Cardiac imaging is a cornerstone in the initial diagnosis, management, and follow-up of cardiac sarcoidosis. However, ordering thresholds, access, and follow-up imaging vary across the globe. A Delphi study was conducted to define areas of consensus and areas requiring further study in the use of cardiac imaging in suspected or established cardiac sarcoidosis. An international, multidisciplinary panel of experts in cardiac sarcoidosis completed a modified 2-round Delphi study. The study evaluated clinical decision making regarding the use of cardiac imaging, including indication thresholds, interpretation, and interval follow-up imaging. Consensus was defined a priori as ≥70% agreement or disagreement. A total of 89 experts in cardiac sarcoidosis (89 in round 1 and 75 in round 2) participated, representing 61 centers in 13 countries. Consensus was reached on 22 of 46 items (48%) in round 1 and 21 of 29 items (72%) in round 2. There was a low threshold to order advanced cardiac imaging for new rhythm abnormalities or ventricular dysfunction detected on echocardiography in patients with established extracardiac sarcoidosis. 18F-fluorodeoxyglucose (FDG) positron emission tomography was an important co-primary modality with cardiac magnetic resonance (CMR) for initial diagnosis. If CMR was the first test, there was consensus to proceed to FDG-positron emission tomography after any abnormal CMR result or even after normal CMR result in the setting of moderate or high pretest probability for cardiac sarcoidosis. There was consensus that late gadolinium enhancement quantification was important, but there was no consensus on the threshold of risk or on how best to quantify late gadolinium enhancement. Similarly, reduction in FDG uptake was an important factor in guiding treatment response, but there was no consensus on how to best quantify FDG uptake or what constituted an adequate radiographic response. Several consensus areas for cardiac imaging in suspected and established cardiac sarcoidosis were identified. This consensus study identified areas of priority for future prospective, controlled, multicenter research studies.

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelligence (AI) approach using the results to derive enhanced total perfusion deficit (TPD) and 17-segment summed scores. Methods: We used a cohort of patients undergoing myocardial perfusion imaging within 180 d of invasive coronary angiography. Obstructive CAD was defined as any stenosis of at least 70% or at least 50% in the left main coronary artery. We used per-vessel DL predictions to modulate polar map pixel scores. These transformed polar maps were then used to derive TPD-DL and summed stress score-DL. We compared diagnostic performance using area under the receiver operating characteristic curve (AUC). Results: In the 555 patients held out for testing, the median age was 65 y (interquartile range, 57-73 y), and 381 (69%) were male. Obstructive CAD was present in 329 (59%) patients. The prediction performance for obstructive CAD of stress TPD-DL (AUC, 0.837; 95% CI, 0.804-0.870) was higher than AI prediction alone (AUC, 0.795; 95% CI, 0.758-0.831; P = 0.005) and traditional stress TPD (AUC, 0.737; 95% CI, 0.696-0.778; P < 0.001). Summed stress score-DL had the second highest prediction performance (AUC, 0.822; 95% CI, 0.788-0.857) and higher AUC than traditional quantitative summed stress score (AUC, 0.728; 95% CI, 0.686-0.769; P < 0.001). At a threshold of 5%, the sensitivity and specificity of TPD rose from 72% to 79% and from 62% to 70%, respectively. Conclusion: Integrating AI predictions with traditional quantitative approaches leads to a simplified AI approach, presenting clinicians with familiar measures but operating with higher accuracy than traditional quantitative scoring. This approach may facilitate integration of new AI methods into clinical practice.

Myocardial perfusion imaging (MPI) is frequently used to improve cardiac risk prediction in patients undergoing non-cardiac surgery. However, the data supporting this practice is derived from single center studies and predates current therapy for coronary artery disease (CAD). We evaluated the association between pre-operative testing indication and outcomes as well as predictors of death or myocardial infarction (MI) in these patients. We include patients from the international, multicenter REFINE-SPECT registry (13 sites). Based on referral indication, patients were classified as pre-operative testing indications and non-pre-operative testing. We evaluated the associations between pre-operative testing and incidence of death or MI. We then evaluated associations with death or MI in patients referred for pre-operative testing compared to other patients. In total, 32,711 patients were included with pre-operative testing as the indication in 2,173(6.6

Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74–0.87]), which was higher than for coronary calcium (0.64 [0.57–0.71]) or perfusion (0.62 [0.55–0.70]), with p < 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.

