Computed tomography (CT) has become an essential part of modern science and
medicine. A CT scanner consists of an X-ray source that is spun around an
object of interest. On the opposite end of the X-ray source, a detector
captures X-rays that are not absorbed by the object. The reconstruction of an
image is a linear inverse problem, which is usually solved by filtered back
projection. However, when the number of measurements is small, the
reconstruction problem is ill-posed. This is for example the case when the
X-ray source is not spun completely around the object, but rather irradiates
the object only from a limited angle. To tackle this problem, we present a deep
neural network that is trained on a large amount of carefully-crafted synthetic
data and can perform limited-angle tomography reconstruction even for only
30{\deg} or 40{\deg} sinograms. With our approach we won the first place in the
Helsinki Tomography Challenge 2022.

基于深度神经网络，本论文提出了一个训练于大量精心制作的合成数据上的模型，可以进行受限角度的断层投影重建，即便仅有 30° 或 40° 的正弦图像，取得了 2022 年赫尔辛基断层成像挑战赛的第一名。

通过合成数据上的深度端到端学习实现有限角度层析重建

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on  Synthetic Data

Limited angle problem is a challenging issue in x-ray computed tomography
(CT) field. Iterative reconstruction methods that utilize the additional prior
can suppress artifacts and improve image quality, but unfortunately require
increased computation time. An interesting way is to restrain the artifacts in
the images reconstructed from the practical filtered back projection (FBP)
method. Frikel and Quinto have proved that the streak artifacts in FBP results
could be characterized. It indicates that the artifacts created by FBP method
have specific and similar characteristics in a stationary limited-angle
scanning configuration. Based on this understanding, this work aims at
developing a method to extract and suppress specific artifacts of FBP
reconstructions for limited-angle tomography. A data-driven learning-based
method is proposed based on a deep convolutional neural network. An end-to-end
mapping between the FBP and artifact-free images is learned and the implicit
features involving artifacts will be extracted and suppressed via nonlinear
mapping. The qualitative and quantitative evaluations of experimental results
indicate that the proposed method show a stable and prospective performance on
artifacts reduction and detail recovery for limited angle tomography. The
presented strategy provides a simple and efficient approach for improving image
quality of the reconstruction results from limited projection data.

本文提出了一种基于深度卷积神经网络的数据驱动学习方法，用于在限角摄影术下提取和抑制 FBP 重建的特定伪影。 实验结果的定性和定量评估表明，该方法在伪影抑制和细节恢复方面表现出稳定和有前途的性能，为从有限投影数据中重构出的图像质量的提高提供了简单和高效的途径。