The continuous development and extensive use of CT in medical practice has raised a public concern over the associated radiation dose to the patient. Lowering the radiation dose increases the noise and artifacts in reconstructed images, which can compromise diagnostic information. Hence,advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance. This model introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, and promises to improve the performance of GAN. Proposed method is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.
Input variables : Low dose CT image
Output Variables : Denoised CT image
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | April, 2018 |
| Healthcare Domain | : | Medical Technology |
| Code | : | github.com |
| Medical Imaging |
| Image Denoising |