REGISTRATION OF MR AND SPECT WITHOUT USING EXTERNAL FIDUCIAL MARKERS

The aim of our work is to present, test and validate an automated regi stration method used for matching brain SPECT scans with corresponding MR scans. The method was applied on a data set consisting of ten brai n IDEX SPECT scans and ten T-1- and T-2-weighted MR scans of the same subjects. Of two subjects a CT scan was also made. (Semi-) automated a lgorithms were used to extract the brain from the MR, CT and SPECT ima ges. Next, a surface registration technique called chamfer matching wa s used to match the segmented brains. A perturbation study was perform ed to determine the sensitivity of the matching results to the choice of the starting values. Furthermore, the SPECT segmentation threshold was varied to study its effect on the resulting parameters and a compa rison between the use of MR T-1- and T-2-weighted images was made. Fin ally, the two sets of CT scans were used to estimate the accuracy by m atching MR to CT and comparing the MR-SPECT match to the SPECT-CT matc h. The perturbation study showed that for initial perturbations up to 6 cm the algorithm fails in less than 4% of the cases. A variation of the SPECT segmentation threshold over a realistic range (25%) caused a n average variation in the optimal match of 0.28 cm vector length. Whe n T-2 is used instead of T-1 the stability of the algorithm is compara ble but the results are less realistic due the large deformations. Fin ally, a comparison of the direct SPECT-MR match and the indirect match with CT as intermediate yields a discrepancy of 0.4 cm vector length. We conclude that the accuracy of our automatic matching algorithm for SPECT and MR, in which no external markers were used, is comparable t o the accuracies reported in the literature for non-automatic methods or methods based on external markers. The proposed method is efficient and insensitive to small variations in SPECT segmentation.