2023
Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging
Feher A, Pieszko K, Shanbhag A, Lemley M, Miller R, Huang C, Miras L, Liu Y, Gerber J, Sinusas A, Miller E, Slomka P. Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging. The International Journal Of Cardiovascular Imaging 2023, 40: 185-193. PMID: 37845406, PMCID: PMC466934, DOI: 10.1007/s10554-023-02980-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedCalcinosisCoronary Artery DiseaseFemaleHumansMaleMiddle AgedMyocardial Perfusion ImagingPredictive Value of TestsPrognosisTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsMajor adverse cardiovascular eventsCoronary artery calcificationMyocardial perfusion imagingCT myocardial perfusion imagingSPECT/CT myocardial perfusion imagingArtery calcificationPrognostic valuePrior coronary stentingAdverse cardiovascular eventsSimilar prognostic valueSPECT myocardial perfusionREFINE SPECT registrySPECT/CTCardiovascular eventsCAC scoringCalcium scoreCoronary calcificationCoronary stentingPrognostic utilitySingle centerCoronary arteryLow doseMyocardial perfusionPerfusion imagingSurvival analysisDuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.Peer-Reviewed Original ResearchMeSH KeywordsHeartHumansImage Processing, Computer-AssistedPhantoms, ImagingTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsCross-modality registrationConvolutional layersCo-attention mechanismMultiple convolutional layersCo-attention moduleDifferent convolutional layersMedical image registrationInput data streamDeep learning strategiesLow registration errorIntensity-based registration methodCardiac SPECTΜ-mapsDeep learningFeature fusionData streamsInput imageSource codeFeature mapsNeural networkImage registrationSpatial featuresRegistration performanceRegistration methodInput informationUnsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, Liang J, Huang C, Sharir T, Dorbala S, Di Carli M, Einstein A, Sinusas A, Miller E, Bateman T, Fish M, Ruddy T, Acampa W, Hauser M, Kaufmann P, Dey D, Berman D, Slomka P. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 50: 2656-2668. PMID: 37067586, PMCID: PMC10317876, DOI: 10.1007/s00259-023-06218-z.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseExercise TestFemaleHumansMaleMyocardial Perfusion ImagingPrognosisTomography, Emission-Computed, Single-PhotonUnsupervised Machine LearningConceptsCoronary artery diseaseMyocardial perfusion imagingCause mortalityTotal perfusion deficitArtery diseaseRisk stratificationPerfusion deficitsHigher body mass indexTomography myocardial perfusion imagingMore diabetes mellitusSPECT myocardial perfusion imagingBetter risk stratificationRisk-stratify patientsBody mass indexREFINE SPECT registrySingle photon emissionDiabetes mellitusMass indexExternal cohortStress imagingMyocardial perfusionPatientsRisk phenotypePerfusion imagingImaging characteristics
2022
Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study
Yousefi H, Shi L, Soufer A, Tsatkin V, Bruni W, Avendano R, Greco K, McMahon D, Thorn S, Miller E, Sinusas A, Liu C. Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study. Journal Of Nuclear Cardiology 2022, 30: 292-297. PMID: 36319815, DOI: 10.1007/s12350-022-03123-0.Peer-Reviewed Original ResearchAnimalsBlood VolumeErythrocytesHumansMicrocirculationPilot ProjectsSingle Photon Emission Computed Tomography Computed TomographyTomography, Emission-Computed, Single-PhotonDirect Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning
Singh A, Miller RJH, Otaki Y, Kavanagh P, Hauser MT, Tzolos E, Kwiecinski J, Van Kriekinge S, Wei CC, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC Cardiovascular Imaging 2022, 16: 209-220. PMID: 36274041, PMCID: PMC10980287, DOI: 10.1016/j.jcmg.2022.07.017.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseDeep LearningHumansMyocardial InfarctionMyocardial Perfusion ImagingPredictive Value of TestsPrognosisRisk AssessmentTomography, Emission-Computed, Single-PhotonConceptsMyocardial perfusion imagingTotal perfusion deficitNonfatal myocardial infarctionMyocardial infarctionPerfusion imagingTomography myocardial perfusion imagingIschemic total perfusion deficitStress total perfusion deficitTesting groupReceiver-operating characteristic curvePatient-level riskPrediction of deathSingle photon emissionLogistic regression modelsCause mortalityPrimary outcomeHighest quartileRisk stratificationAbnormal perfusionNormal perfusionPerfusion deficitsAdverse event predictionPrognostic accuracyHigh riskMyocardial perfusionDeep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events
