From d640b5c5d2a762a8e659390d80aa9d513bf0aa74 Mon Sep 17 00:00:00 2001 From: Martin Craig Date: Thu, 16 Mar 2017 08:52:12 +0000 Subject: [PATCH] Add README --- README.md | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..4ffa01b --- /dev/null +++ b/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.* \ No newline at end of file