Abstract
Ziel/Aim Personalized therapy was shown to have clinical benefits e.g. in liver selective internal radiation therapy (SIRT). Yet, there is no broad consensus about its introduction into clinical practice, mainly as Monte Carlo (MC) simulations (its gold standard) involve massive computation time. Moreover, faster methods like organ-S-values (IDAC, Internal Dose Assessment by Computer) or kernel-dosimetry show poor accuracy. This work compares different approaches for internal dosimetry and evaluates them in terms of accuracy and computational performance. At the same time, we evaluate our patch-based MC approach that yields dose maps with comparable accuracy to full whole-body MC simulations at reasonable computation time. The patch-based approach will help to create large training datasets for Deep Neural Networks (DNNs), which will potentially replace MC simulations in the coming years [1].
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