Improved Algorithm for Motion Estimation in the Reconstruction of PROPELLER MRI Data

Algorithms to reliably and accurately extract inter-strip motion are crucial for motion artifacts suppression in PROPELLER MRI. The current algorithm is based on image registration through maximizing correlation coefficients of k-space data. To decouple rotational and translational estimation, only magnitude of k-space data is taken into account while performing rotational estimation. Because little data are contained in the central overlapped sampling area, the robustness and precision of the k-space based rotational estimation are degraded by the discarding of useful phase information. In this paper, an improved algorithm through optimizing correlation in image space is proposed. First, k-space data are transformed into image space by zero-padded reconstruction of each strip. Then the rotational and translational parameters are acquired simultaneously through maximizing correlation coefficients between these images with POWELL optimization. Phantom and in vivo imaging demonstrate that the proposed image-space-based algorithm is of a much higher accuracy than the k-space-based algorithm.