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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Updated: Jun 22, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence

Motoki Fukuda¹, Michihito Nozawa², Hironori Akiyama²

¹Department of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan. fukuda-m@cc.osaka-dent.ac.jp.

|June 28, 2024

Summary

CycleGAN image synthesis significantly improved soft tissue visibility on cone-beam computed tomography (CBCT) scans. This AI-driven enhancement aids in visualizing anatomical structures, though its effect on cystic lesions is limited.

Keywords:

Artificial intelligence Cone-beam CT CycleGAN Deep learning Soft tissue

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Cone-beam computed tomography (CBCT) has limitations in soft tissue visualization.
Enhancing soft tissue contrast in CBCT is crucial for accurate diagnosis.

Purpose of the Study:

To improve soft tissue visibility on CBCT using CycleGAN.
To evaluate the effectiveness of CycleGAN-generated synthetic CBCT (sCBCT) images.

Main Methods:

CycleGAN network trained on CT and CBCT images.
Comparative analysis of sCBCT, original CBCT (oCBCT), and CT images.
Evaluation using histogram analysis and expert human scoring.

Main Results:

sCBCT images showed significant shifts in voxel intensity towards CT-like values.
Visibility scores for soft tissue anatomical structures were significantly increased.
Improvement in cystic lesion visibility was limited.

Conclusions:

CycleGAN effectively enhances soft tissue visibility on CBCT, especially in the submandibular and floor of mouth regions.
Further refinement of training methods may improve visualization of cystic lesions.
AI-based image synthesis shows promise for improving CBCT diagnostic capabilities.