized calculations of the images with other data sources are provided.