Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction.

Unlabelled: We present software for integrated analysis of brain PET studies and coregistered segmented MRI that couples a module for automated placement of regions of interest (ROI) with 4 alternative methods for partial-volume-effect correction (PVEc). The accuracy and precision of these methods have been measured using 4 simulated (18)F-FDG PET studies with increasing degrees of atrophy.

Methods: The software allows the application of a set of labels, defined a priori in the Talairach space, to segmented and coregistered MRI. Resulting ROIs are then transferred onto the PET study, and corresponding values are corrected according to the 4 PVEc techniques under investigation, providing corresponding corrected values. To evaluate the PVEc techniques, the software was applied to 4 simulated (18)F-FDG PET studies, introducing increasingly larger experimental errors, including errors in coregistration (0- to 6-pixel misregistration), segmentation (-13.7% to 14.1% gray matter [GM] volume change) and resolution estimate errors (-16.9% to 26.8% full-width-at-half-maximum mismatch).

Results: Even in the absence of segmentation and coregistration errors, uncorrected PET values showed -37.6% GM underestimation and 91.7% WM overestimation. Voxel-based correction only for the loss of GM activity as a result of spill-out onto extraparenchymal tissues left a residual underestimation of GM values (-21.2%). Application of the method that took into account both spill-in and spill-out effects between any possible pair of ROIs (R-PVEc) and of the voxel-based method that corrects also for the WM activity derived from R-PVEC (mMG-PVEc) provided an accuracy above 96%. The coefficient of variation of the GM ROIs, a measure of the imprecision of the GM concentration estimates, was 8.5% for uncorrected PET data and decreased with PVEc, reaching 6.0% for mMG-PVEc. Coregistration errors appeared to be the major determinant of the imprecision.

Conclusion: Coupling of automated ROI placement and PVEc provides a tool for integrated analysis of brain PET/MRI data, which allows a recovery of true GM ROI values, with a high degree of accuracy when R-PVEc or mMG-PVEc is used. Among the 4 tested PVEc methods, R-PVEc showed the greatest accuracy and is suitable when corrected images are not specifically needed. Otherwise, if corrected images are desired, the mMG-PVEc method appears the most adequate, showing a similar accuracy.