# Self-supervised Physics-based Denoising for Computed Tomography

arXiv: https://arxiv.org/abs/2211.00745 · HTML: https://mlsanity.com/paper/2211.00745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 1 Nov 2022.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [High-Quality Self-Supervised Deep Image Denoising](https://mlsanity.com/paper/1901.10277/) (2019)
- [Robust and interpretable blind image denoising via bias-free convolutional neural networks](https://mlsanity.com/paper/1906.05478/) (2019)
- [Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising](https://mlsanity.com/paper/2010.11971/) (2020)
- [3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network](https://mlsanity.com/paper/1802.05656/) (2018)
- [Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography](https://mlsanity.com/paper/1806.09748/) (2018)
- [Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging](https://mlsanity.com/paper/2106.12175/) (2021)
- [Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples](https://mlsanity.com/paper/1906.03639/) (2019)
- [Unpaired image denoising using a generative adversarial network in X-ray CT](https://mlsanity.com/paper/1903.06257/) (2019)
- [Microscopy Image Restoration with Deep Wiener-Kolmogorov filters](https://mlsanity.com/paper/1911.10989/) (2019)
- [Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture](https://mlsanity.com/paper/2012.03623/) (2020)
- [Self-Supervised Training For Low Dose CT Reconstruction](https://mlsanity.com/paper/2010.13232/) (2020)
- [No-reference denoising of low-dose CT projections](https://mlsanity.com/paper/2102.02662/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learned denoising with simulated and experimental low-dose CT data](https://mlsanity.com/paper/2408.08115/) (2024)

## Abstract (arXiv)

Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Ultimately, these issues could affect the perception of medical personnel and could cause misdiagnosis. Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training, rarely collected in regular clinical workflows. In this work, we introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images. Unlike previously proposed self-supervised techniques, the introduced method exploits the connections between the adjacent projections and the actual model of CT noise distribution. Such a combination allows for interpretable no-reference denoising using nothing but the original noisy LDCT projections. Our experiments with LDCT data demonstrate that the proposed method reaches the level of the fully supervised models, sometimes superseding them, easily generalizes to various noise levels, and outperforms state-of-the-art self-supervised denoising algorithms.