Deep learning-based metal and scatter artifact reduction in conebeam computed tomography

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2025-10-24
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Authors

Date

2025

Department

Sähkötekniikan ja automaation laitos
Department of Electrical Engineering and Automation

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Mcode

Degree programme

Language

en

Pages

104 + app. 61

Series

Aalto University publication series Doctoral Theses, 193/2025

Abstract

Cone-Beam Computed Tomography (CBCT) provides high-quality three-dimensional X-ray imaging and offers advantages such as reduced radiation dose, lower cost, and a smaller physical footprint compared to Multi-Detector CT (MDCT). Due to these features, CBCT is well-suited for a range of clinical applications, including dentistry, orthopedics, interventional radiology, and image-guided therapies, as well as for use in mobile clinics and remote deployments, thereby contributing to broader accessibility in healthcare. However, CBCT image quality is often compromised by inherent artifacts, including scatter and metal artifacts, which pose significant challenges to diagnostic applications. The emergence of deep learning methods presents a promising avenue for addressing these imaging challenges, potentially offering substantial improvements. However, practical constraints, such as the need for large training datasets, and the seamless integration of deep learning models into existing artifact correction pipelines, must be addressed to ensure clinical feasibility. The main purpose of this thesis is to develop clinically applicable deep learning techniques to mitigate metal and scatter artifacts in CBCT imaging. For instance, to overcome the challenge of data scarcity, simulated datasets are leveraged for network training. Additionally, lightweight Convolutional Neural Network (CNN) models are introduced to facilitate efficient integration into established artifact correction workflows. To ensure real-world applicability, the proposed methods are evaluated using real CBCT datasets. The research is structured around four key contributions. Publication I introduces a learning-based inpainting method of metal traces to reduce metal artifacts. Publication II employs simulated data to train a neural network for metal trace segmentation to improve the effectiveness of existing inpainting-based metal artifact reduction methods. Publication III presents a neural network for scatter estimation under clinically relevant variations in the Field of Measurement (FOM). Finally, in Publication IV, an ultrafast scatter estimation approach is proposed for deployment in mobile CBCT systems and on-device applications. The findings demonstrate that the developed models substantially enhance the state-of-the-art in artifact correction, advancing the clinical viability of CBCT imaging through deep learning-driven lightweight solutions.

Description

Supervising professor

Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland

Thesis advisor

Hietanen, Ari, Dr., Planmeca Oy, Finland

Keywords

deep learning, cone-beam computed tomography, metal artifacts, scatter artifacts

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Parts

  • [Publication 1]: Harshit Agrawal, Ari Hietanen, Simo Särkkä. Metal artifact reduction in cone-beam extremity images using gated convolutions. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, pp. 1087–1090, April 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202106167360
    DOI: 10.1109/ISBI48211.2021.9434163 View at publisher
  • [Publication 2]: Harshit Agrawal, Ari Hietanen, Simo Särkkä. Deep learning based projection domain metal segmentation for metal artifact reduction in cone beam computed tomography. IEEE Access, Volume 11, Pages 100371–100382, November 2023.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202310046151
    DOI: 10.1109/ACCESS.2023.3314700 View at publisher
  • [Publication 3]: Harshit Agrawal, Ari Hietanen, Simo Särkkä. Deep learning architecture for scatter estimation in cone-beam computed tomography head imaging with varying field-of-measurement settings. Journal of Medical Imaging, Volume 11, Issue 5, Article number: 053501, October 2024.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202411267449
    DOI: 10.1117/1.JMI.11.5.053501 View at publisher
  • [Publication 4]: Harshit Agrawal, Ari Hietanen, Simo Särkkä. Ultrafast deep learningbased scatter estimation in cone-beam computed tomography. Submitted to Medical & Biological Engineering & Computing, arXiv preprint arXiv:2509.08973, 2025.
    DOI: 10.48550/arXiv.2509.08973 View at publisher

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https://urn.fi/URN:ISBN:978-952-64-2756-0

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[diss] Sähkötekniikan korkeakoulu / ELEC