Miller R, Pieszko K, Shanbhag A, Feher A, Lemley M, Killekar A, Kavanagh PB, Van Kriekinge SD, Liang JX, Huang C, Miller EJ, Bateman T, Berman DS, Dey D, Slomka PJ. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. Journal Of Nuclear Medicine 2022, 64: 652-658. PMID: 36207138, PMCID: PMC10071789, DOI: 10.2967/jnumed.122.264423.Peer-Reviewed Original ResearchCalciumCoronary AngiographyCoronary Artery DiseaseDeep LearningHumansRisk FactorsSingle Photon Emission Computed Tomography Computed TomographyTomography, Emission-Computed, Single-PhotonMitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images
Miller RJH, Singh A, Otaki Y, Tamarappoo BK, Kavanagh P, Parekh T, Hu LH, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli MF, Liang JX, Dey D, Berman DS, Slomka PJ. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 387-397. PMID: 36194270, PMCID: PMC10042590, DOI: 10.1007/s00259-022-05972-w.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCoronary AngiographyCoronary Artery DiseaseDeep LearningFemaleHumansMyocardial Perfusion ImagingPerfusionSensitivity and SpecificityTomography, Emission-Computed, Single-PhotonConceptsCoronary artery diseaseMyocardial perfusion imagingArtery diseaseInvasive angiographyObstructive coronary artery diseaseDisease probabilityLow-risk patientsLow-risk populationHigh-risk populationTotal perfusion deficitHigh diagnostic accuracyS-TPDPerfusion deficitsPatient managementPatientsPerfusion imagingDiagnostic accuracyPerfusion SPECT imagesLower likelihoodGood calibrationCharacteristic curveAngiographySPECT imagesSelection biasDiseaseIntegration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging
Feher A, Pieszko K, Miller R, Lemley M, Shanbhag A, Huang C, Miras L, Liu YH, Sinusas AJ, Miller EJ, Slomka PJ. Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging. Journal Of Nuclear Cardiology 2022, 30: 590-603. PMID: 36195826, DOI: 10.1007/s12350-022-03099-x.Peer-Reviewed Original ResearchMeSH KeywordsAgedCalciumCoronary Artery DiseaseFemaleHumansMachine LearningMaleMiddle AgedMyocardial Perfusion ImagingPrognosisTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsMajor adverse cardiovascular eventsMyocardial perfusion imagingAdverse cardiovascular eventsSPECT myocardial perfusion imagingCAC scoringCardiovascular eventsPrediction of MACECoronary artery calcification (CAC) scoringMACE-free survivalClinical risk factorsCoronary artery calciumCT myocardial perfusion imagingReceiver operator characteristic curveSPECT/CT myocardial perfusion imagingSPECT/CTOperator characteristic curveCT myocardial perfusionArtery calciumCAC scoreAnalysis patientsMACE predictionSingle centerHigher event ratesRisk factorsRisk scoreDuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT
Chen X, Zhou B, Xie H, Miao T, Liu H, Holler W, Lin M, Miller EJ, Carson RE, Sinusas AJ, Liu C. DuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Medical Physics 2022, 50: 89-103. PMID: 36048541, PMCID: PMC9868054, DOI: 10.1002/mp.15958.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsDeep LearningHeartHominidaeHumansImage Processing, Computer-AssistedTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsLow reconstruction accuracySynthetic projectionsAbsolute percent errorImage predictionSPECT image reconstructionImage domainSinogram synthesisGround truthReconstruction accuracyImage reconstructionSinogram domainProjection angleData acquisitionMean square errorFast data acquisitionImagesReconstruction artifactsSPECT imagesSquare errorDeep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.
