Relationship of Subendocardial Perfusion to Myocardial Injury, Cardiac Structure, and Clinical Outcomes Among Patients With Hypertension.

BACKGROUND: Coronary microvascular dysfunction has been implicated in the development of hypertensive heart disease and heart failure, with subendocardial ischemia identified as a driver of sustained myocardial injury and fibrosis. We aimed to evaluate the relationships of subendocardial perfusion with cardiac injury, structure, and a composite of major adverse cardiac and cerebrovascular events consisting of death, heart failure hospitalization, myocardial infarction, and stroke. METHODS: Layer-specific blood flow and myocardial flow reserve (MFR; stress/rest myocardial blood flow) were assessed by 13N-ammonia perfusion positron emission tomography in consecutive patients with hypertension without flow-limiting coronary artery disease (summed stress score <3) imaged at Brigham and Women's Hospital (Boston, MA) from 2015 to 2021. In this post hoc observational study, biomarkers, echocardiographic parameters, and longitudinal clinical outcomes were compared by tertiles of subendocardial MFR (MFRsubendo). RESULTS: Among 358 patients, the mean age was 70.6±12.0 years, and 53.4% were male. The median MFRsubendo was 2.57 (interquartile range, 2.08-3.10), and lower MFRsubendo was associated with older age, diabetes, lower renal function, greater coronary calcium burden, and higher systolic blood pressure (P<0.05 for all). In cross-sectional multivariable regression analyses, the lowest tertile of MFRsubendo was associated with myocardial injury and with greater left ventricular wall thickness and volumes compared with the highest tertile. Relative to the highest tertile, low MFRsubendo was independently associated with an increased rate of major adverse cardiac and cerebrovascular events (adjusted hazard ratio, 2.99 [95% CI, 1.39-6.44]; P=0.005) and heart failure hospitalization (adjusted hazard ratio, 2.76 [95% CI, 1.04-7.32; P=0.042) over 1.1 (interquartile range, 0.6-2.8) years median follow-up. CONCLUSIONS: Among patients with hypertension without flow-limiting coronary artery disease, impaired MFRsubendo was associated with cardiovascular risk factors, elevated cardiac biomarkers, cardiac structure, and clinical events.

Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.

AIMS: Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS: Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION: LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.

AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

Quantification of Aortic Valve Fibrotic and Calcific Tissue from CTA: Prospective Comparison with Histology.

Background Quantifying the fibrotic and calcific composition of the aortic valve at CT angiography (CTA) can be useful for assessing disease severity and outcomes of patients with aortic stenosis (AS); however, it has not yet been validated against quantitative histologic findings. Purpose To compare quantification of aortic valve fibrotic and calcific tissue composition at CTA versus histologic examination. Materials and Methods This prospective study included patients who underwent CTA before either surgical aortic valve replacement for AS or orthotopic heart transplant (controls) at two centers between January 2022 and April 2023. At CTA, fibrotic and calcific tissue composition were quantified using automated Gaussian mixture modeling applied to the density of aortic valve tissue components, calculated as [(volume/total tissue volume) × 100]. For histologic evaluation, explanted valve cusps were stained with Movat pentachrome as well as hematoxylin and eosin. For each cusp, three 5-µm slices were obtained. Fibrotic and calcific tissue composition were quantified using a validated artificial intelligence tool and averaged across the aortic valve. Correlations were assessed using the Spearman rank correlation coefficient. Intermodality and interobserver variability were measured using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Twenty-nine participants (mean age, 63 years ± 10 [SD]; 23 male) were evaluated: 19 with severe AS, five with moderate AS, and five controls. Fibrocalcific tissue composition strongly correlated with histologic findings (r = 0.92; P < .001). The agreement between CTA and histologic findings for fibrocalcific tissue quantification was excellent (ICC, 0.94; P = .001), with underestimation of fibrotic composition at CTA (bias, -4.9%; 95% limits of agreement [LoA]: -18.5%, 8.7%). Finally, there was excellent interobserver repeatability for fibrotic (ICC, 0.99) and calcific (ICC, 0.99) aortic valve tissue volume measurements, with no evidence of a difference in measurements between readers (bias, -0.04 cm3 [95% LoA: -0.27 cm3, 0.19 cm3] and 0.02 cm3 [95% LoA: -0.14 cm3, 0.19 cm3], respectively). Conclusion In a direct comparison, standardized quantitative aortic valve tissue characterization at CTA showed excellent concordance with histologic findings and demonstrated interobserver reproducibility. Clinical trial registration no. NCT06136689 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Almeida in this issue.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

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.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

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.

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.

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.

BACKGROUND: Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. METHODS: Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. RESULTS: Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). CONCLUSIONS: In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.

Noninvasive In Vivo Thrombus Imaging in Patients With Ischemic Stroke or Transient Ischemic Attack-Brief Report.

