HIMAC: HISTOGRAM-DRIVEN AND MASK-AWARE CONSISTENCY MODELS FOR MRI RECONSTRUCTION

Diffusion-based methods have achieved remarkable success in accelerated Magnetic Resonance Imaging (MRI) reconstruction; however, they often suffer from two key limitations: (1) they rely on slow, multi-step sampling to produce high-quality reconstructions, and (2) they are conditioned on a limited number of undersampling patterns, leading to poor generalization capability. In this work, we present HiMaC (Histogram-driven and Mask-aware Consistency Model)-a unified framework that leverages Consistency Models (CMs) to enable one-step or few-step MRI reconstruction with sub-stantially reduced inference cost. However, we observe that plain CM outputs exhibit a brightness shift due to intensity distribution mismatch with fully sampled references. To address this, we introduce a distribution alignment regularization that enforces distributional consistency between the reconstructed and input images by minimizing the divergence between their histograms. Moreover, since undersampling patterns in practical MRI are pre-coded into the scanner, our approach incorporates the downsampling mask as an auxiliary input, explicitly embedding sampling information to guide the reconstruction process. This design not only enhances reconstruction fidelity but also improves generalization across diverse undersampling patterns, achieving fast, high-fidelity, and robust MRI reconstruction.