Abstract: An electronic device including a protection layer, a display panel, and a sound broadcasting element is provided. The protection layer has an inner surface and a side surface directly connected to the inner surface. The display panel is disposed on the inner surface of the protection layer and has a back surface and a side surface directly connected to the back surface. The sound broadcasting element is located adjacent to the side surface of the display panel, and the sound broadcasting element includes a piezoelectric component and a connection component.

Abstract: The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.

Abstract: An information processing method processes an x-ray image including the steps of: obtaining first lower-radiation dose three-dimensional image data during a first scan of a patient; and detecting, using a trained neural network, a presence of an artifact (e.g., a metal artifact) in the first lower-radiation dose three-dimensional image data. An information processing apparatus includes processing circuitry for performing the detection method, and computer instructions stored in a non-transitory computer readable storage medium cause a computer processor to performing the detection method.

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

Abstract: A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.

Abstract: A medical data processing method according to an embodiment includes inputting first medical data relating to a subject imaged with a medical image capture apparatus to a learned model to configured to generate second medical data having lower noise than that of the first medical data and having a super resolution compared with the first medical data based on the first medical data to output the second medical data.

Abstract: A method and apparatus is provided to improve the image quality of images generated by analytical reconstruction of a computed tomography (CT) image. This improved image quality results from a deep learning (DL) network that is used to filter a sinogram before back projection but after the sinogram has been filtered using a ramp filter or other reconstruction kernel.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).

Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

Abstract: A method and apparatus is provided to improve the image quality of images generated by analytical reconstruction of a computed tomography (CT) image. This improved image quality results from a deep learning (DL) network that is used to filter a sinogram before back projection but after the sinogram has been filtered using a ramp filter or other reconstruction kernel.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).

Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.