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The Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) has been expanded to include more patients and CT attenuation correction imaging. We present the design and initial results from the updated registry. Methods: The updated REFINE SPECT is a multicenter, international registry with clinical data and image files. SPECT images were processed by quantitative software and CT images by deep learning software detecting coronary artery calcium (CAC). Patients were followed for major adverse cardiovascular events (MACEs) (death, myocardial infarction, unstable angina, late revascularization). Results: The registry included scans from 45,252 patients from 13 centers (55.9% male, 64.7 ± 11.8 y). Correlating invasive coronary angiography was available for 3,786 (8.4%) patients. CT attenuation correction imaging was available for 13,405 patients. MACEs occurred in 6,514 (14.4%) patients during a median follow-up of 3.6 y (interquartile range, 2.5-4.8 y). Patients with a stress total perfusion deficit of 5% to less than 10% (unadjusted hazard ratio [HR], 2.42; 95% CI, 2.23-2.62) and a stress total perfusion deficit of at least 10% (unadjusted HR, 3.85; 95% CI, 3.56-4.16) were more likely to experience MACEs. Patients with a deep learning CAC score of 101-400 (unadjusted HR, 3.09; 95% CI, 2.57-3.72) and a CAC of more than 400 (unadjusted HR, 5.17; 95% CI, 4.41-6.05) were at increased risk of MACEs. Conclusion: The REFINE SPECT registry contains a comprehensive set of imaging and clinical variables. It will aid in understanding the value of SPECT myocardial perfusion imaging, leverage hybrid imaging, and facilitate validation of new artificial intelligence tools for improving prediction of adverse outcomes incorporating multimodality imaging., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

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.)

Background: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction., Methods: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction., Findings: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36)., Interpretation: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results., Funding: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002]., Competing Interests: Declaration of interests Dr. Robert Miller has received consulting and research support from Pfizer. Drs Berman and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Williams serves as the President-Elect of the British Society of Cardiovascular Imaging and is on the Board of Directors for the Society of Cardiovascular Computed Tomography; she has received consulting support from FEOPS and has given lectures for Canon Medical Systems, Siemens Healthineers and Novartis. Dr. Pieszko has served as a consultant for Medicalgorithmics S.A. Dr. Slomka has received consulting fees from Synektik. Drs. Berman, Sharir, Kaufmann, and Edward Miller have served as consultants for GE Healthcare. Dr. Dorbala has received honoraria from Novo Nordisk and Pfizer; her institution has received grant support from Attralus, Pfizer, GE Healthcare, Siemans, and Phillips. Dr. DiCarli has received institutional research grant support from Gilead Sciences and Amgen and consulting honoraria from Sanofi, Valo Health and MedTrace. Dr. Ruddy has received research grant support from GE Healthcare and Pfizer. Dr. Edward Miller has served as a consultant for ROIVANT; has received grant support from Anylam, Pfizer and Siemens, and has participated on the study advisory board of BioBridge. Dr. Sinusas serves a leadership role on the Society of Nuclear Medicine and Molecular Imaging Cardiovascular Council. Dr. Einstein receives royalties from Wolters Kluwer UpToDate and the American Society of Nuclear Cardiology/Society of Nuclear Medicine and Molecular Imaging, consulting fees from W.L Gore & Associates, support through patents with Columbia Technology Ventures, and has given lectures for Ionetix. Dr. Einstein's institution has received research support from GE Healthcare, Roche Medical Systems, W. L. Gore & Associates, Eidos Therapeutics, Attralus, Pfizer, Neovasc, Intellia Therapeutics, Ionis Pharmaceuticals, Canon Medical Systems, the International Atomic Energy Agency, National Council on Radiation Protection and Measurements, and the United States Regulatory Commission. The remaining authors have nothing to disclose., (Copyright © 2023. Published by Elsevier B.V.)

