New algorithm for extracting motion information from PROPELLER data and head motion correction in T1-weighted MRI

PROPELLER (Periodically Rotated Overlapping ParallEl Lines with Enhanced Reconstruction) MRI, proposed by J. G. Pipe [1], offers a novel and effective means for compensating motion. For the reconstruction of PROPLLER data, algorithms to retiably and accurately extract inter-strip motion from data in central overlapped area are crucial to motion artifacts suppression. When implemented on T1-weighted MR data, the reconstruction algorithm, with motion estimated by registration based on maximizing correlation energy in frequency domain (CF), produces images with low quality due to the inaccurate estimation of motion. In this paper, a new algorithm is proposed for motion estimation based on the registration by maximizing mutual information in spatial domain (MIS). Furthermore, the optimization process is initialized by CF algorithm, so the algorithm is abbreviated as CF-MIS algorithm in this paper. With phantom and in vivo MR imaging, the CF-AIIS algorithm was shown to be of higher accuracy in rotation estimation than CF algorithm. Consequently, the head motion in T1-weighted PROPELLER MRI was better corrected.