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Background: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features., Objectives: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility., Methods: The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization., Results: In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%., Conclusions: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging., Competing Interests: Funding Support and Author Disclosures This research was supported in part by the National Heart, Lung, and Blood Institute at the National Institutes of Health grant R35HL161195 (Principal Investigator: Dr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Mr Kavanagh has received software royalties for QPS software at Cedars-Sinai Medical Center. Dr Miller has received consulting fees and research support from Pfizer; and has served as a consultant for GE Healthcare. Dr Newby is supported by the British Heart Foundation; is a recipient of a Wellcome Trust Senior Investigator Award (WT103782AIA); and has received honoraria for consultancy and lectures from AstraZeneca. Dr Berman has received software royalties for QPS software at Cedars-Sinai Medical Center; and has served as a consultant for GE Healthcare. Dr Slomka has received software royalties for QPS software at Cedars-Sina Medical Center, research grant support from Siemens Medical Systems, and consulting fees from Synektik., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex., Methods and Results: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex., Conclusion: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients., Competing Interests: Conflict of interest: D.S.B. and P.J.S. participated in software royalties for QPS software at Cedars-Sinai Medical Center. P.J.S. has received research grant support from Siemens Medical Systems. D.S.B., S.D., A.J.E., and E.J.M. are consultants for GE Healthcare. S.D. is a consultant for Bracco Diagnostics and has received a grant through her institution from Astellas. M.D.C. has received research grant support from Spectrum Dynamics; and he is a consultant for Sanof and GE Healthcare. D.S.B.’s institution has received grant support from HeartFlow. E.J.M. has served as a consultant for Bracco Inc, and he and his institution have received grant support from Bracco Inc. T.D.R. has received research grant support from GE Healthcare and Advanced Accelerator Applications. A.J.E. reports receiving a speaker's fee from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare—UpToDate, and serving on a scientific advisory board for Canon America Medical Systems USA; his institution has grants/grants pending from Attralus, BridgeBio Pharma, Canon Medical Systems USA, GE Healthcare, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Purpose: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables., Methods: Consecutive patients undergoing regadenoson rest-stress [ 82 Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint., Results: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5)., Conclusions: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI., (© 2024. The Author(s).)

Background: Despite its potential benefits, the utilization of stress-only protocol in clinical practice has been limited. We report utilizing stress-first single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI)., Methods: We assessed 12,472 patients who were referred for SPECT-MPI between 2013 and 2020. The temporal changes in frequency of stress-only imaging were assessed according to risk factors, mode of stress, prior coronary artery disease (CAD) history, left ventricular function, and symptom status. The clinical endpoint was all-cause mortality., Results: In our lab, stress/rest SPECT-MPI in place of rest/stress SPECT-MPI was first introduced in November 2011 and was performed more commonly than rest/stress imaging after 2013. Stress-only SPECT-MPI scanning has been performed in 30-34% of our SPECT-MPI studies since 2013 (i.e.. 31.7% in 2013 and 33.6% in 2020). During the study period, we routinely used two-position imaging (additional prone or upright imaging) to reduce attenuation and motion artifact and introduced SPECT/CT scanner in 2018. The rate of stress-only study remained consistent before and after implementing the SPECT/CT scanner. The frequency of stress-only imaging was 43% among patients without a history of prior CAD and 19% among those with a prior CAD history. Among patients undergoing treadmill exercise, the frequency of stress-only imaging was 48%, while 32% among patients undergoing pharmacologic stress test. In multivariate Cox analysis, there was no significant difference in mortality risk between stress-only and stress/rest protocols in patients with normal SPECT-MPI results (p = 0.271)., Conclusion: Implementation of a stress-first imaging protocol has consistently resulted in safe cancellation of 30% of rest SPECT-MPI studies., Competing Interests: Declaration of competing interest Dr. Berman participates in software royalties for QPS software at Cedars-Sinai Medical Center. All other authors have no conflicts of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82 Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training ( n = 112) and validation ( n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group ( n = 112), MBF measurements with automated and manual MC showed strong correlations ( r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group ( n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82 Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables., Methods: Consecutive patients who underwent pharmacological stress-rest 82 Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM)., Results: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1)., Conclusion: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction., (© 2023. The Author(s).)

Purpose: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI)., Methods: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%)., Results: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference)., Conclusions: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone., (© 2023. The Author(s).)

To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT. Methods: SPECT myocardial perfusion imaging was performed using 99m Tc-sestamibi or 99m Tc-tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that applies a deep learning model (DeepAC) to generate simulated AC SPECT images. The model was trained with short-axis NC and AC images performed at 1 site ( n = 4,886) and was tested on patients from 2 separate external sites ( n = 604). We assessed the diagnostic accuracy of the stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver-operating-characteristic curve. We also quantified the direct count change among AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in less than 1 s from NC images; area under the receiver-operating-characteristic curve for obstructive CAD was higher for DeepAC TPD (0.79; 95% CI, 0.72-0.85) than for NC TPD (0.70; 95% CI, 0.63-0.78; P < 0.001) and similar to AC TPD (0.81; 95% CI, 0.75-0.87; P = 0.196). The normalcy rate in the low-likelihood-of-coronary-disease population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) than for NC TPD (54.6%, P < 0.001 for both). The positive count change (increase in counts) was significantly higher for AC versus NC (median, 9.4; interquartile range, 6.0-14.2; P < 0.001) than for AC versus DeepAC (median, 2.4; interquartile range, 1.3-4.2). Conclusion: In an independent external dataset, DeepAC provided improved diagnostic accuracy for obstructive CAD, as compared with NC images, and this accuracy was similar to that of actual AC. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed., Objectives: The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups., Methods: Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC)., Results: During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external)., Conclusions: The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction., Competing Interests: Funding Support and Author Disclosures This research (principal investigator: Dr Slomka) was supported in part by grant R01HL089765 from the National Heart, Lung and Blood Institute of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Mr Kavanagh has participated in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Einstein has served as a consultant to GE Healthcare and W.L. Gore & Associates; and his institution has received research support from GE Healthcare, Philips Healthcare, Toshiba America Medical Systems, Roche Medical Systems, and W.L. Gore & Associates. Dr Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr Edward Miller has served as a consultant to GE Healthcare. Dr Dorbala has served as a consultant to GE Healthcare and Bracco Diagnostics; and her institution has received grant support from Astellas. Dr Di Carli has received research grant support from Spectrum Dynamics; and has received consulting honoraria from Sanofi and GE Healthcare. Dr Berman has participated in software royalties for QPS software at Cedars-Sinai Medical Center; and has served as a consultant to GE Healthcare. Dr Slomka has participated in software royalties for QPS software at Cedars-Sinai Medical Center; and has received research grant support from Siemens Medical Systems. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Purpose: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing., Methods: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179)., Results: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both)., Conclusion: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed., Methods: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia., Results: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center., Conclusion: The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

