# Deep Back Projection for Sparse-View CT Reconstruction

arXiv: https://arxiv.org/abs/1807.02370 · HTML: https://mlsanity.com/paper/1807.02370/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

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

- [Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays](https://mlsanity.com/paper/2303.05297/) (2023)
- [Sparse-View CT Reconstruction using Recurrent Stacked Back Projection](https://mlsanity.com/paper/2112.04998/) (2021)
- [Residual Back Projection With Untrained Neural Networks](https://mlsanity.com/paper/2210.14416/) (2022)
- [Intensity Field Decomposition for Tissue-Guided Neural Tomography](https://mlsanity.com/paper/2411.00900/) (2024)
- [Trans2-CBCT: A Dual-Transformer Framework for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2506.17425/) (2025)
- [Stretched sinograms for limited-angle tomographic reconstruction with neural networks](https://mlsanity.com/paper/2306.10201/) (2023)
- [High-Quality Tomographic Image Reconstruction Integrating Neural Networks and Mathematical Optimization](https://mlsanity.com/paper/2509.06082/) (2025)
- [Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT](https://mlsanity.com/paper/2512.12236/) (2025)
- [Limited-Angle CT Reconstruction Using Multi-Volume Latent Consistency Model](https://mlsanity.com/paper/2603.05183/) (2026)

## Abstract (arXiv)

Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data. FBP is computationally efficient but produces lower quality reconstructions than more sophisticated iterative methods, particularly when the number of views is lower than the number required by the Nyquist rate. In this paper, we use a deep convolutional neural network (CNN) to produce high-quality reconstructions directly from sinogram data. A primary novelty of our approach is that we first back project each view separately to form a stack of back projections and then feed this stack as input into the convolutional neural network. These single-view back projections convert the encoding of sinogram data into the appropriate spatial location, which can then be leveraged by the spatial invariance of the CNN to learn the reconstruction effectively. We demonstrate the benefit of our CNN based back projection on simulated sparse-view CT data over classical FBP.