BACKGROUND:The proposed cause of Takotsubo syndrome (TTS) includes coronary microvascular dysfunction. This study aimed to investigate coronary microvascular dysfunction and its recovery in patients with TTS using serial positron emission tomography myocardial perfusion imaging. METHODS:Patients with TTS who underwent cardiac positron emission tomography within 30 days of admission and at 6-month follow-up (May 2017-June 2023) were analyzed. Changes in positron emission tomography parameters, including extent of myocardial perfusion abnormality, left ventricular function, rest and stress myocardial blood flow (MBF), myocardial flow reserve, and coronary vascular resistance (CVR), were assessed from baseline to follow-up. In apical TTS, segmental analyses (basal, mid, distal segments, and apex) and intersegment differences were evaluated. RESULTS:Of 130 patients screened, 62 patients (median age, 70 years, 97% women) were included. After a median follow-up of 178 (121-282) days, global rest and stress MBF, myocardial flow reserve, and CVR significantly improved at follow-up (0.81-0.89 mL/min per gram, P=0.004; 1.56-2.61 mL/min per gram, P<0.001; 1.96-2.65, P<0.001; 52.0-36.2 mm Hg·min·g/mL, P<0.001, respectively). Among 53 patients with apical TTS, improvements in stress MBF, myocardial flow reserve, and CVR were noted in all myocardial segments (all P<0.001), including the basal segment; however, persistent MBF and CVR abnormalities were identified in the distal segment and apex, despite full recovery of left ventricular function. CONCLUSIONS:Patients who underwent serial positron emission tomography perfusion imaging for TTS demonstrated reversible reductions in rest and stress MBF, myocardial flow reserve, and increases in CVR, suggestive of TTS-related coronary microvascular dysfunction and subsequent subtotal recovery. Coronary microvascular dysfunction extended beyond regions of wall motion abnormalities, and regional coronary flow abnormalities persisted in the medium term even after recovery of left ventricular function.

Background: Observational data have suggested that patients with moderate to severe ischemia benefit from revascularization. However, this was not confirmed in a large, randomized trial. Objectives: Using a contemporary, multicenter registry, the authors evaluated differences in the association between quantitative ischemia, revascularization, and outcomes across important subgroups. Methods: Patients who underwent myocardial perfusion imaging in 12 centers were included in this retrospective analysis. The population was divided into original (2009-2014) and recent (2014-2021) registry sites. Early revascularization was defined as any revascularization within 90 days of myocardial perfusion imaging. A propensity score was developed to adjust for nonrandomization. Propensity score-adjusted survival analyses were used to evaluate the associations between quantitative ischemia, early revascularization, and death or myocardial infarction (MI) to identify at what severity of ischemia the HR for early revascularization crosses 1 (threshold for potential benefit). Results: Overall, 40,449 patients were included with a median follow-up of 3.5 (IQR: 2.4-4.6) years, during which death or MI occurred in 2,797 (6.9%). Early revascularization was associated with reduced death or MI in patients with >9.0% myocardial ischemia (95% upper CI: 11.2%, interaction P < 0.001). The threshold for ischemia, above which patients may benefit from revascularization, was higher in more recent patients (14.0% vs 6.5%), but similar in female (>10.0%) and male patients (>8.6%). Conclusions: Early revascularization was associated with reduced risk in patients with a higher burden of quantitative ischemia in more recent populations. These findings suggest that methods integrating more factors than just ischemia are needed to improve patient selection for revascularization.

Background:Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction. Methods:This study included 27039 consecutive patients [57% male] with MPI scans from nine sites. We used an AI model to segment liver and spleen on low dose CTAC scans and quantify the liver measures, and the difference of liver minus spleen (LmS) measures. HS was defined as mean liver attenuation < 40 Hounsfield units (HU) or LmS attenuation < -10 HU. Additionally, we used seven sites to develop an AI liver risk index (LIRI) for comprehensive hepatic assessment by integrating the hepatic measures and two external sites to validate its improved prognostic value and generalizability for all-cause mortality prediction over HS. Findings:Median (interquartile range [IQR]) age was 67 [58, 75] years and body mass index (BMI) was 29.5 [25.5, 34.7] kg/m2, with diabetes in 8950 (33%) patients. The algorithm identified HS in 6579 (24%) patients. During median [IQR] follow-up of 3.58 [1.86, 5.15] years, 4836 (18%) patients died. HS was associated with increased mortality risk overall (adjusted hazard ratio (HR): 1.14 [1.05, 1.24], p=0.0016) and in subpopulations. LIRI provided higher prognostic value than HS after adjustments overall (adjusted HR 1.5 [1.32, 1.69], p<0.0001 vs HR 1.16 [1.02, 1.31], p=0.0204) and in subpopulations. Interpretations:AI-based hepatic measures automatically identify HS from CTAC scans in patients undergoing MPI without additional radiation dose or physician interaction. Integrated liver assessment combining multiple hepatic imaging measures improved risk stratification for all-cause mortality.