Background: Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) vs other clinical variables is not well delineated., Methods and Results: From 19,658 patients who underwent SPECT-MPI, we identified 3122 patients with DM without known coronary artery disease (CAD) (DM+/CAD-) and 3564 without DM with known CAD (DM-/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD- and DM-/CAD+ groups. There was comparable MACE in the matched DM+/CAD- and DM-/CAD+ groups (HR 1.15, 95% CI 0.97-1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and DM status. The combined consideration of mode of stress, TPD, and DM provided synergistic stratification, an 8.87-fold (HR 8.87, 95% CI 7.27-10.82) increase in MACE among pharmacologically stressed patients with DM and TPD > 10% (vs non-ischemic, exercised stressed patients without DM)., Conclusions: Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients., (© 2021. American Society of Nuclear Cardiology.)

Background: The frequency of inducible myocardial ischemia has declined in contemporary stress test cohorts, suggesting a need to re-evaluate its optimal use. To-date, however, a comprehensive analysis of the most potent predictors of myocardial ischemia among cardiac stress test patients has not been conducted., Methods: We assessed 27,615 patients referred for stress-rest SPECT myocardial perfusion imaging between January 1, 2004 and December 31, 2017. Chi-square analysis was used to ascertain the most potent predictors of ischemia., Results: Among our cohort, CAD status (presence/absence of known CAD), rest left ventricular ejection fraction (LVEF), and typical angina were the most potent predictors of ischemia. The frequency of ischemia was only 6.6% among patients with an LVEF > 55% but 38.1% for patients with LVEF < 45% (P < 0.001). The frequency of myocardial ischemia was fourfold higher among patients with known CAD vs no known CAD (28.0% vs 6.5%, P < 0.001) and approximately threefold higher among patients with typical angina vs patients with atypical symptoms (P < 0.001)., Conclusions: The frequency of myocardial ischemia varies markedly according to the common clinical parameters and is particularly high among patients with known CAD, low LVEF, and typical angina. These observations may be used to develop more cost-effective strategies for referring patients for cardiac stress testing., (© 2021. American Society of Nuclear Cardiology.)

Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results ( P < 0.001), but not compared with readers interpreting with DL results ( P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD)., Methods and Results: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P &lt; .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P &lt; .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P &lt; .01)., Conclusions: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging., (© 2021. American Society of Nuclear Cardiology.)

Background: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features., Methods: We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation., Results: In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p &lt; 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p &lt; 0.001)., Conclusion: ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

Background: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing., Methods: A total of 4735 consecutive patients referred for stress and rest 82 Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24-6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation., Results: In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64-0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69-0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%-46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia., Conclusions: The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.

Aims: Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI., Methods and Results: This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models., Conclusion: Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)

Background: The utility of performing early myocardial revascularization among patients presenting with inducible myocardial ischemia and low left ventricular ejection fraction (LVEF) is currently unknown., Objectives: In this study, we sought to assess the relationship between stress-induced myocardial ischemia, revascularization, and all-cause mortality (ACM) among patients with normal vs low LVEF., Methods: We evaluated 43,443 patients undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging from 1998 to 2017. Median follow-up was 11.4 years. Myocardial ischemia was assessed for its interaction between early revascularization and mortality. A propensity score was used to adjust for nonrandomization to revascularization, followed by multivariable Cox modeling adjusted for the propensity score and clinical variables to predict ACM., Results: The frequency of myocardial ischemia varied markedly according to LVEF and angina, ranging from 6.7% among patients with LVEF ≥55% and no typical angina to 64.0% among patients with LVEF <45% and typical angina (P < 0.001). Among 39,883 patients with LVEF ≥45%, early revascularization was associated with increased mortality risk among patients without ischemia and lower mortality risk among patients with severe (≥15%) ischemia (HR: 0.70; 95% CI: 0.52-0.95). Among 3,560 patients with LVEF <45%, revascularization was not associated with mortality benefit among patients with no or mild ischemia, and was associated with decreased mortality among patients with moderate (10%-14%) (HR: 0.67; 95% CI: 0.49-0.91) and severe (≥15%) (HR: 0.55; 95% CI: 0.38-0.80) ischemia., Conclusions: Within this cohort, early myocardial revascularization was associated with a significant reduction in mortality among both patients with normal LVEF and severe inducible myocardial ischemia and patients with low LVEF and moderate or severe inducible myocardial ischemia., Competing Interests: Funding Support and Author Disclosures This work was supported in part by the Dr Miriam and Sheldon G. Adelson Medical Research Foundation and by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award numbers R01HL089765. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr Berman participates in software royalties for QPS software at Cedars-Sinai Medical Center. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022. Published by Elsevier Inc.)

Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in single-center studies with conventional single-photon emission computed tomography (SPECT) cameras. We evaluated sex-specific difference in the prognostic value of automated quantification of ischemic total perfusion defect (ITPD) and the interaction between sex and ITPD using high-efficiency SPECT cameras with solid-state detectors in an international multicenter imaging registry (REFINE SPECT [Registry of Fast Myocardial Perfusion Imaging With Next-Generation SPECT])., Methods: Rest and exercise or pharmacological stress SPECT myocardial perfusion imaging were performed in 17 833 patients from 5 centers. MACE was defined as the first occurrence of death or myocardial infarction. Total perfusion defect (TPD) at rest, stress, and ejection fraction were quantified automatically by software. ITPD was given by stressTPD-restTPD. Cox proportional hazards model was used to evaluate the association between ITPD versus MACE-free survival and expressed as a hazard ratio., Results: In 10614 men and 7219 women, with a median follow-up of 4.75 years (interquartile range, 3.7-6.1), there were 1709 MACE. In a multivariable Cox model, after adjusting for revascularization and other confounding variables, ITPD was associated with MACE (hazard ratio, 1.08 [95% CI, 1.05-1.1]; P <0.001). There was an interaction between ITPD and sex ( P <0.001); predicted survival for ITPD<5% was worse among men compared to women, whereas survival among women was worse than men for ITPD≥5%, P <0.001., Conclusions: In the international, multicenter REFINE SPECT registry, moderate and severe ischemia as quantified by ITPD from high-efficiency SPECT is associated with a worse prognosis in women compared with men.

Background: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk., Methods: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD)., Results: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs., Conclusion: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation., Objectives: This study sought to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease-deep learning [CAD-DL]) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI)., Methods: A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis., Results: In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 [95% CI: 0.82-0.85]) than stress TPD (AUC: 0.78 [95% CI: 0.77-0.80]) or reader diagnosis (AUC: 0.71 [95% CI: 0.69-0.72]; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 [95% CI: 0.76-0.84]) than stress TPD (AUC: 0.73 [95% CI: 0.69-0.77]) or reader diagnosis (AUC: 0.65 [95% CI: 0.61-0.69]; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow., Conclusions: The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI., Competing Interests: Funding Support and Author Disclosures This research was supported in part by National Heart, Lung, and Blood Institute/National Institutes of Health grant R01HL089765 (principal investigator: Piotr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Drs Berman and Slomka and Mr Kavanagh participated in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Slomka has received research grant support from Siemens Medical Systems. Drs Berman, Dorbala, Einstein, and Edward Miller are consultants for GE Healthcare. Dr Einstein is a consultant for W.L. Gore & Associates. Dr Dorbala is a consultant for Bracco Diagnostics; and has received a grant through her institution from Astellas. Dr Di Carli has received research grant support from Spectrum Dynamics; and he is a consultant for Sanofi and GE Healthcare. Dr Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr Einstein has received research support through his institution from GE Healthcare, Philips Healthcare, Toshiba America Medical Systems, Roche Medical Systems, and W.L. Gore & Associates. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment., Methods: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared., Results: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003)., Conclusions: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity., (© 2020. American Society of Nuclear Cardiology.)

Shape index and eccentricity index are measures of left ventricular morphology. Although both measures can be quantified with any stress imaging modality, they are not routinely evaluated during clinical interpretation. We assessed their independent associations with major adverse cardiovascular events (MACE), including measures of poststress change in shape index and eccentricity index. Methods: Patients undergoing SPECT myocardial perfusion imaging between 2009 and 2014 from the Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) were studied. Shape index (ratio between the maximum left ventricular diameter in short axis and ventricular length) and eccentricity index (calculated from orthogonal diameters in short axis and length) were calculated in end-diastole at stress and rest. Multivariable analysis was performed to assess independent associations with MACE (death, nonfatal myocardial infarction, unstable angina, or late revascularization). Results: In total, 14,016 patients with a mean age of 64.3 ± 12.2 y (8,469 [60.4%] male were included. MACE occurred in 2,120 patients during a median follow-up of 4.3 y (interquartile range, 3.4-5.7). Rest, stress, and poststress change in shape and eccentricity indices were associated with MACE in unadjusted analyses (all P < 0.001). However, in multivariable models, only poststress change in shape index (adjusted hazard ratio, 1.38; P < 0.001) and eccentricity index (adjusted hazard ratio, 0.80; P = 0.033) remained associated with MACE. Conclusion: Two novel measures, poststress change in shape index and eccentricity index, were independently associated with MACE and improved risk estimation. Changes in ventricular morphology have important prognostic utility and should be included in patient risk estimation after SPECT myocardial perfusion imaging., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Phase analysis of single-photon emission computed tomography myocardial perfusion imaging provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of single-photon emission computed tomography-myocardial perfusion imaging phase analysis in the largest multinational registry to date across all modalities., Methods: From the REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging With Next Generation SPECT), a total of 19 210 patients were included (mean age 63.8±12.0 years and 56% males). Poststress total perfusion deficit, left ventricular ejection fraction, and phase variables (phase entropy, bandwidth, and SD) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE)., Results: During a follow-up of 4.5±1.7 years, 2673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were ≈4-fold higher between the second and highest decile group for entropy (1.7% versus 6.7%). Optimal phase variable cutoff values stratified MACE risk in patients with normal and abnormal total perfusion deficit and left ventricular ejection fraction. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with total perfusion deficit and left ventricular ejection fraction ( P <0.0001)., Conclusions: In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and left ventricular ejection fraction assessment alone. Phase analysis can be obtained fully automatically, without additional radiation exposure or cost to improve MACE risk prediction and, therefore, should be routinely reported for single-photon emission computed tomography-myocardial perfusion imaging studies.

