Optimal linear combinations of array elements for B 1 mapping
نویسندگان
چکیده
منابع مشابه
Considerations for using linear combinations of array elements in B1 mapping
Introduction: Accurate B1 mapping of transmit coil arrays is vital for parallel transmission MRI. Current mapping methods accommodate a limited range of B1 amplitude, but can be adapted for use with coil arrays by mapping B1 fields from linear combinations of coils and solving for individual elements [1,2]. Existing formulations can be represented by an encoding matrix A, with Ai,j = 1 for i≠j,...
متن کاملOptimal Linear Combinations of Neural Networks 1
Neural network (NN)-based modeling often involves trying multiple networks with diierent architectures and training parameters in order to achieve acceptable model accuracy. Typically, one of the trained NNs is chosen as best, while the rest are discarded. Hashem and Schmeiser 25] proposed using optimal linear combinations of a number of trained neural networks instead of using a single best ne...
متن کاملOptimal Linear Combinations of Neural Networks
Neural network-based modeling often involves trying multiple networks with different architectures and training parameters in order to achieve acceptable model accuracy. Typically, one of the trained networks is chosen as best, while the rest are discarded. [Hashem and Schmeiser (1995)] proposed using optimal linear combinations of a number of trained neural networks instead of using a single b...
متن کاملExploiting AUC for optimal linear combinations of dichotomizers
The combination of classifiers is an established technique to improve the classification performance. The possible combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two class problems. In this case, a good alternative is given by the Area under the R...
متن کاملOptimal Linear Combinations of Neural Networks: An Overview
| Neural networks based model-ing often involves trying multiple networks with diierent architectures and/or training parameters in order to achieve acceptable model accuracy. Typically, one of the trained NNs is chosen as best, while the rest are discarded. Hashem and Schmeiser 1] propose using optimal linear combinations of a number of trained neural networks instead of using a single best ne...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Magnetic Resonance in Medicine
سال: 2009
ISSN: 0740-3194
DOI: 10.1002/mrm.22068