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Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review

Mohammadreza Amirian, Daniel Barco, Ivo Herzig, Frank-Peter Schilling

TL;DR

One of the key findings is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.

Abstract

Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or low-dose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the type of artifacts rather than the architecture of neural networks, is lacking in the literature. In this review, the data generation and simulation pipelines, and artifact reduction techniques are specifically investigated for each type of artifact. We provide an overview of deep learning techniques that have successfully been shown to reduce artifacts in 3D, as well as in time-resolved (4D) CBCT through the use of projection- and/or volume-domain optimizations, or by introducing neural networks directly within the CBCT reconstruction algorithms. Research gaps are identified to suggest avenues for future exploration. One of the key findings of this work is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.

Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review

TL;DR

One of the key findings is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.

Abstract

Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or low-dose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the type of artifacts rather than the architecture of neural networks, is lacking in the literature. In this review, the data generation and simulation pipelines, and artifact reduction techniques are specifically investigated for each type of artifact. We provide an overview of deep learning techniques that have successfully been shown to reduce artifacts in 3D, as well as in time-resolved (4D) CBCT through the use of projection- and/or volume-domain optimizations, or by introducing neural networks directly within the CBCT reconstruction algorithms. Research gaps are identified to suggest avenues for future exploration. One of the key findings of this work is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.
Paper Structure (14 sections, 4 figures, 1 table)

This paper contains 14 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Cone-Beam Geometry Reconstruction and Deep Learning
  4. Evaluation Metrics
  5. Clinical Evaluation
  6. Cone-Beam Image Quality
  7. Low dose
  8. Sparse-View
  9. Limited angle
  10. Scatter and Beam Hardening
  11. Metal
  12. Motion
  13. Discussion and Recommendations
  14. Conclusions

Figures (4)

  • Figure 1: Visual Abstract: An illustration of the CBCT acquisition process in IGRT for lung CBCT and the application of deep learning for artifact correction. The diagram depicts the acquisition of 2D projections (initial corrections such as scatter corrections have already been applied), including (optionally) time- and motion-related information (e.g. breathing amplitude signal), standard CBCT reconstruction (typically 2D$\rightarrow$3D), and DL-based components for image enhancement. Incorporating acquired temporal and motion information provides the opportunity to apply a projection binning which can be used to reconstruct 4D CBCT images (3D images at various states of motion). During the course of CBCT reconstruction, several types of artifacts (e.g. arising from cone-beam geometry, low dose, sparse view or limited angle scans, scatter, metal or beam hardening) can be mitigated through DL-based optimization in the projection and/or volume domain, or by improving (parts of) the reconstruction algorithm itself using neural networks. The illustration of a commerical radiotherapy system is adapted from Shende2016CommissioningOT.
  • Figure 2: Visualisation of the content of this survey and the literature covered.
  • Figure 3: Examples of different kinds of artifacts appearing in CBCT scans. Shown are several artifact-free motion states obtained with a simulated 4D CBCT scan (1st row), sparse-view artifacts at various sub-sampling rates (2nd row), limited-angle, scatter and metal artifacts (3rd row), as well as motion artifacts (4th row).
  • Figure 4: A visual summary of the distribution of the covered research literature in CBCT artifact mitigation using deep learning, separately for two time periods, (a) based on three generic deep learning architecture categories given a broad categorization by artifact type, and (b) based on the distribution according to the type of artifact.