ESTRO 36 Abstract Book

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S875 ESTRO 36
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References
[1] Brouwers PJ et al. Set-up verification and 2-
dimensional electronic portal imaging device dosimetry
during breath hold compared with free breathing in breast
cancer radiation therapy. Pract Radiat Oncol. 2015 May-
Jun;5(3):e135-41
[2] Cerviño L et al. Using surface imaging and visual
coaching to improve the reproducibility and stability of
deep-inspiration breath hold for left-breast-cancer
radiotherapy. Phys. Med. Biol. 54 (2009) 6853–6865
EP-1618 Can diaphragm motion function as a surrogate
for motion of esophageal tumors during treatment?
S.E. Heethuis1, L. Goense1, A.S. Borggreve 1, P.S.N. Van
Rossum1, R. Van Hillegersberg2, J.P. Ruurda2, S. Mook 1,
G.J. Meijer1, J.J.W. Lagendijk1, A.L.H.M.W. Van Lier1
1 University Medical Center Utrecht, Department of
Radiotherapy, Amsterdam, The Netherlands
2 University Medical Center Utrecht, Department of
Surgery, Amsterdam, The Netherlands
Purpose or Objective
Esophageal tumors show large motion in cranio-caudal
direction (CC), with a Peak-to-Peak (P-t-P) range of 2.7 to
24.5mm [Lever F. et al. (2013)]. In case the motion of the
tumor could be followed during radiotherapy treatment,
this would enable treatment margin reduction. The aim of
this research is to investigate whether the motion of the
diaphragm is correlated with breathing motion and drift
we can detect in esophageal tumors. As such, the
diaphragm could function as a surrogate for esophageal
tumor motion during treatment.
Material and Methods
In total, 46 coronal cine MR scans were obtained from 4
patients whom were treated with neoadjuvant
chemoradiotherapy (nCRT) for distal esophageal cancer.
In this study, one MR scan was performed prior to nCRT,
followed by 5 weekly MR scans during nCRT (in one patient
only 4 scans). Cine MR scans included 75 frames acquired
in approximately 45 seconds, with a resolution of
2.01x2.01mm. The scan was acquired twice within one
session, separated by circa 10 minutes. To estimate
motion in the cine MR series an optical flow algorithm
(RealTITracker, [Zachiu C. et al. (2015)]) was used to
calculate motion fields. The tumor was delineated
manually, in which the mean motion for each frame was
calculated in CC direction. Motion was also estimated in
the diaphragm/liver border within a manually placed
rectangle. An in-house tool was designed to find peaks and
estimate drifts in the motion curves. Drift was defined as
the change in the mean between consecutively found local
maxima and minima. Correlation of the CC motion
between diaphragm and tumor was calculated. P-t-P
analysis was performed on tumor motion curves and tumor
motion curves corrected for drift using the diaphragm drift
( Fig. 1).
Results
A strong Pearson’s correlation of r=0.972 was found while
comparing CC motion in diaphragm and tumor, with a
range of 0.849-0.996. The mean P-t-P tumor motion
before and after correction for drift was 10.1 and 9.3mm
respectively (p<0.05). However, for individual scan
sessions the effect of drift could be much larger, as is
exemplified in Fig. 1a. P-t-P amplitude for each patient
before and after drift correction is shown in Fig. 2.
Although the amplitude of the diaphragm motion was
higher, mean P-t-P motion of 12.6mm, when the tumor
motion showed a drift or sudden movement, this was also
found in the diaphragm motion ( Fig. 1&2).
Conclusion
In this study it was found that diaphragm motion shows a
strong correlation with esophageal tumor motion. Using
the diaphragm motion for drift correction resulted on
average in a reduction of the P-t-P range over all patients.
This reduction can be used for adaptive treatment
strategies, which reduce margins. For example, in case an
MR-linac is taken in mind [Lagendijk J.J.W. et al (2008)],
MR-based gating to compensate for respiratory motion
and/or base-line shift (drifting) detection using the
diaphragm as surrogate will be well feasible.
EP-1619 Determination of Lung Tumour Motion from
PET Raw Data used for Accelerometer Based Motion
Prediction
G. Hürtgen1, S. Von Werder2, V. Berneking 1, K. Gester 1,
O. Winz 3, P. Hallen4, F. Büther5, C. Schubert1, N.
Escobar-Corral1, J. Hatakeyama Zeidler6, H. Arenbeck6,
C. Disselhorst-Klug 2, A. Stahl7, M.J. Eble 1
1RWTH Aachen University Hospital, Department of