mlsanityData to 7 Oct 2026

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

arXiv 2303.01678 ↗First posted 3 Mar 2023eess.IV cs.CV physics.med-phcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 123 references that are in the map. First posted on arXiv 3 Mar 2023.

Builds on

18 of 123 references.

References · 18

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6 more
  1. 2021A fully automated method for 3D individual tooth identification and segmentation in dental CBCTcited by 18
  2. 2020Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imagingcited by 14
  3. 2019Unpaired image denoising using a generative adversarial network in X-ray CTcited by 10
  4. 2020A two-stage approach for beam hardening artifact reduction in low-dose dental CBCTcited by 5
  5. 2021Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identificationcited by 5
  6. 2020Automated 3D cephalometric landmark identification using computerized tomographycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of Viewcited by 2
  2. 2025Adaptive Multi-resolution Hash-Encoding Framework for INR-based Dental CBCT Reconstruction with Truncated FOVcited by 1
  3. 2026MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomographycited by 0