MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment
This addresses the problem of MRI quality assessment for clinical decision-making by bridging quantitative analysis with interpretability, though it is incremental as it builds on existing MLLM and signal processing methods.
The paper tackles MRI quality assessment by introducing the MMRQA framework, which integrates multimodal large language models with signal processing to combine quantitative metrics and semantic understanding, achieving state-of-the-art performance on benchmarks like MR-ART, FastMRI, and MyConnectome with strong zero-shot generalization.
Magnetic resonance imaging (MRI) quality assessment is crucial for clinical decision-making, yet remains challenging due to data scarcity and protocol variability. Traditional approaches face fundamental trade-offs: signal-based methods like MRIQC provide quantitative metrics but lack semantic understanding, while deep learning approaches achieve high accuracy but sacrifice interpretability. To address these limitations, we introduce the Multimodal MRI Quality Assessment (MMRQA) framework, pioneering the integration of multimodal large language models (MLLMs) with acquisition-aware signal processing. MMRQA combines three key innovations: robust metric extraction via MRQy augmented with simulated artifacts, structured transformation of metrics into question-answer pairs using Qwen, and parameter-efficient fusion through Low-Rank Adaptation (LoRA) of LLaVA-OneVision. Evaluated on MR-ART, FastMRI, and MyConnectome benchmarks, MMRQA achieves state-of-the-art performance with strong zero-shot generalization, as validated by comprehensive ablation studies. By bridging quantitative analysis with semantic reasoning, our framework generates clinically interpretable outputs that enhance quality control in dynamic medical settings.