Program Information

Combining Proton Radiography and X-Ray CT Information to Better Estimate Relative Proton Stopping Power in a Clinical Environment

C Collins Fekete^1,2*, M Dias^2,3 , P Doolan^2,4, David C Hansen⁵, L Beaulieu¹, J Seco² (1) Departement de physique, de genie physique et d'optique et Centre de recherche sur le cancer, Universite Laval, Quebec (2) Massachusetts General Hospital, Boston, MA, (3) Dipartamento di Elettronica, Informazione e Bioingegneria - DEIB, Politecnico di Milano, Italy (4) University College London, London, U.K (5) Experimental Clinical Oncology, Aarhus University, 8000 Aarhus C, Denmark

Presentations

SU-E-J-37 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:In standard proton therapy clinical practice, proton stopping power uncertainties are in the order of 3.5%, which affects the ability of placing the proton Bragg peak at the edge of the tumor. The innovating idea of this project is to approach the uncertainty problem in RSP by using combined information from X-ray CT and proton radiography along a few beam angles. In addition, this project aims to quantify the systematic error introduced by the theoretical models (Janni, ICRU49, Bischel) for proton stopping power in media.

Methods:A 3D phantom of 36 cm3 composed of 9 materials randomly placed is created. Measured RSP values are obtained using a Gammex phantom with a proton beam. Theoretical RSP values are calculated with Beth-Block equation in combination with three databases (Janni, ICRU49 and Bischel). Clinical RSP errors are simulated by introducing a systematic (1.5%, 2.5%, 3.5%) and a random error (+/-0.5%) to the theoretical RSP. A ray-tracing algorithm uses each of these RSP tables to calculate energy loss for proton crossing the phantom through various directions. For each direction, gradient descent (GD) method is done on the clinical RSP table to minimize the residual energy difference between the simulation with clinical RSP and with theoretical RSP. The possibility of a systematic material dependent error is investigated by comparing measured RSP to theoretical RSP as calculated from the three models.

Results:Using 10,000 iterations on GD algorithm, RSP differences between theoretical values and clinical RSP have converged (<1%) for each error introduced. Results produced with ICRU49 have the smallest average difference (0.021%) to the measured RSP. Janni (1.168%) and Bischel (-0.372%) database shows larger systematic errors.

Conclusion:Based on these results, ray-tracing optimisation using information from proton radiography and X-ray CT demonstrates a potential to improve the proton range accuracy in a clinical environment.

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