Department of Engineering Physics, Tsinghua University, Beijing 100084, People's Republic of China.
Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084, People's Republic of China.
Phys Med Biol. 2020 Dec 11;65(24):245030. doi: 10.1088/1361-6560/ab8fc1.
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 已广泛应用于临床诊断。在这项工作中，我们专注于一种新型螺旋 CT 原型，该原型在轴向上配备了稀疏多探测器和多狭缝准直器（MSC）。这种系统不仅可以降低辐射剂量并抑制 MSC 散射，还可以降低探测器的制造成本。然而，这种系统面临的主要问题是重建数据不足。因此，我们针对这个不适定的反问题提出了一种基于深度学习的函数优化方法。通过结合 Radon 反演算子和分离每一层，我们大大简化了我们的三维重建网络的复杂性。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）来估计缺失的投影数据，并将螺旋投影数据转换为 2D 扇形束投影数据。然后，部署一个解析线性算子将数据从投影域转换到图像域。最后，在图像域中添加另一个 CNN 以进一步进行图像细化。这三个步骤共同作用，可以端到端训练。整个网络在基于美国医学物理学家协会（AAPM）低剂量 CT 大挑战中的八位患者的模拟 CT 数据集上进行训练。我们在模拟数据集和临床数据集上评估训练好的网络。基于视觉检查和定量评估，广泛的实验研究取得了非常令人鼓舞的结果。这些结果证明了我们的方法的有效性及其在临床应用中的潜力。该方法为完全三维不适定问题提供了一种新的解决方案。