Lasiodiplodia theobromae attacks over 500 plant species and is an important pathogen of tropical and subtropical fruit. Due to global warming and climate change, the incidence of disease associated with L. theobromae is rising. Virulence tests performed on avocado and mango branches and fruit showed a large diversity of virulence of different L. theobromae isolates. Genome sequencing was performed for two L. theobromae isolates, representing more virulent (Avo62) and less-virulent (Man7) strains, to determine the cause of their variation. Comparative genomics, including orthologous and single-nucleotide polymorphism (SNP) analyses, identified SNPs in the less-virulent strain in genes related to secreted cell wall-degrading enzymes, stress, transporters, sucrose, and proline metabolism, genes in secondary metabolic clusters, effectors, genes involved in the cell cycle, and genes belonging to transcription factors that may contribute to the virulence of L. theobromae . Moreover, carbohydrate-active enzyme analysis revealed a minor increase in gene counts of cutinases and pectinases and the absence of a few glycoside hydrolases in the less-virulent isolate. Changes in gene-copy numbers might explain the morphological differences found in the in-vitro experiments. The more virulent Avo62 grew faster on glucose, sucrose, or starch as a single carbon source. It also grew faster under stress conditions, such as osmotic stress, alkaline pH, and relatively high temperature. Furthermore, the more virulent isolate secreted more ammonia than the less-virulent one both in vitro and in vivo. These study results describe genome-based variability related to L. theobromae virulence, which might prove useful for the mitigation of postharvest stem-end rot. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license., Competing Interests: The author(s) declare no conflict of interest.

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making., (© 2023. The Author(s).)

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.)

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.)

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: 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: 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.)

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.)

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.)

Objectives: This study compared the ability of automated myocardial perfusion imaging analysis to predict major adverse cardiac events (MACE) to that of visual analysis., Background: Quantitative analysis has not been compared with clinical visual analysis in prognostic studies., Methods: A total of 19,495 patients from the multicenter REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) study (64 ± 12 years of age, 56% males) undergoing stress Tc-99m-labeled single-photon emission computed tomography (SPECT) myocardial perfusion imaging were followed for 4.5 ± 1.7 years for MACE. Perfusion abnormalities were assessed visually and categorized as normal, probably normal, equivocal, or abnormal. Stress total perfusion deficit (TPD), quantified automatically, was categorized as TPD = 0%, TPD >0% to <1%, ≤1% to <3%, ≤3% to <5%, ≤5% to ≤10%, or TPD >10%. MACE consisted of death, nonfatal myocardial infarction, unstable angina, or late revascularization (>90 days). Kaplan-Meier and Cox proportional hazards analyses were performed to test the performance of visual and quantitative assessments in predicting MACE., Results: During follow-up examinations, 2,760 (14.2%) MACE occurred. MACE rates increased with worsening of visual assessments, that is, the rate for normal MACE was 2.0%, 3.2% for probably normal, 4.2% for equivocal, and 7.4% for abnormal (all p < 0.001). MACE rates increased with increasing stress TPD from 1.3% for the TPD category of 0% to 7.8% for the TPD category of >10% (p < 0.0001). The adjusted hazard ratio (HR) for MACE increased even in equivocal assessment (HR: 1.56; 95% confidence interval [CI]: 1.37 to 1.78) and in the TPD category of ≤3% to <5% (HR: 1.74; 95% CI: 1.41 to 2.14; all p < 0.001). The rate of MACE in patients visually assessed as normal still increased from 1.3% (TPD = 0%) to 3.4% (TPD ≥5%) (p < 0.0001)., Conclusions: Quantitative analysis allows precise granular risk stratification in comparison to visual reading, even for cases with normal clinical reading., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: Prevalence and prognostic impact of cardiovascular disease differ between patients with or without diabetes. We aimed to explore differences in the prevalence and prognosis of myocardial ischemia by automated quantification of total perfusion deficit (TPD) among patients with and without diabetes., Research Design and Methods: Of 20,418 individuals who underwent single-photon emission computed tomography myocardial perfusion imaging, 2,951 patients with diabetes were matched to 2,951 patients without diabetes based on risk factors using propensity score. TPD was categorized as TPD = 0%, 0% < TPD < 1%, 1% ≤ TPD < 5%, 5% ≤ TPD ≤ 10%, and TPD >10%. Major adverse cardiovascular events (MACE) were defined as a composite of all-cause mortality, myocardial infarction, unstable angina, or late revascularization., Results: MACE risk was increased in patients with diabetes compared with patients without diabetes at each level of TPD above 0 ( P < 0.001 for interaction). In patients with TPD >10%, patients with diabetes had greater than twice the MACE risk compared with patients without diabetes (annualized MACE rate 9.4 [95% CI 6.7-11.6] and 3.9 [95% CI 2.8-5.6], respectively, P < 0.001). Patients with diabetes with even very minimal TPD (0% < TPD < 1%) experienced a higher risk for MACE than those with 0% TPD (hazard ratio 2.05 [95% CI 1.21-3.47], P = 0.007). Patients with diabetes with a TPD of 0.5% had a similar MACE risk as patients without diabetes with a TPD of 8%., Conclusions: For every level of TPD >0%, even a very minimal deficit of 0% < TPD < 1%, the MACE risk was higher in the patients with diabetes compared with patients without diabetes. Patients with diabetes with minimal ischemia had comparable MACE risk as patients without diabetes with significant ischemia., (© 2019 by the American Diabetes Association.)

