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.

翻译：近期Mamba模型在视觉表示学习方面展现出显著适应性，包括医学成像任务。本研究提出了MambaMIR——一种基于Mamba的医学图像重建模型，以及其基于生成对抗网络的变体MambaMIR-GAN。所提出的MambaMIR继承了原始Mamba模型的若干优势，例如线性复杂度、全局感受野和动态权重。创新的任意掩码机制有效将Mamba适配到图像重建任务中，为后续基于蒙特卡洛的不确定性估计提供随机性。在涵盖膝关节、胸部、腹部等解剖区域的快速MRI和SVCT等多种医学图像重建任务上的实验表明，MambaMIR和MambaMIR-GAN在重建结果上达到或优于现有最先进方法。此外，估计的不确定性图为进一步理解重建质量的可靠性提供了新视角。代码已开源：https://github.com/ayanglab/MambaMIR。