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This paper introduces Sequence Search, a neural architecture search (NAS) framework for automated MRI pulse sequence design. It optimizes sequences by iteratively generating candidates and refining them using a differentiable Bloch simulator and objective-specific loss functions. The framework rediscovers standard sequences and identifies novel sequences with improved properties, such as reduced RF energy.
AI can now design MRI sequences that human experts might miss, potentially leading to faster, safer, and more effective medical imaging.
Developing an MR sequence is challenging and remains largely constrained by human intuition. Recently, AI-driven approaches have been proposed; however, most require an initial sequence for parameter optimization or extensive training datasets, limiting their general applicability. In this study, we propose"Sequence Search,"an automated sequence design framework based on neural architecture search. The method takes tissue properties, imaging parameters, and design objectives as inputs and generates pulse sequences satisfying the design objectives, without requiring prior knowledge of conventional sequence structures. Sequence Search iteratively generates candidate sequences through neural architecture search and optimizes them via a differentiable Bloch simulator and objective-specific loss functions using gradient-based learning. The framework successfully replicated conventional spin-echo, T2-weighted spin-echo, and inversion recovery sequences. Less intuitive solutions were also discovered, such as three-RF spin-echo-like sequences with reduced RF energy and refocusing phases deviating from the conventional Hahn-echo. This work establishes a generalizable framework for automated MR sequence design, highlighting the potential to explore configurations beyond conventional designs based on human intuition.