Development and evolution of nuclear cardiology and cardiac PET in Canada.

Gated radionuclide angiography and myocardial perfusion imaging were developed in the United States and Europe in the 1970's and soon adopted in Canadian centers. Much of the early development of nuclear cardiology in Canada was in Toronto, Ontario and was quickly followed by new programs across the country. Clinical research in Canada contributed to the further development of nuclear cardiology and cardiac PET. The Canadian Nuclear Cardiology Society (CNCS) was formed in 1995 and became the Canadian Society of Cardiovascular Nuclear and CT Imaging (CNCT) in 2014. The CNCS had a major role in education and advocacy for cardiovascular nuclear medicine testing. The CNCS established the Dr Robert Burns Lecture and CNCT named the Canadian Society of Cardiovascular Nuclear and CT Imaging Annual Achievement Award for Dr Michael Freeman in memoriam of these two outstanding Canadian leaders in nuclear cardiology. The future of nuclear cardiology in Canada is exciting with the expanding use of SPECT imaging to include Tc-99m-pyrophosphate for diagnosis of transthyretin cardiac amyloidosis and the ongoing introduction of cardiac PET imaging.

Myocardial blood flow quantification with SPECT.

INTRODUCTION: The addition of absolute myocardial blood flow (MBF) data improves the diagnostic and prognostic accuracy of relative perfusion imaging with nuclear medicine. Cardiac-specific gamma cameras allow measurement of MBF with SPECT. METHODS: This paper reviews the evidence supporting the use of SPECT to measure myocardial blood flow (MBF). Studies have evaluated SPECT MBF in large animal models and compared it in humans with invasive angiographic measurements and against the clinical standard of PET MBF. The repeatability of SPECT MBF has been determined in both single-site and multi-center trials. RESULTS: SPECT MBF has excellent correlation with microspheres in an animal model, with the number of stenoses and fractional flow reserve, and with PET-derived MBF. The inter-user coefficient of variability is ∼20% while the COV of test-retest MBF is ∼30%. SPECT MBF improves the sensitivity and specificity of the detection of multi-vessel disease over relative perfusion imaging and provides incremental value in predicting adverse cardiac events. CONCLUSION: SPECT MBF is a promising technique for providing clinically valuable information in the assessment of coronary artery disease.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry.

AIMS: Variation in diagnostic performance of 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 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 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 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 to 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 to 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. CONCLUSIONS: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study.

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].

Multicenter Evaluation of the Feasibility of Clinical Implementation of SPECT Myocardial Blood Flow Measurement: Intersite Variability and Imaging Time.

BACKGROUND: Single-center studies have shown that single photon emission computed tomography myocardial blood flow (MBF) measurement is accurate compared with MBF measured with microspheres in a porcine model, positron emission tomography, and angiography. Clinical implementation requires consistency across multiple sites. The study goal is to determine the intersite processing repeatability of single photon emission computed tomography MBF and the additional camera time required. METHODS: Five sites (Canada, Italy, Japan, Germany, and Singapore) each acquired 25 to 35 MBF studies at rest and with pharmacological stress using technetium-99m-tetrofosmin on a pinhole-collimated cadmium-zinc-telluride-based cardiac single photon emission computed tomography camera with standardized list-mode imaging and processing protocols. Patients had intermediate to high pretest probability of coronary artery disease. MBF was measured locally and at a core laboratory using commercially available software. The time a room was occupied for an MBF study was compared with that for a standard rest/stress myocardial perfusion study. RESULTS: With motion correction, the overall correlation in MBF between core laboratory and local site was 0.93 (range, 0.87-0.97) at rest, 0.90 (range, 0.84-0.96) at stress, and 0.84 (range, 0.70-0.92) for myocardial flow reserve. The local-to-core difference in global MBF (bias-MBF) was 5.4% (-3.8% to 14.8%; median [interquartile range]) at rest and 5.4% (-6.2% to 19.4%) at stress. Between the 5 sites, bias-MBF ranged from -1.6% to 11.0% at rest and from -1.9% to 16.3% at stress; the interquartile range in bias-MBF was between 9.3% (4.8%-14.0%) and 22.3% (-10.3% to 12.0%) at rest and between 17.0% (-11.3% to 5.6%) and 33.3% (-10.4% to 22.9%) at stress and was not significantly different between most sites. Both bias and interquartile range were like previously reported interobserver variability and less than the SD of the test-retest difference of 30%. The overall difference in myocardial flow reserve was 1.52% (-10.6% to 11.3%). There were no significant differences between with and without motion correction. The average additional acquisition time varied between sites from 44 to 79 minutes. CONCLUSIONS: The average bias-MBF and bias-MFR values were small with standard deviations substantially less than the test-retest variability. This demonstrates that MBF can be measured consistently across multiple sites and further supports that this technique can be reliably implemented. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03427749.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging.

