IEEE Trans Med Imaging. 2019 Oct;38(10):2469-2481. doi: 10.1109/TMI.2019.2910760. Epub 2019 Apr 11.
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have discovered many solutions to accurately reconstruct the image. However, if these conditions are violated, accurate image reconstruction from line integrals remains an intellectual challenge. In this paper, a deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy. Particularly, accurate reconstructions were achieved for the case when the sparse view reconstruction problem (i.e., compressed sensing problem) is entangled with the classical interior tomographic problems.
计算机断层扫描（CT）在医学诊断和无损检测中得到了广泛的应用。CT 中的图像重建旨在从测量的线积分中准确地恢复像素值，即沿直线的像素值总和。只要获得的数据满足数据充足条件以及关于视角采样间隔和横向数据截断严重程度的其他条件，研究人员就已经发现了许多准确重建图像的解决方案。然而，如果违反了这些条件，那么从线积分准确重建图像仍然是一个具有挑战性的问题。在本文中，开发并训练了一种具有通用网络架构的深度学习方法，称为 iCT-Net，用于以高精度准确重建先前解决和未解决的 CT 重建问题的图像。特别是，当稀疏视图重建问题（即压缩感知问题）与经典内部层析问题纠缠在一起时，实现了准确的重建。