BACKGROUND:Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) uses computed tomography (CT)-based attenuation correction (AC) to improve diagnostic accuracy. Deep learning (DL) has the potential to generate synthetic AC images, as an alternative to CT-based AC. OBJECTIVES:This study evaluated whether DL-generated synthetic SPECT images could enhance accuracy of conventional SPECT MPI. METHODS:Study investigators developed a DL model in a multicenter cohort of 4,894 patients from 4 sites to generate simulated SPECT AC images (DeepAC). The model was externally validated in 746 patients from 72 sites in a clinical trial (A Phase 3 Multicenter Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD; NCT01347710) and in 320 patients from another external site. In the first external cohort, the study assessed the diagnostic accuracy for obstructive coronary artery disease (CAD)-defined as left main coronary artery stenosis ≥50% or ≥70% in other vessels-for total perfusion deficit (TPD). In the latter, the study completed change analysis and compared quantitative scores for AC, DeepAC, and nonattenuation correction (NC) with clinical scores. RESULTS:In the first external cohort (mean age, 63 ± 9.5 years; 69.0% male), 206 patients (27.6%) had obstructive CAD. The area under the receiver-operating characteristic curve (AUC) of DeepAC TPD (0.77; 95% CI: 0.73-0.81) was higher than the NC TPD (AUC: 0.73; 95% CI: 0.69-0.77; P < 0.001). In the second external cohort, DeepAC quantitative scores had closer agreement with actual AC scores compared with NC. CONCLUSIONS:In a multicenter external cohort, DeepAC improved prediction performance for obstructive CAD. This approach could enhance diagnostic accuracy in facilities using conventional SPECT systems without requiring additional equipment, imaging time, or radiation exposure.

BACKGROUND:CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification. METHODS:We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves. FINDINGS:The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<0·0001, 1·55, 1·26-1·90; p<0·0001, and 1·30, 1·06-1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62-0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44-0·71; p<0·0001). INTERPRETATION:CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value. FUNDING:The National Heart, Lung, and Blood Institute, National Institutes of Health.

Background and Aims:Revascularization in stable coronary artery disease often relies on ischemia severity, but we introduce an AI-driven approach that uses clinical and imaging data to estimate individualized treatment effects and guide personalized decisions. Methods:Using a large, international registry from 13 centers, we developed an AI model to estimate individual treatment effects by simulating outcomes under alternative therapeutic strategies. The model was trained on an internal cohort constructed using 1:1 propensity score matching to emulate randomized controlled trials (RCTs), creating balanced patient pairs in which only the treatment strategy-early revascularization (defined as any procedure within 90 days of MPI) versus medical therapy-differed. This design allowed the model to estimate individualized treatment effects, forming the basis for counterfactual reasoning at the patient level. We then derived the AI-REVASC score, which quantifies the potential benefit, for each patient, of early revascularization. The score was validated in the held-out testing cohort using Cox regression. Results:Of 45,252 patients, 19,935 (44.1%) were female, median age 65 (IQR: 57-73). During a median follow-up of 3.6 years (IQR: 2.7-4.9), 4,323 (9.6%) experienced MI or death. The AI model identified a group (n=1,335, 5.9%) that benefits from early revascularization with a propensity-adjusted hazard ratio of 0.50 (95% CI: 0.25-1.00). Patients identified for early revascularization had higher prevalence of hypertension, diabetes, dyslipidemia, and lower LVEF. Conclusions:This study pioneers a scalable, data-driven approach that emulates randomized trials using retrospective data. The AI-REVASC score enables precision revascularization decisions where guidelines and RCTs fall short.

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.

