# Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

arXiv: https://arxiv.org/abs/1711.10388 · HTML: https://mlsanity.com/paper/1711.10388/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 30 of 31)

- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2211.12340/) (2022)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction](https://mlsanity.com/paper/2405.20693/) (2024)
- [Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.06681/) (2023)
- [3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation](https://mlsanity.com/paper/2312.15676/) (2023)
- [Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data](https://mlsanity.com/paper/2201.09318/) (2022)
- [Differentiable Forward Projector for X-ray Computed Tomography](https://mlsanity.com/paper/2307.05801/) (2023)
- [Low-Dose CT Denoising via Sinogram Inner-Structure Transformer](https://mlsanity.com/paper/2204.03163/) (2022)
- [NeAT: Neural Adaptive Tomography](https://mlsanity.com/paper/2202.02171/) (2022)
- [Computed Tomography Reconstruction using Generative Energy-Based Priors](https://mlsanity.com/paper/2203.12658/) (2022)
- [X-LRM: X-ray Large Reconstruction Model for Extremely Sparse-View Computed Tomography Recovery in One Second](https://mlsanity.com/paper/2503.06382/) (2025)
- [X2-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction](https://mlsanity.com/paper/2503.21779/) (2025)
- [X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography](https://mlsanity.com/paper/2505.15235/) (2025)
- [Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion](https://mlsanity.com/paper/2505.19385/) (2025)
- [GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2508.02408/) (2025)
- [RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations](https://mlsanity.com/paper/2409.13930/) (2024)
- [Clean Implicit 3D Structure from Noisy 2D STEM Images](https://mlsanity.com/paper/2203.15434/) (2022)
- [Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction](https://mlsanity.com/paper/2410.10836/) (2024)
- [A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2101.07613/) (2021)
- [Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction](https://mlsanity.com/paper/2603.06852/) (2026)
- [SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networks](https://mlsanity.com/paper/2309.04960/) (2023)
- [Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization](https://mlsanity.com/paper/2312.01689/) (2023)
- [SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing](https://mlsanity.com/paper/2404.12142/) (2024)
- [PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT](https://mlsanity.com/paper/2507.05317/) (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)
- [Two-stage deep learning framework for the restoration of incomplete-ring PET images](https://mlsanity.com/paper/2504.00816/) (2025)
- [Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions](https://mlsanity.com/paper/2507.05647/) (2025)
- [TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction](https://mlsanity.com/paper/2602.11705/) (2026)

## Abstract (arXiv)

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180^circ view of the object. This is impractical in a limited angle scenario, when the viewing angle is less than 180^circ, which can occur due to different factors including restrictions on scanning time, limited flexibility of scanner rotation, etc. The sinograms obtained as a result, cause existing techniques to produce highly artifact-laden reconstructions. In this paper, we propose to address this problem through implicit sinogram completion, on a challenging real world dataset containing scans of common checked-in luggage. We propose a system, consisting of 1D and 2D convolutional neural networks, that operates on a limited angle sinogram to directly produce the best estimate of a reconstruction. Next, we use the x-ray transform on this reconstruction to obtain a "completed" sinogram, as if it came from a full 180^circ measurement. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by our network. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction preserves the 3D structure of objects effectively.