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

Synopsis

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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