Aims: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost but require clinicians to make immediate decisions regarding rest scan cancellation. We developed a machine learning (ML) approach for automatic rest scan cancellation and evaluated its prognostic safety., Methods and Results: In total, 20 414 patients from a solid-state SPECT MPI international multicentre registry with clinical data and follow-up for major adverse cardiac events (MACE) were used to train ML for MACE prediction as a continuous probability (ML score), using 10-fold repeated hold-out testing to separate test from training data. Three ML score thresholds (ML1, ML2, and ML3) were derived by matching the cancellation rates achieved by physician interpretation and two clinical selection rules. Annual MACE rates were compared in patients selected for rest scan cancellation between approaches. Patients selected for rest scan cancellation with ML had lower annualized MACE rates than those selected by physician interpretation or clinical selection rules (ML1 vs. physician interpretation: 1.4 ± 0.1% vs. 2.1 ± 0.1%; ML2 vs. clinical selection: 1.5 ± 0.1% vs. 2.0 ± 0.1%; ML3 vs. stringent clinical selection: 0.6 ± 0.1% vs. 1.7 ± 0.1%, all P < 0.0001) at matched cancellation rates (60 ± 0.7, 64 ± 0.7, and 30 ± 0.6%). Annualized all-cause mortality rates in populations recommended for rest cancellation by physician interpretation, clinical selection approaches were higher (1.3%, 1.2%, and 1.0%, respectively) compared with corresponding ML thresholds (0.6%, 0.6%, and 0.2%)., Conclusion: ML, using clinical and stress imaging data, can be used to automatically recommend cancellation of rest SPECT MPI scans, while ensuring higher prognostic safety than current clinical approaches., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)

Objectives: Using a contemporary, multicenter international single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) registry, this study characterized the potential major adverse cardiovascular event(s) (MACE) benefit of early revascularization based on automatic quantification of ischemia., Background: Prior single-center data reported an association between moderate to severe ischemia SPECT-MPI and reduced cardiac death with early revascularization., Methods: Consecutive patients from a multicenter, international registry who underwent 99m Tc SPECT-MPI between 2009 and 2014 with solid-state scanners were included. Ischemia was quantified automatically as ischemic total perfusion deficit (TPD). Early revascularization was defined as within 90 days. The primary outcome was MACE (death, myocardial infarction, and unstable angina). A propensity score was developed to adjust for nonrandomization of revascularization; then, multivariable Cox modeling adjusted for propensity score and demographics was used to predict MACE., Results: In total, 19,088 patients were included, with a mean follow-up of 4.7 ± 1.6 years, during which MACE occurred in 1,836 (9.6%) patients. There was a significant interaction between ischemic TPD modeled as a continuous variable and early revascularization (interaction p value: 0.012). In this model, there was a trend toward reduced MACE in patients with >5.4% ischemic TPD and a significant association with reduced MACE in patients with >10.2% ischemic TPD., Conclusions: In this large, international, multicenter study reflecting contemporary cardiology practice, early revascularization of patients with >10.2% ischemia on SPECT-MPI, quantified automatically, was associated with reduced MACE., Competing Interests: Funding Support and Author Disclosures This research was supported in part by grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institutes of Health (principal investigator: Dr. Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work was also supported in part by the Miriam and Sheldon Adelson Medical Research Foundation. Dr. R.J.H. Miller received funding support from the Arthur J E Child Fellowship grant. Dr. Einstein has served as a consultant for GE Healthcare, and his institution has received research support from Toshiba America Medical Systems and Roche Medical Systems. Dr. Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr. E. Miller has served as a consultant for GE Healthcare and Bracco Inc.; and he and his institution have received grant support from Bracco Inc. Dr. Dorbala has served as a consultant for GE Healthcare and Bracco Diagnostics; and her institution has received grant support from Astellas. Dr. Di Carli has received research grant support from Spectrum Dynamics and consulting honoraria from Sanofi and GE Healthcare. Dr. Berman participates in software royalties for QPS software at Cedars-Sinai Medical Center; has served as a consultant for GE Healthcare; and his institution has received grant support from HeartFlow. Drs. Slomka participates in software royalties for QPS software at Cedars-Sinai Medical Center; and has received research grant support from Siemens Medical Systems. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

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.

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.

Aims: Ischaemia on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is strongly associated with cardiovascular risk. Transient ischaemic dilation (TID) and post-stress wall motion abnormalities (WMA) are non-perfusion markers of ischaemia with incremental prognostic utility. Using a large, multicentre SPECT MPI registry, we assessed the degree to which these features increased the risk of major adverse cardiovascular events (MACE) in patients with less than moderate ischaemia., Methods and Results: Ischaemia was quantified with total perfusion deficit using semiautomated software and classified as: none (<1%), minimal (1 to <5%), mild (5 to <10%), moderate (10 to <15%), and severe (≥15%). Univariable and multivariable Cox proportional hazard analyses were used to assess associations between high-risk imaging features and MACE. We included 16 578 patients, mean age 64.2 and median follow-up 4.7 years. During follow-up, 1842 patients experienced at least one event. Patients with mild ischaemia and TID were more likely to experience MACE compared with patients without TID [adjusted hazard ratio (HR) 1.42, P = 0.023], with outcomes not significantly different from patients with moderate ischaemia without other high-risk features (unadjusted HR 1.15, P = 0.556). There were similar findings in patients with post-stress WMA. However, in multivariable analysis of patients with mild ischaemia, TID (adjusted HR 1.50, P = 0.037), but not WMA, was independently associated with increased MACE., Conclusion: In patients with mild ischaemia, TID or post-stress WMA identify groups of patients with outcomes similar to patients with moderate ischaemia. Whether these combinations identify patients who may derive benefit from revascularization deserves further investigation., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Aims: To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting., Methods and Results: A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed., Conclusion: In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient)., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99m Tc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) ( P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) ( P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read ( P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: We investigated fully automatic coronary artery calcium (CAC) scoring and cardiovascular disease (CVD) risk categorization from CT attenuation correction (CTAC) acquired at rest and stress during cardiac PET/CT and compared it with manual annotations in CTAC and with dedicated calcium scoring CT (CSCT)., Methods and Results: We included 133 consecutive patients undergoing myocardial perfusion 82 Rb PET/CT with the acquisition of low-dose CTAC at rest and stress. Additionally, a dedicated CSCT was performed for all patients. Manual CAC annotations in CTAC and CSCT provided the reference standard. In CTAC, CAC was scored automatically using a previously developed machine learning algorithm. Patients were assigned to a CVD risk category based on their Agatston score (0, 1-10, 11-100, 101-400, >400). Agreement in CVD risk categorization between manual and automatic scoring in CTAC at rest and stress resulted in Cohen's linearly weighted κ of 0.85 and 0.89, respectively. The agreement between CSCT and CTAC at rest resulted in κ of 0.82 and 0.74, using manual and automatic scoring, respectively. For CTAC at stress, these were 0.79 and 0.70, respectively., Conclusion: Automatic CAC scoring from CTAC PET/CT may allow routine CVD risk assessment from the CTAC component of PET/CT without any additional radiation dose or scan time.

