Registration of MR and SPECT without using external fiducial markers.
The aim of our work is to present, test and validate an automated registration method used for matching brain SPECT scans with corresponding MR scans. The method was applied on a data set consisting of ten brain IDEX SPECT scans and ten T1- and T2-weighted MR scans of the same subjects. Of two subjects a CT scan was also made. (Semi-) automated algorithms were used to extract the brain from the MR, CT and SPECT images. Next, a surface registration technique called chamfer matching was used to match the segmented brains. A perturbation study was performed 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 comparison between the use of MR T1- and T2-weighted images was made. Finally, the two sets of CT scans were used to estimate the accuracy by matching MR to CT and comparing the MR-SPECT match to the SPECT-CT match. 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 an average variation in the optimal match of 0.28 cm vector length. When T2 is used instead of T1 the stability of the algorithm is comparable but the results are less realistic due the large deformations. Finally, 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 to 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.
de Munck
J C
JC
The Netherlands Cancer Institute (Antoni van Leeuwenhoek Huis), Radiotherapy Department, Amsterdam. jc.munck@azvu.nl
Verster
F C
FC
Dubois
E A
EA
Habraken
J B
JB
Boltjes
B
B
Claus
J J
JJ
van Herk
M
M
D001331:Automation; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000295:instrumentation / Q000379:methods; D014057:Tomography, X-Ray Computed