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J Nucl Med. 2004 Feb;45(2):192-201.

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

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  • 1Biostructure and Bioimaging Institute, National Council for Research, Building 10, Via Pansini 5, 80131 Naples, Italy.


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.


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).


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.


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.

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