Deep Learning-Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99mTc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

PURPOSE OF REVIEW: Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. RECENT FINDINGS: Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also 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)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Self-reported exercise activity influences the relationship between coronary computed tomography angiographic finding and mortality.

AIM: Recent studies suggest that the application of exercise activity questionnaires, including the use of a single-item exercise question, can be additive to the prognostic efficacy of imaging findings. This study aims to evaluate the prognostic efficacy of exercise activity in patients undergoing coronary computed tomography angiography (CCTA). METHODS AND RESULTS: We assessed 9772 patients who underwent CCTA at a single center between 2007 and 2020. Patients were divided into 4 groups of physical activity as no exercise (n â€‹= â€‹1643, 17%), mild exercise (n â€‹= â€‹3156, 32%), moderate exercise (n â€‹= â€‹3542, 36%), and high exercise (n â€‹= â€‹1431,15%), based on a single-item self-reported questionnaire. Coronary stenosis was categorized as no (0%), non-obstructive (1-49%), borderline (50-69%), and obstructive (≥70%). During a median follow-up of 4.64 (IQR 1.53-7.89) years, 490 (7.6%) died. There was a stepwise inverse relationship between exercise activity and mortality (p â€‹< â€‹0.001). Compared with the high activity group, the no activity group had a 3-fold higher mortality risk (HR: 3.3, 95%CI (1.94-5.63), p â€‹< â€‹0.001) after adjustment for age, clinical risk factors, symptoms, and statin use. For any level of CCTA stenosis, mortality rates were inversely associated with the degree of patients' exercise activity. The risk of all-cause mortality was similar among the patients with obstructive stenosis with high exercise versus those with no coronary stenosis but no exercise activity (p â€‹= â€‹0.912). CONCLUSION: Physical activity as assessed by a single-item self-reported questionnaire is a strong stepwise inverse predictor of mortality risk among patients undergoing CCTA.

Duration of adenosine-induced myocardial hyperemia - Insights from quantitative 13N-ammonia positron emission tomography myocardial perfusion imaging.

AIMS: To assess the impact of adenosine on quantitative myocardial blood flow (MBF) in a rapid stress-rest protocol compared to a rest-stress protocol using 13N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) and to gain insights into the time dependency of such effects. METHODS AND RESULTS: Quantitative MBF at rest (rMBF), during adenosine-induced stress (sMBF) and myocardial flow reserve (MFR) were obtained from 331 retrospectively identified patients who underwent 13N-ammonia PET-MPI for suspected chronic coronary syndrome and who all exhibited no perfusion defects. Of these, 146 (44.1%) underwent a rapid stress-rest protocol with a time interval (Δtstress-rest) of 20 ± 4 minutes between adenosine infusion offset and rest-imaging, as per clinical routine. The remaining 185 (55.9%) patients underwent a rest-stress protocol and served as the reference. Groups did not differ regarding demographics, risk factors, medication, left ventricular function, and calcium scores. rMBF was significantly higher in the stress-rest vs. the rest-stress group (0.80 [IQR 0.66-1.00] vs. 0.70 [0.58-0.83] ml·min-1·g-1, p < 0.001) and, as sMBF was identical between groups (2.52 [2.20-2.96] vs. 2.50 [1.96-3.11], p = 0.347), MFR was significantly lower in the stress-rest group (3.07 [2.43-3.88] vs. 3.50 [2.63-4.10], p < 0.001). There was a weak correlation between Δtstress-rest and rMBF (r = -0.259, p = 0.002) and between Δtstress-rest and MFR (r = 0.163, p = 0.049), and the proportion of patients with abnormally high rMBF was significantly decreasing with increasing Δtstress-rest. CONCLUSIONS: Intravenously applied adenosine induces a long-lasting hyperemic effect on the myocardium. Consequently, rapid stress-rest protocols could lead to an overestimation of rMBF and an underestimation of MFR.

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.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. Methods: The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort). The algorithm used clinical risk factors, stress variables, SPECT imaging parameters, and fully automated deep learning-generated calcium scores from attenuation CT scans. The model was trained and validated using repeated hold-out (10-fold cross-validation). External validation was conducted on a separate cohort of 2,912 patients. During a median follow-up of 1.9 y, 297 patients (6%) in the internal cohort were admitted for HF exacerbation. Results: The final model demonstrated a higher area under the receiver-operating-characteristic curve (0.87 ± 0.03) for predicting HF admissions than did stress left ventricular ejection fraction (0.73 ± 0.05, P < 0.0001) or a model developed using only clinical parameters (0.81 ± 0.04, P < 0.0001). These findings were confirmed in the external validation cohort (area under the receiver-operating-characteristic curve: 0.80 ± 0.04 for final model, 0.70 ± 0.06 for stress left ventricular ejection fraction, 0.72 ± 0.05 for clinical model; P < 0.001 for all). Conclusion: Integrating SPECT myocardial perfusion imaging into an artificial intelligence-based risk assessment algorithm improves the prediction of HF hospitalization. The proposed method could enable early interventions to prevent HF hospitalizations, leading to improved patient care and better outcomes.

