Quantification of myocardial blood flow by CZT-SPECT with motion correction and comparison with 15O-water PET.

BACKGROUND: We compared quantification of MBF and myocardial flow reserve (MFR) with a 99mTc-sestamibi CZT-SPECT to 15O-water PET. METHODS: SPECT MBF for thirty patients in the WATERDAY study was re-analyzed by QPET software with motion correction and optimal placement of the arterial input function. 15O-water PET MBF was re-quantified using dedicated software. Inter-operator variability was assessed using repeatability coefficients (RPC). RESULTS: Significant correlations were observed between global (r = 0.91, P < 0.001) and regional MBF (r = 0.86, P < 0.001) with SPECT compared to PET. Global MBF (rest 0.95 vs 1.05 ml/min/g, P = 0.07; stress 2.62 vs 2.68 mL/min/g, P = 0.17) and MFR (2.65 vs 2.75, P = 0.86) were similar between SPECT and PET. Rest (0.81 vs 0.98 mL/min/g, P = 0.03) and stress MBF (1.98 vs 2.61 mL/min/g, P = 0.01) in right coronary artery (RCA) were lower with SPECT compared to PET. However, MFR in the RCA territory was similar (2.54 vs 2.77, P = 0.21). The SPECT-PET RPC for global MBFs and MFR were 0.95 mL/min/g and 0.94, with inter-observer RPC of 0.59 mL/min/g and 0.74, respectively. CONCLUSIONS: MBF and MFR derived from CZT-SPECT with motion correction and optimal placement of the arterial input function showed good agreement with 15O-water PET, as well as low inter-operator variability.

Quantitative Clinical Nuclear Cardiology, Part 1: Established Applications.

Single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has attained widespread clinical acceptance as a standard of care for patients with known or suspected coronary artery disease (CAD). A significant contribution to this success has been the use of computer techniques to provide objective quantitative assessment in the standardization of the interpretation of these studies. Software platforms have been developed as a pipeline to provide the quantitative algorithms researched, developed and validated to be clinically useful so diagnosticians everywhere can benefit from these tools. The goal of this CME article (PART 1) is to describe the many quantitative tools that are clinically established and more importantly how clinicians should use them routinely in the interpretation, clinical management and therapy guidance of patients with CAD.

Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT).

BACKGROUND: We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners. METHODS: REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) uses a collaborative design with multicenter contribution of clinical data and images into a comprehensive clinical-imaging database. All images are processed by quantitative software. Over 290 individual imaging variables are automatically extracted from each image dataset and merged with clinical variables. In the prognostic cohort, patient follow-up is performed for major adverse cardiac events. In the diagnostic cohort (patients with correlating invasive angiography), angiography and revascularization results within 6 months are obtained. RESULTS: To date, collected prognostic data include scans from 20,418 patients in 5 centers (57% male, 64.0 ± 12.1 years) who underwent exercise (48%) or pharmacologic stress (52%). Diagnostic data include 2079 patients in 9 centers (67% male, 64.7 ± 11.2 years) who underwent exercise (39%) or pharmacologic stress (61%). CONCLUSION: The REFINE SPECT registry will provide a resource for collaborative projects related to the latest generation SPECT-MPI. It will aid in the development of new artificial intelligence tools for automated diagnosis and prediction of prognostic outcomes.

Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry.

BACKGROUND: Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to derive TID limits for common myocardial perfusion imaging protocols utilizing a large, multicenter registry (REFINE SPECT). METHODS: One thousand six hundred and seventy-two patients with low likelihood of coronary artery disease with normal perfusion findings were identified. Images were processed with Quantitative Perfusion SPECT software (Cedars-Sinai Medical Center, Los Angeles, CA). Non-attenuation-corrected, camera-, radiotracer-, and stress protocol-specific TID limits in supine position were derived from 97.5th percentile and mean + 2 standard deviations (SD). Reference limits were compared for different solid-state cameras (D-SPECT vs. Discovery), radiotracers (technetium-99m-sestamibi vs. tetrofosmin), different types of stress (exercise vs. four different vasodilator-based protocols), and different vasodilator-based protocols. RESULTS: TID measurements did not follow Gaussian distribution in six out of eight subgroups. TID limits ranged from 1.18 to 1.52 (97.5th percentile) and 1.18 to 1.39 (mean + 2SD). No difference was noted between D-SPECT and Discovery cameras (P = 0.71) while differences between exercise and vasodilator-based protocols (adenosine, regadenoson, or regadenoson-walk) were noted (all P < 0.05). CONCLUSIONS: We used a multicenter registry to establish camera-, radiotracer-, and protocol-specific upper reference limits of TID for supine position on CZT camera systems. Reference limits did not differ between D-SPECT and Discovery camera.

