Iterative optimization method for accelerated acquisition and parameter estimation in quantitative magnetization transfer imaging

We investigate the effect of reducing the total number of measurements in qMTI on the model parameters (qMT parameters) of a binary spin bath. The parameters estimation is driven by artificial neural networks (ANNs). The major goal is to find the minimal number of measurements including their optimal settings while maintaining quality and quantitative comparability of the calculated qMT parameters as obtained from a much higher number of measurements. A small number of only 5 measurements is mostly sufficient for the presented experiments with limited saturation parameters. Further spread of the saturation parameters may lead to 4 overall sufficient measurements.

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