MambaMIR：用于联合医学图像重建与不确定性估计的任意掩码Mamba
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
AI总结：
本研究提出基于Mamba的医学图像重建模型MambaMIR及其GAN变体，通过创新的任意掩码机制结合蒙特卡洛方法实现不确定性估计，在快速MRI和SVCT等任务上取得优于现有方法的重建效果。
摘要：
近期Mamba模型在视觉表示学习（包括医学成像任务）中展现出卓越的适应性。本研究引入了用于医学图像重建的基于Mamba的模型MambaMIR，及其基于生成对抗网络的变体MambaMIR-GAN。提出的MambaMIR继承了原始Mamba模型的线性复杂度、全局感受野和动态权重等优势。创新的任意掩码机制有效地将Mamba适配于图像重建任务，为后续基于蒙特卡洛的不确定性估计提供了随机性。在涵盖膝关节、胸部和腹部等解剖区域的多种医学图像重建任务（包括快速MRI和SVCT）上进行的实验表明，MambaMIR和MambaMIR-GAN相较于最先进方法取得了相当或更优的重建结果。此外，估计的不确定性图进一步提供了对重建质量可靠性的深入见解。代码已在https://github.com/ayanglab/MambaMIR公开发布。
英文摘要：
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.
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