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Session: Proton Therapy III [Return to Session]

Sensitivity Analysis of Biological Washout and Depth Selection for a Machine Learning-Based Dose Verification Framework in Proton Therapy

S Yu1*, Y Liu1, H PENG1,2, M jia4, (1) Wuhan University, Wuhan, China, 430072 (2) ProtonSmart Ltd, Wuhan, China, 430072 (3) Tianjin University, ,Tianjin, China, 300072


WE-C930-IePD-F7-1 (Wednesday, 7/13/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]

Exhibit Hall | Forum 7

Purpose: Dose verification based on proton-induced positron emitters is a promising quality assurance tool. To move a step closer towards practical application, the sensitivity analysis of two factors was investigated: biological washout and depth selection.

Methods: The training dataset was generated based upon a CT image-based phantom and multiple beam energies/pathways, using Monte-Carlo simulation. For the study of biological washout, an analytical model was applied to modify the change of activity profiles over 5 minutes, including both physical decay and biological washout. For the study of depth selection (a challenge linked to multiple-field irradiation), truncations were applied at different lengths (100, 125, 150 mm) to the raw activity profiles. The performances of a worst-case scenario were examined, combining both factors (depth selection: 125 mm, biological washout: 5 mins). The accuracy was evaluated in terms of ∆_range, mean absolute error (MAE) and mean relative errors (MRE over peak region, MRE95).

Results: The machine learning model is found to be more sensitive to depth selection, relative to biological washout. For the worst-case (only activity as input), the mean ∆_range and MRE95 (activity only) are 6.26 mm and 10.1%, respectively. Including anatomical and stopping power prior as auxiliary inputs (activity+HU+SP), is able to significantly improve accuracy/robustness (∆_range : 0.38 mm, MRE95: 3.11%).

Conclusion: The framework shows good immunity to the perturbation associated with the two factors, maintaining good accuracy even in the worst-case. The detection of proton-induced positron emitters, combined with machine learning, has potential to realize online patient-specific verification in proton therapy.


Dosimetry, PET


TH- Radiation Dose Measurement Devices: Development (new technology and techniques)

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