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 this https URL

本研究介绍了 MambaMIR 模型及其基于生成对抗网络的变体 MambaMIR-GAN，利用 Mamba 模型的优势，如线性复杂度、全局感受野和动态权重，通过创新的任意掩码机制使 Mamba 适应图像重建任务，并提供蒙特卡洛法的不确定性估计。实验证明 MambaMIR 和 MambaMIR-GAN 在多个医学图像重建任务中（包括快速 MRI 和 SVCT）取得了与当今最先进方法相媲美甚至更好的重建结果，并提供可靠性的重建质量的估计不确定性图。