Multi-objective optimization as a novel weight-tuning strategy for deformable image registration applied to pre-operative partial- breast radiotherapy

Deformable image registration (DIR) has potential to enable novel approaches in radiotherapy (RT) such as dose accumulation, online adaptive planning, and response monitoring. Although DIR is predominantly formulated as a single-objective optimization problem, its inherent nature is multi-objective, i.e., there are multiple, conflicting objectives that need to be optimized simultaneously. A major challenge that limits its use in clinical practice, however, is the difficulty in choosing the optimal trade-off of these multiple objectives. Currently, primarily trial-and-error approaches are used to find weights to linearly combine multiple objectives into a single-objective function. Their success relies on a logical relation between the weights, objective values, and registration outcome, which is not well established. In this work, for the task of RT tumor response monitoring, we employ a multi-objective optimization approach that is not necessarily dependant on this logical relation and provides insightful tuning of weights even for hard registration cases.