NOISY MRI RECONSTRUCTION VIA MAP ESTIMATION WITH AN IMPLICIT DEEP-DENOISER PRIOR.

Authors

Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang, Li Feng

Published in

Proceedings. International Conference on Image Processing. Volume 2026. Epub Aug 13, 2026.

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 Implicit-MAP (ImMAP), 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.

PMID:
42829777
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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