Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Demonstrated superiority over baseline and state-of-the-art methods

• Primary Outcome: Superior reconstruction quality with lowest normalized root-mean-square error and highest structural similarity index

• Nnt Or Sensitivity: Approximately fivefold improvement in computational efficiency

• Confidence Interval: Not reported

Clinical Application

High feasibility for implementation in research and clinical MRI facilities with appropriate computational resources Relevant to Australian radiology departments and research institutions; may require TGA consideration for clinical implementation; potential PBS implications for reduced scan times and improved throughput Patients requiring multiparametric quantitative MRI, particularly for neuroimaging applications

Abstract

PURPOSE: To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). METHODS: We propose REFINE-MORE (REference-Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement module that incorporates MR physics constraints. The INR component enables informative learning of spatiotemporal correlations to initialize multiparametric quantitative maps, which are then further refined through an unrolled optimization scheme enforcing data consistency. To improve computational efficiency, REFINE-MORE integrates a low-rank adaptation strategy that promotes rapid model convergence. We evaluated REFINE-MORE on accelerated multiparametric quantitative magnetization transfer imaging for simultaneous estimation of free water spin-lattice relaxation, tissue macromolecular proton fraction, and magnetization exchange rate, using both phantom and in vivo brain data. RESULTS: Under 4× and 5× accelerations on in vivo data, REFINE-MORE achieved superior reconstruction quality, demonstrating the lowest normalized root-mean-square error and highest structural similarity index compared to baseline methods and other state-of-the-art model-based and deep learning approaches. Phantom experiments further showed strong agreement with reference values, underscoring the robustness and generalizability of the proposed framework. Additionally, the model adaptation strategy improved reconstruction efficiency by approximately fivefold. CONCLUSION: REFINE-MORE enables accurate and efficient scan-specific multiparametric qMRI reconstruction, providing a flexible solution for high-dimensional, accelerated qMRI applications.

References

1. 1.Feng, R., Jang, A., He, X., & Liu, F. (2026). Accelerating multiparametric quantitative MRI using self-supervised scan-specific implicit neural representation with model reinforcement. Magnetic Resonance in Medicine. https://doi.org/10.1016/0893-6080(89)90020-8