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on lung-like phantoms with clinical proton and carbon
beams at the Heidelberg ion-therapy center (HIT). We
adopted the benchmarked model to provide a
parametrization of the Bragg peak degradation on the
beam and on the previously mentioned lung parameters.
Throughout this work, we tested and used a Gaussian
convolution of the undegraded Bragg peak (U. Titt et al,
2015) to parametrize the degradation. Furthermore, the
model was used to investigate the effects on clinical
spread out Bragg peak (SOBP) and on the relative
biological effectiveness (RBE).
Results
Fluctuations in the WET were found the major degradation
factor, contributing more than 75% (40%) to the
cumulative distal falloff widening for a carbon (proton)
Bragg peak. The simulated lung parenchyma model (Figure
1) was capable to reproduce the experimental data with a
slight underestimation of the degradation parameters, yet
guaranteeing the correct reproduction of all the relevant
characteristics in the degraded dose distribution. The
Gaussian filtration unified the description for different
beam particles and provided a compact and complete
characterization with specific dependencies with respect
to each lung parameter. Moreover, the description was
found independent from the initial beam energy resulting
in deviations mainly about the SOBP distal falloff while the
plateau remains unaffected. Finally, the impact on the
biological dose was mainly driven by changes to the
physical dose due to the limited deviations in the RBE.
Conclusion
We provide a comprehensive characterization of Bragg
peak degradation that can readily be implemented in a
TPS. Such implementation is crucial for a more complete
description of lung treatments, adding to the effect of
macroscopic structures (e.g. bronchi, CT resolvable) the
contribution of microscopic lung parenchyma (below CT
resolution).
PO-0788 First assessment of Delivery Analysis tool for
pre-treatment verification on the new Radixact system
A. Girardi
1
, T. Gevaert
1
, C. Jaudet
1
, M. Boussaer
1
, M.
Burghelea
2
, J. Dhont
1
, T. Reynders
1
, K. Tournel
1
, M. De
Ridder
1
1
Universitair Ziekenhuis Brussel, Department of
Radiotherapy- Universitair Ziekenhuis Brussel- Vrije
Universiteit Brussel- Brussels- Belgium, Brussels, Belgium
2
Brainlab AG, BRAINLAB AG Feldkirchen Germany,
Brussels, Belgium
Purpose or Objective
To evaluate the accuracy of the Delivery Analysis (DA) tool
for patient-specific pre-treatment verification and the
sensitivity to detect discrepancies in dose delivery in
comparison with widespread detectors.
Material and Methods
The Radixact machine is equipped with the DA device for
pre-treatment Quality Assurance (QA) and interfraction
verification. This tool is designed to assess the consistency
of the delivered treatment through the detector data and
to show anatomical changes of the patient. The latter
representing a powerful tool to be coupled with Adaptive
Radiotherapy. The idea is to use the detector: a) to
measure the Multileaf Collimator (MLC) leaf open time, b)
to compare the planned sinogram to the delivered one and
c) for dose reconstruction purposes. In this study, we
performed pre-treatment verification on the very first
twenty heterogeneous patients treated worldwide (target
volumes ranging between 98 to 4179 cc) using the DA, the
Sun Nuclear MapCheck2 (MC2) and the ScandiDos Delta4
(D4).The Gamma Index was used to show the agreement
between dose planning calculations and measurements.
To compare the three methods, criteria were set to 2% and
3% in local dose and to 2mm and 3mm in distance,
respectively, excluding doses lower than 20% of the
maximum doses. The performances of the systems were
analysed with a single factor ANOVA test, with a
significance level of α=0.05. A possible dependence of the
results from the target volume was furthermore explored
with a simple linear regression analysis.
Results
The ANOVA test showed no statistically significance
differences between the performances of the three
systems, both for the 2%/2 mm and the 3%/3mm criteria
(p-values equal to 0.351 and 0.660 respectively). The
linear regression indicated a variation of performance as
a function of target volume for the MC2 (R
2
2%/2mm
=0.819
and R
2
3%/3mm
=0.979) and the D4 detectors (R
2
2%/2mm
=0.991
and R
2
3%/3mm
=0.990), which is not highlighted for the DA
system (R
2
2%/2mm
=0.283 and R
2
3%/3mm
=0.290). This
difference could be related to the missing data due to the
larger dimension of the dose map with respect to the
detection area of the MC2 and D4 systems.
Conclusion
This study showed that the performances of the Delivery
Analysis tool for the new Radixact machine is not different
from those of two other widespread detectors for pre-
treatment verification. Moreover, the linear regression
test showed that the performances of the system are not
correlated with the target volume, as is the case for two
other detectors used in the study, proving its sensitivity as
a patient specific QA tool.