Abstract: The present invention relates lo a light-tuned antenna device. The light-tuned antenna device comprises a First support element with one or more through-holes, and a photosensitive substrate disposed in one of the through-holes in the first support element, the photosensitive substrate having a first surface and a second surface. In addition, the antenna device comprises one or more patch-type antennas disposed on the first surface of the photosensitive substrate, and a second support element provided with one or more through-holes and disposed distally from the first support element. The device further comprises one or more focusing systems disposed in one of the through-holes in the second support element, and a control unit that Controls the focusing system.

Abstract: Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences.

Abstract: Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

Abstract: A method includes inserting a flexible cable into a cleaning region of a channel of a component via a cleaning port, rotating the flexible cable about an axis within the channel to interact a tip with deposits disposed within the cleaning region of the channel, moving the flexible cable within the cleaning region of the channel along a length of the channel, and removing portions of the deposits from the cleaning region of the channel via a second port. The flexible cable includes the axis and the tip. A diameter of the tip is different than a diameter of the flexible cable. The tip is configured to loosen the portions of the deposits.

Abstract: A method includes inserting a flexible cable into a cleaning region of a channel of a component via a cleaning port, rotating the flexible cable about an axis within the channel to interact a tip with deposits disposed within the cleaning region of the channel, moving the flexible cable within the cleaning region of the channel along a length of the channel, and removing portions of the deposits from the cleaning region of the channel via a second port. The flexible cable includes the axis and the tip. A diameter of the tip is different than a diameter of the flexible cable. The tip is configured to loosen the portions of the deposits.

Abstract: A segmentation/prediction method is described for differentiating between infarct, penumbra and healthy regions in a tomographic (e.g. MRI or CT) image dataset of the brain of a stroke patient under examination. The method comprises deriving (7, 11) a multidimensional set of feature vectors from a plurality of baseline modalities, where the modalities comprising both structural and functional modalities. For each volume element of image dataset, an n-dimensional feature vector is extracted (8, 12), such that it represents both structural and functional modalities of the volume element. A classification (13) is performed on the volume element and the classification is used to inform the segmentation (14) in order to label the volume element as belonging to healthy tissue, penumbra tissue, or infarct tissue.