MRI-SPET and SPET-SPET brain co-registration: Evaluation of the performance of eight different algorithms

The aim of this study was to assess the accuracy and computing time needed for MRI-SPET and SPET-SPET brain co-registration using eight different algo rithms (Hermes software from Nuclear Diagnostics Ltd run on a SUN Ultra Spa re 2) to determine the clinically most suitable algorithm. MRI-SPET coregis tration was evaluated using phantom studies. To approximate clinical dual-h eaded SPET studies, a Hoffman brain phantom was filled with Tc-99(m). For M RI imaging (1.5 Tesla), the phantom was filled with water and doped with Gd -DTPA for contrast enhancement. For both modalities, phantom images were ac quired and reconstructed using a routine clinical protocol. MRI and SPET im ages were matched by Downhill Simplex minimization of the sum of absolute C ount Differences (CD), the sum of the Square Root of absolute count differe nces (SR), the Difference in Shape between the binary masks (SD), the numbe r of Sign Changes in the subtracted image (SC), the Variance of intensities between corresponding pixels (VAR), the sum of absolute count differences between the 2D- and 3D-Gradient images (2DG-3DG) and, finally, the standard deviation of the Uniformity Index (UI), that is the intensity ratio betwee n spatially corresponding voxels. Six degrees of freedom were allowed (thre e translation and three rotation parameters, three scaling parameters were constrained). The accuracy of the matching process with these different sim ilarity measures was evaluated via the residual mismatch between external m arkers. We found that CD, SR, VAR and UI give the most accurate registratio n compared with the other similarity measures. For the evaluation of SPET-S PFT co-registration, five Tc-99(m)-ECD brain perfusion SPET scans were perf ormed with a dual-headed gamma camera. These studies were then manually mis aligned, and subsequently re-aligned using the methods outlined above. For this application, CD, SR and VAR were also found to give the most accurate registration. For all of these algorithms, the computing time required was clinically acceptable (i.e. less than 10 min). ((C) 1999 Lippincott William s & Wilkins).