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Project: Visualization of variability in radiotherapy dose planning

Description

In radiotherapy, the goal is to deliver high radiation doses to destroy the cancer cells,while sparing the adjacent healthy tissues. To ensure that disease is treated and normal structures are spared, a dose planning is required. Nowadays, through the use of image guidance techniques and advanced planning software, tumor control has significantly improved and radiation-induced toxicity has diminished. However, radiotherapy planning involves several sources of uncertainty, which are often neglected. One source of uncertainty is variability due to different assumptions, parameter settings or choices that can be made during planning, which may result in several alternative dose plans. It is not known a priori which of these assumptions or parameter settings lead to better results. Still, it is valuable for clinical researchers to understand how the different assumptions and parametrizations can affect the final dose plan. In this way, they can become aware of the variability and evaluate whether it has an impact on the final clinical decision and treatment. Currently, there is no system able to offer a visual analysis and comparison of multiple possible dose plans.The aim of this thesis is to investigate visualization methods and strategies that can be used for the interactive exploration and analysis of the variability involved in radiotherapy planning data. The main contribution is the design and development of an initial prototyping visual framework. It enables clinical researchers, working on radiotherapy planning,to interactively explore and gain insight into the variability in an ensemble of possible dose plans, as well as to detect relevant trends and patterns in the alternatives of planning data.The proposed framework consists of multiple linked views, to allow for detailed variability assessment, with interactive functionalities to further facilitate the exploration and analysis.The potential usefulness of the framework is illustrated with two usage scenarios, namely, a simulated case and a real-world case, both from prostate cancer data. Also, a discussion with domain experts resulted in a positive feedback for the developed framework.

Details

Student

PS

Pedro Silva

Supervisor

Anna Vilanova

External location

Renata Raidou - Biomedical Technology

Link

Thesis