Unifying linear prior-information-driven methods for accelerated image acquisition

In the ongoing quest for faster imaging and higher spatial resolution, several methods have been developed to speed up data acquisition by incorporating prior information about the object being imaged. This study shows that many of these methods can be integrated into a single common equation. The unified framework provides a conceptual link that facilitates comparison among these methods to reveal their strengths and weaknesses. By considering the limitations of existing methods, a new member in this class of methods was developed. The broad-use linear acquisition speed-up technique (BLAST) uses the estimated amount of change within the FOV as prior information. BLAST has the flexibility of incorporating a variable amount of prior information to avoid the misleading appearance of "phantom features," which arise from overconstraining the reconstruction. Examples from dynamic imaging and MR thermometry are shown.