Triple-gated motion and blood pool clearance corrections improve reproducibility of coronary 18F-NaF PET.

PURPOSE: To improve the test-retest reproducibility of coronary plaque 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) uptake measurements. METHODS: We recruited 20 patients with coronary artery disease who underwent repeated hybrid PET/CT angiography (CTA) imaging within 3 weeks. All patients had 30-min PET acquisition and CTA during a single imaging session. Five PET image-sets with progressive motion correction were reconstructed: (i) a static dataset (no-MC), (ii) end-diastolic PET (standard), (iii) cardiac motion corrected (MC), (iv) combined cardiac and gross patient motion corrected (2 × MC) and, (v) cardiorespiratory and gross patient motion corrected (3 × MC). In addition to motion correction, all datasets were corrected for variations in the background activities which are introduced by variations in the injection-to-scan delays (background blood pool clearance correction, BC). Test-retest reproducibility of PET target-to-background ratio (TBR) was assessed by Bland-Altman analysis and coefficient of reproducibility. RESULTS: A total of 47 unique coronary lesions were identified on CTA. Motion correction in combination with BC improved the PET TBR test-retest reproducibility for all lesions (coefficient of reproducibility: standard = 0.437, no-MC = 0.345 (27% improvement), standard + BC = 0.365 (20% improvement), no-MC + BC = 0.341 (27% improvement), MC + BC = 0.288 (52% improvement), 2 × MC + BC = 0.278 (57% improvement) and 3 × C + BC = 0.254 (72% improvement), all p < 0.001). Importantly, in a sub-analysis of 18F-NaF-avid lesions with gross patient motion > 10 mm following corrections, reproducibility was improved by 133% (coefficient of reproducibility: standard = 0.745, 3 × MC = 0.320). CONCLUSION: Joint corrections for cardiac, respiratory, and gross patient motion in combination with background blood pool corrections markedly improve test-retest reproducibility of coronary 18F-NaF PET.

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.

BACKGROUND: We developed machine-learning (ML) models to estimate a patient's risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient's risk to provide insight to clinicians beyond that of a "black box." METHODS: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model's rationale to facilitate clinical interpretation. RESULTS: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. CONCLUSIONS: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

Detection and quantitation of right ventricular reversible perfusion defects by stress SPECT myocardial perfusion imaging: A proof-of-principle study.

BACKGROUND: In patients with right dominant coronary circulation, the right ventricular (RV) myocardium and the inferior region of the left ventricular (LV) myocardium share a common source of blood flow. We hypothesized that stress/rest SPECT myocardial perfusion imaging (MPI) could detect reversible perfusion defects in the RV in some patients with LV inferior wall perfusion abnormalities. MATERIAL AND METHODS: We identified 2 groups of patients with LV inferior wall perfusion defects (with or without defects in other regions of LV myocardium) from our database. Patients in group 1 (n = 17) had reversible perfusion defects in the RV free wall by visual analysis, while patients in group 2 (n = 17) did not. The images were processed with filtered back projection and, separately, with iterative reconstruction. The images were then re-processed using an automated quantitative software that is specifically designed to include the RV in the region of interest. RESULTS: There were 76% men in group 1 and 94% in group 2 (P <0.05). The mean age was 65±20 in group 1 vs. 63±18 years in group 2 (P < 0.05). The stress type was exercise in 30% in group 1 and 35% in group 2, with the remaining patients studied with pharmacological stress testing (P = NS). The presence of RV reversible perfusion defects using filtered back projection was more evident in 13 patients (75%), while it was better seen with iterative reconstruction in 4 patients (25%). By automated analysis, the RV reversible perfusion defect size was 19 ± 14% of RV myocardium. CONCLUSION: This proof-of-principle study demonstrates that reversible RV perfusion defects suggestive of ischemia can be detected by SPECT myocardial perfusion imaging in some patients with LV inferior ischemia by visual analysis and can be quantitated by automated programs. Further studies on the diagnostic and prognostic relevance of assessing RV ischemia on SPECT MPI are needed.