Objectives: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD)., Background: Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI., Methods: A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99m Tc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure., Results: A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01)., Conclusions: Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods., (Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: Non-invasive fractional flow reserve derived from coronary CT angiography (FFR CT ) has been shown to be predictive of lesion-specific ischemia as assessed by invasive fractional flow reserve (FFR). However, in practice, clinicians are often faced with an abnormal distal FFR CT in the absence of a discrete obstructive lesion. Using quantitative plaque analysis, we sought to determine the relationship between an abnormal whole vessel FFR CT (V-FFR CT ) and quantitative measures of whole vessel atherosclerosis in coronary arteries without obstructive stenosis., Methods: FFR CT was calculated in 155 consecutive patients undergoing coronary CTA with ≥25% but less than 70% stenosis in at least one major epicardial vessel. Semi-automated software was used to quantify plaque volumes (total plaque [TP], calcified plaque [CP], non-calcified plaque [NCP], low-density non-calcified plaque [LD-NCP]), remodeling index [RI], maximal contrast density difference [CDD] and percent diameter stenosis [%DS]. Abnormal V-FFR CT was defined as a minimum value of ≤0.75 across the vessel (at the most distal region where FFR CT was computed)., Results: Vessels with abnormal V-FFR CT had higher per-vessel TP (554 vs 331 mm 3 ), CP (59 vs 25 mm 3 ), NCP (429 vs 295 mm 3 ), LD-NCP (65 vs 35 mm 3 ) volume and maximum CDD (21 vs 14%) than those with normal V-FFR CT (median, p < 0.05 for all). Using a multivariate analysis to adjust for CDD and %DS, all measures of plaque volume were predictive of abnormal V-FFR CT (OR 2.09, 1.36, 1.95, 1.95 for TP, CP, NCP and LD-NCP volume, respectively; p < 0.05 for all)., Conclusion: Abnormal V-FFR CT in vessels without obstructive stenosis is associated with multiple markers of diffuse non-obstructive atherosclerosis, independent of stenosis severity. Whole vessel FFR CT may represent a novel measure of diffuse coronary plaque burden., (Copyright © 2018. Published by Elsevier Inc.)

Objectives: This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning (ML) to predict major adverse cardiac events (MACE)., Background: Traditionally, prognostication by MPI has relied on visual or quantitative analysis of images without objective consideration of the clinical data. ML permits a large number of variables to be considered in combination and at a level of complexity beyond the human clinical reader., Methods: A total of 2,619 consecutive patients (48% men; 62 ± 13 years of age) who underwent exercise (38%) or pharmacological stress (62%) with high-speed SPECT MPI were monitored for MACE. Twenty-eight clinical variables, 17 stress test variables, and 25 imaging variables (including total perfusion deficit [TPD]) were recorded. Areas under the receiver-operating characteristic curve (AUC) for MACE prediction were compared among: 1) ML with all available data (ML-combined); 2) ML with only imaging data (ML-imaging); 3) 5-point scale visual diagnosis (physician [MD] diagnosis); and 4) automated quantitative imaging analysis (stress TPD and ischemic TPD). ML involved automated variable selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross validation., Results: During follow-up (3.2 ± 0.6 years), 239 patients (9.1%) had MACE. MACE prediction was significantly higher for ML-combined than ML-imaging (AUC: 0.81 vs. 0.78; p < 0.01). ML-combined also had higher predictive accuracy compared with MD diagnosis, automated stress TPD, and automated ischemic TPD (AUC: 0.81 vs. 0.65 vs. 0.73 vs. 0.71, respectively; p < 0.01 for all). Risk reclassification for ML-combined compared with visual MD diagnosis was 26% (p < 0.001)., Conclusions: ML combined with both clinical and imaging data variables was found to have high predictive accuracy for 3-year risk of MACE and was superior to existing visual or automated perfusion assessments. ML could allow integration of clinical and imaging data for personalized MACE risk computations in patients undergoing SPECT MPI., (Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99m Tc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) ( P < 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) ( P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same ( P = NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) ( P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same ( P = NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) ( P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification., (© 2017 by the Society of Nuclear Medicine and Molecular Imaging.)

Aims: To compare the predictive value of epicardial and intrathoracic fat volume (EFV, IFV), coronary artery calcium (CAC) score, and single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) for major adverse cardiac events (MACE)., Methods and Results: Follow-up was obtained in 275 patients with known or suspected coronary artery disease (CAD), who underwent SPECT-MPI including non-contrast cardiac computed tomography (CT) for attenuation correction to evaluate ischaemic heart disease and in whom EFV, IFV, and CAC score were calculated from non-contrast CT. Associations between fat volume, traditional cardiovascular risk factors, CAC score, and SPECT-MPI results were assessed and MACE predictors identified by Cox proportional hazard regression and global χ(2) statistics. After a median follow-up of 2.9 years, MACE were recorded in 38 patients. In univariate Cox regression analysis, EFV and IFV were predictors of MACE (P = 0.013 and P = 0.004, respectively). In multivariate analysis, EFV and IFV provided incremental predictive value beyond traditional cardiovascular risk factors (P < 0.05 and P < 0.01). However, after adjustment for CAC score and SPECT-MPI results, EFV and IFV fell short of statistical significance as independent outcome predictors., Conclusion: Quantification of EFV and IFV is associated with MACE and may improve risk stratification beyond traditional cardiovascular risk factors. However, once CAC score and/or SPECT-MPI results are known, EFV and IFV do not provide any added clinically relevant prognostic value. Further studies may identify the subpopulation with the largest relative merit of EFV and IFV as an adjunct to SPECT-MPI and CAC score., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.)

