Feasibility study on the integration of a deep learning dose computation algorithm in an automated proton therapy treatment planning system

Feasibility study on the integration of a deep learning dose computation algorithm in an automated proton therapy treatment planning system

Graauw, Mitchel (TU Delft Applied Sciences)

Perko, Z. (mentor)
Breedveld, Sebastiaan (graduation committee)

Delft University of Technology

Applied Physics

2024-03-22

The dose computation algorithm, or dose engine, is one of the fundamental parts of radiotherapy treatment planning. These algorithms predict how the dose will be distributed inside the patient.
Current dose engines are mainly based on either Monte Carlo simulations (MC) or pencil beam algorithms (PBA). MC being very precise, but relatively slow. PBA being quicker, but generally lacking accuracy. Since treatment planning requires both high speed and accuracy, one would prefer MC accuracy with even higher speeds than PBA. A recent study showed a possible solution based on deep-learning called the Dose Transformation Algorithm (DoTA). This deep-learning algorithm is capable of doing MC accurate dose calculations and is faster than PBA (Pastor-Serrano and Perk ́o 2022).
In this project, a feasibility study has been performed on the integration of DoTA as a dose engine in actual treatment planning. The treatment planning system (TPS) in this study is Erasmus- iCycle (Breedveld, Storchi, et al. 2012). This study included the creation of an algorithm to do dose computations with DoTA for any given set of parameters given by the TPS. Subsequently, the dose computations by DoTA have been compared to those computed by Erasmus-iCycle’s current dose engine, ASTROID (Kooy et al. 2010). Analyses on these dose computations included comparisons in a homogeneous water box, alternative homogeneous matter and patient geometries. Two main sources of discrepancy between DoTA and ASTROID where the beam’s range and the beam model used by ASTROID, compared to what DoTA was trained on. Both dose engines likely use a different interpretation of the proton stopping power, leading to range discrepancies up to 14.9% for 200 MeV beamlets when projected in a homogeneous matter of 1000 Hounsfield Units (HUs). Comparing the dose distributions in water, the maximum dose discrepancy around the Bragg peak (BP) for a 80 MeV beam was about 60.0%, due to the width of the beam being larger for DoTA. The mean dose discrepancy in water reached a maximum of 18.9%. In a patient geometry, the range differences made the mean discrepancies go up to a maximum of 22.9%, as expected from the range discrepancies found earlier. Implementation of different gantry and beamlet angles increased the discrepancies, likely caused by the interpolation required to perform calculations under these angles. In terms of distributed energy, the models were closer, with the mean discrepancy decreasing to maximum of 7.1%. Computations of two treatment plan dose distributions showed that the discrepancies arising from this beam model and range difference were to large to achieve viable dose volume histograms. A two lateral beam plan showed the better results of the two plans with an under dosing of 20.8%, likely due to robustness occasionally compensating for the range discrepancy.

Dose Calculation
Deep Learning
Optimization
treatment
Proton
Proton therapy

http://resolver.tudelft.nl/uuid:b662f36f-b60d-4741-90f1-1f9cb8b30c10

Student theses

master thesis

© 2024 Mitchel Graauw