Added prognostic value of left ventricular shape by gated SPECT imaging in patients with suspected coronary artery disease and normal myocardial perfusion.

BACKGROUND: Left ventricular (LV) remodeling is associated with adverse cardiovascular events. We evaluated the added prognostic value of LV shape index (SI) assessed by gated single-photon emission tomography (SPECT) in patients without known coronary artery disease (CAD). METHODS AND RESULTS: We studied 674 patients with normal myocardial perfusion and normal LV ejection fraction (EF) on stress gated SPECT imaging. An automated software program was used to calculate end-diastolic and end-systolic LVSI. An LVSI ≤ 0.54 at end-systole was considered normal. Follow-up was 96% complete with a median follow-up of 37 months. During follow-up, 25 events occurred (3.8% cumulative event rate). Event-free survival was lower in patients with abnormal end-systolic LVSI (P < .001). Age (P = .021), diabetes (P = .048), and end-systolic LVSI (P < .001) were independent predictors of events. LVSI added prognostic information increasing the global chi-square of the model including age and diabetes from 15.15 to 25.97 (P < .001). The effect of diabetes on hazard ratio increased with increasing values of end-systolic LVSI. The probability of events at 48 months predicted by Weibull analysis progressively increased with increasing values of end-systolic LVSI and was higher in patients with diabetes as compared to those without. Decision curve analyses indicate that the model including end-systolic LVSI resulted in an increased net benefit between 5% and 30% threshold probability, indicating superior estimation of outcomes at low threshold probability levels. CONCLUSIONS: The evaluation of LVSI may identify patients with early-stage LV remodeling and at higher risk of adverse cardiac events, even in the presence of normal myocardial perfusion.

Prognostic value of vasodilator response using rubidium-82 positron emission tomography myocardial perfusion imaging in patients with coronary artery disease.

BACKGROUND: Prognostic value of positron emission tomography (PET) myocardial perfusion imaging (MPI) is well established. There is paucity of data on how the prognostic value of PET relates to the hemodynamic response to vasodilator stress. We hypothesize that inadequate hemodynamic response will affect the prognostic value of PET MPI. METHODS AND RESULTS: Using a multicenter rubidium (Rb)-82 PET registry, 3406 patients who underwent a clinically indicated rest/stress PET MPI with a vasodilator agent were analyzed. Patients were categorized as, "responders" [increase in heart rate ≥ 10 beats per minute (bpm) and decrease in systolic blood pressure (SBP) ≥10 mmHg], "partial responders" (either a change in HR or SBP), and "non-responders" (no change in HR or SBP). Primary outcome was all-cause death (ACD), and secondary outcome was cardiac death (CD). Ischemic burden was measured using summed stress score (SSS) and % left ventricular (LV) ischemia. After a median follow-up of 1.68 years (interquartile range = 1.17- 2.55), there were 7.9% (n = 270) ACD and 2.6% (n = 54) CD. Responders with a normal PET MPI had an annualized event rate (AER) of 1.22% (SSS of 0-3) and 1.58% (% LV ischemia = 0). Partial and non-responders had higher AER with worsening levels of ischemic burden. In the presence of severe SSS ≥12 and LV ischemia of ≥10%, partial responders had an AER of 10.79% and 10.36%, compared to non-responders with an AER of 19.4% and 12.43%, respectively. Patient classification was improved when SSS was added to a model containing clinical variables (NRI: 42%, p < 0.001) and responder category was added (NRI: 61%, p < 0.001). The model including clinical variables, SSS and hemodynamic response has good discrimination ability (Harrell C statistics: 0.77 [0.74-0.80]). CONCLUSION: Hemodynamic response during a vasodilator Rb-82 PET MPI is predictive of ACD. Partial and non-responders may require additional risk stratification leading to altered patient management.

Fully automated analysis of attenuation-corrected SPECT for the long-term prediction of acute myocardial infarction.

