ESTRO 36 Abstract Book

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S498 ESTRO 36
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Material and Methods
Ten patients who were referred for brain metastasis
radiosurgery were analysed in this study. A planning CT (1
mm slice thickness), a contrast-enhanced T1 3D MRI scan
(1.5T, 1 mm isotropic voxel size, surface coils) with
patient immobilized in a 3-point thermoplastic shell
(mask-MR) and a contrast-enhanced T1 3D MRI scan (1.5T,
1 mm isotropic voxel size, multi-channel head coil)
without immobilization mask (no mask-MR) were acquired.
First, a clinician stated which of the MRI scans had superior
quality, to assure that the no-mask MR had at least the
same image quality compared to the clinically used mask-
MR. Then, the two MRIs were registered independently to
the planning CT by a normalized mutual information
algorithm which was restricted to rigid registration. The
GTV was delineated by 3 clinicians on 1) mask-MR and 2)
no mask-MR. The brain stem, chiasm and right eye were
delineated by one clinician. Furthermore, 8 well-defined
landmarks were marked by an observer in both scans.
Residual registration errors were estimated for both MRIs
by measuring the absolute coordinate differences in the
three orthogonal directions between the set of landmarks
on both imaging series after registration. Moreover, the
absolute differences in the centres-of-gravity coordinates
of GTV (median of 3 observers), brain stem, chiasm and
right eye on mask-MR and no mask-MR were compared.
Results
The no mask-MR image quality was found to be superior in
9 of the 10 patients. The average coordinate difference
between mask-MR and no mask-MR for all landmarks along
the three orthogonal directions were within 0.5 mm (table
1). Similar results were found for the coordinates of the
centre-of-gravity of all delineated OARs and GTV.
Deviations in OAR registration > 1mm could be attributed
to variations in delineation (figure 1). Only in one case, a
registration error was observed. All GTV deviations were
within 1mm.
Conclusion
The registration of MRIs obtained with or without
immobilization mask to a planning-CT generally differs
less than the MRI resolution (1 mm isotropic). Therefore,
immobilization of the head during MRI for patients
undergoing radiotherapy of brain metastasis is not
necessary.
However, to guarantee high accuracy of image registration
when omitting an immobilization device during MRI, more
attention should be paid to the quality of MR-CT fusion.
Furthermore, consecutive MR images should be matched
separately to CT, to correct for intra-scan motion.
We foresee two benefits of scanning without mask. Firstly,
the patient comfort during the MRI scan sessions will be
improved. Secondly, omission of the immobilization mask
permits the use of a multi-channel head coil which results
in higher image quality. Moreover, using a head coil allows
for introduction of MRI techniques which require high
signal-to-noise ratios or acceleration (e.g. DWI and FLAIR).
PO-0902 Identifying the dominant prostate cancer focal
lesion using 3D image texture analysis
D. Montgomery1, K. Cheng1, Y. Feng 1, D.B. McLaren2, S.
McLaughlin3, W. Nailon1
1Edinburgh Cancer Centre Western General Hospital,
Department of Oncology Physics, Edinburgh, United
Kingdom
2Edinburgh Cancer Centre Western General Hospital,
Department of Clinical Oncology, Edinburgh, United
Kingdom
3Heriot Watt University, School of Engineering and
Physical Sciences, Edinburgh, United Kingdom
Purpose or Objective
Prostate cancer is one of the few solid organs where
radiotherapy is applied to the whole organ. This is because
accurately identifying the dominant cancer foci on
magnetic resonance (MR) images, which can then be
mapped onto computerised tomography (CT) images for
radiotherapy planning, is difficult. The aim of this study
was to investigate the use of three-dimensional (3D)
texture analysis for automatically identifying the
dominant cancer foci on MR images acquired for diagnosis
and prior to the administration of androgen deprivation
therapy, which may shrink the tumour foci.
Material and Methods
On 14 patients with confirmed prostate cancer, 3D image
texture analysis was carried out on T2-weighted MR
images acquired for diagnosis on a Symphony 1.5T scanner
(Siemens, Erlangen, Germany). The prostate, bladder,
rectum and the location of the main cancer foci were
outlined on all images. In 5x5x5 pixel3 volumes within the
prostate 446 3D texture analysis features were calculated.
These features were used to train an AdaBoost model,
which was used to predict the class of each 5x5x5 region
as either 'prostate” or 'focal lesion.” Morphological
filtering was applied to each region to remove invalid
elements and to clean the final outline. The Dice similarity
coefficient was used to assess the agreement between the
clinical and predicted contours.
Results
Figure 1 shows an example of a contour produced by the
algorithm where the Dice similarity coefficient was 0.98.
Table 1 shows the Dice coefficients calculated between
the clinical contours and the contours predicted by 3D
image analysis. 11 of the 14 cases had a Dice score greater
than 0.65 and 8 of the 14 cases had a score greater than
0.9, indicating good agreement between the clinical and
predicted contours. In 3 cases the image analysis
technique failed to identify the focal lesion.
Figure 1: Clinical contour in blue and predicted contour
generated by 3D texture analysis shown in red on three
T2-weighted MR images from the same patient (Patient 6).
Table 1: Dice coefficient between the clinical contours
and the contours predicted by image analysis.
Conclusion
The 3D image analysis results presented are encouraging
and demonstrate the potential of this approach for
automatically identifying focal disease on T2-weighted MR
images. However, further investigation is required to
establish why the approach fails in certain circumstances