mlsanityData to 14 Oct 2026

MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment

arXiv 2509.24888 ↗First posted 29 Sep 2025cs.CV cs.CLcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 8 of its 21 references that are in the map. First posted on arXiv 29 Sep 2025.

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8 of 21 references.

References · 8

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,719
  4. 2024SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuningcited by 355
  5. 2020MRQy: An Open-Source Tool for Quality Control of MR Imaging Datacited by 12
  6. 2021Artifact- and content-specific quality assessment for MRI with image rulerscited by 8
  7. 2023FetMRQC: a robust quality control system for multi-centric fetal brain MRIcited by 4
  8. 2025Ultrasound-QBench: Can LLMs Aid in Quality Assessment of Ultrasound Imaging?cited by 3

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  4. 2025OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation
  5. 2026NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding
  6. 2025Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
  7. 2026CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
  8. 2025Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications