Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography

TL;DR

This paper analyzes the mathematical challenges of low-dose dental CBCT imaging, highlighting its severe ill-posedness and proposing deep learning approaches to improve image reconstruction despite data damage from metal implants.

Contribution

It explains the ill-posed nonlinear structure of dental CBCT and discusses deep learning advantages over traditional methods for better image reconstruction.

Findings

Dental CBCT is more ill-posed than standard CT.

Deep learning offers advantages in image reconstruction.

Utilizing prior information can mitigate data damage.

Abstract

This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations of existing methods in the accurate restoration of the morphological structures of teeth using dental CBCT data severely damaged by metal implants. We further discuss the usefulness of panoramic images generated from CBCT data for accurate tooth segmentation. We also discuss the possibility of utilizing radiation-free intra-oral scan data as prior information in CBCT image reconstruction to compensate for the damage to data caused by metal implants.