基于内存高效学习原始-对偶架构的三维螺旋CT重建
3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture
AI总结：
本文提出一种内存高效的学习原始-对偶架构，通过将螺旋轨迹分段并依次应用展开的LPD迭代，首次在单块24GB GPU上实现了全尺寸临床三维螺旋CT的深度学习重建。
摘要：
基于深度学习的计算机断层扫描（CT）重建已在模拟的二维低剂量CT数据上展现出卓越性能。这一点尤其适用于域自适应神经网络，这类网络结合了手工设计的CT成像物理模型。经验证据表明，采用此类架构可减少对训练数据的需求并提升泛化能力。然而，其训练需要大量计算资源，在三维螺旋CT——医学成像中最常用的采集几何结构——中很快变得难以承受。此外，临床数据还带来模拟中未考虑的其他挑战，例如通量测量误差、分辨率不匹配，以及最重要的是缺乏真实基准真值。在计算上可行的训练需求与解决这些问题的必要性，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。本文对一种域自适应神经网络架构——学习原始-对偶（Learned Primal-Dual，LPD）——进行了修改，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成若干段，并对这些段依次应用展开的LPD迭代来实现这一目标。据我们所知，本工作是首次将展开的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图像和投影数据集（Low dose CT image and projection data set，LDCT）中的数据。此外，训练和测试均在单块24GB显存的GPU卡上完成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
