This thesis contributes to the topic of image-based feature localization and tracking in fluoroscopic (2D x-ray) image sequences. Such tracking is needed to automatically measure organ motion in cancer patients treated with radiation therapy. While the use of 3D cone-beam computed tomography (CBCT) images is a standard clinical practice for verifying the agreement of the patient's position to a plan, it is done before the treatment procedure. Hence, measurement of the motion during the procedure could improve plan design and the accuracy of treatment delivery. Using an existing CBCT imaging system is one way of collecting fluoroscopic sequences for such analysis. Since x-ray images of soft tissues are typically characterized with low contrast and high noise, radio-opaque fiducial markers are often inserted in or around the target. This thesis describes techniques that comprise a complete system for automated detection and tracking of the markers in fluoroscopic image sequences.
One of the cornerstone design ideas in this thesis is the use of the 3D CBCT image of the patient, from which the markers can be extracted more easily, to initialize the tracking in the fluoroscopic image sequences. To do this, a specific marker-based image registration framework was proposed. It includes multiple novel techniques, such as marker segmentation and modelling, the marker enhancement filter, and marker-specific template image generation approaches.
Through extensive experiments on testing data sets, these novel techniques were combined with appropriate state-of-the-art methods to produce a sleek, computationally efficient, fully automated system that achieved reliable marker localization and tracking. The accuracy of the system is sufficient for clinical implementation. The thesis demonstrates an application of the system to the images of prostate cancer patients, and includes examples of statistical analysis of organ motion that can be used to improve treatment planning. / Dissertation / Doctor of Philosophy (PhD) / This thesis presents the development of a software system that analyzes sequences of 2D x-ray images to automatically measure organ motion in patients undergoing radiation therapy for cancer treatment. The knowledge of motion statistics obtained from this system creates opportunities for patient-specific treatment design that may lead to a better outcome.
Automated processing of organ motion is challenging due to the low contrast and high noise levels in the x-ray images. To achieve reliable detection, the proposed system was designed to make use of 3D cone-beam computed tomography images of the patient, where the features (markers) are easier to identify. This required the development of a specific image registration framework for aligning the images, including a number of novel feature modelling and image processing techniques.
The proposed motion tracking approach was implemented as a complete software system that was extensively validated on phantom and patient studies. It achieved a level of accuracy and reliability that is suitable for clinical implementation.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19136 |
| Date | January 2016 |
| Creators | Peshko, Olesya |
| Contributors | Davidson, Timothy N., Modersitzki, Jan, Moseley, Douglas J., Terlaky, Tamas, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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