# On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

arXiv: https://arxiv.org/abs/2211.01111 · HTML: https://mlsanity.com/paper/2211.01111/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [TorchRadon: Fast Differentiable Routines for Computed Tomography](https://mlsanity.com/paper/2009.14788/) (2020)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching](https://mlsanity.com/paper/2112.03696/) (2021)
- [Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography](https://mlsanity.com/paper/2201.10345/) (2022)
- [Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks](https://mlsanity.com/paper/2005.09766/) (2020)
- [Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT](https://mlsanity.com/paper/2207.07368/) (2022)
- [Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning](https://mlsanity.com/paper/2203.14794/) (2022)

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

- [Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2305.15887/) (2023)
- [Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction](https://mlsanity.com/paper/2212.02177/) (2022)
- [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://mlsanity.com/paper/2602.18589/) (2026)
- [Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography](https://mlsanity.com/paper/2302.06436/) (2023)
- [Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction](https://mlsanity.com/paper/2312.12644/) (2023)
- [Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows](https://mlsanity.com/paper/2410.17543/) (2024)
- [PPORLD-EDNetLDCT: A Proximal Policy Optimization-Based Reinforcement Learning Framework for Adaptive Low-Dose CT Denoising](https://mlsanity.com/paper/2509.03185/) (2025)
- [An update to PYRO-NN: A Python Library for Differentiable CT Operators](https://mlsanity.com/paper/2511.08427/) (2025)
- [Agentic Autoresearch for CT Reconstruction](https://mlsanity.com/paper/2607.22824/) (2026)

## Abstract (arXiv)

Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates methods already intervening in the raw detector data due to limited access to suitable projection data or correct reconstruction algorithms. In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data. Our experiments demonstrate that including an additional projection denoising operator improved the overall denoising performance by 82.4-94.1%/12.5-41.7% (PSNR/SSIM) on abdomen CT and 1.5-2.9%/0.4-0.5% (PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire helical CT reconstruction framework publicly available that contains a raw projection rebinning step to render helical projection data suitable for differentiable fan-beam reconstruction operators and end-to-end learning.