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.

Unlabelled: Hybrid PET myocardial perfusion imaging (MPI) with CT allows the incorporation of coronary artery calcium (CAC) into the clinical protocol. We aimed to determine whether the combined analysis of MPI and CAC could improve the diagnostic accuracy of PET MPI in detection of obstructive coronary artery disease (CAD)., Methods: Consecutive patients (n = 152; mean age ± SD, 69 ± 12 y) without prior CAD, referred to (82)Rb PET MPI followed by invasive coronary angiography performed within 14 days, were studied. Myocardial perfusion was quantified automatically for left anterior descending, left circumflex, and right coronary artery territories as an ischemic total perfusion deficit (ITPD) for 456 vessels. Global and per-vessel CAC Agatston scores were calculated. Obstructive CAD was defined as 50% or greater stenosis of the left main and 70% or greater stenosis in the left anterior descending, left circumflex, and right coronary arteries. Logistic regression and 10-fold cross validation were used to derive and validate the combined ITPD/logCAC (logarithm of coronary calcium) scores., Results: In the prediction of per-vessel obstructive CAD, the receiver-operating-characteristic area under the curve for combined per-vessel ITPD/logCAC score was higher, 0.85 (95% confidence interval [CI], 0.81-0.89), than standalone ITPD area under the curve, 0.81 (95% CI: 0.76-0.85), and logCAC score, 0.73 (95% CI, 0.68-0.78; P < 0.05). The integrated discrimination improvement of combined per-vessel ITPD/logCAC analysis was 0.07 (95% CI, 0.04-0.09; P < 0.0001), as compared with ITPD alone., Conclusion: Combined automatically derived per-vessel ITPD and logCAC score improves accuracy of (82)Rb PET MPI for detection of obstructive CAD., (© 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Objective: We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms., Methods: 1,181 rest (201)Tl/stress (99m)Tc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD <5%] were considered. Cases with stenosis <70% by ICA and LLk of CAD were considered normal. Total stress perfusion deficit (TPD) for supine/prone data, stress/rest perfusion change, and transient ischemic dilatation were derived by automated perfusion quantification software and were combined with age, sex, and post-electrocardiogram CAD probability by a boosted ensemble ML algorithm (LogitBoost). The diagnostic accuracy of the model for prediction of obstructive CAD ≥70% was compared to standard prone/supine quantification and to visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Tenfold stratified cross-validation was performed., Results: The diagnostic accuracy of ML (87.3% ± 2.1%) was similar to Expert 1 (86.0% ± 2.1%), but superior to combined supine/prone TPD (82.8% ± 2.2%) and Expert 2 (82.1% ± 2.2%) (P < .01). The receiver operator characteristic areas under curve for ML algorithm (0.94 ± 0.01) were higher than those for TPD and both visual readers (P < .001). The sensitivity of ML algorithm (78.9% ± 4.2%) was similar to TPD (75.6% ± 4.4%) and Expert 1 (76.3% ± 4.3%), but higher than that of Expert 2 (71.1% ± 4.6%), (P < .01). The specificity of ML algorithm (92.1% ± 2.2%) was similar to Expert 1 (91.4% ± 2.2%) and Expert 2 (88.3% ± 2.5%), but higher than TPD (86.8% ± 2.6%), (P < .01)., Conclusion: ML significantly improves diagnostic performance of MPS by computational integration of quantitative perfusion and clinical data to the level rivaling expert analysis.

Unlabelled: We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for the prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for noncorrected (NC) data by Support Vector Machine (SVM) algorithm, a computer method for machine learning., Methods: Rest-stress gated (99m)Tc MPS NC studies (n = 957) from 623 consecutive patients with correlating invasive coronary angiography and 334 with a low likelihood of CAD (<5%) were assessed. Stenosis ≥50% in left main or ≥70% in all other vessels was considered abnormal. Total perfusion deficit (TPD) was computed automatically. In addition, ischemic changes (ISCHs) and ejection fraction changes (EFCs) between stress and rest were derived by quantitative software. The SVM was trained using a group of 125 patients (25 with low-likelihood, 25 with 0-vessel, 25 with 1-vessel, 25 with 2-vessel, and 25 with 3-vessel CAD) with the above quantitative variables and second-order polynomial fitting. The remaining patients (n = 832) were categorized using probability estimates, with CAD defined as a probability estimate ≥ 0.50. The diagnostic accuracy of SVM was also compared with visual segmental scoring by 2 experienced readers., Results: The sensitivity of SVM (84%) was significantly better than ISCH (75%, P < 0.05) and EFC (31%, P < 0.05). The specificity of SVM (88%) was significantly better than TPD (78%, P < 0.05) and EFC (77%, P < 0.05). The diagnostic accuracy of SVM (86%) was significantly better than TPD (81%), ISCH (81%), or EFC (46%) (P < 0.05 for all). The receiver-operating-characteristic (ROC) area under the curve for SVM (0.92) was significantly better than TPD (0.90), ISCH (0.87), and EFC (0.64) (P < 0.001 for all). The diagnostic accuracy of SVM was comparable to the overall accuracy of both visual readers (86% vs. 84%, P = NS). The ROC area under the curve for SVM (0.92) was significantly better than that of both visual readers (0.87 and 0.88, P < 0.03)., Conclusion: Computational integration of quantitative perfusion and functional variables using the SVM approach significantly improves the diagnostic accuracy of MPS and can significantly outperform visual assessment based on ROC analysis.

