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Abstract #2796

Integrating Spatial and Temporal Correlations into a Deep Neural Network for Low-delay Reconstruction of Highly Undersampled Radial Dynamic Images

Hidenori Takeshima1,2

1Clinical Application Research Department, Research and Development Center, Toshiba Medical Systems Corporation, Kanagawa, Japan, 2Analytics AI Laboratory, Corporate Research & Development Center, Toshiba Corporation, Kanagawa, Japan

This paper proposes a novel method for the reconstruction of dynamic images from highly undersampled radial k-space data. In order to take advantage of spatial and temporal correlations and reducing the reconstruction time delay, a deep neural network (DNN) was trained with additional input images displaying the aforementioned correlations. It is shown that the image quality from the proposed method is superior to that of the method based on the conventional DNN reconstruction scheme from a single input to a single output.

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