BACKGROUND: Most prior studies assessing the prognostic value of SPECT myocardial perfusion imaging (MPI) have used semi-quantitative visual analysis. We assessed the feasibility of large-scale fully automated quantitative analysis of SPECT MPI to predict acute myocardial infarction (AMI). Additionally, we examined the impact of attenuation correction (AC) in automated strategies. METHODS AND RESULTS: 5960 patients underwent rest/stress SPECT MPI with AC. Left ventricular (LV) segmentation, contour QC check, and quantitation of stress and ischemic total perfusion deficit (sTPD, iTPD) were performed. Only contours flagged for potential errors by QC were visually checked (10%). During long-term follow-up (6.1 ± 2.7 years), 522 patients (9%) had AMI. In Cox models, adjusted for ejection fraction (LVEF) and other relevant covariates, there was a stepwise increase in risk hazard ratios by quartile for sTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001) and iTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001). Area under curve for AMI prediction by automated measures was similar for AC and non-AC data (sTPD: 0.63 vs 0.64, P = 0.85; iTPD: 0.61 vs 0.61, P = 0.70). Higher AUCs for both AC and non-AC data were seen for AMI occurring in the first 1 year of follow-up (sTPD: 0.71, 0.72; iTPD: 0.70, 0.68). CONCLUSIONS: Fully automated sTPD was an independent predictor of future AMI events even after adjusting for LVEF and other relevant covariates. AC did not significantly impact predictive accuracy.

Comparison of left ventricular shape by gated SPECT imaging in diabetic and nondiabetic patients with normal myocardial perfusion: A propensity score analysis.

BACKGROUND: Diabetes mellitus induces structural and functional cardiac alterations that can result in heart failure. Left ventricular (LV) shape is a dynamic component of cardiac geometry influencing its contractile function. However, few data are available comparing LV shape index in diabetic and nondiabetic patients without overt coronary artery disease after balancing for coronary risk factors. METHODS: We studied 1168 patients with normal myocardial perfusion and normal LV ejection fraction on stress gated single-photon emission computed tomography (SPECT) imaging. To account for differences in baseline characteristics between diabetic and nondiabetic patients, we created a propensity score-matched cohort considering clinical variables, coronary risk factors, and stress type. RESULTS: Before matching, diabetic patients were older, had higher prevalence of male gender and coronary risk factors, and higher end-diastolic and end-systolic LV shape index. After matching, all clinical characteristics were comparable between diabetic and nondiabetic patients, but diabetic patients still had higher end-diastolic and end-systolic LV shape index (both P < .001). At multivariable linear regression analysis, diabetes was a strong predictor of end-systolic LV shape index in the overall study population and in the propensity-matched cohort. CONCLUSIONS: Diabetic patients have higher values of LV shape index compared to nondiabetic patients also after balancing clinical characteristics by propensity score analysis. Shape indexes assessment by gated SPECT may be useful for identifying early LV remodeling in patients with diabetes.

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

AIMS: Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. METHODS AND RESULTS: The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). CONCLUSIONS: Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.

Prognostic significance of blood pressure response during vasodilator stress Rb-82 positron emission tomography myocardial perfusion imaging.

BACKGROUND: A drop in blood pressure (BP) or blunted BP response is an established high-risk marker during exercise myocardial perfusion imaging (MPI); however, data are sparse regarding the prognostic value of BP response in patients undergoing vasodilator stress rubidium-82 (Rb-82) Positron Emission Tomography (PET) MPI. METHODS AND RESULTS: From the PET Prognosis Multicenter Registry, a cohort of 3413 patients underwent vasodilator stress Rb-82 PET MPI with dipyridamole or adenosine. We used multivariable Cox proportional hazard regression to analyze the association with mortality of four BP variables: stress minus rest systolic BP (∆SBP), stress minus rest diastolic BP (∆DBP), resting systolic BP (rSBP), and resting diastolic BP (rDBP). Covariates that had univariate P values <.10 were entered into the multivariable model. After median 1.7 years follow-up, 270 patients died. In univariate analyses, ∆SBP (P = .082), rSBP (P = .008), and rDBP (P < .001) were of potential prognostic value (P < .10), but ∆DBP was not (P = .96). After adjustment for other clinical and MPI variables, ∆SBP no longer independently predicted mortality (P = .082); only lower rSBP (P = .026) and lower rDBP (P = .045) remained independently prognostic. CONCLUSIONS: In patients undergoing vasodilator stress MPI, only lower resting BP is an independent predictor of mortality along with other clinical and MPI variables; BP response does not appear to add to risk stratification in these patients.

