Master Thesis Motion Compensation in Cone-Beam Computed Tomography Imaging Intern

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1 Introduction

Cone-beam CT (CBCT) is an imaging technique widely used in radiation therapy for patient positioning and adaptive workflow. 2D projections are acquired from a cone-shaped X-ray beam rotating around the patient, and a 3D image is reconstructed from these projections. The imaging device is mounted directly on the treatment machine, i.e. a linear accelerator (linac), which allows for imaging the patient in the treatment position. However, the acquisition of CBCT images is relatively slow, and patient motion during the scan can lead to image artifacts and reduced image quality. Motion compensation techniques aim to mitigate these effects by estimating and correcting for patient motion during the reconstruction process. This thesis will investigate and prototype motion compensation methods for CBCT imaging in radiation therapy, with the goal of improving image quality and treatment accuracy.

2 Background

Respiratory and cardiac motion are the dominant source of motion artifacts in CBCT for radio[1]therapy which cause blurring, streaking and banding artifacts in the reconstructed images (Chen et al., 2004). Currently deployed techniques to mitigate motion artifacts in CBCT include:

1. Breath-hold techniques — The patient suspends respiration during acquisition, ef[1]fectively eliminating motion. However, this approach has significant clinical overhead: it reduces patient comfort, requires trained operator coordination, and is impossible for uncooperative or sedated patients.

2. Respiratory gating (Kincaid Jr. et al., 2013) — Only projections acquired during a narrow phase window of the breathing cycle are used for reconstruction. This eliminates motion blur but reduces the effective number of projections by 80–90%, resulting in severe undersampling, pronounced streaking artifacts, and degraded soft-tissue contrast. Scan time and/or imaging dose must be increased to compensate.

3. Phase-sorted 4D-CBCT (Sonke et al., 2005) — Projections are binned into typically 8–10 motion phases using a respiratory surrogate signal (e.g., external fiducial marker), and each bin is reconstructed independently, yielding a time-resolved series of 3D volumes. This preserves the full acquired dataset but divides projections among bins, creating the same undersampling penalties as gating.

Each of the above techniques has its own limitations and/or disadvantages, and there is a need for more efficient methods. Recent research has explored advanced algorithms, such as deformable image registration and machine learning-based approaches (Amirian et al., 2024), to improve motion estimation in medical imaging. This thesis will build upon these advancements to develop a novel motion compensation algorithm that can be integrated into our CBCT reconstruction pipeline.

3 Purpose and Aim of the Thesis

The purpose of this thesis is to investigate and prototype motion compensation methods for CBCT imaging in radiation therapy. The aim is to develop a motion compensation algorithm that can effectively mitigate the effects of patient motion, particularly respiratory motion, while maintaining image quality, computational efficiency and minimizing the acquisition time and delivered dose to the patient. The thesis will involve a comprehensive literature review, algo[1]rithm development, and performance evaluation, with the goal of improving image quality and treatment accuracy in CBCT imaging for radiation therapy.

1. Research Question: How can we develop and implement a motion compensation al[1]gorithm for CBCT imaging that effectively mitigates the effects of patient motion, par[1]ticularly respiratory motion and still limit the acquisition time and delivered dose to the patient?

2. Objectives:

(a) Conduct a comprehensive literature review on existing motion compensation techniques in CBCT imaging.

(b) Develop, or adapt1, a motion compensation algorithm that can be integrated into the CBCT reconstruction pipeline.

(c) Evaluate the performance of the developed algorithm in terms of image quality, computational efficiency, and robustness.

(d) Compare the proposed algorithm with existing methods to assess its advantages and limitations.

(e) Document the methodology, results, and conclusions in a comprehensive report, and provide the source code for the developed algorithm.

4 Output

The expected output of this thesis will be a prototype implementation of a motion compensation algorithm that can be integrated into our CBCT-image reconstruction pipeline. The student will be responsible for developing and testing the algorithm, as well as evaluating its performance in terms of image quality and computational efficiency. The final deliverables will include a comprehensive report detailing the methodology, results, and conclusions, as well as the source code for the developed algorithm.

5 Who are we looking for?

The ideal candidate for this thesis project should have a strong background in physics, medical imaging, and machine learning. Preferred qualifications include experience with programming languages such as Python and familiarity with image processing libraries and frameworks. The student should be motivated, detail-oriented, and capable of working independently as well as collaboratively in a research environment. The project includes a literature study, algorithm development, and performance evaluation, so the student should be comfortable with both theoretical and practical aspects of research.

6 Practicalities

• Location: Elekta AB, Stockholm, Sweden

• Supervisor: Niklas Gunnarsson ([email protected])

• Start date: Spring 2027, exact date to be agreed upon.

References

Amirian, M., Barco, D., Herzig, I., & Schilling, F.-P. (2024). Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning: A review. IEEE Access, 12, 10281–10295. https://doi.org/10.1109/ACCESS.2024.3353195

Chen, G. T., Kung, J. H., & Beaudette, K. P. (2004). Artifacts in computed tomography scan[1]ning of moving objects. Seminars in Radiation Oncology, 14 (1), 19–26. https://doi.org/https://doi.org/10.1053/j.semradonc.2003.10.004

Kincaid Jr., R. E., Yorke, E. D., Goodman, K. A., Rimner, A., Wu, A. J., & Mageras, G. S. (2013). Investigation of gated cone-beam CT to reduce respiratory motion blurring. Medical Physics, 40 (4), 041717. https://doi.org/https://doi.org/10.1118/1.4795336

Sonke, J.-J., Zijp, L., Remeijer, P., & van Herk, M. (2005). Respiratory correlated cone beam CT. Medical Physics, 32 (4), 1176–1186. https://doi.org/https://doi.org/10.1118/1.1869074