An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

2025-03-06

AI Summary

Quantitative analysis of proton magnetic resonance spectroscopy (¹H-MRS) metabolites in the human anterior cingulate cortex remains challenging due to methodological variability and limited comparability between deep learning–based and conventional modeling approaches. Method: This study conducts the first statistically rigorous comparison of the deep learning model QNet and the established linear combination modeling method LCModel for quantifying tNAA, tCho, and Ins—implemented within the unified cloud platform CloudBrain-MRS. Bland–Altman analysis and Pearson correlation tests assess agreement and consistency. Contribution/Results: QNet and LCModel demonstrate high quantitative concordance (limits of agreement half-width ≤18.5%; r = 0.469–0.927), yet QNet yields metabolite concentrations closer to literature-reported population means, exhibiting superior biological plausibility. Critically, this work establishes the first cloud-based MRS quantification framework enabling fair, standardized evaluation of both paradigms—advancing clinical ¹H-MRS automation with a new paradigm balancing accuracy and interpretability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age/standard deviation = 27.4/3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively; Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel.

Problem

• Compare QNet and LCModel for brain metabolite quantification.
• Evaluate consistency and correlation between two MRS methods.
• Assess QNet's accuracy versus LCModel on CloudBrain-MRS.

Innovation

• Deep learning QNet for MRS quantification
• CloudBrain-MRS platform for analysis
• Comparison with classical LCModel method