Add README

README.md

@@ -0,0 +1,53 @@
+# VERBENA: Vascular Model Based Perfusion Quantification for DSC-MRI
+
+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 BASIL tools 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.
+
+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 scheduled for a future release of FSL (it is not to be found in the 
+current release). However, if you are interested in using VERBENA, it is 
+possible to provide a pre-release copy that is compatible with more recent 
+FSL releases.
+
+## Referencing
+
+If you use VERBENA in your research, please make sure that you reference the 
+first article listed below.
+
+*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*
+
+The following articles provide more background on the original vascular model 
+from which the VERBENA model is derived:
+
+*Mouridsen K, Friston K, Hjort N, Gyldensted L, Østergaard L, Kiebel S. Bayesian 
+estimation of cerebral perfusion using a physiological model of microvasculature. 
+NeuroImage 2006;33:570–579. doi: 10.1016/j.neuroimage.2006.06.015.*
+
+*Ostergaard L, Chesler D, Weisskoff R, Sorensen A, Rosen B. Modeling Cerebral 
+Blood Flow and Flow Heterogeneity From Magnetic Resonance Residue Data. J Cereb 
+Blood Flow Metab 1999;19:690–699.*
+
+An alternative Bayesian 'non-parametric' deconvolution approach has been 
+published in:
+
+*Mehndiratta A, MacIntosh BJ, Crane DE, Payne SJ, Chappell MA. A control point 
+interpolation method for the non-parametric quantification of cerebral 
+haemodynamics from dynamic susceptibility contrast MRI. NeuroImage 
+2013;64:560–570. doi: 10.1016/j.neuroimage.2012.08.083.*