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