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TREATMENT RESPONSE ASSESSMENT IN HEAD AND NECK
CANCER USING DIFFUSION-WEIGHTED MRI
H. Van Herck1,2, P. Slagmolen1,2,3, M. Lambrecht2,
S. Nuyts2, V. Vandecaveye2, R. Hermans2, F. De Keyzer2, P. Suetens1,2
1Catholic
University of Leuven, Department of Electrical Engineering (ESAT-PSI), Belgium
2University
3IBBT-KULeuven
Hospitals Gasthuisberg, Belgium
Future Health Department, Leuven, Belgium
Abstract
In this paper we present a novel, semi-automatic
method to fuse anatomical T1-weighted and
functional diffusion-weighted magnetic resonance
(DW-MR) images of head and neck (H&N) cancer
patients treated with radiotherapy. The aim is to
obtain quantifiable results about tumor response
during radiotherapy treatment. Our method was
applied to a database of 21 patients with manually
annotated landmarks used for validation. Results
show a significant decrease in mean distance
between the validation points after registration. This
demonstrates that our method is well-suited to
enable treatment follow-up using diffusion-weighted
imaging.
thin-plate spline (TPS) warps to reduce
susceptibility induced artifacts [2], with markers
manually placed on distinct landmark positions in
the registered T1pre, DW pre and DW 2w images.
Figure 1. Overview of method components
Keywords: medical imaging
3
1
Introduction
DW-MRI, a functional imaging technique which
characterizes tissues based on differences in water
mobility, has recently shown promising results in
identifying treatment resistant tumors, of which
precise visualization might help deliver treatment
more accurately. However, the acquisition
sequence used in DW imaging introduces
susceptibility and eddy current induced distortions,
which inhibit the fusion of functional and anatomical
information. Traditional approaches using a single
rigid or nonrigid registration do not suffice in aligning
T1-weighted with DW H&N images. Hence, a more
extended post processing solution was developed.
Results
After registration, DW pre, T12w and DW 2w showed a
strong visual correlation with T1pre. Additionally,
applying leave-one-out validation to 104 slices, with
10 to 35 markers each, demonstrated a decrease in
mean registration error from 5.88 mm to 3.19 mm.
Also, a statistically valid linear regression analysis
portrays a smaller error with more registration
markers, reaching 1 mm with 48 markers.
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Conclusion
Our method can help assess RT-induced changes
and identify treatment-resistant anatomical regions.
References
2
Method
Before and two weeks into radiotherapy (RT), 21
patients underwent both an anatomical and a DW
MRI, yielding two T1-weighted and DW image pairs
(Figure 1). To align all images with the anatomically
correct T1pre image, the proposed method consists
of three components: a rigid initialization to counter
patient relocation and patient motion [1], a nonrigid
registration using B-splines to account for smaller,
anatomical deformations and two marker-based
[1] F. Maes, A. Collignon, D. Vandermeulen, P.
Suetens, "Multimodality image registration by
maximization of mutual information," IEEE
Transactions on Medical Imaging, vol. 16, no. 2,
p. 187-198, 1997.
[2] F. L. Bookstein, "Principal warps: thin-plate
spline warps and the decomposition of
deformations," IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 11, no.
6, p. 567-585, 1989.
10th Belgian Day on Biomedical Engineering – joint meeting with IEEE EMBS Benelux Chapter
December 2, 2011