Deep learning based computed tomography (CT) reconstruction has demonstrated outstanding performance on simulated 2D low-dose CT data. This applies in particular to domain adapted neural networks, which incorporate a handcrafted physics model for CT imaging. Empirical evidence shows that employing such architectures reduces the demand for training data and improves upon generalisation. However, their training requires large computational resources that quickly become prohibitive in 3D helical CT, which is the most common acquisition geometry used for medical imaging. Furthermore, clinical data also comes with other challenges not accounted for in simulations, like errors in flux measurement, resolution mismatch and, most importantly, the absence of the real ground truth. The necessity to have a computationally feasible training combined with the need to address these issues has made it difficult to evaluate deep learning based reconstruction on clinical 3D helical CT. This paper modifies a domain adapted neural network architecture, the Learned Primal-Dual (LPD), so that it can be trained and applied to reconstruction in this setting. We achieve this by splitting the helical trajectory into sections and applying the unrolled LPD iterations to those sections sequentially. To the best of our knowledge, this work is the first to apply an unrolled deep learning architecture for reconstruction on full-sized clinical data, like those in the Low dose CT image and projection data set (LDCT). Moreover, training and testing is done on a single GPU card with 24GB of memory.

翻译：基于深度学习的计算机断层扫描（CT）重建在模拟二维低剂量CT数据上展现出卓越性能，尤其适用于结合了CT成像手工物理模型的领域自适应神经网络。实验证据表明，此类架构可降低训练数据需求并提升泛化能力。然而，其训练需要大量计算资源，这在使用最广泛的三维螺旋CT医疗成像几何结构中很快变得难以承受。此外，临床数据还面临模拟中未考虑的其他挑战，如通量测量误差、分辨率失配，以及最关键的——真实金标准缺失。计算可行性训练的需求与解决上述问题的必要性，使得在临床三维螺旋CT上评估基于深度学习的重建变得困难。本文对一种领域自适应神经网络架构——学习型原始对偶网络（LPD）进行改进，使其能够在此场景下进行训练并应用于重建。我们通过将螺旋轨迹分割为多个段，并将展开的LPD迭代依次应用于这些段来实现这一目标。据我们所知，本工作是首次将展开式深度学习架构应用于全尺寸临床数据的重建，例如低剂量CT图像与投影数据集（LDCT）中的数据。此外，训练与测试均在单块24GB内存的GPU显卡上完成。