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.)

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.)

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.)

Objectives: This prospective, multicenter trial compared quantitative results of myocardial perfusion imaging and function using a high-speed single-photon emission computed tomography (SPECT) system with those obtained with conventional SPECT., Background: A novel SPECT camera was shown in a pilot study to detect a similar amount of myocardial perfusion abnormality compared with conventional SPECT in one-seventh of the acquisition time., Methods: A total of 238 patients underwent myocardial perfusion imaging with conventional and high-speed SPECT at 4 U.S. centers. An additional 63 patients with a low pre-test likelihood of coronary artery disease underwent myocardial perfusion imaging with both technologies to develop method- and sex-specific normal limits. Rest/stress acquisition times were, respectively, 20/15 min and 4/2 min for conventional and high-speed SPECT. Stress and rest quantitative total perfusion deficit, post-stress left ventricular end-diastolic volume, and ejection fraction were derived for the 238 patients by the 2 methods., Results: High-speed stress and rest total perfusion deficit correlated linearly with conventional SPECT total perfusion deficit (r = 0.95 and 0.97, respectively, p < 0.0001), with good concordance in the 3 vascular territories (kappa statistics for the left anterior descending coronary artery, left circumflex coronary artery, and right coronary artery were 0.73, 0.73, and 0.70, respectively; >90% agreement). The percentage of ischemic myocardium by both imaging modalities was significantly larger in patients with a high coronary artery disease likelihood than in those with a low and intermediate likelihood (p < 0.001). The average amount of ischemia was slightly but significantly larger by high-speed SPECT compared with conventional SPECT in high-likelihood patients (4.6 +/- 4.6% vs. 3.9 +/- 4.0%, respectively; p < 0.05). Post-stress ejection fraction and end-diastolic volume by the 2 methods were linearly correlated (r = 0.89 and 0.97, respectively)., Conclusions: The high-speed SPECT technology provides quantitative measures of myocardial perfusion and function comparable to those with conventional SPECT in one-seventh of the acquisition time., (Copyright 2010 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Unlabelled: This study described and validated a new solid-state single-photon gamma-camera and compared it with a conventional-SPECT Anger camera. The compact new camera uses a unique method for localizing gamma-photon information with a bank of 9 solid-state detector columns with tungsten collimators that rotate independently., Methods: Several phantom studies were performed comparing the new technology with conventional-SPECT technology. These included measurements of line sources and single- and dual-radionuclide studies of a torso phantom. Simulations were also performed using a cardiothoracic phantom. Furthermore, 18 patients were scanned with both the new camera and a conventional-SPECT camera., Results: The new camera had a count sensitivity that was 10 times higher than that of the conventional camera and a compensated spatial resolution that was moderately better. Dual-radionuclide studies using a phantom show the further potential of the new camera for a 2-tracer simultaneous acquisition. Two-minute clinical studies with the new camera and 11-min studies with the conventional camera qualitatively showed good-to-excellent image quality and improved myocardial edge definition for the new camera., Conclusion: These initial performance characteristics of a new solid-state single-photon gamma-camera offer great promise for clinical dynamic SPECT protocols, with important implications for applications in nuclear cardiology and molecular imaging.

