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Fluence Map Optimization in IMRT Planning under Uncertainty

Alimohammadi, Maryam | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49790 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rafiee, Majid
  7. Abstract:
  8. Cancer is one of the main causes of death among all people around the world. This shows that more and more attention should be paid to cure cancer. One of the most common cancer treatments is radiation therapy. Among all radiation therapy treatments, Intensity-Modulated Radiation Therapy is a very effective and flexible ways to treat cancer. There are numerous mathematical problems to find the perfect treatment plan. One of these mathematical problems is Fluence Map Optimization in which we try to find the best dose intensity for each tumor and surrounding organs. The main purpose in this problem is to minimize the number of cancerous cells which are being destroyed while minimizing the number of healthy cells that are getting injured. In this thesis, we want to find the best treatment plan for patients by providing two biological models. One of them is based on tumor control probability and the other one is based on equivalent uniform dose. Biological models are more efficient because of considering cell’s reaction against specific amount of dose. However, they will have uncertainties in their parameters. We will overcome this problem by using expected optimization method and a less common technique in radiotherapy named CVaR optimization. CVaR optimization has been used a lot in finance problems but it is new to the IMRT optimization. Another merit of the models is that they are convex which helps us to optimize the problem with nonlinear convex algorithms. In this thesis, we will be using Generalized Reduced Gradient and Interior Point algorithms. At the end, a comparison between all the provided models and their outcomes
  9. Keywords:
  10. Fluence Map Optimization ; Conditional Value at Risk ; Convex Programming ; Radiotherapy ; Intersity Modulated Radiation Therapy ; Nonlinear Convex Programming

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