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  • Title: 99mTc-ECD brain perfusion SPET: variability, asymmetry and effects of age and gender in healthy adults.
    Author: Van Laere K, Versijpt J, Audenaert K, Koole M, Goethals I, Achten E, Dierckx R.
    Journal: Eur J Nucl Med; 2001 Jul; 28(7):873-87. PubMed ID: 11504084.
    Abstract:
    Reliable and high-resolution reference data for regional cerebral blood flow measured with single-photon emission tomography (SPET) are necessary for optimal clinical and research use. Therefore, a large dataset of normal technetium-99m labelled ethylene cysteine dimer (ECD) perfusion SPET in carefully screened healthy volunteers with an age range spanning six decades was created, with correction for non-uniform attenuation and scatter and based on an anatomically standardised analysis. Eighty-nine healthy volunteers, stratified for gender (46 females, 43 males; age 20-81 years), were included. Twelve volunteers underwent repeated 99mTc-ECD SPET after 2.5+/-2.3 weeks. An automated whole-brain volume of interest analysis with MANOVA as well as voxelwise analysis using SPM99 was conducted. Average intersubject variability was 4.8% while intrasubject reproducibility was 3.0%. An age-related decline in tracer uptake was found in the anterior cingulate gyrus, bilateral basal ganglia, left prefrontal, left lateral frontal and left superior temporal and insular cortex (all P=0.001-0.02). There was an overall increase in right/left asymmetry with age, which was most pronounced in the frontal and temporal neocortex. The most significant correlations between AI and age decade were found in the prefrontal (R=0.35, P=0.001) and superior temporal neocortex (R=0.43, P<0.001). Women had significantly higher uptake in the right parietal cortex (P<0.001), while men showed higher uptake in the cerebellum and the left anterior temporal and orbitofrontal cortex (all P<0.01). This normative dataset allows age- and gender-specific patient and group assessment of 99mTc-ECD perfusion SPET under a wide variety of clinical circumstances in relation to normal variations and highlights the importance of both age- and gender-specific normal datasets for optimal analysis sensitivity.
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