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Abstract #3623

k-t CNN for Modeling Spatio-temporal Mappings and an Application to Reconstruction of k-space Data with Stack-of-spirals Trajectory

Hidenori Takeshima1 and Hideaki Kutsuna2
1Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Kanagawa, Japan, 2MRI Systems Development Department, MRI Systems Division, Canon Medical Systems Corporation, Kanagawa, Japan

The authors propose a new model using a convolutional neural network (CNN) named k-t CNN for approximating non-linear spatio-temporal mappings used in various applications. Existing studies imply that spatio-temporal mappings are non-linear. Most existing studies developed various methods using linear models for spatio-temporal applications. Meanwhile, the effectiveness of non-linear models was shown for spatial-domain applications.
As an application of k-t CNN, the effectiveness of the proposed method is shown experimentally in the case of reconstruction of stack-of-spirals k-space data.

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