Mohammadi Khoroushadi, Mohammad Sadegh

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.

Simulated Annealing algorithm is utilized for Intensity Modulated Radiation Therapy IMRT optimization.
The goal in IMRT is to give the prescribed radiation dose to the tumor while minimizing the dose given
to normal organs.

Intensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse treatment planning, the treatment plan is achieved by using an optimization process. The optimized plan results to a high-quality dose distribution in the planning target volume (PTV), which receives the prescribed dose while the dose that is received by the organs at risk (OARs) is reduced. Accordingly, an objective function has to be defined for the PTV, while some constraints have to be considered to handle the dose limitations for the OARs.