MambaRecon：基于结构化状态空间模型的MRI重建
MambaRecon: MRI Reconstruction with Structured State Space Models
AI总结：
提出MambaRecon框架，利用结构化状态空间模型（Mamba）进行MRI重建，增强长距离上下文敏感性，在公开脑部数据集上超越现有最先进基线。
摘要：
磁共振成像（MRI）是最重要的医学成像模态之一，因为它能提供优越的软组织分辨率，尽管扫描速度存在显著限制。深度学习的出现推动了利用卷积神经网络以及更近期的视觉变换器来加速MRI扫描重建的前沿方法的发展。最近提出的结构化状态空间模型（例如Mamba）因其相对于变换器模型的效率和低计算需求而获得了一些关注。我们提出了一种创新的MRI重建框架，其核心采用结构化状态空间模型，旨在增强长距离上下文敏感性和重建效能。在公开的脑部MRI数据集上的综合实验表明，我们的模型树立了新的基准，超越了最先进的重建基线。代码将提供（https://github.com/yilmazkorkmaz1/MambaRecon）。
英文摘要：
Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues, albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development of cutting-edge methods for the expedited reconstruction of MRI scans, utilizing convolutional neural networks and, more recently, vision transformers. Recently proposed structured state space models (e.g., Mamba) have gained some traction due to their efficiency and low computational requirements compared to transformer models. We propose an innovative MRI reconstruction framework that employs structured state space models at its core, aimed at amplifying both long-range contextual sensitivity and reconstruction efficacy. Comprehensive experiments on public brain MRI datasets show that our model sets new benchmarks beating state-of-the-art reconstruction baselines. Code will be available (https://github.com/yilmazkorkmaz1/MambaRecon).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
