# Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography

arXiv: https://arxiv.org/abs/1806.09748 · HTML: https://mlsanity.com/paper/1806.09748/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 23 of 23)

- [CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization](https://mlsanity.com/paper/2304.01814/) (2023)
- [Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey](https://mlsanity.com/paper/2203.15725/) (2022)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond](https://mlsanity.com/paper/2103.14528/) (2021)
- [Federated CycleGAN for Privacy-Preserving Image-to-Image Translation](https://mlsanity.com/paper/2106.09246/) (2021)
- [Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment](https://mlsanity.com/paper/2302.13251/) (2023)
- [Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging](https://mlsanity.com/paper/2311.08972/) (2023)
- [MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising](https://mlsanity.com/paper/2103.12995/) (2021)
- ["One-Shot" Reduction of Additive Artifacts in Medical Images](https://mlsanity.com/paper/2110.12274/) (2021)
- [Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography](https://mlsanity.com/paper/2303.01678/) (2023)
- [InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration](https://mlsanity.com/paper/2211.05622/) (2022)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [Self-supervised Physics-based Denoising for Computed Tomography](https://mlsanity.com/paper/2211.00745/) (2022)
- [Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models](https://mlsanity.com/paper/2408.03156/) (2024)
- [Depth-induced NTK: Bridging Over-parameterized Neural Networks and Deep Neural Kernels](https://mlsanity.com/paper/2511.05585/) (2025)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising](https://mlsanity.com/paper/2105.07146/) (2021)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images](https://mlsanity.com/paper/2112.12810/) (2021)
- [Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution](https://mlsanity.com/paper/2309.00995/) (2023)
- [Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning](https://mlsanity.com/paper/2311.12071/) (2023)
- [Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction](https://mlsanity.com/paper/2601.20304/) (2026)
- [GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization](https://mlsanity.com/paper/2607.11941/) (2026)

## Abstract (arXiv)

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images in the two phases are closely related so that we propose a cycle-consistent adversarial denoising network to learn the non-degenerate mapping between the low and high dose cardiac phases. Experimental results showed that the proposed method effectively reduces the noise in the low-dose CT image while the preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality.