Abstract: Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.

Abstract: This invention relates to methods of producing aerogels and composites thereof. In particular, the invention relates to methods of producing silica aerogels and composites thereof. The invention also relates to doped aerogels and doped silica aerogels. The method involves the use of alkaline solutions, and particularly aqueous alkaline solutions, during the aerogel drying process. The method is more energy efficient and cheaper than prior art methods.

Abstract: Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

Abstract: A communication method and a communications node includes sending, by a sending node, a first frame to a receiving node on a first channel, and sending at least one second frame to the receiving node on a second channel, where a frequency of the first channel is less than a frequency of the second channel, where each second frame corresponds to a sending direction, and a length of each second frame is less than a preset frame length.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RAR? agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency.

Abstract: Embodiments of the present invention provide a data transmission method and a station. The method includes: transmitting, by a station, data of a primary access category (AC) of the station within a current transmission opportunity (TXOP), where to-be-transmitted data of the station includes the data of the primary AC and data of at least one secondary AC; after transmission of the data of the primary AC is completed, determining, by the station, whether there is remaining time in the TXOP; and if there is remaining time in the TXOP, transmitting, by the station, the data of the at least one secondary AC within the remaining time. According to the method provided in the embodiments of the present invention, the current TXOP is fully utilized without a channel resource wasted, thereby improving data transmission efficiency of the station.

Abstract: Network attack defense includes: obtaining a set of one or more statistical attributes for a protected site by gathering statistics for a set of one or more site attributes of the protected site, the site attributes indicating an operation mode of the protected site; determining, based on the set of one or more statistical attributes, that the protected site is to transition from a current operation mode to a target operation mode, wherein the current operation mode has a current defense strategy different from a target defense strategy of the target operation mode; and if the protected site is to transition from the current operation mode to the target operation mode, transitioning from the current operation mode to the target operation mode and applying the target defense strategy for the protected site instead of the current operation mode.

Abstract: Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical images. Embodiments of the present disclosure may relate to a method for segmenting medical images. The method may be implemented by a processor device executing a plurality of computer executable instructions. The method may comprise receiving an image from a memory, and identifying at least one landmark point within the image. The method may further comprise selecting an image point in the image, and determining at least one feature for the image point relative to the at least one landmark point. The method may also comprise associating the image point with an anatomical structure by using a classification model based on the at least one determined feature.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RAR? agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency.

Abstract: Network attack defense includes: obtaining a set of one or more statistical attributes for a protected site by gathering statistics for a set of one or more site attributes of the protected site, the site attributes indicating an operation mode of the protected site; determining, based on the set of one or more statistical attributes, that the protected site is to transition from a current operation mode to a target operation mode, wherein the current operation mode has a current defense strategy different from a target defense strategy of the target operation mode; and if the protected site is to transition from the current operation mode to the target operation mode, transitioning from the current operation mode to the target operation mode and applying the target defense strategy for the protected site instead of the current operation mode.

Abstract: Systems, computer-implemented methods, and computer readable media for generating a synthetic image of an anatomical portion based on an origin image of the anatomical portion acquired by an imaging device using a first imaging modality are disclosed. These systems may be configured to receive the origin image of the anatomical portion acquired by the imaging device using the first imaging modality, receive a convolutional neural network model trained for predicting the synthetic image based on the origin image, and convert the origin image to the synthetic image through the convolutional neural network model. The synthetic image may resemble an imaging of the anatomical portion using a second imaging modality differing from the first imaging modality.

Abstract: An image display apparatus includes a display panel including a front surface on which an image is displayed, a base plate disposed behind the display panel, a light guide plate disposed between the display panel and the base plate, a light source, a light source cover, and a corner component. The light source is disposed along an edge of the base plate, and emits light incident on an edge face of the light guide plate. The light cover is elongated and covers a front of the light source. The corner component is disposed along a corner of the base plate, laterally supports the display panel, and includes a portion that overlaps the light source cover in a front to back direction.

Abstract: An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image.

Abstract: Techniques for generating an enhanced cone-beam computed tomography (CBCT) image using a trained model are provided. A CBCT image of a subject is received, a synthetic computed tomography (sCT) image corresponding to the CBCT image is generated, using a generative model. The generative model is trained in a generative adversarial network (GAN). The generative model is further trained to process the CBCT image as an input and provide the sCT image as an output. The sCT image is presented for medical analysis of the subject.

Abstract: A deformable radiotherapy phantom can be produced using an additive manufacturing process, based on a medical image of the patient. The deformable phantom can include dosimeters for measuring radiation dose distribution. A smart material can allow deformation in response to an applied stimulus. Among other things, the phantom can be used to validate radiation dose warping, a radiotherapy treatment plan, to determine a maximum acceptable deformation of the patient, to validate a cumulative accuracy of dose warping and deformable image registration, or the like.

Abstract: A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.