# Sinogram interpolation for sparse-view micro-CT with deep learning neural network

arXiv: https://arxiv.org/abs/1902.03362 · HTML: https://mlsanity.com/paper/1902.03362/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

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

- [Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2211.13926/) (2022)
- [GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis](https://mlsanity.com/paper/2404.03126/) (2024)
- [Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2508.10313/) (2025)
- [SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networks](https://mlsanity.com/paper/2309.04960/) (2023)
- [A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2202.09609/) (2022)
- [Continuous Filtered Backprojection by Learnable Interpolation Network](https://mlsanity.com/paper/2505.01768/) (2025)
- [Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model](https://mlsanity.com/paper/2511.14310/) (2025)
- [LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2606.16212/) (2026)
- [Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction](https://mlsanity.com/paper/2608.17255/) (2026)

## Abstract (arXiv)

In sparse-view Computed Tomography (CT), only a small number of projection images are taken around the object, and sinogram interpolation method has a significant impact on final image quality. When the amount of sparsity (the amount of missing views in sinogram data) is not high, conventional interpolation methods have yielded good results. When the amount of sparsity is high, more advanced sinogram interpolation methods are needed. Recently, several deep learning (DL) based sinogram interpolation methods have been proposed. However, those DL-based methods have mostly tested so far on computer simulated sinogram data rather experimentally acquired sinogram data. In this study, we developed a sinogram interpolation method for sparse-view micro-CT based on the combination of U-Net and residual learning. We applied the method to sinogram data obtained from sparse-view micro-CT experiments, where the sparsity reached 90%. The interpolated sinogram by the DL neural network was fed to FBP algorithm for reconstruction. The result shows that both RMSE and SSIM of CT image are greatly improved. The experimental results demonstrate that this sinogram interpolation method produce significantly better results over standard linear interpolation methods when the sinogram data are extremely sparse.