3D radial sampling can enable ultrashort echo times, reduce sensitivity to motion, and provide high levels of acceleration. These methods have recently been used to perform free-running pediatric body imaging. However, the optimization of projection sampling remains an open problem and current schemes often produce high acoustic noise with an unfavorable noise texture. In this work, we demonstrate the optimization of trajectories using a joint consideration of sampling and acoustic performance. To perform this we introduce a neural network framework and investigate variable TR optimization as means to simultaneously distribute projections optimally and minimize acoustic noise autocorrelation.
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