High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction

2026-07-01

AI Summary

This work addresses the significant degradation in k-space reconstruction quality of magnetic resonance imaging (MRI) under undersampled and high-noise conditions by proposing a unified high-dimensional k-space reconstruction framework. The method enhances the representational capacity of the data space through the incorporation of high-dimensional embedding priors, enabling existing diffusion-based inverse problem solvers to operate more robustly in an enriched k-space without altering the underlying diffusion model architecture or optimization procedure. As a model-agnostic enhancement mechanism, the proposed framework consistently improves reconstruction performance across multiple public and internal datasets, with the most pronounced gains observed in high-noise scenarios.

Abstract

Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While diffusion models have recently shown strong potential as generative prior for inverse problems,existingapproachesstruggletohandlenoisyreconstruction settings, especially when operating directly in k-space domain. In this work, we propose a unified high-dimensional k-space reconstruction framework tailored for noisy inverse problems, whichenhancesdiffusion-based solversthroughrepresentation lifting.Ratherthanmodifyingthe underlying optimization procedures, the proposed framework augments the data representation space, enabling existing diffusion-based solvers to operate on enriched k-space embeddings with improved expressiveness. Extensive experiments on both in-house and public datasets across varying noise levels and undersampled factors demonstrate that the proposed frame work consistently improves reconstruction quality for multiple diffusion-based inverse solvers. Notably, the largest gains are observed in high-noise regimes, which is consistent with our theoretical analysis of error propagation under high-dimensional representation. These results suggest that high-dimensional representation provides a general and model-agnostic mechanism for improving diffusion-based MRI reconstruction in noisy settings, offering a new perspective on robust k-space generative modeling for practical inverse problems. The code will be available at https://github.com/yqx7150/HEP-MRIRec.

Problem

• MRI reconstruction
• k-space
• noisy inverse problems
• diffusion models
• high-dimensional embedding

Innovation

• high-dimensional embedding
• k-space MRI reconstruction
• diffusion models
• noisy inverse problems
• generative prior