Premium
Technical Note: A direct ray‐tracing method to compute integral depth dose in pencil beam proton radiography with a multilayer ionization chamber
Author(s) -
Farace Paolo,
Righetto Roberto,
Deffet Sylvain,
Meijers Arturs,
Vander Stappen Francois
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4966703
Subject(s) - imaging phantom , monte carlo method , ray tracing (physics) , pencil (optics) , proton therapy , beam tracing , physics , ionization chamber , optics , tracing , distributed ray tracing , range (aeronautics) , beam (structure) , algorithm , computational physics , mathematics , ionization , computer science , materials science , statistics , ion , composite material , quantum mechanics , operating system
Purpose: To introduce a fast ray‐tracing algorithm in pencil proton radiography (PR) with a multilayer ionization chamber (MLIC) for in vivo range error mapping. Methods: Pencil beam PR was obtained by delivering spots uniformly positioned in a square (45 × 45 mm 2 field‐of‐view) of 9 × 9 spots capable of crossing the phantoms (210 MeV). The exit beam was collected by a MLIC to sample the integral depth dose (IDD MLIC ). PRs of an electron‐density and of a head phantom were acquired by moving the couch to obtain multiple 45 × 45 mm 2 frames. To map the corresponding range errors, the two‐dimensional set of IDD MLIC was compared with (i) the integral depth dose computed by the treatment planning system (TPS) by both analytic (IDD TPS ) and Monte Carlo (IDD MC ) algorithms in a volume of water simulating the MLIC at the CT, and (ii) the integral depth dose directly computed by a simple ray‐tracing algorithm (IDD direct ) through the same CT data. The exact spatial position of the spot pattern was numerically adjusted testing different in‐plane positions and selecting the one that minimized the range differences between IDD direct and IDD MLIC . Results: Range error mapping was feasible by both the TPS and the ray‐tracing methods, but very sensitive to even small misalignments. In homogeneous regions, the range errors computed by the direct ray‐tracing algorithm matched the results obtained by both the analytic and the Monte Carlo algorithms. In both phantoms, lateral heterogeneities were better modeled by the ray‐tracing and the Monte Carlo algorithms than by the analytic TPS computation. Accordingly, when the pencil beam crossed lateral heterogeneities, the range errors mapped by the direct algorithm matched better the Monte Carlo maps than those obtained by the analytic algorithm. Finally, the simplicity of the ray‐tracing algorithm allowed to implement a prototype procedure for automated spatial alignment. Conclusions: The ray‐tracing algorithm can reliably replace the TPS method in MLIC PR for in vivo range verification and it can be a key component to develop software tools for spatial alignment and correction of CT calibration.