In multiphase coronary CT angiography (CTA), a series of CT images are taken at different levels of radiation dose during the examination. This reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. The model is using unsupervised learning technique that can remove the noise of the CT images in the low-dose phases by learning from the CT images in the routine dose phases. Cycle-consistent adversarial denoising network is proposed to learn the 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 preserving detailed texture and edge information.
Input variables : Low dose CT Images
Output Variables : Routine dose CT images
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | November, 2018 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Health Risk Management |
| Disease Detection |