Exploring the potential of Optimal Transport for Monte Carlo methods in Medical physics – ExOTics

This project focuses on the integration of the optimal transport (OT) method into Monte Carlo (MC) simulations in medical physics, with a particular emphasis on dosimetry, which calculates the dose distribution in the patient. MC simulations are commonly used in this field, but they are notoriously slow due to the iterative and stochastic estimation of probability density functions. Various variance reduction techniques (VRT) have been introduced to accelerate MC without biasing the simulation. Despite this, limitations persist in terms of precision and uncertainty. OT provides a powerful mathematical framework for measuring the "distance" between two distributions, making it a promising tool for improving the accuracy and fidelity of dose maps in MC simulations. The project proposes three components: (1) Integrating OT into deep learning training phases to predict doses, (2) Using OT as a new metric to evaluate novel MC simulation methods, and (3) Using OT to enhance transfer learning (TL) and thereby extend MC simulation capabilities. These components aim to improve the precision, robustness, and efficiency of dose predictions, which could revolutionize the approach to dose estimation and analysis, offering significant potential for personalized treatment planning in radiotherapy, specifically for targeted radionuclide therapy of prostate cancer.