Determination of the Point Spread Function for Compressed Sensing Reconstruction

Iulius Dragonu¹, Guobin Li¹, Jeff Snyder¹, Jrgen Hennig¹, Maxim Zaitsev¹

Compressed Sensing (CS) is a technique that allows accelerating data acquisition in the presence of sparse or compressible signals. This is accomplished by using a pseudo-random undersampling in the phase-encoding direction. The Point Spread Function (PSF) is a fundamental tool allowing the evaluation of the quality of reconstruction and the spatial resolution of images. Previously the concept of PSF approximation was extended to non-linear and non-stationary imaging systems. The PSF has different values in all imaging points due to the non-linearity and non-stationary proprieties of the CS algorithms. In this work, we propose a technique of evaluating the PSF of the CS reconstruction based on an acquisition pattern used in PSF for echo-planar imaging.