End-to-End Sequential Sampling and Reconstruction for MR Imaging
End-to-End Sequential Sampling and Reconstruction for MR Imaging
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发表时间：
2021-05
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通讯作者：
Tianwei Yin;Zihui Wu;He Sun;Adrian V. Dalca;Yisong Yue;K. Bouman
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作者：
Tianwei Yin;Zihui Wu;He Sun;Adrian V. Dalca;Yisong Yue;K. Bouman
加速MRI通过测量$\kappa$-空间中的二次采样缩短采集时间。从二次采样测量恢复高保真解剖图像需要两个组件之间的密切合作：（1）选择二次采样模式的采样器和（2）从不完整测量恢复图像的重建器。在本文中，我们利用MRI测量的顺序性质，并提出了一个完全可区分的框架，联合学习顺序采样策略，同时重建策略。这种共同设计的框架能够在采集过程中进行调整，以便为特定目标捕获最具信息量的测量结果。在fastMRI膝关节数据集上的实验结果表明，该方法成功地利用了采样过程中的中间信息，以提高重建性能。特别是，我们提出的方法可以优于目前最先进的学习$\kappa$-空间采样基线超过96%的测试样本。我们还调查了序贯抽样和协同设计策略的个人和集体利益。
Accelerated MRI shortens acquisition time by subsampling in the measurement $\kappa$-space. Recovering a high-fidelity anatomical image from subsampled measurements requires close cooperation between two components: (1) a sampler that chooses the subsampling pattern and (2) a reconstructor that recovers images from incomplete measurements. In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy. This co-designed framework is able to adapt during acquisition in order to capture the most informative measurements for a particular target. Experimental results on the fastMRI knee dataset demonstrate that the proposed approach successfully utilizes intermediate information during the sampling process to boost reconstruction performance. In particular, our proposed method can outperform the current state-of-the-art learned $\kappa$-space sampling baseline on over 96% of test samples. We also investigate the individual and collective benefits of the sequential sampling and co-design strategies.