Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator (MSC) in the axis direction. This type of system can not only lower radiation dose, and suppress scattering by MSC, but also cuts down the manufacturing cost of the detector. The major problem to overcome with such a system, however, is that of insufficient data for reconstruction. Hence, we propose a deep learning-based function optimization method for this ill-posed inverse problem. By incorporating a Radon inverse operator, and disentangling each slice, we significantly simplify the complexity of our network for 3D reconstruction. The network is composed of three subnetworks. Firstly, a convolutional neural network (CNN) in the projection domain is constructed to estimate missing projection data, and to convert helical projection data to 2D fan-beam projection data. This is follwed by the deployment of an analytical linear operator to transfer the data from the projection domain to the image domain. Finally, an additional CNN in the image domain is added for further image refinement. These three steps work collectively, and can be trained end to end. The overall network is trained on a simulated CT dataset based on eight patients from the American Association of Physicists in Medicine (AAPM) Low Dose CT Grand Challenge. We evaluate the trained network on both simulated datasets and clinical datasets. Extensive experimental studies have yielded very encouraging results, based on both visual examination and quantitative evaluation. These results demonstrate the effectiveness of our method and its potential for clinical usage. The proposed method provides us with a new solution for a fully 3D ill-posed problem.

螺旋CT已被广泛用于临床诊断。在这项工作中，我们重点研究一种新的螺旋CT原型，该原型在轴向方向上配备了稀疏的多探测器和多缝准直仪（MSC）。这种类型的系统不仅可以降低辐射剂量，并抑制MSC的散射，还可以降低探测器的制造成本。然而，用这样的系统要克服的主要问题是数据不足以进行重建。因此，我们针对此不适定逆问题提出了一种基于深度学习的函数优化方法。通过合并Radon逆算子并解开每个切片，我们显着简化了3D重建网络的复杂性。该网络由三个子网组成。首先，在投影域中构建卷积神经网络（CNN）以估计丢失的投影数据，并将螺旋投影数据转换为2D扇形光束投影数据。这是通过部署解析线性算子将数据从投影域传输到图像域来解决的。最后，在图像域中添加了一个额外的CNN，以进行进一步的图像细化。这三个步骤共同起作用，并且可以进行端到端的培训。整个网络在模拟的CT数据集上进行了训练，该数据集来自美国医学物理学会（AAPM）低剂量CT大型挑战赛的八名患者。我们评估模拟数据集和临床数据集上的训练网络。广泛的实验研究取得了令人鼓舞的结果，基于目视检查和定量评估。这些结果证明了我们方法的有效性及其在临床上的潜力。所提出的方法为我们提供了针对完全3D不适定问题的新解决方案。