Constrained Voxel Based Analysis for Diffusion Tensor
Imaging
Introduction
Diffusion tensor imaging (DTI) is an emerging magnetic resonance imaging (MRI)
modality that assesses the microstructural organization of brain white matter (WM) in
vivo, based on the self-diffusion of water molecules [1]. Combining DTI with fiber
tractography (FT) additionally enables the investigation of WM fiber pathways and has
already shown a great potential for biomedical applications.
One of the preferred methods to analyze DTI data is “voxel based analysis” (VBA), e.g.
[2]. With this approach, statistical tests are performed for each voxel of spatially
normalized data sets (e.g., comparison between patients and controls, see Fig. 1).
Although VBA is fully automated and does not require prior hypotheses, its results are
highly dependent on several parameter settings and specific implementations [3]. In
addition, current VBA methods do not take uncertainty of diffusion profiles into
account.
The project
In this research project, you will work on a novel VBA framework. In contrast to
conventional VBA, in this approach you will incorporate complementary diffusion and FT
information, such as local fiber orientation(s) and precision measures, as constraints
when performing statistical analyses. This information can either be derived from DTI
or more complex models based on high angular resolution diffusion imaging (HARDI),
which can deal with crossing fibers within a voxel (Fig. 2) [4].
You will evaluate this VBA processing framework – coined “constrained VBA” (CVBA) –
both on synthetic data with simulated pathologies, and real human data. The method
will be used to examine differences in diffusion measures between healthy controls and
diabetic patients.
Fig. 1: VBA example result. Fractional anisotropy is a
diffusion measure derived from DTI, and is decreased
in patients with vascular parkinsonism [5]
Fig. 2: HARDI techniques can reveal crossing fibers
within a voxel[4]
Your background
We are looking for students with a background in Biomedical Engineering, Computer
Science, Physics or similar. Matlab programming skills are appreciated.
More information
For more information on this particular project, please contact Chantal Tax
(Chantal@isi.uu.nl).
References
[1] Jones, D. K., and Leemans, A. (2011). Diffusion tensor imaging. Methods Mol
Biol, 711(2), 127-144.
[2] Hsu, J. L., Leemans, A., Bai, C. H., Lee, C. H., Tsai, Y. F., Chiu, H. C., and Chen, W.
H. (2008). Gender differences and age-related white matter changes of the human
brain: a diffusion tensor imaging study. NeuroImage, 39(2), 566-577.
[3] Jones, D. K., Chitnis, X. A., Job, D., Khong, P. L., Leung, L. T., Marenco, S., and
Symms, M. R. (2007). What happens when nine different groups analyze the same DTMRI data set using voxel-based methods. In Proceedings of the 15th Annual Meeting of
the International Society for Magnetic Resonance in Medicine, Berlin (p. 74).
[4] Tournier, J. D., Mori, S., and Leemans, A. (2011). Diffusion tensor imaging and
beyond. Magnetic Resonance in Medicine, 65(6), 1532-1556.
[5] Wang, H. J., Hsu, J.L. and Leemans, A. (2011). Diffusion tensor imaging of
vascular parkinsonism: structural changes in cerebral white matter and the association
with clinical severity. Arch Neurol, 69(10), 1340-8.