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interest, only 0.2% of voxels differ by >5%, showing good
agreement throughout. Results from measured delivery
errors, such as those in figure 1, will also be presented.
Figure 1: Dosimetric comparison between planned and
delivered doses for a HDR brachytherapy treatment in a
phantom with an introduced error.
Conclusion
Real-time dosimetric treatment verification is possible
with our source tracking system combined with MaxiCalc.
Fast dose calculation based on measured source dwell
positions is achieved and overcomes the limitation of
current TPSs.
References
1.
Smith, R L., et al. Medical physics 43.5 (2016):
2435-2442.
2.
Daskalov, G M., et al. Medical physics 25.11
(1998): 2200-2208.
PV-0187 Source dwell time and transit time
measurement for a HDR afterloading unit
T.L. Chiu
1
, B. Yang
1
, H. Geng
1
, W.W. Lam
1
, C.W. Kong
1
,
K.Y. Cheung
1
, S.K. Yu
1
1
Hong Kong Sanatorium & Hospital, Medical Physics &
Research Department, Happy Valley, Hong Kong SAR
China
Purpose or Objective
To evaluate dwell time and transit time of HDR
brachytherapy treatment by an in-house fluorescent
screen based QA system. Since dosimetric effect would be
directly affected by source dwell time, an accurate QA
method on temporal accuracy is essential.
Material and Methods
The system included a fluorescent screen (Kodak, Lanex
regular screen) which converts the radiation signal to
optical signal and a high-speed camera with frame rate up
to 500 fps and pixel resolution of 1280X720. The temporal
resolution was 2 ms. A catheter in which an Ir-192 source
would be loaded was fixed on the fluorescent screen and
the camera was placed 30 cm away from the screen. The
whole system was light-shielded. When the source
travelled inside the catheter, the camera would capture
images on the fluorescent screen sequentially. Source
position was traced out by locating the centroid of the
captured image. The accuracy of dwell time was assessed
by measuring 3 different dwell times, namely, 1 s, 0.5 s &
0.1 s. According to a white paper from vendor, transit time
for separations below 35 mm would occupied part of the
next dwell time and those for separations above 35 mm
would have 0.1 s compensation. Thus, the influence of
transit time on dwell time was studied by measuring 0.5 s
dwell time under 3 different dwell separations, namely, 6
cm, 4 cm & 0.5 cm. Dwell time was assessed by counting
the number of images in which source positions were
unchanged to the subsequent image. Transit time was the
time between two dwell positions.
Results
Fig. 1 demonstrated the capability of this QA system by
showing a source transit process and Fig. 2 indicated that
measured dwell time was affected by source separation.
Table 1(a) tabulated the measured dwell time for 3
different assigned dwell times with 5 mm separation
between source dwell positions. In all three scenarios, the
dwell time at starting position was close to the assigned
value. Dwell time at next dwell position experienced a
larger discrepancy up to 40% for 0.1 s dwell time. This
discrepancy in dwell time was due to the transit time for
which control computer could not fully account. Hence,
dwell time would be shorter than the assigned value
except at the starting position. Table 1(b) tabulated
measured dwell times at 3 different source separations
with 0.5 s assigned dwell time to assess the compensation
method stated. Discrepancy could be up to 0.33 s in 6 cm
separation. Transit time occupied a larger portion of the
dwell time for longer source separation.
Conclusion
Dwell time and transit time could be measured using the
fluorescent QA system with uncertainty down to 2 ms.
High temporal resolution in this system helped measure
the transit time accurately which could hardly be achieved
in commonly used QA systems. The effect of transit time
on actual source dwell time could be significant and was
not fully accounted for by treatment computer. Clinically
possible combinations, like 0.5 s dwell time and 5 mm
separation, could have a dosimetric error of 8%.
PV-0188 Improved class solutions for prostate
brachytherapy planning via evolutionary machine
learning
S.C. Maree
1
, P.A.N. Bosman
2
, Y. Niatsetski
3
, C. Koedood
er
1
, N. Van Wieringen
1
, A. Bel
1
, B.R. Pieters
1
, T.
Alderliesten
1
1
A cademic Medical Center, Radiation oncology,
Amsterdam, The Netherlands
2
Centrum Wiskunde & Informatica, Amsterdam, The