# Radon Inversion via Deep Learning

arXiv: https://arxiv.org/abs/1808.03015 · HTML: https://mlsanity.com/paper/1808.03015/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 30 of 35)

- [MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation](https://mlsanity.com/paper/2402.18451/) (2024)
- [C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2406.03902/) (2024)
- [Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey](https://mlsanity.com/paper/2203.15725/) (2022)
- [NeAT: Neural Adaptive Tomography](https://mlsanity.com/paper/2202.02171/) (2022)
- [Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT](https://mlsanity.com/paper/2310.06949/) (2023)
- [Glimpse: Generalized Locality for Scalable and Robust CT](https://mlsanity.com/paper/2401.00816/) (2024)
- [Discretized Gaussian Representation for Tomographic Reconstruction](https://mlsanity.com/paper/2411.04844/) (2024)
- [Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference](https://mlsanity.com/paper/2210.01125/) (2022)
- [Convergent Data-driven Regularizations for CT Reconstruction](https://mlsanity.com/paper/2212.07786/) (2022)
- [DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2505.02628/) (2025)
- [Clean Implicit 3D Structure from Noisy 2D STEM Images](https://mlsanity.com/paper/2203.15434/) (2022)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution](https://mlsanity.com/paper/2409.07171/) (2024)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography](https://mlsanity.com/paper/2211.00002/) (2022)
- [A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems](https://mlsanity.com/paper/2305.11120/) (2023)
- [Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2401.00135/) (2023)
- [Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model](https://mlsanity.com/paper/2407.12956/) (2024)
- [Zero-Shot Low-dose CT Denoising via Sinogram Flicking](https://mlsanity.com/paper/2504.07927/) (2025)
- [3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions](https://mlsanity.com/paper/2105.14130/) (2021)
- [HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization](https://mlsanity.com/paper/2110.01698/) (2021)
- [Simulator-Based Self-Supervision for Learned 3D Tomography Reconstruction](https://mlsanity.com/paper/2212.07431/) (2022)
- [Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography](https://mlsanity.com/paper/2304.10839/) (2023)
- [Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems](https://mlsanity.com/paper/2311.17248/) (2023)
- [Evaluating Adversarial Robustness of Low dose CT Recovery](https://mlsanity.com/paper/2402.11557/) (2024)
- [Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2409.01544/) (2024)
- [CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates](https://mlsanity.com/paper/2409.01571/) (2024)
- [Unsupervised Deep Learning for Inverse Problems in Computed Tomography](https://mlsanity.com/paper/2508.05321/) (2025)
- [AI-Enabled Ultra-Low-Dose CT Reconstruction](https://mlsanity.com/paper/2106.09834/) (2021)
- [Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions](https://mlsanity.com/paper/2307.09624/) (2023)

## Abstract (arXiv)

Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health examination. The Radon inversion or image reconstruction is challenging due to the potentially defective radon projections. Conventionally, the reconstruction process contains several ad hoc stages to approximate the corresponding Radon inversion. Each of the stages is highly dependent on the results of the previous stage. In this paper, we propose a novel unified framework for Radon inversion via deep learning (DL). The Radon inversion can be approximated by the proposed framework with an end-to-end fashion instead of processing step-by-step with multiple stages. For simplicity, the proposed framework is short as iRadonMap (inverse Radon transform approximation). Specifically, we implement the iRadonMap as an appropriative neural network, of which the architecture can be divided into two segments. In the first segment, a learnable fully-connected filtering layer is used to filter the radon projections along the view-angle direction, which is followed by a learnable sinusoidal back-projection layer to transfer the filtered radon projections into an image. The second segment is a common neural network architecture to further improve the reconstruction performance in the image domain. The iRadonMap is overall optimized by training a large number of generic images from ImageNet database. To evaluate the performance of the iRadonMap, clinical patient data is used. Qualitative results show promising reconstruction performance of the iRadonMap.