2024
Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging
Feher A, Bednarski B, Miller R, Shanbhag A, Lemley M, Miras L, Sinusas A, Miller E, Slomka P. Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266761. PMID: 38548351, PMCID: PMC11064832, DOI: 10.2967/jnumed.123.266761.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingHF hospitalizationHeart failureStress left ventricular ejection fractionPerfusion imagingHF exacerbationPredictive of HF hospitalizationsSPECT/CT myocardial perfusion imagingMyocardial perfusionInternational cohortAcute heart failure exacerbationMedian follow-upVentricular ejection fractionReceiver-operating-characteristic curveClinical risk factorsHeart failure exacerbationExternal validation cohortAcute HF exacerbationPrevent HF hospitalizationsImaging parametersCalcium scoreEjection fractionClinical parametersCT scanValidation cohortAI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Kavanagh P, Feher A, Miller E, Bateman T, Builoff V, Liang J, Newby D, Dey D, Berman D, Slomka P. AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovascular Imaging 2024, 17: 780-791. PMID: 38456877, PMCID: PMC11222053, DOI: 10.1016/j.jcmg.2024.01.006.Peer-Reviewed Original ResearchComputed tomography attenuation correctionMajor adverse cardiovascular eventsCoronary artery calciumLV massMyocardial perfusion imagingPerfusion imagingSingle-photon emission computed tomography/computed tomographyIncreased risk of MACEAssociated with major adverse cardiovascular eventsRisk of Major Adverse Cardiovascular EventsCardiac chamber volumesMedian follow-upCT-based volumesRight ventricular volumesImprove cardiovascular risk assessmentAdverse cardiovascular eventsQuartile of LV massContinuous net reclassification indexCoronary artery diseaseNet reclassification indexCardiovascular risk assessmentTomography/computed tomographyConsecutive patientsImprove risk predictionPrognostic utilityImpact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography
Randazzo M, Elias P, Poterucha T, Sharir T, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Castillo M, Liang J, Miller R, Dey D, Berman D, Slomka P, Einstein A. Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography. European Heart Journal - Cardiovascular Imaging 2024, 25: 996-1006. PMID: 38445511, PMCID: PMC11210974, DOI: 10.1093/ehjci/jeae055.Peer-Reviewed Original ResearchSPECT myocardial perfusion imagingMyocardial perfusion imagingCoronary artery diseaseCardiac sizeDetect obstructive coronary artery diseasePerfusion imagingObstructive coronary artery diseaseDetection of coronary artery diseaseNegative predictive valueLeft ventricular volumeSolid-state scannersSmall cardiac sizeCoronary angiographyMale patientsYounger patientsElderly patientsMale sexDiagnostic performanceVentricular volumeArtery diseaseVisual assessmentPatientsPredictive valueElderly ageSPECTAI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Van Kriekinge S, Kavanagh P, Feher A, Miller E, Einstein A, Ruddy T, Liang J, Builoff V, Berman D, Dey D, Slomka P. AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. Npj Digital Medicine 2024, 7: 24. PMID: 38310123, PMCID: PMC10838293, DOI: 10.1038/s41746-024-01020-z.Peer-Reviewed Original ResearchEpicardial adipose tissueMyocardial infarctionPerfusion imagingEpicardial adipose tissue measurementsEpicardial adipose tissue volumeEAT attenuationMedian follow-upIncreased risk of deathEpicardial fat measurementMyocardial perfusion imagingAssociated with cardiovascular riskCoronary artery diseaseAssociated with deathEating measuresRisk of deathEAT volumeLow-dosePrognostic insightsFollow-upCardiovascular riskCardiovascular risk predictionUngated CTArtery diseaseIncreased riskCardiac silhouette
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 ResearchConceptsMajor 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 analysisTime and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging
Pieszko K, Shanbhag A, Singh A, Hauser M, Miller R, Liang J, Motwani M, Kwieciński J, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Berman D, Dey D, Slomka P. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging. Npj Digital Medicine 2023, 6: 78. PMID: 37127660, PMCID: PMC10151323, DOI: 10.1038/s41746-023-00806-x.Peer-Reviewed Original ResearchAcute coronary syndromeMajor adverse cardiovascular eventsMyocardial perfusion imagingCause deathAdverse cardiovascular eventsModifiable risk factorsPrediction of deathPersonalized risk assessmentCardiovascular eventsCoronary syndromeClinical featuresPrognostic valueRisk factorsExternal cohortStandard clinical interpretationPerfusion imagingAbnormality measuresCardiac perfusionTime pointsPatients' explanationsClinical interpretationExternal validationDeathPatientsRisk assessmentUnsupervised 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 ResearchConceptsCoronary 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 characteristicsIN PATIENTS UNDERGOING NUCLEAR MYOCARDIAL PERFUSION IMAGING, RHEUMATOID ARTHRITIS IS ASSOCIATED WITH INCREASED RISK OF ADVERSE CARDIOVASCULAR EVENTS INDEPENDENT OF MYOCARDIAL ISCHEMIA
