SNR Analysis of Apodization Filters on Raw Data for MR image reconstruction

The raw data acquired from MR scanners are susceptible to noises resulting from truncation, inhomogeneity and patient motion. Truncation or Gibb's artifact is one of the most commonly seen artifact in MR images. Since the raw MR data are samples of Fourier transform, reconstructing images from this data will generate truncation artifacts. The application of apodization filters can reduce truncation artifacts in MR images. In this paper, we discuss and analyze the image quality improvements on applying two popular apodization filters called Fermi and Hamming in the image reconstruction pipeline.