High-efficiency SPECT MPI: comparison of automated quantification, visual interpretation, and coronary angiography

Abstract

Background: Recently introduced high-efficiency (HE) SPECT cameras with solid-state CZT detectors have been shown to decrease imaging time and reduce radiation exposure to patients. An automated, computer-derived quantification of HE MPI has been shown to correlate well with coronary angiography on one HE SPECT camera system (D-SPECT), but has not been compared to visual interpretation on any of the HE SPECT platforms.

Methods: Patients undergoing a clinically indicated Tc-99m sestamibi HE SPECT (GE Discovery 530c with supine and prone imaging) study over a 1-year period followed by a coronary angiogram within 2 months were included. Only patients with a history of CABG surgery were excluded. Both MPI studies and coronary angiograms were reinterpreted by blinded readers. One hundred and twenty two very low (risk of CAD < 5%) or low (risk of CAD < 10%) likelihood subjects with normal myocardial perfusion were used to create normal reference limits. Computer-derived quantification of the total perfusion deficit at stress and rest was obtained with QPS software. The visual and automated MPI quantification were compared to coronary angiography (≥70% luminal stenosis) by receiver operating curve (ROC) analysis.

Results: Of the 3,111 patients who underwent HE SPECT over a 1-year period, 160 patients qualified for the correlation study (66% male, 52% with a history of CAD). The ROC area under the curve (AUC) was similar for both the automated and the visual interpretations using both supine only and combined supine and prone images (0.69-0.74). Using thresholds determined from sensitivity and specificity curves, the automated reads showed higher specificity (59%-67% vs 27%-60%) and lower sensitivity (71%-72% vs 79%-93%) than the visual reads. By including prone images sensitivity decreased slightly but specificity increased for both. By excluding patients with known CAD and cardiomyopathies, AUC and specificity increased for both techniques (0.72-0.82). The use of a difference score to evaluate ischemic burden resulted in lower sensitivities but higher specificities for both automated and visual quantification. There was good agreement between the visual interpretation and automated quantification in the entire cohort of 160 unselected consecutive patients (r = 0.70-0.81, P < .0001).

Conclusions: Automated and visual quantification of high-efficiency SPECT MPI with the GE Discovery camera provides similar overall diagnostic accuracy when compared to coronary angiography. There was good correlation between the two methods of assessment. Combined supine and prone stress imaging provided the best diagnostic accuracy.

Figure 1

Flow diagram of patient testing over 12 month period.

Figure 2

17-Segment normal average segmental uptake for supine and prone images in low likelihood subjects.

Figure 3

Correlation between visual interpretation and automated quantification and Bland-Altman analysis with 95% limits of agreement for (A) supine images and (B) supine and prone images.

Figure 3

Correlation between visual interpretation and automated quantification and Bland-Altman analysis with 95% limits of agreement for (A) supine images and (B) supine and prone images.