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Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a singlescan have tremendous potential to streamline exams and reduce imaging time.However, maintaining clinically feasible scan time necessitates significantundersampling, pushing the limits on compressed sensing and otherlow-dimensional techniques. During MRI scanning, one of the possible solutionsis by using undersampling designs which can effectively improve the acquisitionand achieve higher reconstruction accuracy. However, existing undersamplingoptimization methods are time-consuming and the limited performance preventstheir clinical applications. In this paper, we proposed an improvedundersampling trajectory optimization scheme to generate an optimizedtrajectory within seconds and apply it to subsequent multi-contrast MRIdatasets on a per-subject basis, where we named it OUTCOMES. By using adata-driven method combined with improved algorithm design, GPU acceleration,and more efficient computation, the proposed method can optimize a trajectorywithin 5-10 seconds and achieve 30%-50% reconstruction improvement with thesame acquisition cost, which makes real-time under-sampling optimizationpossible for clinical applications.

OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences