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

Goli Khaleghi; Mohammad Hosntalab; Mahdi Sadeghi; Reza Reiazi; Seied Rabi Mahdavi

doi:10.4103/jmss.jmss_159_21

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

J Med Signals Sens. 2022 Nov 10;12(4):269-277. doi: 10.4103/jmss.jmss_159_21. eCollection 2022 Oct-Dec.

Authors

Goli Khaleghi¹, Mohammad Hosntalab¹, Mahdi Sadeghi², Reza Reiazi^{2

3}, Seied Rabi Mahdavi²

Affiliations

¹ Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
² Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
³ Princess Margaret Cancer Center, Toronto, Ontario, Canada.

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 showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images.

Conclusion: The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks.

Keywords: Denoizing; head-and-neck cancer; metal artifacts; neural networks.