@thesis{PB-VRVis-2022-055,
author = {Schlachter, Matthias},
title = {Visual Computing Methods for Radiotherapy Planning},
year = {2022},
type = {PhD Thesis},
institution = {Fakult{\"a}t f{\"u}r Informatik},
subtitle = {PhD Thesis (TU Wien)},
url = {https://www.vrvis.at/publications/PB-VRVis-2022-055},
abstract = {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.},
}