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Target Volume Delineation in Dynamic Positron Emission Tomography Based on Time Activity Curve Differences

  • Author / Creator
    Teymurazyan, Artur
  • Tumor volume delineation plays a critical role in radiation treatment planning and simulation, since inaccurately defined treatment volumes may lead to the overdosing of normal surrounding structures and potentially missing the cancerous tissue. However, the imaging modality almost exclusively used to determine tumor volumes, X-ray Computed Tomography (CT), does not readily exhibit a distinction between cancerous and normal tissue. It has been shown that CT data augmented with PET can improve radiation treatment plans by providing functional information not available otherwise. Presently, static PET scans account for the majority of procedures performed in clinical practice. In the radiation therapy (RT) setting, these scans are visually inspected by a radiation oncologist for the purpose of tumor volume delineation. This approach, however, often results in significant interobserver variability when comparing contours drawn by different experts on the same PET/CT data sets. For this reason, a search for more objective contouring approaches is underway. The major drawback of conventional tumor delineation in static PET images is the fact that two neighboring voxels of the same intensity can exhibit markedly different overall dynamics. Therefore, equal intensity voxels in a static analysis of a PET image may be falsely classified as belonging to the same tissue. Dynamic PET allows the evaluation of image data in the temporal domain, which often describes specific biochemical properties of the imaged tissues. Analysis of dynamic PET data can be used to improve classification of the imaged volume into cancerous and normal tissue. In this thesis we present a novel tumor volume delineation approach (Single Seed Region Growing algorithm in 4D (dynamic) PET or SSRG/4D-PET) in dynamic PET based on TAC (Time Activity Curve) differences. A partially-supervised approach is pursued in order to allow an expert reader to utilize the information available from other imaging modalities routinely used in conjunction with PET. In our scheme, this includes the definition of a tumor encompassing mask and selection of a seed site within the suspected tumor, while further delineation is performed automatically by the algorithm. The development of this method is examined and improved classification of the imaged volume into cancerous and normal tissue compared to methods currently used in the clinic is demonstrated.

  • Subjects / Keywords
  • Graduation date
    2013-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3KC8Z
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Physics
  • Specialization
    • Medical Physics
  • Supervisor / co-supervisor and their department(s)
    • Riauka, Terry (Department of Oncology)
    • Robinson, Don (Department of Oncology)
  • Examining committee members and their departments
    • Jans, Hans-Sönke (Department of Oncology)
    • Marchand, Richard (Department of Physics)
    • Stodilka, Robert (Medical Imaging, Western University)
    • Sloboda, Ron (Department of Oncology)