Work

Noise-aware adaptive diffusion sampling for accelerated knee MRI reconstruction

Year: 2026

Type: article

Abstract: We present noise-aware adaptive diffusion sampling (NAD), a novel approach combining a classical noise estimation method with diffusion models for accelerated MRI reconstruction. NAD incorporates a data-consistent least-squares reconstruction as an informed starting point and uses patch-based principal component analysis to estimate the current noise level, thereby guiding adaptive sampling in the diffusion process. The method further incorporates conjugate gradient-based data consistency updates and controlled noise injection, meaning it re-injects Gaussian noise calibrated to the estimated noise level σ ^ ( t ) and scaled by γ to efficiently explore the solution space. Evaluated on the Stanford knee MRI dataset, NAD achieves higher peak signal-to-noise ratio than competing diffusion-based methods across the tested sampling budgets, with competitive or superior structural similarity index, while reducing reconstruction time in the main low-to-moderate sampling regimes. The proposed method not only advances accelerated MRI reconstruction but also provides insights into efficiently leveraging diffusion models for inverse problems in medical imaging. Code is available at here.

Source: Physics in Medicine and Biology

DOI: 10.1088/1361-6560/ae6af5

Authors: Dabin Kim, Hongki Lim

Institution: Inha University

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,325