Objectives: The purpose of this study was to compare myocardial perfusion imaging (MPI) with high-speed single-photon emission computed tomography (SPECT) with conventional SPECT imaging for the evaluation of myocardial perfusion in patients with known or suspected coronary artery disease., Background: A novel technology has been developed for high-speed SPECT MPI by employing a bank of independently controlled detector columns with large-hole tungsten collimators and multiple cadmium zinc telluride crystal arrays., Methods: A total of 44 patients (39 men) underwent same-day Tc-99m sestamibi stress/rest MPI. High-speed SPECT images were performed within 30 min after conventional SPECT. Stress and rest acquisition times were 16 and 12 min for conventional imaging and 4 and 2 min for high-speed SPECT, respectively. Myocardial counts/min (cpm) were calculated for both conventional SPECT and high-speed SPECT. Images were visually analyzed, and the summed stress score (SSS) and summed rest score (SRS) were calculated. Image quality and diagnostic confidence were qualitatively assessed., Results: High-speed SPECT SSS and SRS correlated linearly with conventional SPECT respective scores (r = 0.93, p < 0.0001 for SSS, and r = 0.93, p < 0.0001 for SRS). Image quality was rated good and higher in 17 (94%) cases for high-speed SPECT and 16 (89%) cases for conventional SPECT. Of the 44 patients studied, 36 (81.8%) and 35 (79.5%) were diagnosed definitely normal or abnormal by conventional and high-speed SPECT, respectively (p = NS). Myocardial count rate was significantly higher in high-speed versus conventional SPECT (384 x 10(-3) +/- 134 x 10(-3) cpm/min vs. 47 x 10(-3) +/- 14 x 10(-3) cpm/min, respectively, p < 0.0001) for stress and (962 x 10(-3) +/- 426 x 10(-3) cpm/min vs. 136 x 10(-3) +/- 37 x 10(-3) cpm/min, respectively, p < 0.001) for rest., Conclusions: High-speed SPECT provides fast MPI with high image quality and up to 8 times increased system sensitivity. The amount of perfusion abnormality visualized by high-speed SPECT is highly correlated to conventional SPECT, with an equivalent level of diagnostic confidence.

Unlabelled: Quantitation of regional myocardial function is valuable in patients with coronary artery disease. This study assessed normal heterogeneity and developed and validated normal limits for quantitative regional motion and thickening by gated myocardial perfusion SPECT., Methods: Patients underwent rest (201)Tl/exercise (99m)Tc-sestamibi gated SPECT. Reference values of motion and thickening for 20 myocardial segments were obtained in 105 patients with <5% likelihood of coronary disease (low-likelihood group). Criteria for abnormality of motion and thickening were defined for each segment, using receiver operator characteristic analysis, in 101 patients with coronary disease (training group). Semiquantitative visual interpretation was used as the gold standard. These criteria were prospectively validated in 100 patients (validation group). Criteria for grading motion and thickening abnormalities by severity levels were also defined and validated., Results: Normal thickening decreased substantially along the longitudinal axis of the left ventricle, from 69% +/- 13% at the apex to 25% +/- 11% at the basal segments, whereas normal motion varied within the same ventricular plane. Validation of the criteria for abnormality yielded high accuracy in the detection of motion abnormalities (sensitivity, 88%; specificity, 92%) and thickening abnormalities (sensitivity, 87%; specificity, 89%). Quantitative motion and thickening segmental scores showed good agreement with visual scores., Conclusion: Normal regional myocardial contraction by gated myocardial perfusion SPECT is characterized by a substantial apex-to-base decline in thickening and by circumferential heterogeneity in endocardial motion. The assignment of segment-specific threshold values for defining motion and thickening abnormalities provided reasonably accurate identification and grading of regional myocardial dysfunction.

