无需真实数据，利用运动几何特性重建加速动态核磁影像。
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance
- 基于动态微分同胚等变性，从欠采样数据中无监督重建动态图像。
- 在高度加速心脏动态成像上，性能显著优于SSDU等现有无监督方法。
- 不依赖特定网络结构，可适配最新模型与后处理技术，适合临床实时成像。
从欠采样的加速测量中重建动态MRI序列对于实现快速、高时空分辨率的实时心脏运动成像、自由呼吸运动成像等应用至关重要。传统方法如门控电影MRI假设周期性，无法捕捉真实运动；而监督深度学习方法本质上存在缺陷，因为在动态成像中无法获得真正完整的真值视频。本文提出一种无监督框架，仅利用自然的几何时空等变性，从欠采样数据中学习重建动态MRI序列。动态微分同胚等变成像（DDEI）在高度加速的心脏动态成像任务中显著优于当前最先进的无监督方法SSDU。该方法对底层神经网络架构无关，可兼容最新模型与后处理技术。代码及视频演示见https://github.com/Andrewwango/ddei。
原文摘要 · Abstract (English)
Reconstructing dynamic MRI image sequences from undersampled accelerated measurements is crucial for faster and higher spatiotemporal resolution real-time imaging of cardiac motion, free breathing motion and many other applications. Classical paradigms, such as gated cine MRI, assume periodicity, disallowing imaging of true motion. Supervised deep learning methods are fundamentally flawed as, in dynamic imaging, ground truth fully-sampled videos are impossible to truly obtain. We propose an unsupervised framework to learn to reconstruct dynamic MRI sequences from undersampled measurements alone by leveraging natural geometric spatiotemporal equivariances of MRI. Dynamic Diffeomorphic Equivariant Imaging (DDEI) significantly outperforms state-of-the-art unsupervised methods such as SSDU on highly accelerated dynamic cardiac imaging. Our method is agnostic to the underlying neural network architecture and can be used to adapt the latest models and post-processing approaches. Our code and video demos are at https://github.com/Andrewwango/ddei.
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