On MR experiment design with quadratic regularization
The design of MRI experiments represents a trade-off between acquisition time, signal-to-noise ratio (SNR), and resolution. For fixed acquisition time and reconstruction resolution, it has been widely believed that the optimal acquisition strategy is to avoid collecting k-space data at frequencies higher than the nominal image resolution. While this belief is true under certain metrics, we observe in this work that a high-resolution acquisition strategy, combined with an appropriate linear filtering/regularization strategy, leads to significantly improved SNR/resolution efficiency for the majority of common resolution metrics. Analysis of this surprising result leads to practical methods for the improved design of imaging experiments and the selection of efficient quadratic regularization penalties.