Shanbhag AD, Miller RJH, Pieszko K, Lemley M, Kavanagh P, Feher A, Miller EJ, Sinusas AJ, Kaufmann PA, Han D, Huang C, Liang JX, Berman DS, Dey D, Slomka PJ. Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT. Journal Of Nuclear Medicine 2022, 64: 472-478. PMID: 36137759, PMCID: PMC10071806, DOI: 10.2967/jnumed.122.264429.Peer-Reviewed Original ResearchCoronary Artery DiseaseDeep LearningHumansMyocardial Perfusion ImagingROC CurveSensitivity and SpecificityTomography, Emission-Computed, Single-PhotonDifferences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study
Tamarappoo BK, Otaki Y, Sharir T, Hu LH, Gransar H, Einstein AJ, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Eisenberg E, Liang JX, Dey D, Berman DS, Slomka PJ. Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study. Circulation Cardiovascular Imaging 2022, 15: e012741. PMID: 35727872, PMCID: PMC9307118, DOI: 10.1161/circimaging.121.012741.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseFemaleHumansMaleMyocardial InfarctionMyocardial Perfusion ImagingPerfusionPrognosisTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiac eventsTotal perfusion defectPrognostic valuePerfusion defectsSingle photon emissionLarge international multicenter studyMyocardial perfusion single-photon emissionPerfusion single-photon emissionStress SPECT myocardial perfusion imagingSPECT myocardial perfusion imagingAdverse cardiac eventsMACE-free survivalMultivariable Cox modelSingle-center studyGreater prognostic valueInternational multicenter studyProportional hazards modelMyocardial perfusion defectsMyocardial perfusion imagingREFINE SPECT registryConventional single-photon emissionCardiac eventsHazard ratioEjection fractionMulticenter studyMachine learning to predict abnormal myocardial perfusion from pre-test features
Miller RJH, Hauser MT, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Huang C, Liang JX, Han D, Dey D, Berman DS, Slomka PJ. Machine learning to predict abnormal myocardial perfusion from pre-test features. Journal Of Nuclear Cardiology 2022, 29: 2393-2403. PMID: 35672567, PMCID: PMC9588501, DOI: 10.1007/s12350-022-03012-6.Peer-Reviewed Original ResearchMeSH KeywordsHumansMachine LearningMyocardial Perfusion ImagingPerfusionROC CurveTomography, Emission-Computed, Single-PhotonConceptsAbnormal myocardial perfusionAbnormal perfusionMyocardial perfusionDiamond-Forrester modelCAD consortiumConsecutive patientsInternational registryPre-test informationSPECT-MPIClinical informationPhysician's decisionPatientsPerfusionTesting populationExpert visual interpretationRegistryPopulationMethodsWePhysiciansExplainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.
Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2022, 63: 1768-1774. PMID: 35512997, PMCID: PMC9635672, DOI: 10.2967/jnumed.121.263686.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCoronary AngiographyCoronary Artery DiseaseDeep LearningHumansMyocardial Perfusion ImagingPhysiciansTomography, Emission-Computed, Single-PhotonConceptsMyocardial perfusion imagingCoronary artery diseaseObstructive coronary artery diseasePhysician interpretationDiagnostic accuracyPerfusion imagingLeft main arteryOverall net reclassification improvementInvasive coronary angiographyNet reclassification improvementTotal perfusion deficitArtery diseaseCoronary angiographyMedian agePhysician diagnosisReclassification improvementPerfusion deficitsClinical historyCoronary segmentsRepresentative cohortMeaningful improvementsMain arteryPatientsDL resultsQuantitative perfusionHandling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry
Rios R, Miller RJH, Manral N, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Van Kriekinge SD, Kavanagh PB, Parekh T, Liang JX, Dey D, Berman DS, Slomka PJ. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers In Biology And Medicine 2022, 145: 105449. PMID: 35381453, PMCID: PMC9117456, DOI: 10.1016/j.compbiomed.2022.105449.Peer-Reviewed Original ResearchHumansMachine LearningMyocardial Perfusion ImagingRegistriesTomography, Emission-Computed, Single-PhotonDirect and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT
Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 49: 3046-3060. PMID: 35169887, PMCID: PMC9253078, DOI: 10.1007/s00259-022-05718-8.Peer-Reviewed Original ResearchPrevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry
