MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction

2026-05-29

AI Summary

This work addresses the challenge of simultaneously achieving spatial information sharing, state-adaptive synthesis, and computational efficiency in undersampled dynamic and quantitative MRI reconstruction. To this end, the authors propose a scan-specific multi-coil MRI reconstruction framework that, for the first time, integrates a state-conditioned Mixture-of-Experts (MoE) mechanism into Implicit Neural Representations (INRs). By leveraging state indices to guide expert routing, the method decouples spatial content sharing from state-dependent synthesis and jointly optimizes the multi-coil forward model in an end-to-end manner. The approach achieves high reconstruction quality while reducing per-scan optimization time to approximately 30 seconds—substantially faster than existing INR-based methods, which typically require hundreds to thousands of seconds—thereby enabling efficient and unified reconstruction for both dynamic and quantitative MRI.

Abstract

Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-space data while preserving state-dependent fidelity for dynamic and quantitative MRI (qMRI). Existing scan-specific implicit neural representations (INRs) often use monolithic spatiotemporal coordinate fields, explicit subspaces, motion or deformation models, calibration variables, or sequence-specific quantitative signal models. These design choices can limit flexibility in sharing spatial information while adapting image synthesis across acquisition states. Moreover, many INR-based baselines remain computationally demanding, typically requiring per-scan optimization times on the order of hundreds to thousands of seconds. We propose MoE-dqINR, a scan-specific multicoil MRI reconstruction framework that factorizes the image-domain representation into shared spatial experts and a state-conditioned routing pathway. Spatial experts encode reusable coordinate-dependent image content, whereas routing weights, conditioned on ordered acquisition states, synthesize each dynamic frame or contrast state from a common expert bank. The representation is coupled to a multicoil MRI forward model, uses the normalized state index to drive routing in both dynamic and quantitative MRI. By separating shared spatial representation from state-dependent synthesis, the framework provides an image-first architecture for dynamic and quantitative MRI while reducing scan-specific INR optimization to approximately 30 s per scan in our experiments. The proposed formulation establishes state-conditioned mixture-of-experts INR as a scan-specific multicoil MRI reconstruction prior that unifies shared spatial representation, dynamic- and qMRI-specific synthesis, and practical per-scan efficiency.

Problem

• undersampled MRI reconstruction
• dynamic MRI
• quantitative MRI
• implicit neural representation
• scan-specific optimization

Innovation

• Mixture-of-Experts
• Implicit Neural Representation
• Scan-specific MRI Reconstruction
• Dynamic MRI
• Quantitative MRI