Automated Sequence Design using Neural Architecture Search: Reliability and Reproducibility

Motivation: An automated sequence design framework utilizing neural architecture search was proposed, and successfully designed optimal sequences for given properties and target objectives without any prior knowledge of MR physics. Goal(s): We aimed to explore the reliability and reproducibility of this method. Approach: The reliability of the method was evaluated by adjusting the weights of the desired objectives for sequence design. The reproducibility was tested through multiple runs of the design process. Results: Our method exhibited reasonable reliability within a certain range of loss weights. Also, it demonstrated reasonable reproducibility in designing SE sequences; however, it exhibited less robustness when designing IR sequences. Impact: Our previous work, an automated sequence design framework utilizing neural architecture search, is further explored. Our methodology successfully designed sequences with reasonable reliability and reproducibility, despite designing without prior knowledge of MR physics.