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

arXiv: https://arxiv.org/abs/2303.01678 · HTML: https://mlsanity.com/paper/2303.01678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 123 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Compressed Sensing with Deep Image Prior and Learned Regularization](https://mlsanity.com/paper/1806.06438/) (2018)
- [Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2009.07469/) (2020)
- [Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography](https://mlsanity.com/paper/1806.09748/) (2018)
- [CT sinogram-consistency learning for metal-induced beam hardening correction](https://mlsanity.com/paper/1708.00607/) (2017)
- [On the Effectiveness of Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1712.06391/) (2017)
- [A fully automated method for 3D individual tooth identification and segmentation in dental CBCT](https://mlsanity.com/paper/2102.06060/) (2021)
- [Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging](https://mlsanity.com/paper/2001.01432/) (2020)
- [Unpaired image denoising using a generative adversarial network in X-ray CT](https://mlsanity.com/paper/1903.06257/) (2019)
- [A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT](https://mlsanity.com/paper/2010.03778/) (2020)
- [Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification](https://mlsanity.com/paper/2112.01784/) (2021)
- [Automated 3D cephalometric landmark identification using computerized tomography](https://mlsanity.com/paper/2101.05205/) (2020)

## Built on by (most cited first, 3 of 3)

- [An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View](https://mlsanity.com/paper/2508.07618/) (2025)
- [Adaptive Multi-resolution Hash-Encoding Framework for INR-based Dental CBCT Reconstruction with Truncated FOV](https://mlsanity.com/paper/2506.12471/) (2025)
- [MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography](https://mlsanity.com/paper/2603.13447/) (2026)

## Abstract (arXiv)

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.