Quantitation of left ventricular ejection fraction reserve from early gated regadenoson stress Tc-99m high-efficiency SPECT.

BACKGROUND: Ejection fraction (EF) reserve has been found to be a useful adjunct for identifying high risk coronary artery disease in cardiac positron emission tomography (PET). We aimed to evaluate EF reserve obtained from technetium-99m sestamibi (Tc-99m) high-efficiency (HE) SPECT. METHODS: Fifty patients (mean age 69 years) undergoing regadenoson same-day rest (8-11 mCi)/stress (32-42 mCi) Tc-99m gated HE SPECT were enrolled. Stress imaging was started 1 minute after sequential intravenous regadenoson .4 mg and Tc-99m injections, and was composed of five 2 minutes supine gated acquisitions followed by two 4 minutes supine and upright images. Ischemic total perfusion deficit (ITPD) ≥5 % was considered as significant ischemia. RESULTS: Significantly lower mean EF reserve was obtained in the 5th and 9th minute after regadenoson bolus in patients with significant ischemia vs patients without (5th minute: -4.2 ± 4.6% vs 1.3 ± 6.6%, P = .006; 9th minute: -2.7 ± 4.8% vs 2.0 ± 6.6%, P = .03). CONCLUSIONS: Negative EF reserve obtained between 5th and 9th minutes of regadenoson stress demonstrated best concordance with significant ischemia and may be a promising tool for detection of transient ischemic functional changes with Tc-99m HE-SPECT.

"Same-Patient Processing" for multiple cardiac SPECT studies. 1. Improving LV segmentation accuracy.

OBJECTIVES: This paper describes a novel approach (same-patient processing, or SPP) aimed at improving left ventricular segmentation accuracy in patients with multiple SPECT studies, and evaluates its performance compared to conventional processing in a large population of 962 patients undergoing rest and stress electrocardiography-gated SPECT MPI, for a total of 5,772 image datasets (6 per patient). METHODS: Each dataset was independently processed using a standard algorithm, and a shape quality control score (SQC) was produced for every segmentation. Datasets with a SQC score higher than a specific threshold, suggesting algorithmic failure, were automatically reprocessed with the SPP-modified algorithm, which incorporates knowledge of the segmentation mask location in the other datasets belonging to the same patient. Experienced operators blinded as to whether datasets had been processed based on the standard or SPP approach assessed segmentation success/failure for each dataset. RESULTS: The SPP approach reduced segmentation failures from 219/5772 (3.8%) to 42/5772 (0.7%) overall, with particular improvements in attenuation corrected (AC) datasets with high extra-cardiac activity (from 100/962 (10.4%) to 12/962 (1.4%) for rest AC, and from 41/962 (4.3%) to 9/962 (0.9%) for stress AC). The number of patients who had at least one of their 6 datasets affected by segmentation failure decreased from 141/962 (14.7%) to 14/962 (1.7%) using the SPP approach. CONCLUSION: Whenever multiple image datasets for the same patient exist and need to be processed, it is possible to deal with the images as a group rather than individually. The same-patient processing approach can be implemented automatically, and may substantially reduce the need for manual reprocessing due to cardiac segmentation failure.

"Same-patient processing" for multiple cardiac SPECT studies. 2. Improving quantification repeatability.

