TL;DR
This paper introduces a novel reordering method for k-space sampling in periodic MRI sequences, significantly reducing eddy current artifacts by formulating the problem as a traveling salesman problem and applying simulated annealing.
Contribution
It presents a new reordering algorithm based on discrete optimization to minimize eddy current artifacts in balanced gradient MRI sequences, leveraging periodicity for improved sampling.
Findings
Substantial reduction of artifacts in reconstructed images.
Slightly better performance of the all-jumps-minimized variant.
Effective artifact reduction without changing overall k-space trajectory.
Abstract
Purpose: To minimize eddy current artifacts in periodic pulse sequences with balanced gradient moments as, e.g., used for quantitative MRI. Theory and Methods: Eddy current artifacts in balanced sequences result from large jumps in k-space. In quantitative MRI, one often samples some spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps in temporal succession without changing the overall trajectory. This reordering can be formulated as a traveling salesman problem and we tackle the discrete optimization with a simulated annealing algorithm. Results: Compared to the default ordering, we observe a substantial reduction of artifacts in the reconstructed images and the derived quantitative parameter maps. Comparing two variants of our algorithm, one that resembles the pairing…
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