Comparison of voxel- and volume-of-interest-based analyses in FDG PET scans of HIV positive and healthy individuals

Abnormal glucose metabolic patterns in the brain have been reported for HIV-1 seropositive (HIV+) patients when compared with seronegative healthy individuals. The metabolic covariance pattern obtained from voxel- or volume-of-interest (VOI)-based multivariate data analysis techniques can be used to characterize disease and potentially to detect and monitor disease progression in the early stage of AIDS dementia complex.

However, the arbitrary smoothing typically applied to PET data during reconstruction and processing to reduce noise has an unknown effect on the data, especially for the voxel-based analysis. To investigate the impact of smoothing on a discrimination task, we applied principal component analysis with scaled subprofile-model preprocessing (SSM/PCA) followed by Fisher discriminant analysis to FDG PET data that were reconstructed and processed with different degrees of smoothing.

Receiver operating characteristic curves were used to compare the ability of derived metabolic covariance patterns to discriminate HIV+ patients from healthy volunteers. For the voxel-based analysis, we found that the spatial distribution of voxel weights from the SSM/PCA analysis suggested edge effects along major tissue and cerebrospinal fluid boundaries, indicative of a disease-specific pattern of cerebral atrophy for the HIV+ patients.

In terms of its discrimination performance, this metabolic covariance pattern is stable and insensitive to a wide range of smoothing kernels, except for ramp reconstruction and Hanning reconstruction with 7 x 7 x 7 block smoothing. In these reconstructions, the discrimination performance decreased as a result of high image noise and excessive smoothing, respectively.

Our results also indicated that if sufficient variance from the VOI measurements is included, the overall performance of a conventional VOI-based analysis can be similar to that of the voxel-based analysis for the same discrimination task. However, the VOI-based analysis performed poorly at low false-positive fraction and is less tolerant to noise in the metabolic covariance pattern than the voxel-based analysis.

We have obtained a unique covariance pattern of brain glucose metabolism for HIV+ patients compared with healthy volunteers. Discrimination based on this covariance pattern was found to be insensitive to a wide range of image smoothness.