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.

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

OBJECTIVES: Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. METHODS: We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). RESULTS: Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). CONCLUSION: Excellent agreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. CLINICAL RELEVANCE STATEMENT: Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. KEY POINTS: • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

BACKGROUND: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. OBJECTIVES: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. METHODS: The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. RESULTS: In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. CONCLUSIONS: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.

Thoracic aortic microcalcification activity in combined positron emission tomography and magnetic resonance imaging.

INTRODUCTION: Non-invasive detection of pathological changes in thoracic aortic disease remains an unmet clinical need particularly for patients with congenital heart disease. Positron emission tomography combined with magnetic resonance imaging (PET-MRI) could provide a valuable low-radiation method of aortic surveillance in high-risk groups. Quantification of aortic microcalcification activity using sodium [18F]fluoride holds promise in the assessment of thoracic aortopathies. We sought to evaluate aortic sodium [18F]fluoride uptake in PET-MRI using three methods of attenuation correction compared to positron emission tomography computed tomography (PET-CT) in patients with bicuspid aortic valve, METHODS: Thirty asymptomatic patients under surveillance for bicuspid aortic valve disease underwent sodium [18F]fluoride PET-CT and PET-MRI of the ascending thoracic aorta during a single visit. PET-MRI data were reconstructed using three iterations of attenuation correction (Dixon, radial gradient recalled echo with two [RadialVIBE-2] or four [RadialVIBE-4] tissue segmentation). Images were qualitatively and quantitatively analysed for aortic sodium [18F]fluoride uptake on PET-CT and PET-MRI. RESULTS: Aortic sodium [18F]fluoride uptake on PET-MRI was visually comparable with PET-CT using each reconstruction and total aortic standardised uptake values on PET-CT strongly correlated with each PET-MRI attenuation correction method (Dixon R = 0.70; RadialVIBE-2 R = 0.63; RadialVIBE-4 R = 0.64; p < 0.001 for all). Breathing related artefact between soft tissue and lung were detected using Dixon and RadialVIBE-4 but not RadialVIBE-2 reconstructions, with the presence of this artefact adjacent to the atria leading to variations in blood pool activity estimates. Consequently, quantitative agreements between radiotracer activity on PET-CT and PET-MRI were most consistent with RadialVIBE-2. CONCLUSION: Ascending aortic microcalcification analysis in PET-MRI is feasible with comparable findings to PET-CT. RadialVIBE-2 tissue attenuation correction correlates best with the reference standard of PET-CT and is less susceptible to artefact. There remain challenges in segmenting tissue types in PET-MRI reconstructions, and improved attenuation correction methods are required.

Rosuvastatin effect on atherosclerotic plaque metabolism: A subclinical atherosclerosis imaging study with 18F-NaF PET-CT.

BACKGROUND AND AIMS: Atherosclerotic plaque fluorine-18 sodium fluoride (18F-NaF) uptake on positron emission tomography with computed tomography (PET-CT) identifies active microcalcification and has been shown to correlate with clinical instability in patients with cardiovascular (CV) disease. Statin therapy promotes coronary macrocalcification over time. Our aim was to investigate rosuvastatin effect on atheroma 18F-NaF uptake. METHODS: Subjects with high CV risk but without CV events underwent 18F-NaF-PET-CT in a single-centre. Those with subclinical atherosclerosis and significant 18F-NaF plaque uptake were included in a single-arm clinical trial, treated with rosuvastatin 20 mg/daily for six months, and re-evaluated by 18F-NaF-PET-CT. Primary endpoint was reduction in maximum atheroma 18F-NaF uptake in the coronary, aortic or carotid arteries, assessed by the tissue-to-background ratio (TBR). The secondary endpoint was corrected uptake per lesion (CUL) variation. RESULTS: Forty individuals were enrolled and 38 included in the pharmacological trial; mean age was 64 years, two-thirds were male and most were diabetic. The 10-year expected CV risk was 9.5% (6.0-15.3) for SCORE2 and 31.7 ± 18.7% for ASCVD systems. After six months of rosuvastatin treatment (n = 34), low-density lipoprotein cholesterol lowered from 133.6 ± 33.8 to 58.8 ± 20.7 mg dL-1 (60% relative reduction, p < 0.01). There was a significant 19% reduction in maximum plaque 18F-NaF uptake after treatment, from 1.96 (1.78-2.22) to 1.53 (1.40-2.10), p < 0.001 (primary endpoint analysis). The secondary endpoint CUL was reduced by 23% (p = 0.003). CONCLUSION: In a single-centre non-randomized clinical trial of high CV risk individuals with subclinical atherosclerosis, the maximum atherosclerotic plaque 18F-NaF uptake was significantly reduced after six months of high-intensity statin.

