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Figure 2
Conclusion
DPF can be estimated and constructed adaptively voxel-
by-voxel in human tumor using multiple FDG-PET imaging
obtained during the treatment course. DPF provides a
potential quantitative objective for adaptive DPbN to plan
the best clinical dose, escalate or de-escalate, in human
tumor based on its own radiosensitivity or radioresistance.
OC-0442 Intensity based synthetic CT generation from
standard T2-weighted MR images with three MR
scanners
L. Koivula
1
, L. Wee
2
, J. Dowling
3
, P. Greer
4
, T. Seppälä
1
,
J. Korhonen
1
1
Comprehensive Cancer Center- Helsinki University
Central Hospital, Department of radiation oncolocy,
Helsinki, Finland
2
Danish Colorectal Cancer Center South, Vejle Hospital,
Vejle, Denmark
3
Commonwealth Scientific and Industrial Research
Organisation CSIRO, CSIRO ICT Centre, Brisbane,
Australia
4
Calvary Mater Newcastle Hospital, Radiation Oncology,
Newcastle, Australia
Purpose or Objective
Recent studies have shown feasibility t o conduct the
entire radiotherapy treatment planning workflow relying
solely on magnetic resonance imaging (M RI). Yet, few
hospitals have implemented the MRI-only workflow into
clinical routine. One limiting issue is the requisite
construction of a synthetic computed tomography (sCT)
image. The majority of published sCT generation methods
necessitate inclusion of extra sequences into the
simulation imaging protocol. This study aims to develop an
intensity-based sCT generation method that relies only on
image data from standard T2-weighted sequence. The
work includes images derived from three different
manufacturers’ MR scanners. The primary target group
was prostate, for which T2-weighted images are already
used as standard target delineation images.
Material and Methods
The study utilized a total of 30 standard T2-weighted
images acquired for prostate target delineation in three
different clinics. The imaging was conducted with MR
scanners (GE Optima 1.5T, Philips Ingenia 1.5T, and
Siemens Skyra 3.0T) of each participating clinic by using
their typical clinical settings. Intensity value variations of
the obtained images were studied locally, and compared
to corresponding Hounsfield units (HUs) of a standard CT
image. The data of 21 of the 30 prostate patients was used
to generate conversion models for bony and soft tissues to
transform the MR image into sCT. The models were
optimized separately for the images obtained by each MR
platform. The sCT generation was tested for 9 of the 30
prostate patients by acquiring the conversion algorithms
within and outside an automatically contoured bone
outline. The quality of the produced sCT images was
quantified by HU and dose distribution comparisons
against standard CT images. The treatment planning was
conducted with VMAT.
Results
Figure 1 shows examples of the constructed sCTs with the
original MR images of each scanner. The mean HU
difference for the sCTs was 11 HUs and 90 HUs in the soft
and bone tissue volumes, respectively (n=9). The target
volume dose differences compared to the CTs were within
0.8% in all cases (0.2±0.5% [average±SD, n=9]). Table 1
presents the HU and dose distribution differe nces
between the sCTs and the actual CT images.
Conclusion
This study revealed the feasibility of generating high
quality sCTs directly from intensity values of standard T2-
weighted MR images. The applied sCT generation method
is adjustable for images applied by multiple
manufacturers’ scanners with different clinical settings.
This work can further contribute to wider clinical
implementation of MRI-only based radiotherapy treatment
planning.
Proffered Papers: Optimatisation algorithms for
treatment planning
OC-0443 Robust optimization of VMAT in head and
neck patients
D. Wagenaar
1
, R.G.J. Kierkels
1
, J. Free
1
, J.A.
Langendijk
1
, E.W. Korevaar
1
1
UMCG University Medical Center Groningen,
Department of Radiation Oncology, Groningen, The
Netherlands