An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (FFR) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

A surgery assistance device includes a processor that sets a predetermined time interval corresponding to a pulsation cycle of a heart and sequentially acquires an angiographic image representing a target blood vessel into which a medical device is inserted at the predetermined time interval. The processor sequentially corrects the angiographic image sequentially acquired and generates a corrected angiographic image by correcting the angiographic image to be corrected in such a manner that a position of a specific portion of the medical device included in the angiographic image to be corrected approaches a position of the specific portion of the medical device included in the angiographic image acquired temporally earlier than the angiographic image to be corrected.

The invention provides an image processing method to assess quantitative myocardial blood flow and/or myocardial flow reserve, comprising the steps of: (a) pre-processing of images comprises: (i) reconstructing dynamic cine 3D tomographic myocardial perfusion imaging (MPI) data, (ii) optionally, denoising to improve the quality of image, (iii) extracting blood input function from a region of interest (ROI) of the left ventricle blood cavity, (iv) estimating the distribution volume (DV), given by the ratio of uptake and washout rates (K.sub.1/k.sub.2) to stabilize and improve estimation of K.sub.1, k.sub.2 and total blood volume (TBV) and subsequent myocardial blood flow measures, and (v) data normalization by dividing by the maximum of the blood input function; (b) assessing the individual signals pre-processed in step (a) to generate K.sub.1 and TBV parametric maps using artificial neural network; (c) post-processing of K.sub.1, k.sub.2 and TBV parametric maps; and of rest and stress myocardial blood flow to estimate myocardial flow reserve (MFR) and/or coronary flow reserve (CFR).

An image processing method is provided. The image processing method includes: setting a first analysis point and a second analysis point on a fundus image so as to be symmetrical about a reference line; finding a first blood vessel running direction at the first analysis point and finding a second blood vessel running direction at the second analysis point; and comparing the first blood vessel running direction against the second blood vessel running direction.

Systems and methods for establishing a patient's current or future clinical or lab values are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the lab values of people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's current or future clinical or lab values.

The disclosure relates to a method including: capturing a first set of image frames, wherein the first set of image frames includes a representation of a user's face; identifying at least one skin patch of the user's face that is represented in the first set of frames; determining a light source configuration and transmitting the light source configuration to a first light source; illuminating, by the first light source, the at least one skin patch according to the light source configuration; capturing a second set of image frames, wherein the second set of image frames includes a representation of the at least one skin patch illuminated by the first light source according to the light source configuration; and processing one or more of the second set of image frames using remote photo-plethysmography, rPPG. The disclosure also relates to a system configured to perform the method.

Provided is a non-contact sleep state measurement system 100 capable of accurately measuring a sleep state regardless of age, disease, or the like of a subject. The sleep state measurement system includes: a frame image acquisition unit that acquires frame images including a subject P during sleep in time series; a difference information calculation unit that calculates difference information that is information indicating a difference between two frame images at different times; a subject attribute information acquisition unit that acquires subject attribute information that is information indicating an attribute of the subject; and a sleep state related information calculation unit that calculates sleep state related information that is information related to a sleep state of the subject using the difference information or secondary information obtained therefrom as an explanatory variable according to the subject attribute information.

The invention relates to a cerebral blood flow (CBF) correction method based on multiple post-labeling delays (PLDs), a system, and a non-transitory computer-readable storage medium, which relate to CBF detection. The CBF correction method based on multiple PLDs includes importing a CBF perfusion image and an arterial transit time (ATT) image corresponding to the CBF perfusion image into a structure space to obtain a CBF perfusion model, the CBF perfusion image including multiple PLDs; registering a brain atlas to the CBF perfusion model to obtain a brain segmented CBF perfusion model; and taking, in the brain segmented CBF perfusion model, a highest CBF in multiple PLDs corresponding to each region as a corrected CBF of the each region. According to the CBF correction method, the CBF is obtained more accurately.

Disclosed in one embodiment of the present invention is a method for estimating a heart rate on the basis of a corrected image, the method comprising the steps of: acquiring a serial image; extracting a region of interest (ROI) from the acquired serial image; converting, from an RGB space to a YCbCr space, the color space of the extracted ROI; calculating a weighted value applied to the serial image, by inputting the converted result to a learning model; applying the calculated weighed value to the serial image and generating a serial image with the ROI corrected; and estimating the heart rate of a person included in the corrected serial image, by analyzing the ROI of the corrected serial image.

A method and apparatus for analyzing diagnostic image data are provided in which correspondence detection between a first diagnostic image and a second diagnostic image of a vessel of interest in a patients vasculature is performed on the basis of at least one functional parameter by matching the one or more values of said functional parameter at particular positions along the vessel of interest as shown in the first diagnostic image and the second diagnostic image to one another, thereby determining a correlation between the positions in the basis of said functional parameter values rather than solely the vessel geometry.