Contents
- Introduction
- User Guide
- FAQ
Research Overview
FDT (FMRIB's Diffusion Toolbox) is a software tool for analysis of diffusion weighted images.
FDT is part of FSL (FMRIB's Software Library). FDT has an easy-to-use graphical user interface (GUI) and its component programmes can also be run from the command line. FDT includes tools for data preprocessing, local diffusion modelling and tractography. Each stage in FDT is run separately. The main FDT programmes, which are accessible from the GUI are:
- eddycorrect - for correction of eddy current distortions
- bedpostx - for local modelling of diffusion parameters
- probtrackx - for tractography and connectivity-based segmentation
- dtifit - for local fitting of diffusion tensors
The FDT GUI also includes a registration option that registers images using FLIRT. Additional FDT utilities, that can be run only from the command line, are:
- proj_thresh - for thresholding some outputs of probtrackx
- find_the_biggest - for performing hard segmentation on the outputs of connectivity-based thresholding in probtrackx
- vecreg - for resampling vector data
- qboot - for estimating fibre orientations and their uncertainty using ODFs and bootstrapping
The probabilistic tractography tools within FDT are very flexible and allow the user to generate connectivity distributions from single or multiple voxels, from FreeSurfer or Caret cortical surfaces; or FIRST sub-cortical surfaces; to limit these distributions based on anatomical criteria and to perform segmentation based on the probability of connection to user-defined target regions.
To call the FDT GUI, either run Fdt (Fdt_gui on Mac or Windows), or run fsl and press the FDT button.
For an overview of the local diffusion modelling and tractography used within FDT see the appendix.
Referencing
If you use FDT in your research, please make sure that you reference the relevant articles amongst the following:
When using bedpostx
Standard ball & stick model (model1)
[Behrens 2003a] T.E.J. Behrens, M.W. Woolrich, M. Jenkinson, H. Johansen-Berg, R.G. Nunes, S. Clare, P.M. Matthews, J.M. Brady, and S.M. Smith. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med, 50(5):1077-1088, 2003.
[Behrens 2007] T.E.J. Behrens, H. Johansen-Berg, S. Jbabdi, M.F.S. Rushworth, and M.W. Woolrich. Probabilistic diffusion tractography with multiple fibre orientations. What can we gain? NeuroImage, 23:144-155, 2007.
Multi-shell ball & stick model (model2)
[Jbabdi 2012] S. Jbabdi, S.N. Sotiropoulos, A. Savio, M. Grana, T.E.J. Behrens. Model-based analysis of multishell diffusion MR data for tractography: How to get over fitting problems. Magn Reson Med, doi: 10.1002/mrm.24204, 2012.
Multi-shell ball & zepellins model (model3)
[Sotiropoulos 2016] S.N. Sotiropoulos, M. Hernandez-Fernandez, A.T. Vu, J.L. Andersson, S. Moeller, E. Yacoub, C. Lenglet, K. Ugurbil, T.E.J. Behrens, S. Jbabdi. Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project. Neuroimage 134:396-409, 2016.
GPU version
[Hernandez 2013] Hernandez M, Guerrero GD, Cecilia JM, Garcia JM, Inuggi A, Jbabdi S, Behrens TEJ, Sotiropoulos SN, (2013) Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs. PLoS ONE 8(4):e61892
When using probtrackx
[Behrens 2003a] T.E.J. Behrens, M.W. Woolrich, M. Jenkinson, H. Johansen-Berg, R.G. Nunes, S. Clare, P.M. Matthews, J.M. Brady, and S.M. Smith. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med, 50(5):1077-1088, 2003.
[Behrens 2007] T.E.J. Behrens, H. Johansen-Berg, S. Jbabdi, M.F.S. Rushworth, and M.W. Woolrich. Probabilistic diffusion tractography with multiple fibre orientations. What can we gain? NeuroImage, 23:144-155, 2007.
GPU version
[Hernandez 2019] M. Hernandez-Fernandez, I. Reguly, S. Jbabdi, M. Giles, S. Smith, S.N. Sotiropoulos. Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes. Neuroimage, 188:598-615, 2019.
When using connectivity-based parcellation
[Behrens 2003b] T.E.J. Behrens, H. Johansen-Berg, M.W. Woolrich, S.M. Smith, C.A.M. Wheeler-Kingshott, P.A. Boulby, G.J. Barker, E.L. Sillery, K. Sheehan, O. Ciccarelli, A.J. Thompson, J.M. Brady, and P.M. Matthews. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7):750-757, 2003.
[Johansen-Berg 2004] H. Johansen-Berg, T.E.J. Behrens, M.D. Robson, I. Drobnjak, M.F.S. Rushworth, J.M. Brady, S.M. Smith, D.J. Higham, and P.M. Matthews. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc Natl Acad Sci U S A, 101(36):13335-13340, 2004.
When using Qboot
[Sotiropoulos 2011] S.N. Sotiropoulos, I. Aganj, S. Jbabdi, G. Sapiro, C. Lenglet, T.E.J. Behrens. Inference on Constant Solid Angle Orientation Distribution Functions from Diffusion-Weighted MRI, p. 609, OHBM, Canada, 2011.