Artificial Bee Colony (ABC) based variable density sampling scheme for CS-MRI

The self-sustained dynamics of the bee population in nature is a result of their hierarchical working culture, efficient organizing skills and unique highly developed foraging ability, which enables them to interact effectively among each other as well as with their environment. In this paper, a novel algorithm utilizing the bee's swarm intelligence, and its heuristics based on quality and quantity of food sources (nectars) is proposed to generate a variable density sampling (VDS) scheme for compressive sampling (CS) based fast MRI data acquisition. The algorithm uses the scout-bees for global random selection process which is further fine-tuned by employed and onlooker-bees who forage locally in the neighborhood giving prime importance to points possessing high fitness values (or high energy) usually located around the center of fc-space. The algorithm introduces the concept of searching for the high quality food sources in annular regions, called as bins, of varying widths. Retrospective CS-MRI simulations show that the proposed fc-ABC based VDS scheme performs significantly better than other sampling schemes.