# Image fusion-based low-dose CBCT enhancement method for visualizing miniscrew insertion in the infrazygomatic crest

## TL;DR

This paper introduces a new image enhancement method for low-dose CBCT scans to better visualize miniscrew insertion in dental applications.

## Contribution

A novel low-dose CBCT enhancement method using image fusion to improve visibility and contrast for subzygomatic screw insertion.

## Key findings

- The proposed method outperforms existing enhancement algorithms in low-dose CBCT image quality.
- It effectively addresses overexposure, underexposure, low visibility, and low contrast in CBCT images.
- The algorithm achieved higher evaluation metrics (0.07–2) compared to the best performing method on two datasets.

## Abstract

Digital dental technology covers oral cone-beam computed tomography (CBCT) image processing and low-dose CBCT dental applications. A low-dose CBCT image enhancement method based on image fusion is proposed to address the need for subzygomatic small screw insertion. Specifically, firstly, a sharpening correction module is proposed, where the CBCT image is sharpened to compensate for the loss of details in the underexposed/over-exposed region. Secondly, a visibility restoration module based on type II fuzzy sets is designed, and a contrast enhancement module using curve transformation is designed. In addition to this, we propose a perceptual fusion module that fuses visibility and contrast of oral CBCT images. As a result, the problems of overexposure/underexposure, low visibility, and low contrast that occur in oral CBCT images can be effectively addressed with consistent interpretability. The proposed algorithm was analyzed in comparison experiments with a variety of algorithms, as well as ablation experiments. After analysis, compared with advanced enhancement algorithms, this algorithm achieved excellent results in low-dose CBCT enhancement and effective observation of subzygomatic small screw implantation. Compared with the best performing method, the evaluation metric is 0.07–2 higher on both datasets. The project can be found at: https://github.com/sunpeipei2024/low-dose-CBCT.