Search

The aims of this study were to develop an objective method for assessing rCBF deficits using a statistical image analysis protocol and to validate its effective use in clinical practice. 99 Tc m -HMPAO brain SPET images were acquired for 40 normal subjects, 10 patients with Alzheimer's disease and 10 patients with depression. Automated image registration was used to standardize the size and shape of the brain structures for all subjects. The images of the first 30 normal subjects were used to construct a normal database. The CBF images of the other 10 normal subjects and the 20 patients were compared voxel by voxel with the normal database to map CBF abnormalities by statistical evaluation. The results were compared with the clinical reports of CBF images. The expert system detected all rCBF deficits reported by the nuclear physicians. Some additional areas with special information, like atrophy and bilateral asymmetry, were also identified by the expert s system. We conclude that this expert system can delineate CBF deficits with sufficiently high accuracy, differentiating normal from abnormal CBF images using voxel-based comparisons. The use of an expert svstem improves rCBF SPET image evaluation.

The purpose of this study was twofold: to calculate relative uptake values for 99mTc-HMPAO in various regions of the normal brain after alignment and registration to a standard shape and size, and to validate the automated image registration (AIR) program for SPECT-to-SPECT transformation.Thirty subjects took part in this study. Technetium-99m-HMPAO brain SPECT and x-ray-CT scans were acquired. SPECT images were normalized to an average activity of 100 counts/pixel. Intersubject accuracy was evaluated on brain images of 17 normal subjects (mean age = 64.9 +/- 8.7 yr). These images were aligned and registered to a standard size and shape with the help of AIR. Realigned images were overlaid on reference images to determine the overlap areas. Intrasubject accuracy was evaluated by realigning 20 degree rotated brain images with an index calculated as: overlap area/(overlap area + nonoverlap area). Anatomical variability between realigned target and reference images was evaluated by measurements on corresponding x-ray-CT scans, realigned using transformations that were established by the SPECT images. Realigned brain SPECT images of 30 normal subjects (mean age = 50.7 +/- 18.7 yr), including those subjects examined in the accuracy validation study, were used to generate mean and s.d. images. Images based on the mean value of each voxel (n = 30) were compared with other mean images prepared by the human brain atlas (HBA) standardization technique on a voxel-by-voxel basis to generate T maps.Accuracy indices were 0.98 +/- 0.006 and 0.99 +/- 0.002 for the intersubject and intrasubject evaluations, respectively. The maximum anatomical variability was 4.7 mm after realignment. Paired Student's t-test comparisons of mean HBA and AIR images revealed statistically significant differences for the deep white matter, pons and occipito-temporal regions. These differences could be explained by variation in the population being studied and the protocol for data handling by AIR and HBA.AIR aligns and registers brain SPECT images with acceptable accuracy, without the necessity of MRI or x-ray-CT scans.

This study assessed the accuracy and reliability of Automated Image Registration (AIR) for standardization of brain SPECT images of patients with Alzheimer's disease (AD). Standardized cerebral blood flow (CBF) images of patients with AD and control subjects were then used for group comparison and covariance analyses.Thirteen patients with AD at an early stage (age 69.8+/-7.1 y, Clinical Dementia Rating Score 0.5-1.0, Mini-Mental State Examination score 19-23) and 20 age-matched normal subjects (age 69.5+/-8.3 y) participated in this study. 99mTc-hexamethyl propylenamine oxime (HMPAO) brain SPECT and CT scans were acquired for each subject. SPECT images were transformed to a standard size and shape with the help of AIR. Accuracy of AIR for spatial normalization was evaluated by an index calculated on SPECT images. Anatomical variability of standardized target images was evaluated by measurements on corresponding CT scans, spatially normalized using transformations established by the SPECT images. Realigned brain SPECT images of patients and controls were used for group comparison with the help of statistical parameter mapping. Significant differences were displayed on the respective voxel to generate three-dimensional Z maps. CT scans of individual subjects were evaluated by a computer program for brain atrophy. Voxel-based covariance analysis was performed on standardized images with ages and atrophy indices as independent variables.Inaccuracy assessed by functional data was 2.3%. The maximum anatomical variability was 4.9 mm after standardization. Z maps showed significantly decreased regional CBF (rCBF) in the frontal, parietal and temporal regions in the patient group (P0.001). Covariance analysis revealed that the effects of aging on rCBF were more pronounced compared with atrophy, especially in intact cortical areas at an early stage of AD. Decrease in rCBF was partly due to senility and atrophy, however these two factors cannot explain all the deficits.AIR can transform SPECT images of AD patients with acceptable accuracy without any need for corresponding structural images. The frontal regions of the brain, in addition to parietal and temporal lobes, may show reduced CBF in patients with AD even at an early stage of dementia. The reduced rCBF in the cortical regions cannot be explained entirely by advanced atrophy and fast aging process.