OBJECTIVES: This paper investigates the ability of grouped quantification (an expression of the same-patient processing approach, or SPP) to improve repeatability of measurements in patients with multiple SPECT studies, and evaluates its performance compared to standard quantification in a population of 100 patients undergoing rest, stress, gated rest, and gated stress SPECT MPI. All acquisitions were performed twice, back-to-back, for a total of 800 image datasets (8 per patient). METHODS: Each dataset was automatically processed (a) independently, using standard quantitative software, and (b) as a group, together with the other 7 datasets belonging to the same patient, using an SPP-modified version of the software that registered the images to one another using a downhill simplex algorithm for the search of optimal translation, rotation, and scaling parameters. RESULTS: Overall, grouped quantification resulted in significantly lower differences between repeated measurements of stress ungated volumes (1.40 ± 2.76 mL vs 3.33 ± 5.06 mL, P < .05), end-diastolic volumes (1.78 ± 2.78 vs 3.49 ± 5.35 mL, P < .05), end-systolic volumes (1.17 ± 1.96 vs 2.44 ± 3.35 mL, P < .05), and LVEFs (-0.45 ± 2.29% vs -1.16 ± 3.30%, P < .05). Additionally, grouped quantification produced better repeatability (lower repeatability coefficients) for stress and rest ungated volumes (5.4 vs 9.9 and 5.2 vs 13.1, respectively), stress TPD (2.6 vs 3.6), stress and rest end-diastolic volumes (5.5 vs 10.5 and 7.2 vs 14.7, respectively), stress and rest end-systolic volumes (3.8 vs 6.6 and 5.3 vs 10.3, respectively), stress and rest LVEFs (4.5 vs 6.5 and 4.7 vs 8.2, respectively), and rest total motion deficit (5.6 vs 9.6). CONCLUSION: It is possible to improve the repeatability of quantitative measurements of parameters of myocardial perfusion and function derived from SPECT MPI studies of a same patient by group processing of image datasets belonging to that patient. This application of the same-patient processing approach is an extension of the "paired processing" technique already described by our group, and can be performed in automated fashion through incorporation in the quantitative algorithm.

Predictors of high-risk coronary artery disease in subjects with normal SPECT myocardial perfusion imaging.

BACKGROUND: While uncommon, normal stress SPECT myocardial perfusion imaging (MPI) can be seen in patients with high-risk coronary artery disease (CAD) by invasive coronary angiography (ICA).The predictors of high-risk CAD in patients with normal SPECT-MPI have not been described. METHODS: We studied 580 patients (age 64 ± 12 years, 49% men) without known CAD who underwent stress-gated SPECT-MPI [exercise (41%) or vasodilator (59%)] <2 months before ICA and had summed stress score (SSS) <4. High-risk CAD was defined as 3 vessels with ≥70% stenosis, 2 vessels with ≥70% stenosis including proximal left anterior descending, or left main with ≥50% stenosis. Obstructive non-high-risk CAD was defined by the presence of a ≥70% stenosis but without having other high-risk criteria. Tenfold cross-validated receiver operating characteristic (ROC) estimates were obtained to assess the predictors of high-risk CAD. RESULTS: Forty-two subjects (7.2%) had high-risk CAD and 168 (29.0%) had obstructive non-high-risk CAD. Variables associated with high-risk CAD were pretest probability of CAD ≥66% (Odds ratio [OR] 3.63, 95% CI 1.6-8.3, P = .002), SSS > 0 (OR 7.46, 95% CI 2.6-21.1, P < 0.001), and abnormal TID (OR 2.16, 95% CI 1.0-4.5, P = 0.044). When substituted for TID, EF change was also predictive of high-risk CAD (OR 0.93, 95% CI 0.9-1.0, P = 0.023). The prevalence of high-risk CAD increased as the number of these predictors increased. In a sub-analysis of patients in whom quantitative total perfusion deficit (TPD) was available, TPD > 0 was also a predictor of high-risk CAD (OR 6.01, 95% CI 1.5-22.2, P = 0.011). CONCLUSION: Several clinical, stress, and SPECT-MPI findings are associated high-risk CAD among patients with normal SPECT-MPI. Consideration of these factors may improve the overall assessment of the likelihood of high-risk CAD in patients undergoing stress SPECT-MPI.

Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

OBJECTIVE: We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach. METHODS: 713 rest (201)Thallium/stress (99m)Technetium MPS studies with correlating invasive angiography with 372 revascularization events (275 PCI/97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation, stress combined supine/prone total perfusion deficit (TPD), supine rest and stress TPD, exercise ejection fraction, and end-systolic volume, along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). Tenfold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. RESULTS: The sensitivity of machine learning (ML) (73.6% ± 4.3%) for prediction of revascularization was similar to one reader (73.9% ± 4.6%) and standalone measures of perfusion (75.5% ± 4.5%). The specificity of ML (74.7% ± 4.2%) was also better than both expert readers (67.2% ± 4.9% and 66.0% ± 5.0%, P < .05), but was similar to ischemic TPD (68.3% ± 4.9%, P < .05). The receiver operator characteristics areas under curve for ML (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02) but superior to reader 2 (0.72 ± 0.02, P < .01) and standalone measure of perfusion (0.77 ± 0.02, P < .01). CONCLUSION: ML approach is comparable or better than experienced readers in prediction of the early revascularization after MPS, and is significantly better than standalone measures of perfusion derived from MPS.

