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Abstract #0467

Fast Acquisition and Low-delay Reconstruction of Stack-of-stars Trajectory Using Temporal Multiresolution Images and a Convolutional Neural Network

Hidenori Takeshima1 and Hideaki Kutsuna2

1Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Yokohama, Japan, 2Software Technologies Group, MRI Systems Development Department, MRI Systems Division, Canon Medical Systems Corporation, Yokohama, Japan

For fast data acquisition and low-delay reconstruction in applications using stack-of-stars trajectory, the authors propose a new reconstruction method using a CNN with temporal multiresolution inputs. Conventionally, stack-of-stars images reconstructed from a few spokes contain streaking artifacts. By utilizing view sharing technique for suppressing the artifacts, reconstructed images are often blurred. For low-delay reconstruction, it is not straightforward to use well-studied methods based on compressed sensing with temporal priors. The proposed method aims to adjust spatio-temporal resolution to a suitable one. Experimental results show that the proposed method could reconstruct highly under-sampled radial dynamic images with reduced artifacts.

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