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Figure 1.
Conclusion
ABAS is a clinically useful tool for segmenting structures in
breast cancer loco-regional radiation therapy in a multi-
institutional setting. The introduction of ABAS in daily
clinical practice will significantly reduce the workload
especially in departments where the radiation therapy
technologists (RTTs) are responsible for target volume
delineation and treatment planning. Manual correction of
some structures is important before clinical use.
Moreover, applying ABAS may be a reasonable alternative
for consistent segmentation and easy quality assurance
testing in multi-institutional trials. Careful selection and
stratification of atlas subjects seems to be the most
influencing factor in the outcome of the ABAS. Further
investigation to find out the best stratification factors is
encouraged.
Based on these results, ABAS is now made
available for Danish patients.
PO-0899 Tumor volume delineation us ing non-EPI
diffusion weighted MRI and FDG-PET in he ad-and-neck
patients.
B. Peltenburg
1
, T. Schakel
1
, J.W. Dankbaar
2
, M.
Aristophanous
3
, C.H.J. Terhaard
1
, J.M. Hoogduin
2
, M.E.P.
Philippens
1
1
UMC Utrecht, Radiation Oncology, Utrecht, The
Netherlands
2
UMC Utrecht, Radiology, Utrecht, The Netherlands
3
MD Anderson Cancer Center, Radiation Physics,
Houston, USA
Purpose or Objective
Diffusion weighted (DW) MRI shows high contrast between
tumor and the surrounding tissue, which makes it a
candidate to facilitate target volume delineation in head-
and-neck (HN) radiotherapy treatment planning. In this
study we assess the performance of geometrically
undistorted DW MRI for target delineation in terms of
interobserver agreement and spatial concordance with
automatic delineation on
18
F-fluorodeoxyglucose (FDG)
positron emission tomography (PET).
Material and Methods
Fifteen head-and-neck cancer patients underwent both
standard echo-planar imaging based (EPI) and undistorted
fast spin-echo based (SPLICE) DW MRI in addition to FDG-
PET for RT treatment planning. Target delineation on DW
MRI was performed by 3 observers, while for PET a semi-
automatic segmentation was performed using a Gaussian
mixture model. Volumes, overlap metrics, defined as dice
similarity coefficient and generalized conformity index,
and hausdorff distances were calculated from the
delineations.
Results
The median volumes delineated by the 3 observers on DW
MRI were 10.8, 10.5 and 9.0 mL respectively. The median
conformity index over all patients was 0.73 (range 0.38 –
0.80). On PET, a significantly smaller median volume of
8.0 mL was found. Compared with PET, the delineations
by the 3 observers showed a median dice similarity
coefficient of 0.71, 0.69 and 0.72 respectively. For all 3
observers the mean hausdorff distance was small with
median (range) distances between PET and DW of 2.3 (1.5
– 6.8), 2.5 (1.6 – 6.9) and 2.0 (1.35 – 7.6) mm respectively.
Over all patients, the median 95
th
percentile distances
were 6.0 (3.0 – 13.4), 6.6 (4.0 – 24.0) and 5.3 (3.4 – 26.0)
mm.
Conclusion
Diffusion weighted imaging optimized for geometric
accuracy resulted in target volume delineation with good
interobserver agreement and a large similarity with PET.
PO-0900 Quantifying the Effect of MRI Geometrical
Distortions on Radiotherapy Treatment Planning Doses.
M. Adjeiwaah
1
, M. Bylund
1
, J. Lundman
1
, J. Jonsson
1
, T.
Nyholm
1
1
Umeå University, Radiation Sciences, Umea, Sweden
Purpose or Objective
The use of MRI for Radiotherapy Treatment Planning (RTP)
is increasing and the proposed MR-only workflow could be
beneficial. One worry of an MR-only RTP is geometrical
distortions. There are at present few studies focusing on
the effect of MR geometrical distortions on planned doses
in an MR-only treatment planning and to our knowledge,
none fully takes into account both gradient non-linearities
and Patient-induced Susceptibility effects. This study
focused on quantifying the effect of gradient non-
linearities and Patient–induced Susceptibility effects on
dose distributions for Prostate Cancers.
Material and Methods
The deformation field was generated by adding measured
machine-specific and simulated patient-induced
susceptibility effect deformation fields for a 3T scanner as
shown in Fig. 1. Different bandwidths and simulated
gradient readouts in the anterior/posterior (A/P) and
right/left (R/L) directions were used. To isolate the effect
of the distortions, the deformation fields were applied to
17 Prostate Patient CT images and their corresponding
clinically delineated structures, giving a distorted CT
(dCT). VMAT optimized plans were generated for all
distorted cases and recalculated on the undistorted CT