A fast and accurate method for registration of MR images of the head.

This paper proposes a new fully automated technique that can be used for the registration of medical images of the head. The method uses Chebyshev polynomials in order to approximate and then minimize a novel multiresolutional, signal intensity independent disparity function, which can generally be defined as the mean squared value of the mean weighted ratio of two images. This function is explicitly computed for n Chebyshev points in a geometric transformation parameter interval [-A, +A] transformation units and is approximated using the Chebyshev polynomials for all other points in the interval. For 3D T2-T1 weighted MR registration, 120 experiments with studies from ten patients were performed and showed that n = 4 Chebyshev points for A = 18 transformation units give mean rotational error 0.36 degrees and a mean translational error 0.36 mm. The different noise conditions did not affect the performance of the method. We conclude that the method is suitable for routine clinical applications and that it has significant potential for future development and improvement.

A new automated method for three-dimensional registration of MR images of the head.

A new technique for three-dimensional image registration was developed and tested using T1 and T2 weighted Magnetic Resonance image studies of the head. The method uses the fuzzy c-means classification algorithm for outlining the surface contours and then minimizes iteratively the mean squared value of the voxel per voxel weighted ratio of the two trilinearly interpolated cubic voxel volumes. A total of 200 two-dimensional and 240 three-dimensional registration experiments were performed and showed that the method is signal intensity independent, it has registration accuracy better than 1 degree for rotations and 1 voxel for translations and it is not affected by the deterioration in the imaging resolution for voxel sizes up to 1.8 mms.