Postprocessing of Spectroscopic Imaging Data with Incomplete k-Space Sampling Using a Maximum Entropy Method
Leibfritz D, Dreher W
University of Bremen, Center of Advanced Imaging (CAI)
Measurements with a reduced range of k&[omega]-values are beneficial for fast spectroscopic imaging (SI) such as spectroscopic RARE or SSFP based SI because the minimum total measurement time can be shortened or the SNR can be increased. Therefore, a maximum entropy method (MEM) was used for processing incomplete SI data. MEM was employed for spectrum reconstruction or time domain data extrapolation. After tests on simulated data, MEM was applied to spectroscopic RARE data measured in vivo on rat brain at 4.7T. The results show that MEM is an efficient and robust tool to process incomplete SI data.