BACKGROUND:Coronary artery disease (CAD) and peripheral artery disease (PAD) are often regarded as analogous risk factors for major adverse cardiovascular events (MACE), given their shared pathophysiology. We aimed to investigate whether the elevated MACE risk in PAD is driven by myocardial perfusion abnormalities or through other PAD-specific mediators. METHODS:We analyzed 45,252 patients from an international, multicentre registry who underwent SPECT myocardial perfusion imaging, excluding those with early coronary revascularization (< 90 days). Myocardial perfusion abnormalities were quantified using total perfusion deficit (TPD). MACE was defined as all-cause mortality, unstable angina admission, myocardial infarction, or late coronary revascularization. PAD was defined using questionnaires or review of electronic medical records. Propensity-score matching was used to select balanced groups of patients with and without PAD. RESULTS:During a median follow-up of 3.6 years (interquartile range [IQR]: 2.6-4.8 years), 5932 patients (13.7%) experienced at least 1 MACE. Compared with patients with neither disease, isolated history of CAD (adjusted hazard ratio [aHR], 1.92; 95% confidence interval [CI], 1.80-2.05) conferred a similar MACE risk as concomitant history of CAD and PAD (aHR, 1.57; 95% CI, 1.44-1.71) and greater risk than isolated history of PAD (aHR, 1.20; 95% CI, 1.09-1.32; P < 0.001). After propensity-score matching, history of PAD alone was not independently associated with increased MACE risk (P = 0.064). CONCLUSIONS:Although patients with PAD often have concomitant CAD and greater myocardial perfusion abnormalities, PAD itself was not linked to higher risk of MACE after adjusting for these factors. These findings highlight the importance of assessing myocardial ischemic burden in PAD for risk stratification and prompt initiation of disease-modifying therapies.

Background: In many contemporary laboratories, a completely normal stress perfusion single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is required for rest imaging cancelation. We hypothesized that an artificial intelligence (AI)-derived coronary artery calcium (CAC) score of 0 from computed tomography attenuation correction (CTAC) scans obtained during hybrid SPECT/CT may identify additional patients at a low risk of major adverse cardiovascular events (MACEs) who could be selected for stress-only imaging. Methods: Patients without known coronary artery disease who underwent SPECT/CT MPI and had stress total perfusion deficit (TPD) <5% were included. Stress TPD was categorized as no abnormality (stress TPD: 0%) or minimal abnormality (stress TPD: 1%-4%). CAC was automatically quantified from the CTAC scans. We evaluated associations with MACEs. Results: In total, 6884 patients (49.4% males and median age: 63 years) were included. Of these, 9.7% experienced MACE (15% non-fatal myocardial infarction, 2.7% unstable angina, 38.5% coronary revascularization and 43.8% deaths). Compared to patients with TPD 0%, those with TPD 1%-4% and CAC 0 had lower MACE risk (hazard ratio [HR]: 0.58; 95% confidence interval [CI]: 0.45-0.76), while those with TPD 1%%-4% and CAC score>0 had a higher MACE risk (HR: 1.90; 95% CI: 1.56-2.30). Compared to canceling rest scans only in patients with normal perfusion (TPD 0%), by canceling rest scans in patients with CAC 0, more than twice as many rest scans (55% vs 25%) could potentially be canceled. Conclusion: Using an AI-derived CAC of 0 on CT scans with hybrid SPECT/CT in patients with a stress TPD <5% can double the proportion of patients in whom stress-only procedures could be safely performed.

BACKGROUND:Transthyretin cardiac amyloidosis (ATTR-CM) is an increasingly recognized cause of heart failure in older adults. Technetium-99m pyrophosphate (PYP) imaging has emerged as a highly effective tool for diagnosing ATTR-CM. We established a multicentre Canadian registry to provide a platform for research regarding the prevalence of ATTR-CM and accuracy of methods for screening or diagnosis. METHODS:We included patients undergoing [99mTc]PYP imaging at 4 Canadian centres. Medical history, red flags for cardiac amyloidosis, and laboratory biomarkers were collected. Diagnosis of ATTR-CM was established using standardized criteria. Deidentified clinical data and [99mTc]PYP image files were transferred to the core laboratories. RESULTS:In total, 2,118 patients are included in the registry with median age 77 (interquartile range: 68-83) and 1452 (68.6%) male patients. ATTR-CM was present in 618 (29.2%) patients and light chain amyloidosis in 112 (5.3%) patients. The volume of [99mTc]PYP scans increased from 18 in 2016 to 515 in 2022, with the proportion of patients with ATTR-CM ranging from 41% to 19% since 2017. The risk score proposed by Davies et al. had higher area under the receiver operating characteristic curves for ATTR-CM (0.792, 95% confidence interval [CI], 0.771-0.813) compared with the score proposed by Nitsche et al. (0.750; 95% CI, 0.727-0.774; P = 0.002). CONCLUSIONS:We assembled a large cohort of patients undergoing [99mTc]PYP imaging at 4 Canadian centres, with detailed clinical and imaging data. This registry, which continues to grow over time, will serve as an important source for evidence regarding diagnosis and management of ATTR-CM.