Abstract: In one embodiment, there is provided a dissectography module for dissecting a two-dimensional (2D) radiograph. The dissectography module includes an input module, an intermediate module, and an output module. The input module is configured to receive a number K of 2D input radiographs, and to generate at least one three-dimensional (3D) input feature set, and K 2D input feature sets based, at least in part, on the K 2D input radiographs. The intermediate module is configured to generate a 3D intermediate feature set based, at least in part, on the at least one 3D input feature set. The output module is configured to generate output image data based, at least in part, on the K 2D input feature sets, and the 3D intermediate feature set. Dissecting corresponds to extracting a region of interest from the 2D input radiographs while suppressing one or more other structure(s).

Abstract: Various systems and methods are provided for MAR in CT images. A corrupted CT image, of a region of interest (ROI) of a subject, including artifacts caused by a metal object in the subject may be acquired. A corrupted sinogram including a corrupted region of corrupted data caused by the metal object and an uncorrupted region of uncorrupted data may be generated. A mask sinogram that delineates the corrupted region of the corrupted data may be generated. A corrected sinogram including the uncorrupted region of the uncorrupted data and an inpainted region of inpainted data corresponding to the corrupted region may be generated using a denoising diffusion probabilistic model, the corrupted sinogram, and the mask sinogram. A corrected CT image, of the ROI of the subject, that includes reduced artifacts relative to the artifacts in the corrupted CT image may be generated based on the corrected sinogram.

Abstract: In one embodiment, there is provided an apparatus for ultra-low-dose (ULD) computed tomography (CT) reconstruction. The apparatus includes a low dimensional estimation neural network, and a high dimensional refinement neural network. The low dimensional estimation neural network is configured to receive sparse sinogram data, and to reconstruct a low dimensional estimated image based, at least in part, on the sparse sinogram data. The high dimensional refinement neural network is configured to receive the sparse sinogram data and intermediate image data, and to reconstruct a relatively high resolution CT image data. The intermediate image data is related to the low dimensional estimated image.

Abstract: One embodiment provides a method of training an artificial neural network (ANN) for denoising. The method includes generating, by a similarity module, a respective set of similar elements for each noisy input element of a number of noisy input elements included in a single noisy input data set. Each noisy input element includes information and noise. The method further includes generating, by a sample pair module, a plurality of training sample pairs. Each training sample pair includes a pair of selected similar elements corresponding to a respective noisy input element. The method further includes training, by a training module, an ANN using the plurality of training sample pairs. Each set of similar elements is generated prior to training the ANN. The plurality of training sample pairs is generated during training the ANN. The training is unsupervised.

Abstract: In one embodiment, there is provided an apparatus for low-dimensional manifold constrained disentanglement for metal artifact reduction (MAR) in computed tomography (CT) images. The apparatus includes a patch set construction module, a manifold dimensionality module, and a training module. The patch set construction module is configured to construct a patch set based, at least in part on training data. The manifold dimensionality module is configured to determine a dimensionality of a manifold. The training module is configured to optimize a combination loss function comprising a network loss function and the manifold dimensionality. The optimizing the combination loss function includes optimizing at least one network parameter.

Abstract: The disclosure can provide a method, an electronic device, and a storage medium for generating a vocal file. The method can include the following. A recording control is displayed on a playing interface in response to a video played on the playing interface being a first type. A recording interface is displayed in response to the recording control being triggered. A user audio is recorded on the recording interface based on a target song. The vocal file is generated based on the user audio and the target song.