In MRI data acquisition, gradient encoding
introduces a non-uniform distribution of tissue contrast and boundary information
in k-space. As a result, data correlation increases with tissue boundary sparsity
from the center to the outer k-space. The presented work investigates a new
approach to accelerating MRI by taking advantage of non-uniform k-space data correlation.
In this approach, k-space data are collected and reconstructed in a region-by-region
fashion using a previously developed high-speed imaging framework,
"correlation imaging"1,2. It is demonstrated that region-by-region
correlation imaging can introduce a gain over parallel imaging in imaging
acceleration by utilizing more information.