Verbena is a Bayesian Inference tool for quantification of perfusion and other haemodynamic parameters from Dynamic Susceptibility Contrast perfusion MRI of the brain.
VERBENA complements the OXASL pipeline for the quantification of perfusion using Arterial Spin Labelling MRI and is built on the same core inference algorithm (FABBER).
VERBENA uses a specific physiological model for capillary transit of contrast within the blood generally termed the 'vascular model' that was first described by Ostergaard (see below). In VERBENA the model has been extended to explicitly infer the mean transit time and also to optionally include correction for macro vascular contamination - contrast agent within arterial vessels - more information on the model can be found in the theory section of the FABBER_DSC documentation.
VERBENA takes a model-based approach to the analysis of DSC-MRI data in contrast to alternative 'non-parametric' approaches, that often use a Singular Value based Deconvolution to quantify perfusion. An alternative Bayesian Deconvolution approach is also available, but not currently distributed as part of FSL. For more information see the reference below and contact the senior author.
VERBENA is included in FSL v6.0.1.
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If you use VERBENA in your research, please make sure that you reference Chappell et al1.
The following articles provide more background on the original vascular model from which the VERBENA model is derived:
An alternative Bayesian 'non-parametric' deconvolution approach has been published in:
Chappell, M.A., Mehndiratta, A., Calamante F., "Correcting for large vessel contamination in DSC perfusion MRI by extension to a physiological model of the vasculature", e-print ahead of publication. doi: 10.1002/mrm.25390↩