Unlabelled: The combination of myocardial perfusion and poststress ejection fraction (EF) provides incremental prognostic information. This study assessed predictors of nonfatal myocardial infarction (MI) versus cardiac death (CD) by gated myocardial SPECT and examined the value of integrating the amount of ischemia and poststress EF data in risk stratification., Methods: We identified 2,686 patients who underwent resting (201)Tl/stress (99m)Tc-sestamibi gated SPECT and were monitored for >1 y. Patients who underwent revascularization < or = 60 d after the nuclear test were censored from the prognostic analysis. Visual scoring of perfusion images used 20 segments and a scale of 0--4. Poststress EF was automatically generated., Results: Cox regression analysis showed that after adjusting for prescan data, the most powerful predictor of CD was poststress EF, whereas the best predictor of MI was the amount of ischemia (summed difference score [SDS]). Integration of the EF and SDS yielded effective stratification of patients into low-, intermediate-, and high-risk subgroups. Patients with EF >50% and a large amount of ischemia were at intermediate risk (2%--3%), whereas those with mild or moderate ischemia were at low risk of CD (<1%/y). Patients with EF between 30% and 50% were at intermediate risk even in the presence of only mild or moderate ischemia. In patients with EF <30%, the CD rate was high (>4%/y) irrespective of the amount of ischemia., Conclusion: Poststress EF is the best predictor of CD, whereas the amount of ischemia is the best predictor of nonfatal MI. Integration of perfusion and function data improves stratification of patients into low, intermediate, and high risk of CD.

Objectives: The study compared the prognostic significance of myocardial perfusion single-photon emission computed tomography (SPECT) (MPS) in patients early and late after coronary artery bypass graft surgery (CABG)., Background: The long-term effectiveness of CABG is limited by graft stenosis. The greatest incidence of graft occlusion occurs between five and eight years after surgery. However, little is known regarding the appropriate time to stress patients post-CABG with respect to risk stratification., Methods: We identified 1,765 patients, who underwent MPS 7.1 +/- 5.0 years post-CABG. All patients underwent rest T1-201/stress Tc-99m sestamibi MPS and were followed up > or =1 year after testing. Patients with early CABG or PTCA (<60 days after MPS) were censored. The prognostic population consisted of 1,544 patients. A semiquantitative visual analysis employing a 20-segment model was used to define summed stress score (SSS), summed rest score (SRS), summed difference score (SDS), and the number of nonreversible segments (NRS)., Results: During follow-up, 53 cardiac deaths (CD) occurred. There was a significant increase in annual CD rates as a function of SSS. A multivariate analysis identified age, ischemia (SDS), and infarct size (NRS) as independent predictors of CD. Nuclear variables added incremental value to prescan information. The annual CD rate was relatively low (1.3%) in patients < or =5 years post-CABG. In this subgroup only age and infarct size (NRS) were predictive of CD., Conclusion: MPS is strongly predictive of subsequent CD in post-CABG patients and adds incremental value over clinical and treadmill test information. Our data suggest that symptomatic patients < or =5 years and all patients >5 years post-CABG may benefit from testing.

