X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.

本文旨在揭示 U-Net 的局限性并提出新的多分辨率深度学习方案，具体而言，我们通过实验表明，新的网络架构可以提供更好的重建性能，并且我们探讨了 U-Net 替代方案，如双框架和紧框架 U-Net 满足所谓的框条件，使其更好地实现高频边缘的有效恢复。