Patterns of Brain Tumor Recurrence Predicted From DTI Tractography - - PowerPoint PPT Presentation
Patterns of Brain Tumor Recurrence Predicted From DTI Tractography Anitha Priya Krishnan 1 , Isaac Asher 2 , Dave Fuller 2 , Delphine Davis 3 , Paul Okunieff 2 , Walter ODell 1,2 Department of Biomedical Engineering 1 , Radiation Oncology 2 and
Department of Biomedical Engineering1, Radiation Oncology2 and Imaging Sciences3 University of Rochester Medical Center, Rochester, NY USA 14642
Radiation oncologists: 4 - 30mm isotropic
Small margin: recurrences Large margin: damage normal tissue
Clinical observation
Recurrence often at the boundary of treatment
Goal:
Develop appropriate anisotropic treatment
Qualitative correlation of paths of
Hypothesis: paths of elevated water
Migration of cancer cells along WM tracts in
1938 Scherer HJ. 1961 Matsukado et al. 1988 Burger et al. 1989 Johnson et al.
Correlate CT with topographic anatomy and
Our approach: Correlate White matter tracts from DTI
Scalars from DTI (Fractional
2003 Price et al [1] 2004 Hein et al [2] 2005 Beppu et al [3]
Vectors from DTI (principal Eigen
2004 Field et al [4]
Category1: distant recurrence
Small island of recurrent tumor in the DTI
Category2: local recurrence
close to (within 2 cm) or on the boundary of
No sign of recurrence in the DTI dataset
GE signa 1.5T MRI scanner EPI sequence, 20 axial slices, voxel
25 diffusion gradient directions, b =
Tractography: DTIStudio and FSL (
FSL [6]:
Probabilistic fiber tracking Calculates uncertainty in the fiber direction Can track fibers in gray matter
Fiber probability maps for seed point in optic tract Yellow – high probability, Red – moderate relative probability
Spot on left horn not treated Tumor growth Primary GBM Fiber tracts passing through: Primary tumor Recurrent site
Primary Glioma SRS treatment plan Yellow – High probability. Red – Medium relative probability of connection Pretreatment MR T1 Fiber Map from FSL Recurrence tumor Post contrast Axial MR T1 3 months after SRS
Pretreatment MR T1 SRS treatment plan Fibers from primary Post contrast Axial MR T1 3 months after SRS Primary tumor Recurrence tumor
Preliminary results on a small number of
Future Work
High resolution DTI dataset using 3T MR Improved Fiber analysis Verification with animal models
1.
Price SJ, Burnet NG, Donovan T, et al. Diffusion tensor imaging of brain tumors at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol. 2003; 58: 455-462.
2.
Hein, Patrick A, Eskey, et al. Diffusion-Weighted Imaging in the Follow-up of Treated High-Grade Gliomas: Tumor Recurrence versus Radiation Injury. AJNR Am J Neuroradiol 2004 25: 201-209.
3.
Beppu T, Inoue T, Shitaba Y, et al. Fractional anisotropy value by diffusion tensor magnetic resonance imaging as a predictor of cell density and proliferation activity of glioblastomas. Surg Neurol. 2005; 63(1): 56-61.
4.
Field Aaron, Alexander Andrew. Diffusion Tensor Imaging in cerebral tumor diagnosis and therapy. Top Magn Reson Imaging. 2004 oct; 15 (5): 315-24.
5.
Huang H, Zhang J, van Zijl PC, Mori S. Analysis of noise effects on DTI-based tractography using the brute-force and multi-ROI
6.
T.E.J. Behrens, M.W. Woolrich, et al. Characterisation and Propagation of Uncertainty in Diffusion Weighted MR imaging. Magn. Reson Med 2003;50:1077-1088.