Unlabelled: This study validated a new automatic algorithm for assessment of lung-to-heart ratio (L/H) of radiotracers in myocardial perfusion SPECT and assessed the diagnostic value of (99m)Tc-sestamibi L/H after exercise., Methods: The new technique extracts a left ventricular region of interest (ROI) from a summed anterior projection image and generates a lung ROI by reshaping and translating the left ventricular ROI. This algorithm was applied to 230 patients who underwent exercise (99m)Tc-sestamibi SPECT (gated SPECT, n = 88) with first-pass ventriculography. Normal values were established in 26 patients in whom the likelihood of coronary artery disease (CAD) was 5% or less. An abnormality threshold for detecting severe and extensive CAD was defined in a subgroup of 109 patients who underwent coronary angiography and was validated in a prospective group (n = 72)., Results: The success rate of the automatic algorithm was 97%. Excellent correlation was found between automatic and manual L/H values (r = 0.95; P < 0.001). The mean L/H was higher in patients with a peak exercise ejection fraction (EF) less than 40% versus 40% or more (0.51 +/- 0.07 versus 0.43 +/- 0.05, P < 0.001) and in patients with a poststress EF less than 40% versus 40% or more (0.50 +/- 0.07 versus 0.44 +/- 0.06, P < 0.01). A threshold of L/H greater than 0.44 yielded a sensitivity and specificity of 63% and 81%, respectively, for identifying severe and extensive CAD in the prospective group and a sensitivity of 86% in identifying stenosis of 90% or more in the proximal left anterior descending artery., Conclusion: The new automatic algorithm for assessing L/H correlated well with manually derived L/H for (99m)Tc-sestamibi as well as (201)TI SPECT. An increased postexercise (99m)Tc-sestamibi L/H adds significant diagnostic value to study myocardial perfusion SPECT as a marker of severe and extensive CAD and reduced ventricular function.

Unlabelled: This study validates a new quantitative perfusion SPECT algorithm for the assessment of myocardial perfusion. The algorithm is not based on slices and provides fully 3-dimensional sampling and analysis independent of assumptions about the geometric shape of the left ventricle., Methods: Radiopharmaceutical- and sex-specific normal limits and thresholds for perfusion abnormality in 20 segments of the left ventricle were developed for separate, dual-isotope rest 201Tl-exercise 99mTc-sestamibi SPECT in 36 patients with <5% before-scanning likelihood of coronary artery disease (CAD) (group 1) and 159 patients with perfusion abnormalities (group 2). These thresholds were validated in 131 patients (group 3) by comparison with expert visual interpretation. Thresholds for automatic segmental scores were developed and validated for groups 2 and 3, respectively. The accuracy of CAD detection was assessed in 94 patients, who underwent coronary angiography (group 4)., Results: Overall sensitivity for detection of stress and rest segmental perfusion abnormality was 91% and 96%, respectively, for men and 89% and 79%, respectively, for women. Overall specificity for stress and rest was 87% and 90%, respectively, for men and 88% and 90%, respectively, for women. Agreement between automatic and visual scores was good (weighted K of 0.71 and 0.60 for stress and rest images, respectively). Sensitivity and specificity were 88% for the detection of > or =70% stenosis. For the detection of left anterior descending, left circumflex, and right coronary artery stenosis, sensitivity was 84%, 86%, and 88%, respectively, and specificity was 84%, 88%, and 81%, respectively., Conclusion: The new quantitative perfusion SPECT approach is highly sensitive and specific for the detection and localization of CAD, provides accurate automatic scores for the assessment of regional perfusion, and overcomes the low-specificity limitations of previous quantitative algorithms.

