通过微分同胚-时间等变性实现完全无监督的动态MRI重建
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance
AI总结：
针对动态MRI无法获得全采样真值的问题，提出利用几何时空等变性的完全无监督DDEI重建框架，在高加速心脏成像上优于现有无监督方法。
摘要：
从欠采样的加速测量数据中重建动态MRI图像序列，对于实现更快、更高时空分辨率的心脏运动、自由呼吸运动及许多其他应用的实时成像至关重要。诸如门控电影MRI之类的经典范式假设运动具有周期性，因此无法对真实运动进行成像。监督深度学习方法存在根本性缺陷，因为在动态成像中，实际上不可能真正获得作为真值的全采样视频。我们提出一种无监督框架，仅利用欠采样测量数据来学习重建动态MRI序列，方法是利用MRI天然的几何时空等变性。动态微分同胚等变成像（Dynamic Diffeomorphic Equivariant Imaging，DDEI）在高度加速的动态心脏成像上显著优于SSDU等最先进的无监督方法。我们的方法不依赖于底层神经网络架构，可用于适配最新模型和后处理方法。代码和视频演示位于 https://github.com/Andrewwango/ddei。
英文摘要：
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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