Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning.

X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model for the radiation attenuation process is usually required to solve the inversion problem of CT scanning, which encodes the subject into a set of intermediate representations in different angular positions. Here, we show that accurate CT image reconstruction can be subsequently achieved by downsampled imaging geometric modeling via deep-learning techniques. Specifically, we first propose a downsampled imaging geometric modeling approach for the data acquisition process and then incorporate it into a hierarchical neural network, which simultaneously combines both geometric modeling knowledge of the CT imaging system and prior knowledge gained from a data-driven training process for accurate CT image reconstruction. The proposed neural network is denoted as DSigNet, i.e., downsampled-imaging-geometry-based network for CT image reconstruction. We demonstrate the feasibility of the proposed DSigNet for accurate CT image reconstruction with clinical patient data. In addition to improving the CT image quality, the proposed DSigNet might help reduce the computational complexity and accelerate the reconstruction speed for modern CT imaging systems.