Radiation-induced pulmonary toxicity poses a serious challenge and limiting factor in delivering a sufficient amount of dose to eradicate thoracic tumors without compromising lung function. Functional avoidance radiation therapy (RT) using lung ventilation mapping techniques would allow for preferential avoidance of functional lung tissue during radiotherapy and reduce RT-induced lung injuries. Conventional methods for lung ventilation imaging include gamma camera scintigraphy and positron emission tomography scan after inhaling a gaseous radionuclide. Recently, a new method has been proposed in which deformable image registration (DIR) is performed on a pair of anatomical lung images at different respiratory phases to obtain the displacement vector field (DVF) between both phases and generate a lung ventilation map. The long-term goal of this application is to develop an accurate DIR-based lung ventilation mapping technique and apply it to advanced radiotherapy of lung cancer. This new DIR-based method is advantageous in its high image resolutions and robust processing, making it a more feasible option for implementation into the clinical workflow. However, current DIR-based lung ventilation methods have been largely hampered due to two major deficiencies: 1) current DIR algorithms are morphologically based, lacking sufficient physiological realism and thus resulting in erroneous ventilation measurements; and 2) there is a lack of validation of DIR-based lung ventilation calculations against clinical ground truth. The objective of the proposed research is to develop and evaluate a highly efficient and robust biomechanically-based hybrid DIR method for lung ventilation applications. The central hypothesis of this application is that the incorporation of biomechanical motion information into the DIR algorithm improves the accuracy of the DIR-based lung ventilation calculation. The central hypothesis will be tested by pursuing two specific aims: 1) Improve the efficiency and accuracy of a DIR algorithm through automatic vessel tree matching for lung ventilation mapping; and 2) Evaluate the performance of the improved DIR algorithm for lung ventilation applications using an existing multi-institutional lung ventilation database. Outcome of the proposed research will yield an improved DIR algorithm with high efficiency and accuracy for accurate lung ventilation mapping. Once fully developed and validated, it can be used for functional-based treatment planning and adaptive radiotherapy to improve the outcome of lung cancer treatments. The co-sponsors will foster an educational and collaborative environment within their multidisciplinary laboratory and assist in the completion of benchmarks. The candidate will be responsible for designing and carrying out the methods described, along with her sponsors? guidance on the feasibility and significance of the approaches. Duke University offers a training environment with the opportunity to consult and collaborate with renowned researchers and clinicians; it offers the candidate all the necessary resources, technology, and equipment for the successful completion of these research goals.

Public Health Relevance

The proposed research is relevant to public health because it represents a novel strategy to develop and evaluate an improved Deformable Image Registration (DIR) algorithm for accurate lung ventilation mapping for functional avoidance radiation therapy (RT). Functional avoidance RT using lung ventilation mapping techniques would allow for preferential avoidance of functional lung tissue during radiotherapy and mitigate radiation-induced lung injuries which affect an estimate of 5-50% of patients receiving radiotherapy treatment for thoracic cancer, resulting in severe complications. Once fully developed and validated, this biomechanically-based DIR algorithm will allow for more precise radiation delivery to lung tumor and will significantly improve the outcome of lung cancer treatments by maximally sparing healthy lung tissue.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Predoctoral Individual National Research Service Award (F31)
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Special Emphasis Panel (ZRG1)
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Mcneil Ford, Nicole
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Duke University
Schools of Medicine
United States
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