Department of Radiology, Center for Advanced Medical Computing and Analysis (CAMCA), Harvard Medical School and Massachusetts General Hospital, Boston, MA, United States of America.
Phys Med Biol. 2022 May 16;67(11). doi: 10.1088/1361-6560/ac6560.
There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, sparse-view and/or low-dose CT settings can be applied together with interior tomography. Interior tomography has various advantages in terms of reducing the number of detectors and decreasing the x-ray radiation dose. However, a large patient or a small field-of-view (FOV) detector can cause truncated projections, and then the reconstructed images suffer from severe cupping artifacts. In addition, although low-dose CT can reduce the radiation exposure dose, analytic reconstruction algorithms produce image noise. Recently, many researchers have utilized image-domain deep learning (DL) approaches to remove each artifact and demonstrated impressive performances, and the theory of deep convolutional framelets supports the reason for the performance improvement.In this paper, we found that it is difficult to solve coupled artifacts using an image-domain convolutional neural network (CNN) based on deep convolutional framelets.To address the coupled problem, we decouple it into two sub-problems: (i) image-domain noise reduction inside the truncated projection to solve low-dose CT problem and (ii) extrapolation of the projection outside the truncated projection to solve the ROI CT problem. The decoupled sub-problems are solved directly with a novel proposed end-to-end learning method using dual-domain CNNs.We demonstrate that the proposed method outperforms the conventional image-domain DL methods, and a projection-domain CNN shows better performance than the image-domain CNNs commonly used by many researchers.
有几种 X 射线计算机断层扫描 (CT) 扫描策略可用于降低辐射剂量，例如 (1) 稀疏视图 CT、(2) 低剂量 CT 和 (3) 感兴趣区域 (ROI) CT（称为内部 CT）。为了进一步降低剂量，可以将稀疏视图和/或低剂量 CT 设置与内部 CT 一起应用。内部 CT 在减少探测器数量和降低 X 射线辐射剂量方面具有多种优势。然而，大患者或小视野 (FOV) 探测器会导致截断投影，然后重建图像会受到严重的杯状伪影的影响。此外，虽然低剂量 CT 可以降低辐射暴露剂量，但分析重建算法会产生图像噪声。最近，许多研究人员利用图像域深度学习 (DL) 方法来去除每种伪影，并展示了令人印象深刻的性能，并且深度卷积框架的理论支持了性能提升的原因。在本文中，我们发现使用基于深度卷积框架的图像域卷积神经网络 (CNN) 很难解决耦合伪影问题。为了解决耦合问题，我们将其分解为两个子问题：(i) 截断投影内的图像域降噪，以解决低剂量 CT 问题，以及 (ii) 截断投影外的投影外推，以解决 ROI CT 问题。使用双域 CNN 的新颖端到端学习方法直接解决解耦子问题。我们证明了所提出的方法优于传统的图像域 DL 方法，并且投影域 CNN 比许多研究人员常用的图像域 CNN 具有更好的性能。
