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Abstract #0052

Automated Sequence Design using Neural Architecture Search

Hongjun An1, Sooyeon Ji1, Sehong Oh2, Jiye Kim1, Dongmyung Shin1, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Korea, Republic of


A new pulse sequence design method that requires no prior knowledge of MR physics or training dataset is proposed. This method utilizes neural architecture search, generating an optimal sequence for given properties (e.g., T2*, B1+) and target objectives. It successfully discovered FLAIR-like and spin-echo-like sequences. Furthermore, a less-intuitive sequence using three RF pulses was created when targeted for spin-echo. In an in-vivo experiment, this new sequence had almost identical contrasts and SNR with spin-echo (our SNR: 297.1±70.1; spin-echo SNR: 295.8±78.2) while utilizing 90% of RF energy of the spin-echo, suggesting a potential of the automated sequence design.

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