Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning

Reducing the amount of radiation in X-ray computed tomography has been an
active area of research in the recent years. The reduction of radiation has the downside of
degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some
techniques must be utilized to enhance the quality of images. In this research, we approach
the denoising problem using two class of algorithms and we reduce the noise in CT scans
that have been acquired with 75% less dose to the patient compared to the normal dose
scans.
Initially, we implemented wavelet denoising to successfully reduce the noise in
low-dose X-ray computed tomography (CT) images. The denoising was improved by
finding the optimal threshold value instead of a non-optimal selected value. The mean
structural similarity (MSSIM) index was used as the objective function for the
optimization. The denoising performance of combinations of wavelet families, wavelet
orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for
low dose CT denoising. In addition, a new shrinkage function is proposed that provides
better denoising results compared to the traditional ones without requiring a selected
parameter.
Alternatively, convolutional neural networks were employed using different
architectures to resolve the same denoising problem. This new approach improved
denoising even more in comparison to the wavelet denoising.