- User Guide
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.
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.
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.