Background: Hybrid PET/CT allows for acquisition of cardiac PET and coronary CT angiography (CCTA) in one session. However, PET and CCTA are acquired with differing breathing protocols and require software registration. We aimed to validate automatic correction for breathing misalignment between PET and CCTA acquired on hybrid scanner., Methods: Single-session hybrid PET/CT studies of rest/stress (13)N-ammonia PET and CCTA in 32 consecutive patients were considered. Automated registration of PET left ventricular (LV) surfaces with CCTA volumes was evaluated by comparing with expert manual alignment by two observers., Results: The average initial misalignments between the position of LV on PET and CCTA were 27.2 ± 11.8, 13.3 ± 11.5, and 14.3 ± 9.1 mm in x, y, and z axes on rest, and 26.3 ± 10.2, 11.1 ± 9.5, and 11.7 ± 7.1 mm in x, y, and z axes on stress, respectively. The automated PET-CCTA co-registration had 95% agreement as judged visually. Compared with expert manual alignment, the translation errors of the algorithm were 5.3 ± 2.8 mm (rest) and 6.0 ± 3.5 mm (stress). 3D visualization of combined coronary vessel anatomy and hypoperfusion from PET could be made without further manual adjustments., Conclusion: Software co-registration of CCTA and PET myocardial perfusion imaging on hybrid PET/CT scanners is necessary, but can be performed automatically, facilitating integrated 3D display on PET/CT.

Recent advances in nuclear cardiology instrumentation have enabled myocardial perfusion imaging (MPI) with improved image quality and faster scan times. These developments also can be exploited to reduce the effective radiation dose to the patient. In this review, we discuss these technologies including new single photon emission computed tomography (SPECT) and positron emission tomography (PET) scanners, as well as novel reconstruction software with regard to their potential for the reduction of the patient radiation dose. New advances in nuclear cardiology instrumentation will allow routine rest/stress MPI imaging with low radiation doses (<5 mSv) and fast imaging times, even by the software-only solutions. It is possible to further reduce the MPI radiation dose to less than 2 to 3 mSv range with standard acquisition times. PET perfusion imaging also can be performed with very low doses especially by the three-dimensional scanners allowing hybrid PET/computed tomographic angiography (CTA) imaging with low overall dose. In addition, stress-only protocols can be utilized to further reduce the radiation dose and the overall test time.

Background: We aimed to characterize normal limits and to determine the diagnostic accuracy for an automated quantification of 3D 82-Rubidium (Rb-82) PET/CT myocardial perfusion imaging (MPI)., Methods: We studied 125 consecutive patients undergoing Rb-82 PET/CT MPI, including patients with suspected coronary artery disease (CAD) and invasive coronary angiography, and 42 patients with a low likelihood (LLk) of CAD. Normal limits for perfusion and function were derived from LLk patients. QPET software was used to quantify perfusion abnormality at rest and stress expressed as total perfusion deficit (TPD)., Results: Relative perfusion databases did not differ in any of the 17 segments between males and females. The areas under the receiver operating characteristic curve for detection of CAD were 0.86 for identification of ≥50% and ≥70% stenosis. The sensitivity/specificity was 86%/86% for detecting ≥50% stenosis and 93%/77% for ≥70% stenosis, respectively. In regard to normal limits, mean rest and stress left ventricular ejection fraction (LVEF) were 67% ± 10% and 75% ± 9%, respectively. Mean transient ischemic dilation ratio was 1.06 ± 0.14 and mean increase in LVEF with stress was 7.4% ± 6.1% (95th percentile of 0%)., Conclusion: Normal limits have been established for 3D Rb-82 PET/CT analysis with QPET software. Fully automated quantification of myocardial perfusion PET data shows high diagnostic accuracy for detecting obstructive CAD.

Objective: We explored whether the presence of 3 known features of plaque vulnerability on coronary CT angiography (CCTA)--low attenuation plaque content (LAP), positive remodeling (PR), and spotty calcification (SC)--identifies plaques associated with greater inducible myocardial hypoperfusion measured by myocardial perfusion imaging (MPI)., Methods: We analyzed 49 patients free of cardiac disease who underwent CCTA and MPI within a 6-month period and were found on CCTA to have focal 70-99% stenosis from predominantly non-calcified plaque in the proximal or mid segment of 1 major coronary artery. Presence of LAP (≤ 30 Hounsfield Units), PR (outer wall diameter exceeds proximal reference by ≥ 5%), and SC (≤ 3 mm long and occupies ≤ 90° of cross-sectional artery circumference) was determined. On MPI, reversible hypoperfusion in the myocardial territory corresponding to the diseased artery was quantified both as percentage of total myocardium (RevTPD(ART)) by an automatic algorithm and as summed difference score (SDS(ART)) by two experienced readers. RevTPD(ART)≥ 3% and SDS(ART)≥ 3 defined significant inducible hypoperfusion in the territory of the diseased artery., Results: Plaques in patients with RevTPD(ART)≥ 3% more frequently exhibited LAP (70% vs. 14%, p < 0.001) and PR (70% vs. 24%, p = 0.001) but not SC (55% vs. 34%, p = 0.154). RevTPD(ART) increased from 1.3 ± 1.2% in arteries with LAP-/PR- plaques to 3.2 ± 4.3% with LAP+/PR- or LAP-/PR+ plaques to 8.3 ± 2.4% with LAP+/PR+ plaques (p < 0.001); SDS(ART) showed a similar increase: 0.3 ± 0.7 to 2.3 ± 2.8 to 6.0 ± 3.8 (p < 0.001). Using the same LAP/PR categorization, there was a marked increase in the frequency of significant hypoperfusion as determined by both RevTPD(ART)≥ 3% (1/19 to 10/21 to 9/9, p < 0.001) and SDS(ART)≥ 3 (1/19 to 8/21 to 8/9, p < 0.001). LAP and PR, but not SC, were strong predictors of RevTPD(ART) and SDS(ART) in regression models adjusting for potential confounders., Conclusions: Presence of low attenuation plaque and positive remodeling in severely stenotic plaques on CCTA is strongly predictive of myocardial hypoperfusion and may be useful in assessing the hemodynamic significance of such lesions., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Purpose: The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and (82)Rb were demonstrated., Methods: The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and (82)Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ((82)Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers., Results: The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for (82)Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for (82)Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress (82)Rb studies and 4.3% in rest (82)Rb studies., Conclusions: Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