Han D, Rozanski A, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Dey D, Berman DS, Slomka PJ. Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry. Journal Of Nuclear Cardiology 2022, 29: 3221-3232. PMID: 35174442, PMCID: PMC9378748, DOI: 10.1007/s12350-021-02829-x.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseFemaleHumansMaleMyocardial IschemiaMyocardial Perfusion ImagingPrevalenceRegistriesTomography, Emission-Computed, Single-PhotonConceptsTotal perfusion deficitMyocardial ischemiaStress testingCAD risk factorsMulticenter international registryPredictor of ischemiaCardiac stress testingNuclear stress testingChi-square testConclusionThe prevalenceMulticenter registryOverall cohortTypical anginaCAD statusPerfusion deficitsInternational registryMale genderRisk factorsLVEFIschemiaPatientsImaging characteristicsPotent predictorPrevalenceRegistry
2021
Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Tzolos E, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Dey D, Berman DS, Slomka PJ. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. Journal Of Nuclear Cardiology 2021, 29: 3003-3014. PMID: 34757571, PMCID: PMC9085969, DOI: 10.1007/s12350-021-02810-8.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseDiabetes MellitusHumansMyocardial Perfusion ImagingPrognosisRegistriesRisk FactorsTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiovascular eventsCoronary artery diseaseTotal perfusion deficitCardiac stress testingStress testingComparison of diabetesAdverse cardiovascular eventsStress test patientsCardiac stress test patientsREFINE SPECT registryDM statusMACE riskBackgroundDiabetes mellitusCardiovascular eventsArtery diseaseVentricular functionPrognostic predictorClinical variablesPerfusion deficitsChi-square analysisSPECT-MPIPatientsTest patientsPropensity scorePotent predictorPost-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation
Liu H, Wu J, Shi L, Liu Y, Miller E, Sinusas A, Liu YH, Liu C. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. Journal Of Nuclear Cardiology 2021, 29: 2881-2892. PMID: 34671940, DOI: 10.1007/s12350-021-02817-1.Peer-Reviewed Original ResearchDeep LearningHumansImage Processing, Computer-AssistedMyocardial Perfusion ImagingMyocardiumSensitivity and SpecificityTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedPrognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry
Kuronuma K, Miller RJH, Otaki Y, Van Kriekinge SD, Diniz MA, Sharir T, Hu LH, Gransar H, Liang JX, Parekh T, Kavanagh PB, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry. Circulation Cardiovascular Imaging 2021, 14: e012386. PMID: 34281372, PMCID: PMC8978932, DOI: 10.1161/circimaging.120.012386.Peer-Reviewed Original ResearchMeSH KeywordsAgedCanadaCoronary CirculationDisease ProgressionFemaleHumansIncidenceIsraelMaleMiddle AgedMyocardial IschemiaMyocardial Perfusion ImagingPredictive Value of TestsPrognosisRegistriesRisk AssessmentRisk FactorsStroke VolumeTomography, Emission-Computed, Single-PhotonUnited StatesVentricular Function, LeftConceptsMajor adverse cardiac eventsVentricular ejection fractionTotal perfusion deficitSingle photon emissionAdverse cardiac eventsEjection fractionPerfusion deficitsCardiac eventsPrognostic valueTomography myocardial perfusion imagingVentricular ejection fraction assessmentHighest decile groupProportional hazards analysisTomography myocardial perfusionIndependent prognostic significanceIndependent prognostic valueLarge multinational registryEjection fraction assessmentMyocardial perfusion imagingREFINE SPECT registryAdditional radiation exposureConventional single-photon emissionMACE riskMACE rateMultinational registryDetermining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry
Rios R, Miller RJH, Hu LH, Otaki Y, Singh A, Diniz M, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, DiCarli M, Van Kriekinge S, Kavanagh P, Parekh T, Liang JX, Dey D, Berman DS, Slomka P. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovascular Research 2021, 118: 2152-2164. PMID: 34259870, PMCID: PMC9302886, DOI: 10.1093/cvr/cvab236.Peer-Reviewed Original ResearchMeSH KeywordsCardiovascular DiseasesCoronary Artery DiseaseHumansMachine LearningMyocardial Perfusion ImagingPrognosisRegistriesTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiac eventsMyocardial perfusion imagingTotal perfusion deficitPrognostic accuracyREFINE SPECT registryImaging variablesTraditional multivariable modelsMultivariable modelStress total perfusion deficitAdverse cardiac eventsReceiver-operating characteristic curveOptimal risk stratificationTomography (SPECT) MPIComparable prognostic accuracyComparable prognostic performanceHigher prognostic accuracySingle photon emissionCardiovascular event predictionCardiac eventsRisk stratificationClinical variablesPerfusion deficitsPrognostic performancePerfusion imagingCollected variables