Improving the Precision of CNNs for Magnetic Resonance Spectral Modeling

TL;DR

This paper discusses methods to improve the precision and reliability of CNN-based spectral modeling in magnetic resonance spectroscopy, emphasizing comprehensive error metrics and their interactions.

Contribution

It introduces techniques to enhance CNN precision for spectral modeling and provides in-depth analysis of their mechanisms and trade-offs.

Findings

Improved CNN models with better error characterization.

Trade-offs between different precision-enhancing techniques.

Insights into interactions among various error mitigation methods.

Abstract

Magnetic resonance spectroscopic imaging is a widely available imaging modality that can non-invasively provide a metabolic profile of the tissue of interest, yet is challenging to integrate clinically. One major reason is the expensive, expert data processing and analysis that is required. Using machine learning to predict MRS-related quantities offers avenues around this problem, but deep learning models bring their own challenges, especially model trust. Current research trends focus primarily on mean error metrics, but comprehensive precision metrics are also needed, e.g. standard deviations, confidence intervals, etc.. This work highlights why more comprehensive error characterization is important and how to improve the precision of CNNs for spectral modeling, a quantitative task. The results highlight advantages and trade-offs of these techniques that should be considered when addressing such regression tasks with CNNs. Detailed insights into the underlying mechanisms of each technique, and how they interact with other techniques, are discussed in depth.