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.

Advances in Single-Photon Emission Computed Tomography: Hardware, Software, and Myocardial Flow Reserve.

The clinical presentation of coronary artery disease (CAD) has changed during the last 20 years with less ischemia on stress testing and more nonobstructive CAD on coronary angiography. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging should include the measurement of myocardial flow reserve and assessment of coronary calcium for the diagnosis of nonobstructive CAD and coronary microvascular disease. SPECT/CT systems provide reliable attenuation correction for better specificity and low-dose CT for coronary calcium evaluation. SPECT MFR measurement is accurate, well validated, and repeatable.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

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

Myocardial Perfusion PET for the Detection and Reporting of Coronary Microvascular Dysfunction: A JACC: Cardiovascular Imaging Expert Panel Statement.

Angina pectoris and dyspnea in patients with normal or nonobstructive coronary vessels remains a diagnostic challenge. Invasive coronary angiography may identify up to 60% of patients with nonobstructive coronary artery disease (CAD), of whom nearly two-thirds may, in fact, have coronary microvascular dysfunction (CMD) that may account for their symptoms. Positron emission tomography (PET) determined absolute quantitative myocardial blood flow (MBF) at rest and during hyperemic vasodilation with subsequent derivation of myocardial flow reserve (MFR) affords the noninvasive detection and delineation of CMD. Individualized or intensified medical therapies with nitrates, calcium-channel blockers, statins, angiotensin-converting enzyme inhibitors, angiotensin II type 1-receptor blockers, beta-blockers, ivabradine, or ranolazine may improve symptoms, quality of life, and outcome in these patients. Standardized diagnosis and reporting criteria for ischemic symptoms caused by CMD are critical for optimized and individualized treatment decisions in such patients. In this respect, it was proposed by the cardiovascular council leadership of the Society of Nuclear Medicine and Molecular Imaging to convene thoughtful leaders from around the world to serve as an independent expert panel to develop standardized diagnosis, nomenclature and nosology, and cardiac PET reporting criteria for CMD. This consensus document aims to provide an overview of the pathophysiology and clinical evidence of CMD, its invasive and noninvasive assessment, standardization of PET-determined MBFs and MFR into "classical" (predominantly related to hyperemic MBFs) and "endogen" (predominantly related to resting MBF) normal coronary microvascular function or CMD that may be critical for diagnosis of microvascular angina, subsequent patient care, and outcome of clinical CMD trials.

Role of nuclear cardiology in diagnosis and risk stratification of coronary microvascular disease.

Coronary flow reserve (CFR) with positron emission tomography/computed tomography (PET/CT) has an important role in the diagnosis of coronary microvascular disease (CMD), aids risk stratification and may be useful in monitoring therapy. CMD contributes to symptoms and a worse prognosis in patients with coronary artery disease (CAD), nonischemic cardiomyopathies, and heart failure. CFR measurements may improve our understanding of the role of CMD in symptoms and prognosis in CAD and other cardiovascular diseases. The clinical presentation of CAD has changed. The prevalence of nonobstructive CAD has increased to about 50% of patients with angina undergoing angiography. Ischemia with nonobstructive arteries (INOCA) is recognized as an important cause of symptoms and has an adverse prognosis. Patients with INOCA may have ischemia due to CMD, epicardial vasospasm or diffuse nonobstructive CAD. Reduced CFR in patients with INOCA identifies a high-risk group that may benefit from management strategies specific for CMD. Although measurement of CFR by PET/CT has excellent accuracy and repeatability, use is limited by cost and availability. CFR measurement with single-photon emission tomography (SPECT) is feasible, validated, and would increase availability and use of CFR. Patients with CMD can be identified by reduced CFR and selected for specific therapies.

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.

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.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

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