Assessing Machine Learning Robustness in MRS Quantification: Impact of Training Strategies on Out-of-Distribution Generalization

Motivation: Despite promising developments, current machine learning methods for magnetic resonance spectroscopy (MRS) suffer from limited robustness and generalization issues, restricting their clinical application. Goal(s): This study compares training strategies for MRS quantification, focusing on neural network resilience to out-of-distribution samples. Approach: Bias towards the training distribution was assessed for various out-of-distribution cases in synthetic data and in-vivo data. Results: Our findings reveal that, while common supervised regression is most accurate for in-distribution cases, it shows the most data bias; physics-informed self-supervised training is more robust; while integrating a least-squares fitting method within the training framework enhances standalone performance while remaining generalizable. Impact: To advance integration in clinical MRS, robust and generalizable machine learning methods are needed. This study's exploration of quantification training strategies offers insights into data biases and advocates hybrid models that combine traditional methods with neural networks to maintain robustness.