Unlabelled: We have developed a new, completely automatic 3-dimensional software approach to quantitative perfusion SPECT. The main features of the software are myocardial sampling based on an ellipsoid model; use of the entire count profile between the endocardial and epicardial surfaces; independence of the algorithm from myocardial shape, size, and orientation and establishment of a standard 3-dimensional point-to-point correspondence among all sampled myocardial regions; automatic generation of quantitative measurements and 5-point semiquantitative scores for each of 20 myocardial segments and automatic derivation of summed perfusion scores; and automatic generation of normal limits for any given patient population on the basis of data fractionally normalized to minimize hot spot artifacts., Methods: The new algorithm was tested on the tomographic images of 420 patients studied with a rest 201TI (111-167 MBq, 35 s/projection)-stress 99mTc-sestamibi (925-1480 MBq, 25 s/projection) separate dual-isotope protocol on a single-detector camera, a dual-detector 90 degrees camera, and a triple-detector camera., Results: The algorithm was successful in 397 of 420 patients (94.5%) and 816 of 840 image datasets (97.1%), with a statistically significant difference between the success rates of the 201TI images (399/ 420, or 95.0%) and the 99mTc images (417/420, or 99.3%; P < 0.001). Algorithm failure was caused by extracardiac uptake (10/24, or 41.7%) or inaccurate identification of the valve plane because of low count statistics (14/24, or 58.3%) and was obviated by simply limiting the image volume in which the software operates. Reproducibility of measurements of summed perfusion scores (r = 0.999 and 1 for stress and rest, respectively), global defect extent (r = 0.999 and 1 for stress and rest, respectively), and segmental perfusion scores (exact agreement = 99.9%, kappa = 0.998 for stress and 0.997 for rest) was extremely high., Conclusion: Automatic 3-dimensional quantitation of perfusion from 201Tl and 99mTc-sestamibi images is feasible and reproducible. The described software, because it is based on the same sampling scheme used for gated SPECT analysis, ensures intrinsically perfect registration of quantitative perfusion with quantitative regional wall motion and thickening information, if gated SPECT is used.

Background: The incremental prognostic value of rest-redistribution (201)Tl compared with stress and rest perfusion abnormalities has not been defined., Methods and Results: We identified 458 patients who underwent rest (201)Tl /stress (exercise or adenosine) (99m)Tc sestamibi single-photon emission computed tomography (SPECT) and had late (18 to 24 hours) (201)Tl imaging, were not revascularized within 60 days of SPECT, and were followed up at >1 year. SPECT images were visually analyzed with the use of a 20-segment model on a scale of 0 to 4. Thirty-seven cardiac deaths (CDs) and 17 nonfatal myocardial infarctions occurred. Univariate Cox proportional hazards analysis showed that the presence of a large amount of rest (201)Tl reversibility (rest-late summed difference score [SDS] of >8) was a significant predictor of CD (chi(2) = 5.77, P = 0.02) and CD or myocardial infarction (chi(2) = 5.3, P = 0.02). The CD rate was 9.3% y(-1) in patients with rest-late SDS of >8 compared with 3.6% y(-1) in patients with a mild/moderate amount of rest reversibility (rest-late SDS 3 to 8) and 3.4% y(-1) in patients with no rest reversibility (rest-late SDS <3) (P = 0.029). Kaplan-Meier survival analysis demonstrated significantly lower cumulative survival rates in patients with rest-late SDS of >8 (P = 0.01). Multivariate Cox proportional hazards analysis demonstrated that the presence of a large amount of resting reversibility was an independent and incremental predictor of CD after adjustment for stress and rest perfusion information. Multivariate logistic regression analysis demonstrated that resting reversibility was not an independent predictor of referral to coronary angiography and revascularization., Conclusions: The identification of a large amount of resting (201)Tl reversibility is an independent predictor of CD over stress and rest perfusion abnormalities.

Background: The incremental prognostic value of post-stress left ventricular ejection fraction (EF) and volume over perfusion has not been investigated., Methods and Results: We identified 1680 consecutive patients who underwent rest Tl-201/stress Tc-99m sestamibi gated single photon emission computed tomography (SPECT) and who were followed-up for 569+/-106 days. Receiver-operator characteristics analysis defined an EF<45%, an end-systolic volume (ESV) >70 mL, and an end-diastolic volume >120 mL as optimal thresholds, yielding moderate sensitivity and high specificity in the prediction of cardiac death. Patients with an EF> or = 45% had mortality rates <1%/year, despite severe perfusion abnormalities, whereas patients with an EF<45% had high mortality rates, even with only mild/moderate perfusion abnormalities (9.2%/year; P<0.00001). Similarly, an ESV< or = 70 mL was related to a low cardiac death rate (<1.2%/year), even for patients with severe perfusion abnormalities, whereas patients with an ESV>70 mL and only mild/moderate perfusion abnormalities had high death rates (8.2%/year; P<0.00001). Patients with an EF<45% and an ESV< or = 70 mL had low cardiac death rates (1.7%/year); those with an EF<45% but an ESV>70 mL had high death rates (7.9%/year; P<0.02). Multivariate Cox proportional hazards regression showed that perfusion variables and ESV were independent predictors of overall coronary events, whereas EF and ESV demonstrated incremental prognostic values over prescan and perfusion information in predicting cardiac death and cardiac death or myocardial infarction., Conclusions: Post-stress EF and ESV by gated-SPECT have incremental prognostic values over prescan and perfusion information in predicting cardiac death, and they provide clinically useful risk stratification.

