#This is a template for a FSL tool section # Create subpages as ToolName/blah # Minimum required subpages are: # UserGuide # Faq # Theory # You should also create a ToolName/Contents which contains nothing but the names of the subpages in the order you wish them to appear in the menu, one per line # eg: #UserGuide #Faq #Theory # Do not include the top level page # Attach an image to this page and change the attachment line at the start to show it on the right of the page. # Remember to add this page to the appropriate category #[[attachment:myimagetoshowontheright]] <> = BayCEST: Bayesian analysis for chemical exchange saturation transfer z-spectra = Quantitative analysis of CEST z-spectra can be achieved by fitting of a multi-pool model to each individual sampled spectrum. There are a large number of parameters within the Bloch-McConnell equations that describe each pool making model fitting prone to inaccuracy and increasing the risk of over fitting. A solution is to provide prior information about the parameters which necessitates the use of a Bayesian method. BayCEST exploits a Bayesian non-linear fitting algorithm, which is essentially a probabilistic version of non-linear least squares, along with a multi-pool implementation of the Bloch-McConnell equations. This algorithm provides a (relatively) fast means to quantify CEST data whilst reducing some of the problems associated with traditional least squares fitting algorithms. ---- = Referencing = If you use BayCEST in your research, please make sure that you reference at least the first of the articles listed below, and ideally the complete list. {{{#!wiki references Chappell, M. A., Donahue, M. J., Tee, Y. K., Khrapitchev, A. A., Sibson, N. R., Jezzard, P., & Payne, S. J. (2012). Quantitative Bayesian model-based analysis of amide proton transfer MRI. Magnetic Resonance in Medicine. doi:10.1002/mrm.24474 Chappell, M., Groves, A., Whitcher, B., & Woolrich, M. (2009). Variational Bayesian Inference for a Nonlinear Forward Model. IEEE Transactions on Signal Processing, 57(1), 223–236. }}} ---- CategoryOther