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Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography

Yuliang Huang, Bjoern Eiben, Kris Thielemans, Jamie R. McClelland

TL;DR

This method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition, and has the potential to improve downstream clinical applications, and enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored.

Abstract

4D Computed Tomography (4DCT) is widely used for many clinical applications such as radiotherapy treatment planning, PET and ventilation imaging. However, common 4DCT methods reconstruct multiple breath cycles into a single, arbitrary breath cycle which can lead to various artefacts, impacting the downstream clinical applications. Surrogate driven motion models can estimate continuous variable motion across multiple cycles based on CT segments `unsorted' from 4DCT, but it requires respiration surrogate signals with strong correlation to the internal motion, which are not always available. The method proposed in this study eliminates such dependency by adapting the hyper-gradient method to the optimization of surrogate signals as hyper-parameters, while achieving better or comparable performance, as demonstrated on digital phantom simulations and real patient data. Our method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition. Our method has the potential to improve downstream clinical applications, and also enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored. Code is avaibale at https://github.com/Yuliang-Huang/4DCT-irregular-motion.

Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography

TL;DR

This method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition, and has the potential to improve downstream clinical applications, and enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored.

Abstract

4D Computed Tomography (4DCT) is widely used for many clinical applications such as radiotherapy treatment planning, PET and ventilation imaging. However, common 4DCT methods reconstruct multiple breath cycles into a single, arbitrary breath cycle which can lead to various artefacts, impacting the downstream clinical applications. Surrogate driven motion models can estimate continuous variable motion across multiple cycles based on CT segments `unsorted' from 4DCT, but it requires respiration surrogate signals with strong correlation to the internal motion, which are not always available. The method proposed in this study eliminates such dependency by adapting the hyper-gradient method to the optimization of surrogate signals as hyper-parameters, while achieving better or comparable performance, as demonstrated on digital phantom simulations and real patient data. Our method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition. Our method has the potential to improve downstream clinical applications, and also enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored. Code is avaibale at https://github.com/Yuliang-Huang/4DCT-irregular-motion.
Paper Structure (15 sections, 10 equations, 3 figures, 1 table)

This paper contains 15 sections, 10 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Problem Formulation
  4. Surrogate-Driven Motion Model Approach
  5. Proposed Method: Surrogate-Free/Optimized Motion Model
  6. Motion Compensated Iterative Reconstruction
  7. Experiments
  8. Digital Phantom Simulation
  9. Real Patient Data
  10. Results
  11. Phantom Experiment Results
  12. Real Patient Results
  13. Discussion and Conclusion
  14. Acknowledgments.
  15. Disclosure of Interests.

Figures (3)

  • Figure 1: Illustration of 4DCT artefacts caused by irregular breathing. Time points of the end-inhalation phase are identified as black dots in the respiration signal plot, with two of them being $t_i$ and $t_i$. $I_{t_i}$ and $I_{t_j}$ are the real-time volumes at $t_i$ and $t_j$ respectively. $P_{t_i}$ and $P_{t_j}$ indicate the CT segments acquired at the couch positions corresponding to $t_i$ and $t_i$, which are stacked into the final end-inhalation phase image. Artefact of duplicated diaphragm can be observed in the end-inhalation phase image.
  • Figure 2: Effects of artefact correction by different methods on digital phantom data.
  • Figure 3: Visual comparison between end-inhalation 4DCT and the deepest/shallowest end-inhalation images estimated by the surrogate-free motion model.