Abstract: The present invention relates to a method of producing a 3D centerline of an organ vessel from a plurality of 2D x-ray images. The method comprises the steps of producing a point-cloud of 3D points which represent intersecting and non-intersecting points between projection lines from the 2D images to their respective x-ray sources; fitting a compound curve to the points in the point-cloud of 3D points; removing outliers from the point-cloud; fitting a new compound curve to the remaining points in the point-cloud of 3D points; and repeating certain steps, wherein the resultant compound curve represents the 3D centerline of the organ vessel. The method minimizes reconstruction errors and produces an optimally-reconstructed 3D vessel skeleton.

Abstract: A method to mitigate near end cross talk (NEXT) interference in networks may include receiving, through NEXT interference, a synchronization signal from a reference master node of a first network at a neighbor master node of a neighbor network. The method may also include scheduling, based on the synchronization signal, a cycle of downstream and upstream communications in the neighbor network synchronized with a cycle of downstream and upstream communications scheduled in the first network.

Abstract: A computer-implemented method is provided for predicting a growth rate of an aortic aneurysm. The method comprises analysing one or more geometric measures of a volumetric model of at least a portion of an aorta having an aneurysm. The method further comprises determining, from the analysis, a growth rate prediction for the aortic aneurysm. Computer-readable media and apparatuses are also described.

Abstract: A method to mitigate near end cross talk (NEXT) interference in networks may include receiving, through NEXT interference, a synchronization signal from a reference master node of a first network at a neighbor master node of a neighbor network. The method may also include scheduling, based on the synchronization signal, a cycle of downstream and upstream communications in the neighbor network synchronized with a cycle of downstream and upstream communications scheduled in the first network.

Abstract: Methods for training an algorithm to identify structural anatomical features, for example of a blood vessel, in a non-contrast computed tomography (NCT) image are described herein. The algorithm may comprise an image segmentation algorithm, a random forest classifier, or a generative adversarial network in examples described herein. In one embodiment, a method comprises receiving a labelled training set for a machine learning image segmentation algorithm. The labelled training set comprising a plurality of NCT images, each NCT image of the plurality of NCT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding NCT image of the plurality of NCT images.

Abstract: A method for training a machine learning image segmentation algorithm to segment structural features of a blood vessel in a computed tomography (CT) image is described herein. The method comprises receiving a labelled training set for the machine learning image segmentation algorithm. The labelled training set comprising a plurality of CT images, each CT image of the plurality of CT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding CT image of the plurality of CT images.

Abstract: The present invention relates to a method of producing a 3D centerline of an organ vessel from a plurality of 2D x-ray images. The method comprises the steps of producing a point-cloud of 3D points which represent intersecting and non-intersecting points between projection lines from the 2D images to their respective x-ray sources; fitting a compound curve to the points in the point-cloud of 3D points; removing outliers from the point-cloud; fitting a new compound curve to the remaining points in the point-cloud of 3D points; and repeating certain steps, wherein the resultant compound curve represents the 3D centerline of the organ vessel. The method minimizes reconstruction errors and produces an optimally-reconstructed 3D vessel skeleton.

Abstract: Aspects of the disclosure provide a receiver. The receiver includes a receiving (Rx) estimation controller configured to set a bit load of a subcarrier in a bit allocation table (BAT) at a transmitter, a receiving unit configured to receive, from the transmitter, a bit sequence that is loaded to the subcarrier based on the bit load of the subcarrier in the BAT wherein the bit sequence is transmitted through a channel from the transmitter to the receiving unit, and a channel estimator configured to estimate a condition of the channel based on the bit sequence that is loaded to the subcarrier.

Abstract: A plurality of images of a common object acquired by ultrasound echo imaging, such as echocardiography, are combined. In respect of each image, a monogenic signal is derived and used to derive, in respect of each pixel, feature measures being measures of phase congruency feature, and alignment measures being measures of the degree of alignment between the normal to said phase congruency feature and the analysis beam. In respect of each pixel, there are derived relative weights for the plurality of images in correspondence with the feature measures for the plurality of images in respect of the corresponding pixel, taking into account the alignment measures for the plurality of images. A combined image is produced by combining the corresponding pixels of each image in accordance with the determined relative weights.