Citation, DOI, disclosures and article data
Citation:
Moore C, Bell D, Noise reduction. Reference article, Radiopaedia.org (Accessed on 09 Mar 2025) https://doi.org/10.53347/rID-72577
Noise reduction, also known as noise suppression or denoising, commonly refers to the various algorithmic techniques to reduce noise in digital images once they are created although a few sources use the term more broadly to imply anything that reduces noise. In digital image processing various techniques, most of which are filtering techniques are applied to images at various stages after acquisition. These methods can involve both spatial filters (convolutions), frequency filters (discrete Fourier transform), morphological filters or even statistical filters.
Practical points
Many radiologists are familiar with CT reconstruction kernels and using a smooth kernel would be an example (in most cases) of noise reduction. The use of CT reconstruction kernels is often a choice, but some noise reduction techniques are automated and performed on raw imaging data without the radiologist necessarily being aware of it. Advanced noise reduction techniques are considered part of AI.
See also
-
1. Eric C. Ehman, Lifeng Yu, Armando Manduca, Amy K. Hara, Maria M. Shiung, Dayna Jondal, David S. Lake, Robert G. Paden, Daniel J. Blezek, Michael R. Bruesewitz, Cynthia H. McCollough, David M. Hough, Joel G. Fletcher. Methods for Clinical Evaluation of Noise Reduction Techniques in Abdominopelvic CT. (2014) RadioGraphics. 34 (4): 849-62. doi:10.1148/rg.344135128 - Pubmed
-
2. Mary Couwenhoven, William Sehnert, Xiaohui Wang, Michael Dupin, John Wandtke M.D., Steven Don M.D., Richard Kraus M.D., Narinder Paul M.D., Neil Halin M.D., Robert Sarno M.D.. Observer study of a noise suppression algorithm for computed radiography images. (2005) Journal of gastroenterology and hepatology. 5749 (5): 318. doi:10.1117/12.595159 - Pubmed
-
3. Zhu, Guangming, Jiang, Bin, Tong, Liz, Xie, Yuan, Zaharchuk, Greg, Wintermark, Max. Applications of Deep Learning to Neuro-Imaging Techniques. (2019) Frontiers in Neurology. 10: 869. doi:10.3389/fneur.2019.00869 - Pubmed
-
4. Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, Matthew S. Rosen. Image reconstruction by domain-transform manifold learning. (2018) Nature. 555 (7697): 487. doi:10.1038/nature25988 - Pubmed
Promoted articles (advertising)
Unable to process the form. Check for errors and try again.