The development of imaging methods to non-invasively identify the white matter pathways of the human connectome is the current frontier of imaging neuroscience. Over the past two decades there has been extensive interest in Magnetic Resonance Imaging (MRI) analysis methods that have led to the advent of fibre tractography techniques that aim to delineate white matter fibres in the brain in vivo1. These MRI analysis methods utilize an MRI acquisition technique known as Diffusion Weighted Imaging (DWI) to generate 3D maps of the underlying white matter pathways. The potential power of these 3D maps is such that there has been significant investment globally to develop commercial software packages designed specifically to aid clinicians in neurosurgical navigation to improve the outcomes of patients with lesions affecting white matter pathways supporting eloquent function undergoing neurosurgery2.
Diffusion MRI tractography requires three essential steps: the acquisition of appropriate DWI data; the correct estimation of fiber orientations; and finally, the application of an appropriate tracking algorithm. The reliability and accuracy of tractography results is dependent on all three steps, and these steps are interdependent that is, data collection needs to be consistent with the intended data analysis method and vice versa3. Despite of its fundamental limitations, the most widely applied model in the neurosurgical setting used to estimate white matter fibre orientations from DWI data is the Diffusion Tensor Imaging (DTI) model4. Although there is ongoing debate regarding the optimal approach with which to replace DTI tractography, there is strong agreement both in the clinical and research settings regarding the need to move beyond DTI-based tractography methods toward the use of more advanced tractography models if we are to achieve biological reliability in tractography information for use in neurosurgery2,5. The specific aims of this session will be to:
1. Provide a summary of key concepts of DWI and DTI
2. Explain the fundamental limitations of DTI for estimating fibre orientations from DWI data, and the consequences for performing tractography analysis.
3. Demonstrate the theoretical and practical advantage of utilizing more advanced diffusion-based tractography models for neurosurgical applications.
1. Mori, S. & Tournier, J.-D. Introduction to diffusion tensor imaging and higher order models. (Elsevier Science, 2014).
2. Nimsky, C. Fiber Tracking—We Should Move Beyond Diffusion Tensor Imaging. World Neurosurg. 82, 35–36 (2014).
3. Tournier, J.-D., Mori, S. & Leemans, A. Diffusion tensor imaging and beyond. Magn. Reson. Med. 65, 1532–1556 (2011).
4. Basser, P. J., Mattiello, J. & LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994).
5. Farquharson, S. et al. White matter fiber tractography: why we need to move beyond DTI. J. Neurosurg. 118, 1367–1377 (2013).