Abstract
BACKGROUND: Objective assessment of wall motion (M) and thickening (T) will aid in diagnosis of coronary artery disease (CAD) from myocardial perfusion SPECT (MPS). We aimed to develop and validate an improved fully automated M/T segmental scoring system for MPS. METHODS: 100 normal gated stress/rest Tc-99m sestamibi MPS scans from patients with low-likelihood (LLk) of CAD were used to derive the regional normal M/T ranges. A new automatic algorithm incorporated regional dependence on the global contractility in polar map coordinates by linear regression analysis and automatically derived 17-segment M (scale 0-5) and T (scale 0-3) scores. We validated this new method in 630 consecutive Tc-99m stress MPS studies in patients with suspected CAD and available correlating angiography, and an additional 241 LLk studies. Two independent observers with 12 and 30 years of experience in nuclear cardiology, blinded to clinical and angiographic data, scored M /T in 17-segments for all 971 studies. RESULTS: Computation time was <1 s per case. In the angiography group, there was a high correlation between the summed scores (averaged for two observers) and automatic scores with r = 0.91 (slope = 1.02, offset = 0.2; P < .0001) for M and r = 0.88 (slope = 1.06, offset = 0.28 for T; P < .0001). Weighted kappa was 0.63 for M and 0.57 for T, with expected agreement of 89% (M) and 91% (T) in individual segments (n = 10710). Weighted kappa between two experts was 0.45 for M and 0.52 for T. The normalcy rate in LLk cases was 96% for automated M and 99% for T (summed score <3). Detection of the angiographically significant disease by automated M or T scoring was better than or equivalent to individual expert observer scoring, and better than the previous automated system. CONCLUSIONS: Fully automated scoring of MPS regional ventricular function can be performed rapidly, is highly correlated with expert visual scoring, can outperform individual experienced observers in the detection of CAD by wall thickening from MPS, and avoids inter-observer variability.