  A central limitation of multiple-acquisition magnetic resonance imaging (MRI)
is the degradation in scan efficiency as the number of distinct datasets grows.
Sparse recovery techniques can alleviate this limitation via randomly
undersampled acquisitions. A frequent sampling strategy is to prescribe for
each acquisition a different random pattern drawn from a common sampling
density. However, naive random patterns often contain gaps or clusters across
the acquisition dimension that in turn can degrade reconstruction quality or
reduce scan efficiency. To address this problem, a statistically-segregated
sampling method is proposed for multiple-acquisition MRI. This method generates
multiple patterns sequentially, while adaptively modifying the sampling density
to minimize k-space overlap across patterns. As a result, it improves
incoherence across acquisitions while still maintaining similar sampling
density across the radial dimension of k-space. Comprehensive simulations and
in vivo results are presented for phase-cycled balanced steady-state free
precession and multi-echo T$_2$-weighted imaging. Segregated sampling achieves
significantly improved quality in both Fourier and compressed-sensing
reconstructions of multiple-acquisition datasets.
