Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms face difficulty when given incomplete data. They usually involve complicated parameter selection operations, which are also sensitive to noise and are time-consuming. In this paper, we report a new deep learning reconstruction framework for incomplete data DPC-CT. It involves the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the domain of DPC projection sinograms. The estimated result is not an artifact caused by the incomplete data, but a complete phase-contrast projection sinogram. After training, this framework is determined and can be used to reconstruct the final DPC-CT images for a given incomplete projection sinogram. Taking the sparse-view, limited-view and missing-view DPC-CT as examples, this framework is validated and demonstrated with synthetic and experimental data sets. Compared with other methods, our framework can achieve the best imaging quality at a faster speed and with fewer parameters. This work supports the application of the state-of-the-art deep learning theory in the field of DPC-CT.

中文翻译：

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包含不完整数据的差分相差计算机断层扫描的深度学习重建框架。

差分相差计算机断层扫描（DPC-CT）是用于软组织和低原子序数样品的强大分析工具。受实施条件的限制，具有不完整预测的DPC-CT经常发生。当给定不完整的数据时，传统的重建算法将面临困难。它们通常涉及复杂的参数选择操作，这些操作也对噪声敏感并且很费时。在本文中，我们报告了针对不完整数据DPC-CT的新的深度学习重建框架。它涉及DPC投影正弦图领域中深度学习神经网络与DPC-CT重建算法的紧密耦合。估计的结果不是由不完整的数据引起的伪影，而是完整的相衬投影正弦图。训练结束后，这个框架是确定的，可以用于为给定的不完整投影正弦图重建最终的DPC-CT图像。以稀疏视图，有限视图和缺少视图的DPC-CT为例，通过综合和实验数据集对该框架进行了验证和演示。与其他方法相比，我们的框架可以以更快的速度和更少的参数获得最佳的成像质量。这项工作支持最新的深度学习理论在DPC-CT领域的应用。我们的框架可以以更快的速度和更少的参数获得最佳的成像质量。这项工作支持最新的深度学习理论在DPC-CT领域的应用。我们的框架可以以更快的速度和更少的参数获得最佳的成像质量。这项工作支持最新的深度学习理论在DPC-CT领域的应用。