# Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network

arXiv: https://arxiv.org/abs/1607.08707 · HTML: https://mlsanity.com/paper/1607.08707/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2016.

## Built on by (most cited first, 9 of 9)

- [Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models](https://mlsanity.com/paper/2211.10655/) (2022)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2101.07613/) (2021)
- [Residual Back Projection With Untrained Neural Networks](https://mlsanity.com/paper/2210.14416/) (2022)
- [HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2403.04207/) (2024)
- [Data-Efficient Limited-Angle CT Using Deep Priors and Regularization](https://mlsanity.com/paper/2502.12293/) (2025)
- [Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information](https://mlsanity.com/paper/2101.07594/) (2021)
- [A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2202.09609/) (2022)

## Abstract (arXiv)

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.