impact of non-specific normal databases on perfusion quantification of low-dose myocardial spect studies

To evaluate the impact of non-specific normal databases on the percent summed rest score (sr%) and stress score (ss%) from simulated low-dose spect studies by shortening the acquisition time/projection. forty normal-weight and 40 overweight/obese patients underwent myocardial studies with a conventional gamma-camera (brightview, philips) using three different acquisition times/projection: 30, 15, and 8 s (100%-counts, 50%-counts, and 25%-counts scan, respectively) and reconstructed using the iterative algorithm with resolution recovery (irr) astonishtm (philips). three sets of normal databases were used: (1) full-counts irr; (2) half-counts irr; and (3) full-counts traditional reconstruction algorithm database (trad). the impact of these databases and the acquired count statistics on the sr% and ss% was assessed by anova analysis and tukey test (p < 0.05). significantly higher sr% and ss% values (> 40%) were found for the full-counts trad databases respect to the irr databases. for overweight/obese patients, significantly higher ss% values for 25%-counts scans (+19%) are confirmed compared to those of 50%-counts scan, independently of using the half-counts or the full-counts irr databases. astonishtm requires the adoption of the own specific normal databases in order to prevent very high overestimation of both stress and rest perfusion scores. conversely, the count statistics of the normal databases seems not to influence the quantification scores.