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Abstract #3059

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

Miha Fuderer1,2, Oscar van der Heide1,2, Cornelis A. T. van den Berg1,2, and Alessandro Sbrizzi1,2
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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