# A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/2304.05359 · HTML: https://mlsanity.com/paper/2304.05359/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Low-dose CT denoising with convolutional neural network](https://mlsanity.com/paper/1610.00321/) (2016)

## Built on by (most cited first, 6 of 6)

- [Multi-Scale Texture Loss for CT denoising with GANs](https://mlsanity.com/paper/2403.16640/) (2024)
- [Texture-Aware StarGAN for CT data harmonisation](https://mlsanity.com/paper/2503.15058/) (2025)
- [Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation](https://mlsanity.com/paper/2508.00766/) (2025)
- [Cross Modality Image Translation In Medical Imaging Using Generative Frameworks](https://mlsanity.com/paper/2605.13686/) (2026)
- [A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis](https://mlsanity.com/paper/2603.13520/) (2026)
- [3D CT-to-PET Translation via Latent Brownian Bridge Diffusion](https://mlsanity.com/paper/2609.12860/) (2026)

## Abstract (arXiv)

The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latter relaxes this constraint. The large availability of unpaired datasets has raised the interest in deepening unpaired denoising strategies that, in turn, need for robust evaluation techniques going beyond the qualitative evaluation. To this end, we can use quantitative image quality assessment scores that we divided into two categories, i.e., paired and unpaired measures. However, the interpretation of unpaired metrics is not straightforward, also because the consistency with paired metrics has not been fully investigated. To cope with this limitation, in this work we consider 15 paired and unpaired scores, which we applied to assess the performance of low-dose CT denoising. We perform an in-depth statistical analysis that not only studies the correlation between paired and unpaired metrics but also within each category. This brings out useful guidelines that can help researchers and practitioners select the right measure for their applications.