Detection and severity classification of extracardiac interference in 82Rb PET myocardial perfusion imaging

Purpose:Myocardial perfusion imaging (MPI) is used for diagnosis and prognosis of coronary artery disease. When MPI studies are performed with positron emission tomography (PET) and the radioactive tracer rubidium‐82 chloride (82Rb), a small but non‐negligible fraction of studies (∼10%) suffer from extracardiac interference: high levels of tracer uptake in structures adjacent to the heart which mask the true cardiac tracer uptake. At present, there are no clinically available options for automated detection or correction of this problem. This work presents an algorithm that detects and classifies the severity of extracardiac interference in 82Rb PET MPI images and reports the accuracy and failure rate of the method.Methods:A set of 200 82Rb PET MPI images were reviewed by a trained nuclear cardiologist and interference severity reported on a four‐class scale, from absent to severe. An automated algorithm was developed that compares uptake at the external border of the myocardium to three thresholds, separating the four interference severity classes. A minimum area of interference was required, and the search region was limited to that facing the stomach wall and spleen. Maximizing concordance (Cohen's Kappa) and minimizing failure rate for the set of 200 clinician‐read images were used to find the optimal population‐based constants defining search limit and minimum area parameters and the thresholds for the algorithm. Tenfold stratified cross‐validation was used to find optimal thresholds and report accuracy measures (sensitivity, specificity, and Kappa).Results:The algorithm was capable of detecting interference with a mean [95% confidence interval] sensitivity/specificity/Kappa of 0.97 [0.94, 1.00]/0.82 [0.66, 0.98]/0.79 [0.65, 0.92], and a failure rate of 1.0% ± 0.2%. The four‐class overall Kappa was 0.72 [0.64, 0.81]. Separation of mild versus moderate‐or‐greater interference was performed with good accuracy (sensitivity/specificity/Kappa = 0.92 [0.86, 0.99]/0.86 [0.71, 1.00]/0.78 [0.64, 0.92]), while separation of moderate versus severe interference severity classes showed reduced sensitivity/Kappa but little change in specificity (sensitivity/specificity/Kappa = 0.83 [0.77, 0.88]/0.82 [0.77, 0.88]/0.65 [0.60, 0.70]). Specificity was greater than sensitivity for all interference classes. Algorithm execution time was <1 min.Conclusions:The algorithm produced here has a low failure rate and high accuracy for detection of extracardiac interference in 82Rb PET MPI scans. It provides a fast, reliable, automated method for assessing severity of extracardiac interference.