M. Schlachter (2022)

Visual Computing Methods for Radiotherapy Planning

communication medium

PhD Thesis (TU Wien)


Radiotherapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process — from imaging to delivery. VC employs elements from computer graphics and image processing to create meaningful, interactive visual representations of medical data, and it has become an influential field of research for many advanced applications like radiation oncology. Interactive VC approaches represent a new opportunity to integrate knowledgeable experts and their cognitive abilities in exploratory processes, which cannot be conducted by solely automatized methods. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. In RT planning medical doctors need to consider a variety of information sources for anatomical and functional target volume delineation. The validation and inspection of the defined target volumes and the resulting RT plan is a complex task, especially in the presence of moving target areas as it is the case for tumors of the chest and the upper abdomen, for instance, caused by breathing motion. Handling RT planning and delivery-related uncertainties, especially in the presence of tumor motion, is essential to improve the efficiency of the treatment and the minimization of side effects. This dissertation contributes to the handling of RT planning related uncertainties by proposing novel VC methods. Quantification and visualization of these types of uncer- tainties will be an essential part of the presented methods, and aims at improving the RT workflow in terms of delineation and registration accuracy, margin definitions and the influence of these uncertainties onto the dosimetric outcome. The publications presented in this thesis address key aspects of the RT treatment planning process, where human interaction is required, and VC has the potential to improve the treatment outcome. First, major requirements for a multi-modal visualization framework are defined and implemented with the aim to improve motion management by including 4D image information. The visualization framework was designed to provide medical doctors with the necessary visual information to improve the accuracy of tumor target delineations and the efficiency of RT plan evaluation. Furthermore, the topic of deformable image registration (DIR) accuracy is addressed in this thesis. DIR has the potential to improve modern RT in many aspects, including volume definition, treatment planning, and image-guided adaptive RT. However, mea- suring DIR accuracy is difficult without known ground truth, but necessary before the integration in the RT workflow. Visual assessment is an important step towards clinical acceptance. A visualization framework is proposed, which supports the exploration and the assessment of DIR accuracy. It offers different interaction and visualization features for exploration of candidate regions to simplify the process of visual assessment, and thereby improve and contribute to its adequate use in RT planning. Finally, the topic of healthy tissue sparing is addressed with a novel visualization approach to interactively explore RT plans, and identify regions of healthy tissue, which can be spared further without compromising the treatment goals defined for tumor targets. For this, overlap volumes of tumor targets and healthy organs are included in the RT plan evaluation process, and the initial visualization framework is extended with quantitative views. This enables quantitative properties of the overlap volumes to be interactively explored, to identify critical regions and to steer the visualization for a detailed inspection of candidates. All approaches were evaluated in user studies covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed, and if available using ground truth data to prove their validity.

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