用结构化状态空间模型提升MRI重建速度与精度
MambaRecon: MRI Reconstruction with Structured State Space Models
- 以结构化状态空间模型为核心，捕捉长距离上下文信息
- 在公开脑部MRI数据集上超越现有最优方法
- 适合需要高效高精度MRI重建的研究者使用
磁共振成像（MRI）是软组织高分辨率成像的重要手段，但扫描速度较慢。深度学习推动了快速MRI重建技术的发展，包括卷积神经网络和近期的视觉变换器。最近提出的结构化状态空间模型（如Mamba）因其相比变换器更低的计算开销而受到关注。本文提出一种创新的MRI重建框架，核心采用结构化状态空间模型，旨在增强长程上下文敏感性和重建效果。在公开脑部MRI数据集上的全面实验表明，该模型超越当前最优基线，创下新纪录。代码将公开于https://github.com/yilmazkorkmaz1/MambaRecon。
原文摘要 · Abstract (English)
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).
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