This repository contains materials and tools to support the implementation and use of the "Free Waveform" (FWF) MRI pulse sequence. The sequence is a diffusion-weighted spin-echo that facilitates the execution of user-defined gradient waveforms for the purposes of tensor-valued diffusion encoding and other methods that require arbitrary modulation of the gradients.
Getting the sequence
Siemens
Please contact Filip Szczepankiewicz at filip.szczepankiewicz@med.lu.se.
Note that the sequence is shared through Lund University by establishing:
C2P between Siemens Healthcare and the receiver,
MTA (material transfer agreement) between Lund University and the receiver.
Check the list of compiled variants to see if the sequence is available for your system. In special cases we may compile the sequence for other versions.
Bruker
An implementation for TopSpin, by Daniel Topgaard at Lund University, is available here.
An implementation for ParaVision, by Mathew Budde at Medical College of Wisconsin, is available here.
Installing the sequence
Siemens
Instructions for sequence installation and setup are found here.
Philips, GE and United Imaging
Instructions for installation and setup are provided by the vendor.
The design of the gradient waveforms (b-tensor shapes) and the signal sampling schemes (b-values, rotations etc.) must be considered when setting up he experiment. A comprehensive review of the factors that need be considered is found here. In general, the design is informed by the hardware, the intended analysis technique and the organ/subject characteristics. Below, we have collected tools and examples related to the experimental design.
Example sampling schemes
Examples of sampling schemes appropriate for a given combination of organ and analysis technique are found in the SamplingSchemes folder.
Data post-processing
Postprocessing can be done using regular tools developed by the diffusion MRI community. Special care is however needed for correction of distortions due to eddy currents and subject movement to avoid artefacts (see Nilsson et al., 2015).
This can be done with e.g. the mddMRI framework and eddy tool from FSL although special conditions apply (see this note).