Abstract #3059

BLAKJac - A computationally efficient noise-propagation performance metric for the analysis and optimization of MR-STAT sequences

Miha Fuderer^1,2, Oscar van der Heide^1,2, Cornelis A. T. van den Berg^1,2, and Alessandro Sbrizzi^1,2

¹Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands

In MR-STAT, data from a sequence of time-varying RF pulses and gradient encodings is reconstructed into multiple quantitative parameter maps by solving a large scale inversion problem. The combined interaction of RF and gradient events determines the noise-propagation into the reconstructed maps. We derive a computationally efficient performance metric to study this effect, by analyzing the block-diagonal of the k-space representation of the Jacobian. This allows for extremely fast prediction of the noise spectrum of the reconstructed parameter maps.

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