Leland Stanford Junior University

Nvidia Corporation

Liyue Shen

Lei Xing

Daguang Xu

Xiaosong Wang

Wentao Zhu

Wei Zhao

Apparatuses, systems, and techniques to generate one or more data items related to an object from a set of multiple data items related to the same object using a generative adversarial network. In at least one embodiment, data objects in a set are disentangled into common and unique components and encoded such that they may be used to train one or more neural networks to generate missing data from the data objects in the set, such as generating missing medical images from a set of related medical images.

Representational disentanglement for multi-modality image completion using neural networks

A method for diagnostic imaging reconstruction uses a prior image xfrom a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.

Implicit neural representation learning with prior embedding for sparsely sampled image reconstruction and other inverse problems

A method for tomographic imaging comprising acquiring [] a set of one or more 2D projection images [] and reconstructing [] a 3D volumetric image [] from the set of one or more 2D projection images [] using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming [] by the encoder network the set of one or more 2D projection images [] to 2D features []; mapping [] by the transform module the 2D features [] to 3D features []; and generating [] by the decoder network the 3D volumetric image [] from the 3D features []. Preferably, the encoder network comprises 2D convolution residual blocks and the decoder network comprises 3D blocks without residual shortcuts within each of the 3D blocks.

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Stanford, CA, United States of America

Liyue Shen