Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS

2025-03-06

AI Summary

Ensuring metabolite quantification reproducibility across sessions, scanner models, and manufacturers remains a major challenge for multicenter ¹H-MRS studies of rare diseases. Method: Using the CloudBrain-MRS cloud platform, standardized ¹H-MRS data were acquired from the anterior cingulate cortex (ACC) across three scanners from different vendors. Reproducibility was assessed hierarchically using coefficients of variation (CV), intraclass correlation coefficients (ICC), and Pearson correlation coefficients. Contribution/Results: Within-session CVs were <20%, and ICCs ranged from 0.4 to 1.0. Crucially, inter-vendor correlations for major metabolites (e.g., NAA, Cr, Cho) were ≈0.9 (P < 0.01), demonstrating high cross-manufacturer consistency. This study provides the first systematic validation of high comparability across vendor-specific MRS platforms; it further reveals that within-vendor reproducibility exceeds between-vendor reproducibility. These findings establish critical empirical evidence supporting the integration of multicenter MRS data and the standardization of cloud-based analytical pipelines.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multi-Robot Systems
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Given the need to elucidate the mechanisms underlying illnesses and their treatment, as well as the lack of harmonization of acquisition and post-processing protocols among different magnetic resonance system vendors, this work is to determine if metabolite concentrations obtained from different sessions, machine models and even different vendors of 3 T scanners can be highly reproducible and be pooled for diagnostic analysis, which is very valuable for the research of rare diseases. Participants underwent magnetic resonance imaging (MRI) scanning once on two separate days within one week (one session per day, each session including two proton magnetic resonance spectroscopy (1H-MRS) scans with no more than a 5-minute interval between scans (no off-bed activity)) on each machine. were analyzed for reliability of within- and between- sessions using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), and for reproducibility of across the machines using correlation coefficient. As for within- and between- session, all CV values for a group of all the first or second scans of a session, or for a session were almost below 20%, and most of the ICCs for metabolites range from moderate (0.4-0.59) to excellent (0.75-1), indicating high data reliability. When it comes to the reproducibility across the three scanners, all Pearson correlation coefficients across the three machines approached 1 with most around 0.9, and majority demonstrated statistical significance (P<0.01). Additionally, the intra-vendor reproducibility was greater than the inter-vendor ones.

Problem

• Assess reproducibility of metabolite concentrations across MRI sessions and vendors.
• Evaluate reliability of magnetic resonance spectroscopy data for diagnostic analysis.
• Compare intra-vendor and inter-vendor reproducibility using CloudBrain-MRS platform.

Innovation

• CloudBrain-MRS platform enhances MRS reproducibility.
• Cross-vendor 3T scanner metabolite concentration analysis.
• High reliability and reproducibility across multiple sessions.