Rafael Simon Maia1, Christian Jacob2, Amy K Hara3, Alvin C Silva3, William Pavlicek3, J Ross Mitchell3. 1. Department of Computer Science, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada. rafaelsimonmaia@gmail.com. 2. Department of Computer Science, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada. 3. Department of Radiology, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA.
Abstract
PURPOSE: Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images METHODS: An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. RESULTS: The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively. CONCLUSION: We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.
PURPOSE: Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images METHODS: An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. RESULTS: The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively. CONCLUSION: We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.
Keywords:
Adaptive Wiener filter; Dual-energy computed tomography; Material density; Noise reduction
Authors: Rafael Simon Maia; Christian Jacob; Amy K Hara; Alvin C Silva; William Pavlicek; Mitchell J Ross Journal: Int J Comput Assist Radiol Surg Date: 2014-05-11 Impact factor: 2.924