NOISY MRI RECONSTRUCTION VIA MAP ESTIMATION WITH AN IMPLICIT DEEP-DENOISER PRIOR.
Authors
Affiliations (3)
Affiliations (3)
- Radiology Department, New York University Grossman School of Medicine.
- Neurology Department, New York University Grossman School of Medicine.
- New York University Tandon School of Engineering, Electrical and Computer Engineering Department.
Abstract
Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability in practice. We introduce <i>Implicit-MAP (ImMAP)</i>, a diffusion-based reconstruction framework that integrates the acquisition noise model directly into a maximum a posteriori (MAP) formulation. Specifically, we build on the stochastic ascent method of Kadkhodaie et al. and generalize it to handle MRI encoding operators and realistic measurement noise. Across both simulated and real noisy datasets, ImMAP consistently outperforms state-of-the-art deep learning (LPDSNet) and diffusion-based (DDS) methods. By clarifying the practical behavior and limitations of diffusion models under realistic noise conditions, ImMAP establishes a more reliable and interpretable baseline for diffusion-based accelerated MRI reconstruction.