This toolbox optimizes the acquisition protocols for spoiled gradient echo qMT sensitivity against noise and also sensitivity-regularization for robustness of a qMT parameter-of-interest.


Mathieu Boudreau1, G. Bruce Pike1,2

Estimated cost



Stable release, MIT License

This work provides a method to incorporate B1 sensitivity considerations into the optimization of quantitative magnetization transfer (qMT) data acquisition. This is achieved by regularizing the Cramer-Rao lower bound optimization condition with a B1 sensitivity term. The framework, qMT-optimization, helps designing qMT acquisition protocols optimized for robustness against inaccuracies of auxiliary measurements. Several demos are included in this package. qMT-optimization toolbox wraps around qMRLab, an open-source software for quantitative MR image analysis. Therefore, some features may require knowledge of qMRLab GUI features.



1Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
2Hotchkiss Brain Institute and Department of Radiology, University of Calgary, Calgary, Alberta, Canada