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

## TL;DR

This paper introduces a new deep learning framework for faster and more accurate MRI scans using physics-based constraints and self-supervised learning.

## Contribution

The novel framework, REFINE-MORE, combines implicit neural representations with model reinforcement for accelerated multiparametric MRI.

## Key findings

- REFINE-MORE achieved the lowest normalized root-mean-square error and highest structural similarity index in in vivo data.
- Phantom experiments showed strong agreement with reference values, indicating robustness and generalizability.
- The model adaptation strategy improved reconstruction efficiency by approximately fivefold.

## Abstract

To develop a self‐supervised scan‐specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI).

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.

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.

REFINE‐MORE enables accurate and efficient scan‐specific multiparametric qMRI reconstruction, providing a flexible solution for high‐dimensional, accelerated qMRI applications.