Automatic registration of misaligned CT attenuation correction maps in Rb-82 PET/CT improves detection of angiographically significant coronary artery disease.

BACKGROUND: We aimed to evaluate the utility of fully automated software registration intended to improve CT attenuation correction (CTAC) map misalignments during cardiac (82)Rb PET/CT myocardial perfusion imaging (MPI). METHODS: 171 consecutive patients (108 males, mean age 69 years), undergoing both rest-stress (82)Rb PET/CT MPI and invasive coronary angiography within 6 months (mean 14 days, range 0-170), were studied. List mode data were automatically processed in batch mode to generate transaxial attenuation corrected slices with four different CTAC alignment correction strategies: (i) no alignment correction (NONE); (ii) manual correction (MANUAL); (iii) automated 6-parameter rigid correction (AUTO); and (iv) targeted use of automated correction only where PET-CTAC alignment was initially judged as incorrect on either stress or rest scan (AUTO for misalignment only). Initial and final registration quality was graded (1-3) by an experienced radiologist (1: satisfactory alignment (<2 mm misalignment), 2: slight misalignment (2-5 mm in any direction), or 3: poor (>5 mm misalignment in any direction). Total perfusion deficit (TPD) and ischemic TPD (ITPD) were computed automatically, and their diagnostic accuracy to detect significant coronary artery disease with each realignment technique was assessed using receiver operating characteristic analysis. RESULTS: The diagnostic accuracy of ITPD, expressed as area under curve, was .81 ± .03 with no alignment correction (NONE), .83 ± .03 with MANUAL correction, .85 ± .03 with AUTO correction (P < .05 vs. NONE and MANUAL), and .87 ± .03 with the targeted use of AUTO correction (P < .05 vs. NONE, MANUAL and AUTO). Both manual and software corrections increased the percentage of cases with satisfactory PET-CTAC map alignment (P < .05 for all) at rest (from 55% for NONE to 80% for MANUAL and 92% for AUTO) and at stress (from 51% for NONE to 78% for MANUAL and 84% for AUTO). CONCLUSION: The diagnostic accuracy of (82)Rb PET/CT MPI with automated rigid alignment is improved compared to data with no CTAC scan alignment or with manual alignment. The optimal strategy for diagnostic performance is to apply automatic alignment only in cases which are visually identified as misaligned.

Quantitative high-efficiency cadmium-zinc-telluride SPECT with dedicated parallel-hole collimation system in obese patients: results of a multi-center study.

BACKGROUND: Obesity is a common source of artifact on conventional SPECT myocardial perfusion imaging (MPI). We evaluated image quality and diagnostic performance of high-efficiency (HE) cadmium-zinc-telluride parallel-hole SPECT MPI for coronary artery disease (CAD) in obese patients. METHODS AND RESULTS: 118 consecutive obese patients at three centers (BMI 43.6 ± 8.9 kg·m(-2), range 35-79.7 kg·m(-2)) had upright/supine HE-SPECT and invasive coronary angiography > 6 months (n = 67) or low likelihood of CAD (n = 51). Stress quantitative total perfusion deficit (TPD) for upright (U-TPD), supine (S-TPD), and combined acquisitions (C-TPD) was assessed. Image quality (IQ; 5 = excellent; < 3 nondiagnostic) was compared among BMI 35-39.9 (n = 58), 40-44.9 (n = 24) and ≥45 (n = 36) groups. ROC curve area for CAD detection (≥50% stenosis) for U-TPD, S-TPD, and C-TPD were 0.80, 0.80, and 0.87, respectively. Sensitivity/specificity was 82%/57% for U-TPD, 74%/71% for S-TPD, and 80%/82% for C-TPD. C-TPD had highest specificity (P = .02). C-TPD normalcy rate was higher than U-TPD (88% vs 75%, P = .02). Mean IQ was similar among BMI 35-39.9, 40-44.9 and ≥45 groups [4.6 vs 4.4 vs 4.5, respectively (P = .6)]. No patient had a nondiagnostic stress scan. CONCLUSIONS: In obese patients, HE-SPECT MPI with dedicated parallel-hole collimation demonstrated high image quality, normalcy rate, and diagnostic accuracy for CAD by quantitative analysis of combined upright/supine acquisitions.