Blood flow information is extracted from speckle information of a time series of structural optical coherence tomography (OCT) images. Flow information can be determined from high-frequency information of the OCT images, a speckle density of the OCT images, from a co-occurrence matrix applied to the OCT image, from a machine learning analysis of an input OCT image, or the like. A flow profile analogous to a pulse waveform is then generated as a time series of the extracted flow information.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

A method for providing a comparison dataset is disclosed. The method includes providing a time-resolved first dataset that maps a first contrast medium flow in a region of interest of an examination object in a first period of time; providing a time-resolved second dataset that maps a second contrast medium flow in the region of interest in a second period of time after the first period of time; spatially registering the first and second datasets; identifying a mapping of at least one vessel section of the region of interest in the first and second datasets; temporally registering the first and second datasets; identifying a difference between the first and second contrast medium flows by a comparison of the registered first and second datasets; and providing the comparison dataset based on the registered first and second datasets, wherein the comparison dataset has at least one parameter characterizing the difference.

A personal carotid artery stenosis detection and stroke prevention device and a personal carotid artery stenosis detection and stroke prevention process are disclosed. The personal carotid artery stenosis detection and stroke prevention device is configured to check carotid arteries of a person for buildup of stenosis. The device is a smart device with a camera and a processing unit configured to analyze video footage captured by the camera and detect possible buildup of stenosis in one carotid artery or both carotid arteries. Analysis and detection is carried out according to the personal carotid artery stenosis detection and stroke prevention process. By detecting stenosis buildup, the device is able to inform the person of the risk of stroke, thereby helping the person to prevent a potential stroke.

An optical coherence tomography angiography (OCT-A) method that includes generating at least two OCT-A images based on different interscan times, de-noising the at least two OCT-A images, and generating a short interscan time (SIT) representative image and a long interscan time (LIT) representative image based on the at least two de-noised OCT-A images. Estimating a relative blood flow velocity based on the SIT representative image and the LIT representative image. Further, generating a blood flow image based on the relative blood flow velocity.

Disclosed herein is a method of facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, the method may include a step of generating, using an intravascular imaging device, at least one intravascular image associated with a patient. Further, the method may include a step of analyzing, using a processing device, the at least one intravascular image. Further, the method may include a step of determining, using the processing device, at least one vein diagnosis based on the analyzing. Further, the method may include a step of displaying, using a display device, the at least one vein diagnosis. Further, the method may include a step of storing, using a storage device, the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database. In other embodiments, an artificial intelligence unit may be configured to reconstruct missing data in at least one intravascular image and determine a value associated with the at least one intravascular image.

The present disclosure relates to perfusion monitoring. In order to provide facilitated peripheral perfusion monitoring, a device (10) for monitoring peripheral perfusion is provided that comprises a data input (12), a data processor (14) and an output interface (16). The data input is configured to provide angiographic image data comprising information about macrovascular blood flow in an area of interest of a subject. The angiographic image data comprises first image data relating to a first point in time, and second image data relating to a second point in time. The data processor is configured to compare the first image data and the second image data to identify, within the area of interest of the subject, a vascular region of interest for macrovascular flow. The data processor is also configured to determine at least one tissue portion of interest for microvascular perfusion based on the identified vascular region of interest and based on feeding information assigned to the identified vascular region of interest. The data processor is further configured to determine a surface portion for assessing microvascular perfusion in the at least one tissue portion of interest with optical perfusion imaging. The data processor is furthermore configured to allocate the surface portion on an outer surface of the subject. The output interface is configured to provide a surface portion identifier based on the allocated surface portion.

An ultrasonic image is obtained from a bias-switchable row-column array transducer. A row channel data set is obtained by applying a bias voltage pattern to groups of row electrodes, the bias voltage pattern being chosen such that row electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of row electrodes; and recording received column signals from each of the plurality of column electrodes. A column channel data set is obtained by applying a bias voltage pattern to groups of column electrodes, the bias voltage pattern being chosen such that column electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of column electrodes; and recording received row signals from each of the plurality of row electrodes.

(a) receiving an image set including at least two reflection images generated at different points in time and an indication of at least one interval between at least two different points in time, where a reflection image is generated while the living organism is illuminated by patterned coherent electromagnetic radiation and includes a pattern with at least one pattern feature, and, where the reflection image shows at least one pattern feature formed by illuminating at least a part of the living organism by the patterned coherent electromagnetic radiation;

(b) determining a feature contrast of the at least two pattern features;

(c) determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval; and

(d) outputting the condition measure.