Abstract #1801

Learning-based Reconstruction using Artificial Neural Network for Higher Acceleration

Kinam Kwon¹, Dongchan Kim¹, Hyunseok Seo¹, Jaejin Cho¹, Byungjai Kim¹, and HyunWook Park¹

¹KAIST, Daejeon, Korea, Republic of

A long imaging time has been regarded as a major drawback of MRI, and many techniques have been proposed to overcome this problem. Parallel imaging (PI) and compressed sensing (CS) techniques utilize different sensitivity of multi-channel RF coils and sparsity of signal in a certain domain to remove aliasing artifacts that are generated by subsampling, respectively. In this study, an artificial neural networks (ANN) are applied to MR reconstruction to reduce imaging time, and it is shown that the ANN model has a potential to be comparable to PI and CS.

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