# Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data

arXiv: https://arxiv.org/abs/2201.09318 · HTML: https://mlsanity.com/paper/2201.09318/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 22 of 22)

- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [CNN-Based Projected Gradient Descent for Consistent Image Reconstruction](https://mlsanity.com/paper/1709.01809/) (2017)
- [Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning](https://mlsanity.com/paper/1904.02816/) (2019)
- [Momentum-Net: Fast and convergent iterative neural network for inverse problems](https://mlsanity.com/paper/1907.11818/) (2019)
- [Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion](https://mlsanity.com/paper/1711.10388/) (2017)
- [A Cone-Beam X-Ray CT Data Collection designed for Machine Learning](https://mlsanity.com/paper/1905.04787/) (2019)
- [A Perspective on Deep Imaging](https://mlsanity.com/paper/1609.04375/) (2016)
- [PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction](https://mlsanity.com/paper/1703.09165/) (2017)
- [Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior](https://mlsanity.com/paper/1908.06792/) (2019)
- [Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning](https://mlsanity.com/paper/1911.04357/) (2019)
- [2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation](https://mlsanity.com/paper/1812.08367/) (2018)
- [Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction](https://mlsanity.com/paper/2010.02761/) (2020)
- [High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features](https://mlsanity.com/paper/2112.01627/) (2021)
- [Extreme Few-view CT Reconstruction using Deep Inference](https://mlsanity.com/paper/1910.05375/) (2019)
- [Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction](https://mlsanity.com/paper/2104.05028/) (2021)
- [Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography](https://mlsanity.com/paper/2110.15424/) (2021)
- [Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner](https://mlsanity.com/paper/1801.01258/) (2018)
- [Deep Learning-Based Reconstruction of Interventional Tools from Four X-Ray Projections for Tomographic Interventional Guidance](https://mlsanity.com/paper/2009.10993/) (2020)
- [Improved low-count quantitative PET reconstruction with an iterative neural network](https://mlsanity.com/paper/1906.02327/) (2019)

## Built on by (most cited first, 17 of 17)

- [Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models](https://mlsanity.com/paper/2211.10655/) (2022)
- [Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems](https://mlsanity.com/paper/2303.05754/) (2023)
- [Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models](https://mlsanity.com/paper/2303.08440/) (2023)
- [Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.06681/) (2023)
- [Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems](https://mlsanity.com/paper/2406.02462/) (2024)
- [C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2406.03902/) (2024)
- [Differentiable Forward Projector for X-ray Computed Tomography](https://mlsanity.com/paper/2307.05801/) (2023)
- [DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction](https://mlsanity.com/paper/2406.10211/) (2024)
- [Discretized Gaussian Representation for Tomographic Reconstruction](https://mlsanity.com/paper/2411.04844/) (2024)
- [X2-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction](https://mlsanity.com/paper/2503.21779/) (2025)
- [Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction](https://mlsanity.com/paper/2410.10836/) (2024)
- [Learning robust parameter inference and density reconstruction in flyer plate impact experiments](https://mlsanity.com/paper/2506.23914/) (2025)
- [Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction](https://mlsanity.com/paper/2512.18161/) (2025)
- [CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging](https://mlsanity.com/paper/2406.14976/) (2024)
- [Material Identification using Multi-Modal Intrinsic Radiation and Radiography](https://mlsanity.com/paper/2603.27036/) (2026)
- [LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2606.16212/) (2026)
- [Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction](https://mlsanity.com/paper/2608.04752/) (2026)

## Abstract (arXiv)

Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the number of available projections decreases, traditional reconstruction techniques such as the FDK algorithm and model-based iterative reconstruction methods perform poorly. Recently, data-driven methods such as deep learning-based reconstruction have garnered a lot of attention in applications because they yield better performance when enough training data is available. However, even these methods have their limitations when there is a scarcity of available training data. This work focuses on image reconstruction in such settings, i.e., when both the number of available CT projections and the training data is extremely limited. We adopt a sequential reconstruction approach over several stages using an adversarially trained shallow network for 'destreaking' followed by a data-consistency update in each stage. To deal with the challenge of limited data, we use image subvolumes to train our method, and patch aggregation during testing. To deal with the computational challenge of learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping network for the 'destreaking' part. Comparisons to other methods over several test examples indicate that the proposed method has much potential, when both the number of projections and available training data are highly limited.