Mathieu Boudreau1, G Bruce Pike1,2. 1. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada. 2. Hotchkiss Brain Institute and Department of Radiology, University of Calgary, Calgary, Alberta, Canada.
Abstract
PURPOSE: To develop and validate a regularization approach of optimizing B1 insensitivity of the quantitative magnetization transfer (qMT) pool-size ratio (F). METHODS: An expression describing the impact of B1 inaccuracies on qMT fitting parameters was derived using a sensitivity analysis. To simultaneously optimize for robustness against noise and B1 inaccuracies, the optimization condition was defined as the Cramér-Rao lower bound (CRLB) regularized by the B1 -sensitivity expression for the parameter of interest (F). The qMT protocols were iteratively optimized from an initial search space, with and without B1 regularization. Three 10-point qMT protocols (Uniform, CRLB, CRLB+B1 regularization) were compared using Monte Carlo simulations for a wide range of conditions (e.g., SNR, B1 inaccuracies, tissues). RESULTS: The B1 -regularized CRLB optimization protocol resulted in the best robustness of F against B1 errors, for a wide range of SNR and for both white matter and gray matter tissues. For SNR = 100, this protocol resulted in errors of less than 1% in mean F values for B1 errors ranging between -10 and 20%, the range of B1 values typically observed in vivo in the human head at field strengths of 3 T and less. Both CRLB-optimized protocols resulted in the lowest σF values for all SNRs and did not increase in the presence of B1 inaccuracies. CONCLUSION: This work demonstrates a regularized optimization approach for improving the robustness of auxiliary measurements (e.g., B1 ) sensitivity of qMT parameters, particularly the pool-size ratio (F). Predicting substantially less B1 sensitivity using protocols optimized with this method, B1 mapping could even be omitted for qMT studies primarily interested in F.
PURPOSE: To develop and validate a regularization approach of optimizing B1 insensitivity of the quantitative magnetization transfer (qMT) pool-size ratio (F). METHODS: An expression describing the impact of B1 inaccuracies on qMT fitting parameters was derived using a sensitivity analysis. To simultaneously optimize for robustness against noise and B1 inaccuracies, the optimization condition was defined as the Cramér-Rao lower bound (CRLB) regularized by the B1 -sensitivity expression for the parameter of interest (F). The qMT protocols were iteratively optimized from an initial search space, with and without B1 regularization. Three 10-point qMT protocols (Uniform, CRLB, CRLB+B1 regularization) were compared using Monte Carlo simulations for a wide range of conditions (e.g., SNR, B1 inaccuracies, tissues). RESULTS: The B1 -regularized CRLB optimization protocol resulted in the best robustness of F against B1 errors, for a wide range of SNR and for both white matter and gray matter tissues. For SNR = 100, this protocol resulted in errors of less than 1% in mean F values for B1 errors ranging between -10 and 20%, the range of B1 values typically observed in vivo in the human head at field strengths of 3 T and less. Both CRLB-optimized protocols resulted in the lowest σF values for all SNRs and did not increase in the presence of B1 inaccuracies. CONCLUSION: This work demonstrates a regularized optimization approach for improving the robustness of auxiliary measurements (e.g., B1 ) sensitivity of qMT parameters, particularly the pool-size ratio (F). Predicting substantially less B1 sensitivity using protocols optimized with this method, B1 mapping could even be omitted for qMT studies primarily interested in F.