Highlights
- •Film was used to assess an MC dose calculator for robotic radiosurgery with an MLC.
- •Within a heterogeneous phantom 91% of pixels pass 2%/1 mm 2D gamma vs. 55% with FSPB.
- •A calculation limitation was identified and fixed. The gamma pass-rate rose to 97%.
- •This demonstrates the value of independent validation for any software solution.
- •The MC algorithm expands the clinical use of robotic radiosurgery with MLC.
Abstract
Purpose
At introduction in 2014, dose calculation for the first MLC on a robotic SRS/SBRT
platform was limited to a correction-based Finite-Size Pencil Beam (FSPB) algorithm.
We report on the dosimetric accuracy of a novel Monte Carlo (MC) dose calculation
algorithm for this MLC, included in the Precision™ treatment planning system.
Methods
A phantom was built of one slab (5.0 cm) of lung-equivalent material (0.09…0.29 g/cc)
enclosed by 3.5 cm (above) and 5 cm (below) slabs of solid water (1.045 g/cc). This
was irradiated using rectangular (15.4 × 15.4 mm2 to 53.8 × 53.7 mm2) and two irregular MLC-fields. Radiochromic film (EBT3) was positioned perpendicular
to the slabs and parallel to the beam. Calculated dose distributions were compared
to film measurements using line scans and 2D gamma analysis.
Results
Measured and MC calculated percent depth dose curves showed a characteristic dose
drop within the low-density region, which was not correctly reproduced by FSPB. Superior
average gamma pass rates (2%/1 mm) were found for MC (91.2 ± 1.5%) compared to FSPB
(55.4 ± 2.7%). However, MC calculations exhibited localized anomalies at mass density
transitions around 0.15 g/cc, which were traced to a simplification in electron transport.
Absence of these anomalies was confirmed in a modified build of the MC engine, which
increased gamma pass rates to 96.6 ± 1.2%.
Conclusions
The novel MC algorithm greatly improves dosimetric accuracy in heterogeneous tissue,
potentially expanding the clinical use of robotic radiosurgery with MLC. In-depth,
independent validation is paramount to identify and reduce the residual uncertainties
in any software solution.
Keywords
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Article info
Publication history
Accepted:
October 13,
2018
Received in revised form:
October 8,
2018
Received:
May 4,
2018
Identification
Copyright
© 2018 Published by Elsevier Ltd on behalf of Associazione Italiana di Fisica Medica.
