Properties of a 2D fat navigator for prospective image domain correction of nodding motion in brain MRI

Abstract

Purpose: A two-dimensional fat navigator (FatNav) image is proposed, designed for future use as a means of prospective motion correction of head-nodding motion. Methods: The proposed FatNav module comprised a fat-selective excitation, followed by an accelerated echo planar imaging readout played out in one central sagittal plane. Stepwise motion experiments with different acceleration factors, blip polarity, and matrix sizes were performed. The accuracy of motion estimates derived from the FatNav data was assessed using water-based, distortion-free, spoiled-gradient echo images as the gold standard. The duration of the FatNav module was 10 ms to 20 ms. Volunteer data were acquired on a 3T system using an 8-channel radiofrequency coil. Methods: It is shown that acceleration factors of R = 8 are feasible for FatNav data. Best results are obtained when parallel imaging calibration data is matched in terms of both geometric distortions and signal content. For head rotations up to about 15 mm and 20 degrees, mean absolute errors of the motion estimates using FatNav data were about 0.5 mm and 1 degree. Conclusion: FatNav is advantageous in that it leaves most of the brain water magnetization unaffected and left to the host pulse sequence. Furthermore, high acceleration factors are possible with FatNav, which reduces estimation bias and the navigator duration.