利用心脏动态MRI的时空旋转对称性，提升低采样率下的图像重建质量。
SRE-CNN: A Spatiotemporal Rotation-Equivariant CNN for Cardiac Cine MR Imaging
- 设计时空旋转等变卷积模块，同时捕捉空间与时间维度的旋转对称性。
- 在20倍超采样数据上重建效果优于现有方法，细节更清晰。
- 适合医学影像重建、低采样率成像研究者参考。
动态磁共振图像具有多种变换对称性，包括图像内部局部特征及时间维度上的旋转对称性。将这些对称性作为先验知识可促进高时空分辨率动态磁共振成像。等变卷积神经网络是有效利用对称性先验的工具，但现有方法未能充分挖掘动态磁共振成像中的对称性。本文提出一种新型时空旋转等变卷积神经网络（SRE-CNN）框架，涵盖高精度滤波器设计、时序等变卷积模块构建及成像模型，全面利用动态磁共振图像固有的旋转对称性。时序等变卷积模块实现了空间与时间维度旋转对称性的联合利用；基于参数化策略的高精度卷积滤波器增强了局部特征旋转对称性的利用，提升了精细解剖结构的重建效果。在高度欠采样的动态心脏电影数据（最高达20倍）上进行的实验表明，所提方法在定量与定性评价上均表现优异。
原文摘要 · Abstract (English)
Dynamic MR images possess various transformation symmetries,including the rotation symmetry of local features within the image and along the temporal dimension. Utilizing these symmetries as prior knowledge can facilitate dynamic MR imaging with high spatiotemporal resolution. Equivariant CNN is an effective tool to leverage the symmetry priors. However, current equivariant CNN methods fail to fully exploit these symmetry priors in dynamic MR imaging. In this work, we propose a novel framework of Spatiotemporal Rotation-Equivariant CNN (SRE-CNN), spanning from the underlying high-precision filter design to the construction of the temporal-equivariant convolutional module and imaging model, to fully harness the rotation symmetries inherent in dynamic MR images. The temporal-equivariant convolutional module enables exploitation the rotation symmetries in both spatial and temporal dimensions, while the high-precision convolutional filter, based on parametrization strategy, enhances the utilization of rotation symmetry of local features to improve the reconstruction of detailed anatomical structures. Experiments conducted on highly undersampled dynamic cardiac cine data (up to 20X) have demonstrated the superior performance of our proposed approach, both quantitatively and qualitatively.
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