Fast image acquisition in magnetic resonance imaging by chaotic compressed sensing

We consider the problem of fast acquisition in magnetic resonance imaging (MRI). A recent breakthrough concept called compressed sensing (CS) shows that sparse or, more generally, compressible signals can be recovered from a small number of linear random measurements. CS, using random measurements, has also been successfully applied to MRI for fast acquisition. In a recent work, we have preliminarily employed deterministic chaos in CS that potentially offers a more practical and efficient CS framework. This paper adapts chaotic CS to MRI acquisition. In particular, we use chaotic logistic map for CS and adapt it to acquire the 2-dimensional MRI. In addition, we numerically analyze the performance of the proposed chaotic CS for MRI and show that it performs better random CS.