Objectives: This study evaluated the diagnostic value of dipyridamole plus low level treadmill exercise (dipyridamole stress) thallium-201 single-photon emission computed tomography (SPECT) in patients taking antianginal drugs., Background: Dipyridamole stress is the major substitute for maximal exercise in patients referred for myocardial perfusion imaging. Although antianginal drugs are commonly suspended before exercise, dipyridamole stress is usually performed without discontinuing these drugs., Methods: Twenty-six patients underwent two dipyridamole perfusion studies: the first without (SPECT-1) and the second with (SPECT-2) antianginal treatment. Twenty-one patients (81%) received calcium antagonists, 19 (73%) received nitrates, and 8 (31%) received beta-blockers. Eighteen of the patients underwent coronary angiography. Data are presented as the mean value +/- SD., Results: Visual scoring yielded significantly larger and more severe reversible perfusion defects for SPECT-1 than for SPECT-2. Quantitative analysis showed larger perfusion defects on stress images of SPECT-1 in the left anterior descending coronary artery (LAD) (25 +/- 21% vs. 17 +/- 15%, p = 0.003), left circumflex coronary artery (LCx) (56 +/- 35% vs. 48 +/- 36%, p = 0.03) and right coronary artery (RCA) (36 +/- 27% vs. 25 +/- 24%, p = 0.008) territories. Individual vessel sensitivities in the LAD, LCx and RCA territories were 93%, 79% and 100% for SPECT-1 and 64%, 50% and 70% for SPECT-2, respectively. These differences were highly significant for the LAD (p = 0.004) and LCx (p = 0.00004) territories. The overall individual vessel sensitivity of SPECT-1 was significantly higher than that of SPECT-2 (92% vs. 62%, p = 0.000003). Specificity was not significantly different in SPECT-1 compared with SPECT-2 (80% and 93%, p = 0.33)., Conclusions: Continued use of antianginal drugs before dipyridamole plus low level treadmill exercise thallium-201 SPECT may reduce the extent and severity of myocardial perfusion defects, resulting in underestimation of coronary artery disease.

Systolic pressure-volume diagrams were obtained noninvasively by measuring the systolic central aortic pressure with a new device and by combining the pressure measurements, thus obtained, with absolute volume measurements obtained by radionuclide ventriculography during ejection. By dividing the peak power by the time elapsed from the beginning of ejection to the peak power point, the ejection rate of change of power (ERCP) was calculated. The ability of this index to assess left ventricular function at rest and exercise was evaluated in ten healthy subjects. ERCP proved to be more sensitive than global left ventricular ejection fraction increasing fivefold from rest to exercise compared with only 20% increase in global ejection fraction. ERCP increased dramatically postexercise from 3411 +/- 2173 to 18,162 +/- 14,633 gm/sec2, median 12,750, 95% confidence interval 9700-29,600, in healthy, while in patients it increased twofold from 2637 +/- 824 to 5062 +/- 1897 gm/sec2, median 4070, 95% confidence interval 2800-7030, p less than 0.001. ERCP had an excellent discriminative power in differentiating healthy subjects from patients, having 100% sensitivity, 90% specificity, 95% accuracy, 95% positive predictive value, and 90% negative predictive value. Thus, this noninvasive index seems to have a more comprehensive ability to evaluate changes in left ventricular function and shows a promising potential for clinical applications.