Background: PET reconstruction incorporating spatially variant 3D Point Spread Function (PSF) improves contrast and image resolution. "Cardiac Motion Frozen" (CMF) processing eliminates the influence of cardiac motion in static summed images. We have evaluated the combined use of CMF- and PSF-based reconstruction for high-resolution cardiac PET., Methods: Static and 16-bin ECG-gated images of 20 patients referred for (18)F-FDG myocardial viability scans were obtained on a Siemens Biograph-64. CMF was applied to the gated images reconstructed with PSF. Myocardium to blood contrast, maximum left ventricle (LV) counts to defect contrast, contrast-to-noise (CNR) and wall thickness with standard reconstruction (2D-AWOSEM), PSF, ED-gated PSF, and CMF-PSF were compared., Results: The measured wall thickness was 18.9 ± 5.2 mm for 2D-AWOSEM, 16.6 ± 4.5 mm for PSF, and 13.8 ± 3.9 mm for CMF-PSF reconstructed images (all P < .05). The CMF-PSF myocardium to blood and maximum LV counts to defect contrasts (5.7 ± 2.7, 10.0 ± 5.7) were higher than for 2D-AWOSEM (3.5 ± 1.4, 6.5 ± 3.1) and for PSF (3.9 ± 1.7, 7.7 ± 3.7) (CMF vs all other, P < .05). The CNR for CMF-PSF (26.3 ± 17.5) was comparable to PSF (29.1 ± 18.3), but higher than for ED-gated dataset (13.7 ± 8.8, P < .05)., Conclusion: Combined CMF-PSF reconstruction increased myocardium to blood contrast, maximum LV counts to defect contrast and maintained equivalent noise when compared to static summed 2D-AWOSEM and PSF reconstruction.

Background: The relationship between luminal stenosis measured by coronary CT angiography (CCTA) and severity of stress-induced ischemia seen on single photon emission computed tomographic myocardial perfusion imaging (SPECT-MPI) is not clearly defined. We sought to evaluate the relationship between stenosis severity assessed by CCTA and ischemia on SPECT-MPI., Methods and Results: ECG-gated CCTA (64 slice dual source CT) and SPECT-MPI were performed within 6 months in 292 patients (ages 26-91, 73% male) with no prior history of coronary artery disease. Maximal coronary luminal narrowing, graded as 0, ≥25%, 50%, 70%, or 90% visual diameter reduction, was consensually assessed by two expert readers. Perfusion defect on SPECT-MPI was assessed by computer-assisted visual interpretation by an expert reader using the standard 17 segment, 5 point-scoring model (stress perfusion defect of ≥5% = abnormal). By SPECT-MPI, abnormal perfusion was seen in 46/292 patients. With increasing stenosis severity, positive predictive value (PPV) increased (42%, 51%, and 74%, P = .01) and negative predictive value was relatively unchanged (97%, 95%, and 91%) in detecting perfusion abnormalities on SPECT-MPI. In a receiver operator curve analysis, stenosis of 50% and 70% were equally effective in differentiating between the presence and absence of ischemia. In a multivariate analysis that included stenosis severity, multivessel disease, plaque composition, and presence of serial stenoses in a coronary artery, the strongest predictors of ischemia were stenosis of 50-89%, odds ratio (OR) 7.31, P = .001, stenosis ≥90%, OR 34.05, P = .0001, and serial stenosis ≥50% OR of 3.55, P = .006., Conclusions: The PPV of CCTA for ischemia by SPECT-MPI rises as stenosis severity increases. Luminal stenosis ≥90% on CCTA strongly predicts ischemia, while <50% stenosis strongly predicts the absence of ischemia. Serial stenosis of ≥50% in a vessel may offer incremental value in addition to stenosis severity in predicting ischemia.

Objectives: We compared electrocardiogram-gated computed tomography (CT) myocardial perfusion imaging (MPI) based on quantification of the extent and severity of perfusion abnormalities to that measured with single-photon emission computed tomography (SPECT) MPI., Background: Contrast-enhanced CT-MPI has been used for the identification of myocardial ischemia., Methods: We performed CT-MPI during intravenous adenosine infusion in 30 patients with perfusion abnormalities on rest/adenosine stress SPECT-MPI acquired within 60 days (18 stress-rest CT-MPI and 12 stress CT-MPI only). The extent and severity of perfusion defects on SPECT-MPI were assessed on a 5-point scale in a standard 17-segment model, and total perfusion deficit (TPD) was quantified by automated software. The extent and severity of perfusion defects on CT-MPI was visually assessed by 2 observers using the same grading scale and expressed as summed stress score and summed rest score; visually quantified TPD was given by summed stress score/(maximal score of 68) and summed rest score/68. The magnitude of perfusion abnormality on CT-MPI in regions of the myocardium was defined., Results: On a per-segment basis, there was good agreement between CT-MPI and SPECT-MPI with a kappa of 0.71 (p < 0.0001) for detection of stress perfusion abnormalities. Automated TPD on SPECT-MPI was similar to visual TPD from CT-MPI (p = 0.65 stress TPD, and p = 0.12 ischemic TPD stress-rest) with excellent agreement (bias = -0.3 for stress TPD, and bias = 1.2 for ischemic TPD) on Bland-Altman analysis. Software-based quantification of the magnitude of stress perfusion deficit and ischemia on CT-MPI were similar to that for automated TPD measured by SPECT (p = 0.88 stress, and p = 0.48 ischemia), with minimal bias (bias = 0.6, and bias = 1.2)., Conclusions: Stress and reversible myocardial perfusion deficit measured by CT-MPI using a visual semiquantitative approach and a visually guided software-based approach show strong similarity with SPECT-MPI, suggesting that CT-MPI-based assessment of myocardial perfusion defects may be of clinical and prognostic value., (Copyright © 2010 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)