Reducing the acquisition time for obtaining fractional anisotropy (FA) is of paramount importance to investigate cerebral microstructures and morphologies non-invasively. This is the first time to introduce the two-dimensional principal component analysis recognition reconstruction (i.e. 2D-PCA-RR) in recovering highly under-sampled FA maps with 5-fold acceleration of data acquisition. An in-house data processing procedure is implemented to optimize signal-to-noise ratio and construct a distortion-free database. Our results from two different under-sampling patterns show a superior performance gain from the 2D-PCA-RR algorithm as compared to conventional reconstruction methods.