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<jats:title>Abstract</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>To present and assess a method for choosing the increment between spokes in radially sampled MRI that can produce higher SNR than golden ratio derived methods.</jats:p></jats:sec><jats:sec><jats:title>Theory and Methods</jats:title><jats:p>Sampling uniformity determines image SNR when reconstructed using linear methods. Thus, for a radial trajectory, uniformly spaced sampling is ideal. However, uniform sampling lacks post-acquisition reconstruction flexibility, which is often needed in dynamic imaging. Golden ratio-based methods are often used for this purpose. The method presented here, Set Increment with Limited Views Encoding Ratio (SILVER), optimizes sampling uniformity when the number of spokes per frame is approximately known a-priori. With SILVER, an optimization algorithm finds the angular increment that provides the highest uniformity for a pre-defined set of reconstruction window sizes. The optimization cost function was based on an electrostatic model of uniformity. SILVER was tested over multiple sets and assessed in terms of uniformity, analytical g-factor, and SNR both in simulation and applied to dynamic arterial spin labeling angiograms in three healthy volunteers.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>All SILVER optimizations produced higher or equal uniformity than the golden ratio within the predefined sets. The SILVER method converged to the golden ratio for broad optimization sets. As hypothesized, the g-factors for SILVER were higher than for uniform sampling, but, on average, 26% lower than golden ratio. Image SNR followed the same trend both in simulation and in vivo.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>SILVER is a simple addition to any sequence currently using golden ratio sampling and it has a small but measurable effect on sampling efficiency.</jats:p></jats:sec>

Original publication

DOI

10.1101/2020.06.25.171017

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

27/06/2020