  The recent Mamba model has shown remarkable adaptability for visual
representation learning, including in medical imaging tasks. This study
introduces MambaMIR, a Mamba-based model for medical image reconstruction, as
well as its Generative Adversarial Network-based variant, MambaMIR-GAN. Our
proposed MambaMIR inherits several advantages, such as linear complexity,
global receptive fields, and dynamic weights, from the original Mamba model.
The innovated arbitrary-mask mechanism effectively adapt Mamba to our image
reconstruction task, providing randomness for subsequent Monte Carlo-based
uncertainty estimation. Experiments conducted on various medical image
reconstruction tasks, including fast MRI and SVCT, which cover anatomical
regions such as the knee, chest, and abdomen, have demonstrated that MambaMIR
and MambaMIR-GAN achieve comparable or superior reconstruction results relative
to state-of-the-art methods. Additionally, the estimated uncertainty maps offer
further insights into the reliability of the reconstruction quality. The code
is publicly available at https://github.com/ayanglab/MambaMIR.
