Sequence Search: Automated Sequence Design using Neural Architecture Search

TL;DR

This paper introduces 'Sequence Search,' an AI-driven framework using neural architecture search to automatically design MRI sequences, surpassing traditional methods and discovering novel configurations without prior sequence knowledge.

Contribution

The study presents a generalizable neural architecture search framework for automated MRI sequence design, capable of replicating and innovating beyond conventional sequences.

Findings

Successfully replicated standard MRI sequences like spin-echo and inversion recovery.

Discovered novel sequences with reduced RF energy and unconventional refocusing phases.

Demonstrated the framework's ability to explore configurations beyond human intuition.

Abstract

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.