Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts.

ABSTRACT:

Background

This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients.

Methods

First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]).

Results

Results s

SUBMITTER: Khaleghi G 

PROVIDER: S-EPMC9885504 | biostudies-literature | 2022 Oct-Dec

REPOSITORIES: biostudies-literature