Noise-aware adaptive diffusion sampling for accelerated knee MRI reconstruction.

Dabin Kim, Hongki Lim

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 levelσ^(t)and scaled by

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