Association of epicardial adipose tissue with early structural and functional cardiac changes in Type 2 diabetes.

BACKGROUND: Dysregulated epicardial adipose tissue (EAT) may contribute to the development of heart failure in Type 2 diabetes (T2D). This study aimed to evaluate the associations between EAT volume and composition with imaging markers of subclinical cardiac dysfunction in people with T2D and no prevalent cardiovascular disease. METHODS: Prospective case-control study enrolling participants with and without T2D and no known cardiovascular disease. Two hundred and fifteen people with T2D (median age 63 years, 60 % male) and thirty-nine non-diabetics (median age 59 years, 62 % male) were included. Using computed tomography (CT), total EAT volume and mean CT attenuation, as well as, low attenuation (Hounsfield unit range -190 to -90) EAT volume were quantified by a deep learning method and volumes indexed to body surface area. Associations with cardiac magnetic resonance-derived left ventricular (LV) volumes and strain indices were assessed using linear regression. RESULTS: T2D participants had higher LV mass/volume ratio (median 0.89 g/mL [0.82-0.99] vs 0.79 g/mL [0.75-0.89]) and lower global longitudinal strain (GLS; 16.1 ± 2.3 % vs 17.2 ± 2.2 %). Total indexed EAT volume correlated inversely with mean CT attenuation. Low attenuation indexed EAT volume was 2-fold higher (18.8 cm3/m2 vs. 9.4 cm3/m2, p < 0.001) in T2D and independently associated with LV mass/volume ratio (ß = 0.002, p = 0.01) and GLS (ß = -0.03, p = 0.03). CONCLUSIONS: Higher EAT volumes seen in T2D are associated with a lower mean CT attenuation. Low attenuation indexed EAT volume is independently, but only weakly, associated with markers of subclinical cardiac dysfunction in T2D.

Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends.

Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.

10-year experience of utilizing a stress-first SPECT myocardial perfusion imaging.

BACKGROUND: Despite its potential benefits, the utilization of stress-only protocol in clinical practice has been limited. We report utilizing stress-first single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: We assessed 12,472 patients who were referred for SPECT-MPI between 2013 and 2020. The temporal changes in frequency of stress-only imaging were assessed according to risk factors, mode of stress, prior coronary artery disease (CAD) history, left ventricular function, and symptom status. The clinical endpoint was all-cause mortality. RESULTS: In our lab, stress/rest SPECT-MPI in place of rest/stress SPECT-MPI was first introduced in November 2011 and was performed more commonly than rest/stress imaging after 2013. Stress-only SPECT-MPI scanning has been performed in 30-34% of our SPECT-MPI studies since 2013 (i.e.. 31.7% in 2013 and 33.6% in 2020). During the study period, we routinely used two-position imaging (additional prone or upright imaging) to reduce attenuation and motion artifact and introduced SPECT/CT scanner in 2018. The rate of stress-only study remained consistent before and after implementing the SPECT/CT scanner. The frequency of stress-only imaging was 43% among patients without a history of prior CAD and 19% among those with a prior CAD history. Among patients undergoing treadmill exercise, the frequency of stress-only imaging was 48%, while 32% among patients undergoing pharmacologic stress test. In multivariate Cox analysis, there was no significant difference in mortality risk between stress-only and stress/rest protocols in patients with normal SPECT-MPI results (p = 0.271). CONCLUSION: Implementation of a stress-first imaging protocol has consistently resulted in safe cancellation of 30% of rest SPECT-MPI studies.

Total-Body PET/CT Applications in Cardiovascular Diseases: A Perspective Document of the SNMMI Cardiovascular Council.

Digital PET/CT systems with a long axial field of view have become available and are emerging as the current state of the art. These new camera systems provide wider anatomic coverage, leading to major increases in system sensitivity. Preliminary results have demonstrated improvements in image quality and quantification, as well as substantial advantages in tracer kinetic modeling from dynamic imaging. These systems also potentially allow for low-dose examinations and major reductions in acquisition time. Thereby, they hold great promise to improve PET-based interrogation of cardiac physiology and biology. Additionally, the whole-body coverage enables simultaneous assessment of multiple organs and the large vascular structures of the body, opening new opportunities for imaging systemic mechanisms, disorders, or treatments and their interactions with the cardiovascular system as a whole. The aim of this perspective document is to debate the potential applications, challenges, opportunities, and remaining challenges of applying PET/CT with a long axial field of view to the field of cardiovascular disease.

Automated vessel specific coronary artery calcification quantification with deep learning in a large multi-center registry.

AIMS: Vessel specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram gated and attenuation correction computed tomography (CT) in a large multicenter registry. METHODS AND RESULTS: Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX) and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated attenuation correction CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400 and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4±1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and attenuation correction CT [(0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing attenuation correction CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, p<0.001) and attenuation correction CT (HR 4.21, 95% CI 3.48, 5.08, p<0.001). CONCLUSION: Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and attenuation correction CT and provides important prognostic information.

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.