Pires J, Oikonomou E, Agarwal R, Liu Y, miller E, Sinusas A, Feher A. IN PATIENTS UNDERGOING NUCLEAR MYOCARDIAL PERFUSION IMAGING, RHEUMATOID ARTHRITIS IS ASSOCIATED WITH INCREASED RISK OF ADVERSE CARDIOVASCULAR EVENTS INDEPENDENT OF MYOCARDIAL ISCHEMIA. Journal Of The American College Of Cardiology 2023, 81: 1467. DOI: 10.1016/s0735-1097(23)01911-3.Peer-Reviewed Original Research
2022
PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients
Feher A, Miller EJ. PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients. Current Cardiology Reports 2022, 24: 1865-1871. PMID: 36279035, DOI: 10.1007/s11886-022-01804-3.Peer-Reviewed Original ResearchConceptsHeart transplant recipientsAllograft vasculopathyTransplant recipientsPositron emission tomography myocardial perfusionOverall graft survivalPost-transplant surveillanceCardiac allograft vasculopathyHeart transplant patientsTomography myocardial perfusionRecent FindingsMultiple studiesMyocardial perfusion imagingMyocardial blood flowMBF quantificationGraft survivalHeart transplantationTransplant patientsTransplant rejectionSerial monitoringPrognostic performancePET myocardial blood flowBlood flowMyocardial perfusionPerfusion imagingMBF assessmentBlood flow quantificationDirect 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 ResearchConceptsMyocardial 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 perfusionMitigating 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 ResearchConceptsCoronary 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 biasDiseaseExplainable 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 ResearchConceptsMyocardial 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 perfusion
2021
Determining 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 ResearchConceptsMajor 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 variablesClinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease
Otaki Y, Singh A, Kavanagh P, Miller RJH, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease. JACC Cardiovascular Imaging 2021, 15: 1091-1102. PMID: 34274267, PMCID: PMC9020794, DOI: 10.1016/j.jcmg.2021.04.030.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingStandard clinical softwareStress total perfusion deficitObstructive CADTomography myocardial perfusion imagingSPECT myocardial perfusion imagingStress myocardial perfusionCoronary artery diseaseReceiver-operating characteristic curveInvasive coronary angiographyClinical softwareReader diagnosisSingle photon emissionTypical clinical workflowArtery diseaseCoronary angiographyPerfusion deficitsDiagnostic findingsMyocardial perfusionPerfusion imagingVentricular volumeStandard clinical workstationPatientsDiagnostic accuracyDiagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT
Eisenberg E, Miller RJH, Hu LH, Rios R, Betancur J, Azadani P, Han D, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2021, 29: 2295-2307. PMID: 34228341, PMCID: PMC9020793, DOI: 10.1007/s12350-021-02698-4.Peer-Reviewed Original ResearchConceptsObstructive coronary artery diseaseCoronary artery diseaseHigh-risk coronary artery diseaseMyocardial perfusion imagingPatient selection algorithmTriple-vessel coronary artery diseasePrediction of CADML thresholdsStress-first protocolInvasive coronary angiographyReceiver operator characteristic curveOperator characteristic curveMyocardial perfusion SPECTArtery diseaseCoronary angiographyAnterior descendingClinical variablesClinical algorithmReader diagnosisRest imagingPerfusion SPECTPerfusion imagingDiagnostic safetyRadiation doseCharacteristic curve
2020
Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT
Hu LH, Miller RJH, Sharir T, Commandeur F, Rios R, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Eisenberg E, Dey D, Berman DS, Slomka PJ. Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. European Heart Journal - Cardiovascular Imaging 2020, 22: 705-714. PMID: 32533137, DOI: 10.1093/ehjci/jeaa134.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsPhysician interpretationMACE rateCancellation rateTomography myocardial perfusion imagingAdverse cardiac eventsInternational multicentre registryCause mortality ratesMyocardial perfusion imagingCurrent clinical approachesSingle photon emissionMulticentre registryCardiac eventsClinical dataMortality ratePerfusion imagingClinical approachPatientsML thresholdsRadiation exposureMPI scansPhoton emissionML scoreMLSafety
2019
Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.
