# Field of View Extension in Computed Tomography Using Deep Learning Prior

arXiv: https://arxiv.org/abs/1911.01178 · HTML: https://mlsanity.com/paper/1911.01178/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)
- [Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images](https://mlsanity.com/paper/2406.04769/) (2024)
- [BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction](https://mlsanity.com/paper/2305.18878/) (2023)
- [Efficient Image-to-Image Schr"odinger Bridge for CT Field of View Extension](https://mlsanity.com/paper/2508.11211/) (2025)
- [Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data](https://mlsanity.com/paper/2409.08800/) (2024)
- [Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction](https://mlsanity.com/paper/2606.06039/) (2026)

## Abstract (arXiv)

In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.