RAIDER: Rapid, anatomy-independent, deep learning-based chemical shift-encoded MRI

Motivation: Despite recent advances, chemical shift-encoded MRI (CSE-MRI) remains a challenging problem and many algorithms are computationally expensive, leading to interest in deep learning-based methods. However, initial attempts have used convolutional neural networks (CNNs), which are limited by data requirements, poor generalisability across different anatomies (‘anatomy-dependence’) and training time. Goal(s): To address these limitations, we propose a deep learning-based method known as RAIDER. Approach: RAIDER uses two multilayer perceptrons (MLPs), each trained separately with simulated single-voxel data, to achieve ultrafast parameter estimation. Results: RAIDER is several orders of magnitude faster than conventional fitting, with similar/better performance, and avoids the inherent limitations of CNN-based methods. Impact: RAIDER delivers ‘ultrafast’ CSE-MRI processing whilst avoiding the data and training-time requirements and anatomy-dependence of CNN-based methods. It could simplify, accelerate and reduce the cost of CSE-MRI processing in both research and clinical practice.