Noise Reduction And Information Extraction Of Dual-Energy Computed Tomography Images
Abstract
With every new generation of computed tomography machinery, big improvements in terms of image quality and acquisition speed were achieved. Nonetheless, a persistent feature of the CT remained: the single energy acquisition of images, which results in images where materials of similar density appear with similar intensity, generating an undesired degree of uncertainty that required other kinds of examinations to be solved. However, it has been known since its invention that the instantaneous acquisition of two or more energies would provide images with better tissue discrimination capabilities and reduce this problem.
Nonetheless, it was only in the last 10 years that CT technology became advanced enough to simultaneously acquire images in dual-energy mode. However, it is necessary to keep the radiation dose to the patient equivalent to a single energy CT image, which results in images that are affected by noise and that need especial algorithms to improve the image quality. A particular feature that has also been know since the invention of dual energy CT is the characteristic negative correlation of its material density information discovered by Kalender et al.
In this work we developed two algorithms that takes advantage of that discovery. We first created an algorithm using a joint anisotropic diffusion that reduced the amount of noise and improved image quality. Finally, we extended this first algorithm by using an adaptive Wiener filter that better approximated the true mean value of each region and drastically improved image quality even in images that were deeply affected by noise.
The proposed techniques were tested in a quantitative way in simulated, real phantom and real patient images to show the improvement in image quality while preserving image information. Finally, we investigated these noise corrected images in the perspective of information extraction, using a modified multi-material decomposition algorithm to obtain a classification of pixel in term of tissue type.
Description
Keywords
Computer Science
Citation
Simon Maia, R. (2016). Noise Reduction And Information Extraction Of Dual-Energy Computed Tomography Images (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27113