James Gerlach

Comparison of Fully Automated Computer Analysis and Visual Scoring for Detection of Coronary Artery Disease from Myocardial Perfusion SPECT in a Large Population

We compared the performance of fully automated quantification of attenuation-corrected (AC) and noncorrected (NC) myocardial perfusion SPECT (MPS) with the corresponding performance of experienced readers for detection of coronary artery disease (CAD). Methods: Rest–stress 99mTc-sestamibi MPS studies (n = 995; 650 consecutive cases with coronary angiography and 345 with likelihood of CAD < 5%) were obtained by MPS with AC. The total perfusion deficit (TPD) for AC and NC data was compared with the visual summed stress and rest scores of 2 experienced readers. Visual reads were performed in 4 consecutive steps with the following information progressively revealed: NC data, AC + NC data, computer results, and all clinical information. Results: The diagnostic accuracy of TPD for detection of CAD was similar to both readers (NC: 82% vs. 84%; AC: 86% vs. 85%–87%; P = not significant) with the exception of the second reader when clinical information was used (89%, P < 0.05). The receiver-operating-characteristic area under the curve (ROC AUC) for TPD was significantly better than visual reads for NC (0.91 vs. 0.87 and 0.89, P < 0.01) and AC (0.92 vs. 0.90, P < 0.01), and it was comparable to visual reads incorporating all clinical information. The per-vessel accuracy of TPD was superior to one reader for NC (81% vs. 77%, P < 0.05) and AC (83% vs. 78%, P < 0.05) and equivalent to the second reader (NC, 79%; and AC, 81%). The per-vessel ROC AUC for NC (0.83) and AC (0.84) for TPD was better than that for the first reader (0.78–0.80, P < 0.01) and comparable to that of the second reader (0.82–0.84, P = not significant) for all steps. Conclusion: For detection of ≥70% stenoses based on angiographic criteria, a fully automated computer analysis of NC and AC MPS data is equivalent for per-patient and can be superior for per-vessel analysis, when compared with expert analysis.

Automated Quality Control for Segmentation of Myocardial Perfusion SPECT

Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies. Methods: A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive 99mTc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers. Results: The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 ± 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 ± 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 ± 0.01; sensitivity, 100%; and specificity, 70%. Conclusion: A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.

Improved Quantification and Normal Limits for Myocardial Perfusion Stress–Rest Change

We aimed to improve the quantification of myocardial perfusion stress–rest changes in myocardial perfusion SPECT (MPS) studies for the optimal automatic detection of ischemia and coronary artery disease (CAD). Methods: Rest–stress 99mTc MPS studies (997 cases; 651 consecutive cases with correlating angiography and 346 cases with less than 5% likelihood (low likelihood [LLK]) of CAD) were analyzed. Normal limits for stress–rest changes were derived from additional LLK patients (40 women, 40 men). We computed the global stress–rest change (C-SR) by integrating direct stress–rest changes for each polar map pixel. Additionally, stress–rest change and total perfusion deficit (TPD) at stress were combined in 1 variable (C-TPD) for the optimal detection of CAD. Results: The area under the receiver-operating-characteristic curve (AUC) for C-SR (0.92) was larger than that for stress TPD–rest TPD (0.88) for the identification of stenosis of 70% or more (P < 0.0001). AUC (0.94) and sensitivity (90%) for C-TPD were higher than those for stress TPD (0.91 and 83%, respectively) (P < 0.0001), whereas specificity remained the same (81%). Conclusion: C-SR and C-TPD provide higher diagnostic performance than difference between stress and rest TPD or stress hypoperfusion analysis.

Quantification of 3D regional myocardial wall thickening from gated magnetic resonance images

To develop 3D quantitative measures of regional myocardial wall motion and thickening using cardiac magnetic resonance imaging (MRI) and to validate them by comparison to standard visual scoring assessment.