Iterative optimization method for accelerated acquisition and parameter estimation in quantitative magnetization transfer imaging
Henrik Marschner 1 , Andr Pampel 1 , and Harald E. Mller 1
Nuclear Magnetic Resonance Unit, Max Planck
Institute for Human Cognitive and Brain Sciences,
Leipzig, Saxony, Germany
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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