Hu LH, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Commandeur F, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019, 21: 549-559. PMID: 31317178, PMCID: PMC7167744, DOI: 10.1093/ehjci/jez177.Peer-Reviewed Original ResearchConceptsEarly coronary revascularizationMyocardial perfusion imagingStress TPDCoronary revascularizationTomography myocardial perfusion imagingTetrofosmin myocardial perfusion imagingSPECT myocardial perfusion imagingInvasive coronary angiographyReceiver operator characteristic curveREFINE SPECT registryPatient-specific explanationsOperator characteristic curveSingle photon emissionMyocardial perfusion SPECTCoronary angiographyIndividual patientsImaging variablesPatientsPerfusion SPECTPerfusion imagingClinical settingCharacteristic curveRevascularizationExpert interpretationStress test
2018
Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
Betancur J, Hu LH, Commandeur F, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Otaki Y, Liang JX, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study. Journal Of Nuclear Medicine 2018, 60: 664-670. PMID: 30262516, PMCID: PMC6495237, DOI: 10.2967/jnumed.118.213538.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingSPECT myocardial perfusion imagingCoronary artery diseaseObstructive diseaseClinical readsArtery diseaseCoronary arteryPerfusion imagingTc-sestamibi myocardial perfusion imagingObstructive coronary artery diseaseLeft main coronary arteryStress myocardial perfusion imagingStress total perfusion deficitMain coronary arteryMajor coronary arteriesLeft ventricular myocardiumHypoperfusion severityRadiotracer countsMulticenter studyPerfusion deficitsNormal limitsVessel sensitivityPatient sensitivityPatientsRationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)
Slomka PJ, Betancur J, Liang JX, Otaki Y, Hu LH, Sharir T, Dorbala S, Di Carli M, Fish MB, Ruddy TD, Bateman TM, Einstein AJ, Kaufmann PA, Miller EJ, Sinusas AJ, Azadani PN, Gransar H, Tamarappoo BK, Dey D, Berman DS, Germano G. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). Journal Of Nuclear Cardiology 2018, 27: 1010-1021. PMID: 29923104, PMCID: PMC6301135, DOI: 10.1007/s12350-018-1326-4.Peer-Reviewed Original ResearchMeSH KeywordsAgedArtificial IntelligenceAutomationCoronary AngiographyCoronary Artery DiseaseData CollectionDatabases, FactualFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMachine LearningMaleMiddle AgedMyocardial Perfusion ImagingPrognosisRegistriesReproducibility of ResultsSoftwareTomography, Emission-Computed, Single-PhotonConceptsMyocardial perfusion imagingPharmacologic stressMajor adverse cardiac eventsAdverse cardiac eventsRevascularization resultsMulticenter registryCardiac eventsClinical variablesPrognostic outcomesResultsTo dateClinical dataPrognostic dataDiagnostic cohortSPECT-MPIImaging variablesMyocardial perfusionPrognostic cohortPerfusion imagingPatientsRegistryArtificial intelligence toolsNew artificial intelligence toolsQuantitative softwareCohortClinical image database
2017
A joint procedural position statement on imaging in cardiac sarcoidosis: from the Cardiovascular and Inflammation & Infection Committees of the European Association of Nuclear Medicine, the European Association of Cardiovascular Imaging, and the American Society of Nuclear Cardiology
Slart R, Glaudemans A, Lancellotti P, Hyafil F, Blankstein R, Schwartz R, Jaber W, Russell R, Gimelli A, Rouzet F, Hacker M, Gheysens O, Plein S, Miller E, Dorbala S, Donal E, Sciagra R, Bucerius J, Verberne H, Lindner O, Übleis C, Agostini D, Signore A, Edvardsen T, Neglia D, Beanlands R, Di Carli M, Chareonthaitawee P, Dilsizian V, Soman P, Habib G, Delgado V, Cardim N, Cosyns B, Flachskampf F, Gerber B, Haugaa K, Lombardi M, Masci P. A joint procedural position statement on imaging in cardiac sarcoidosis: from the Cardiovascular and Inflammation & Infection Committees of the European Association of Nuclear Medicine, the European Association of Cardiovascular Imaging, and the American Society of Nuclear Cardiology. European Heart Journal - Cardiovascular Imaging 2017, 18: 1073-1089. PMID: 28984894, DOI: 10.1093/ehjci/jex146.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsCardiac Imaging TechniquesCardiologyCardiomyopathiesEuropeFemaleHumansMagnetic Resonance ImagingMaleMultimodal ImagingMyocardial Perfusion ImagingNuclear MedicinePositron Emission Tomography Computed TomographyPractice Guidelines as TopicRadionuclide ImagingSarcoidosisSocieties, MedicalUnited StatesConceptsCardiac sarcoidosisEuropean AssociationManagement of patientsCardiovascular magnetic resonanceMyocardial perfusion imagingPositron emission tomographyJoint position paperInfection committeeMulti-center dataClinical trialsSarcoidosisPerfusion imagingEmission tomographyCardiovascular imagingPosition statementAmerican SocietyNuclear cardiologyPosition paperNuclear medicineMagnetic resonanceAssociationCorrect useImagingEchocardiographyInflammation