Ellie Thompson and Anna Schroder, UCL Hawkes Institute
The data you will need for the practicals is included in the data folder. We will be using the Fibercup phantom for all the practicals.
In this exercise we fit the diffusion tensor model to phantom data using non-linear least squares. You will code up the model from scratch and visualise the results.
Key references:
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P.J. Basser, J. Mattiello, D. Lebihan, Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo, Journal of Magnetic Resonance, Series B, Volume 103, Issue 3, (1994) - the first paper introducing the diffusion tensor
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O'Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurg Clin N Am. 2011 Apr;22(2):185-96, viii. doi: 10.1016/j.nec.2010.12.004. - a nice overview of the technique and its applications
In this exercise we fit a simple compartment model: the ball-and-stick model. As in the previous exercise, you will code the model from scratch and fit it to the data using non-linear least squares.
Key references:
- Behrens, T.E.J. et al. (2003), Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med., 50: 1077-1088. https://doi.org/10.1002/mrm.10609 - paper introducing the ball and stick model, as part of a Bayesian framework for uncertainty estimation of the estimated parameters
- Behrens, T.E.J. et al. (2007), Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34.1: 144-155. https://doi.org/10.1016/j.neuroimage.2006.09.018. - extension of the ball and stick model to multiple fibres. The number of fibres in each voxel is estimated from the data by automatic relevance determination.
- Panagiotaki, E. et al. (2012), Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison. NeuroImage, 59.3: 2241-2254, https://doi.org/10.1016/j.neuroimage.2011.09.081. - a taxonomy of different compartment models
In this exercise we will use tools from DIPY to obtain the voxel-wise fibre orientation function by performing constrained spherical deconvolution on our dataset. The practical draws on the example from the DIPY workshop: Reconstruction with Constrained Spherical Deconvolution.
Key references:
- Dell'Acqua F, Tournier JD. (2019) Modelling white matter with spherical deconvolution: How and why? NMR in Biomedicine.; 32:e3945. https://doi.org/10.1002/nbm.3945 - thorough introduction to the topic of spherical deconvoluion in dMRI
- Tournier, J. D., Calamante, F., & Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 35(4), 1459–1472. https://doi.org/10.1016/j.neuroimage.2007.02.016 - initial paper introducing the CSD method
- Jeurissen, B. et al. (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, Volume 103: 411-426, https://doi.org/10.1016/j.neuroimage.2014.07.061. - extension to multi-tissue CSD