Improvement in automated quantitation of myocardial perfusion abnormality by using iterative reconstruction image in combination with resolution recovery, attenuation and scatter corrections for the detection of coronary artery disease.

OBJECTIVE: An iterative reconstruction method in combination with resolution recovery, attenuation and scatter corrections (IR-RASC) can improve image quality. It, however, is undetermined whether this technique can improve the detection of coronary artery disease (CAD) when automated quantitative analysis is used. This study evaluated diagnostic values of IR-RASC in combination with automated quantitative analysis in stress myocardial perfusion imaging (MPI) in the CAD detection.

METHODS: This study enrolled consecutive 64 patients (mean age 66.2 ± 17.3 years, 39 males) who had undergone both (99m)Tc-labeled tetrofosmin stress MPI and coronary angiography within 3 months. Stress MPI abnormalities quantified as summed stress score (SSS), summed rest score (SRS) and summed difference score (SDS) by Heart Risk View-S (HRV-S) and Quantitative Perfusion SPECT (QPS) softwares using IR-RASC images were compared with those by using conventional filtered back-projection method (FBP) images and angiographic findings.

RESULTS: Based on expert visual assessment, SSS and SRS by HRV-S/QPS softwares with IR-RASC were significantly lower than those by HRV-S/QPS softwares with FBP at mid- and basal left ventricular segments. Receiver-operating characteristics analysis showed that areas under the curve assessed by HRV-S (0.687) and QPS (0.678) with IR-RASC were nearly identical to those (0.717-0.724) by expert assessment with FBP, and were significantly (P < 0.05) greater than those by HRV-S (0.505) and QPS (0.522) with FBP. When HRV-S was used, the specificity and diagnostic accuracy of IR-RASC in the CAD detection were significantly greater than those of FBP: 90.3 versus 51.6%, P < 0.0001 and 79.7 versus 54.7%, P = 0.0027, respectively. Likewise, when QPS was used, the specificity and diagnostic accuracy of IR-RASC in the CAD detection were significantly greater than those of FBP: 80.6 versus 41.9%, P < 0.0001, and 78.1 versus 51.6%, P = 0.0018, respectively. There, however, were no significant differences in sensitivity between IR-RASC and FBP images.

CONCLUSIONS: IR-RASC can improve diagnostic accuracy of the CAD detection using an automated scoring system compared to FBP, by reducing false positivity due to artefactual appearance.