BACKGROUND: 18F-GP1 is a novel positron-emitting radiotracer that is highly specific for activated platelets and thrombus. In a proof-of-concept study, we aimed to determine its potential clinical application in establishing the role and origin of thrombus in ischemic stroke. METHODS: Eleven patients with recent ischemic stroke (n=9) or transient ischemic attack (n=2) underwent 18F-GP1 positron emission tomography and computed tomography angiography at a median of 11 (range, 2-21) days from symptom onset. 18F-GP1 uptake (maximum target-to-background ratio) was assessed in the carotid arteries and brain. RESULTS: 18F-GP1 uptake was identified in 10 of 11 patients: 4 in the carotid arteries only, 3 in the brain only, and 3 in both the brain and carotid arteries. In those with carotid uptake, 4 participants had >50% stenosis and 3 had nonstenotic disease. One case had bilateral stenotic disease (>70%), but only the culprit carotid artery demonstrated 18F-GP1 uptake. The average uptake was higher in the culprit (median maximum target-to-background ratio, 1.55 [interquartile range, 1.26-1.82]) compared with the contralateral nonculprit carotid artery (maximum target-to-background ratio, 1.22 [1.19-1.6]). In those with brain 18F-GP1 uptake (maximum target-to-background ratio, 6.45 [4.89-7.65]), areas of acute infarction on computed tomography correlated with brain 18F-GP1 uptake in 6 cases. Ex vivo autoradiography of postmortem infarcted brain tissue showed focal uptake corresponding to intraluminal thrombus within the culprit vessel and downstream microvasculature. There was also evidence of diffuse uptake within some of the infarcted brain tissue reflecting parenchymal petechial hemorrhage. CONCLUSIONS: 18F-GP1 positron emission tomography and computed tomography angiography is a novel noninvasive method of identifying in vivo cerebrovascular thrombosis, which holds major promise in understanding the role and origin of thrombosis in stroke. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03943966.

Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T1-weighted characterization to predict periprocedural myocardial injury.

BACKGROUND: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). METHODS: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit. RESULTS: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-µL increase; 95% CI, 1.01-1.30, p = 0.035). CONCLUSIONS: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.

Head-to-head comparison of 18F-sodium fluoride coronary PET imaging between a silicon photomultiplier with digital photon counting and conventional scanners.

BACKGROUND: We compared silicone photomultipliers with digital photon counting (SiPM) and photomultiplier tubes (PMT) PET in imaging coronary plaque activity with 18F-sodium fluoride (18F-NaF) and evaluated comprehensively SiPM PET reconstruction settings. METHODS: In 25 cardiovascular disease patients (mean age 67±12 years), we conducted 18F-NaF PET on a SiPM (Biograph Vision) and conventional PET (Discovery 710) on the same day as part of a prospective clinical trial (NCT03689946). Following administration of 250 MBq of 18F-NaF, patients underwent a contrast-enhanced CT angiography and a 30-min PET acquisition in list mode on each PET consecutively. Image noise was defined as mean standard deviation of blood pool activity within the left atria. Target-to-background ratio (TBR) and signal-to-noise ratio (SNR) were measured within the whole-vessel tubular 3-dimensional volumes of interest on the cardiac motion and attenuation corrected 18F-NaF PET images using dedicated software. RESULTS: There were significant differences in image noise and background activity between the two PETs (Image noise (%), PMT: 7.6±3.7 vs. SiPM: 4.0±2.3, p<0.001; background activity, PMT: 1.4±0.4 vs. SiPM: 1.0±0.3, p<0.001). Similarly, the SNR and TBR were significantly higher in vessels scanned with the SiPM PET (SNR, PMT: 16.3±11.5 vs. SiPM: 32.7±29.8, p<0.001; TBR, PMT: 0.8±0.4 vs. SiPM: 1.1±0.6, p<0.001). SiPM PET image reconstruction with a 256 matrix, 1.4 mm pixel, and 2 mm Gaussian filter provided best tradeoff in terms of maximal SNR, TBR and clinically practical file size. CONCLUSIONS: In 18F-NaF coronary PET imaging, the SiPM PET showed superior image contrast and less image noise compared to PMT PET.

Emerging Role of Scintigraphy Using Bone-Seeking Tracers for Diagnosis of Cardiac Amyloidosis: AJR Expert Panel Narrative Review.

Amyloidoses are a complex group of clinical diseases that result from progressive organ dysfunction due to extracellular protein misfolding and deposition. The two most common types of cardiac amyloidosis are transthyretin amyloidosis (ATTR) and light-chain (AL) amyloidosis. Diagnosis of ATTR cardiomyopathy (ATTR-CM) is challenging owing to its phenotypic similarity to other more common cardiac conditions, the perceived rarity of the disease, and unfamiliarity with its diagnostic algorithms; endomyocardial biopsy was historically required for diagnosis. However, myocardial scintigraphy using bone-seeking tracers has shown high accuracy for detection of ATTR-CM and has become a key noninvasive diagnostic test for the condition, receiving support from professional society guidelines and transforming prior diagnostic paradigms. This AJR Expert Panel Narrative Review describes the role of myocardial scintigraphy using bone-seeking tracers in the diagnosis of ATTR-CM. The article summarizes available tracers, acquisition techniques, interpretation and reporting considerations, diagnostic pitfalls, and gaps in the current literature. The critical need for monoclonal testing of patients with positive scintigraphy results to differentiate ATTR-CM from AL cardiac amyloidosis is highlighted. Recent updates in guideline recommendations that emphasize the importance of a qualitative visual assessment are also discussed.

Atorvastatin lowers 68Ga-DOTATATE uptake in coronary arteries, bone marrow and spleen in individuals with type 2 diabetes.

AIMS/HYPOTHESIS: Inflammation is a core component of residual cardiovascular risk in type 2 diabetes. With new anti-inflammatory therapeutics entering the field, accurate markers to evaluate their effectiveness in reducing cardiovascular disease are paramount. Gallium-68-labelled DOTATATE (68Ga-DOTATATE) has recently been proposed as a more specific marker of arterial wall inflammation than 18F-fluorodeoxyglucose (18F-FDG). This study set out to investigate whether 68Ga-DOTATATE uptake is amenable to therapeutic intervention in individuals with type 2 diabetes. METHODS: Individuals aged >50 years with type 2 diabetes underwent 68Ga-DOTATATE positron emission tomography (PET)/computed tomography (CT) at baseline and after 3 months treatment with atorvastatin 40 mg once daily. Primary outcome was the difference in coronary 68Ga-DOTATATE uptake, expressed as target-to-background ratio (TBR). The secondary outcome was difference in bone marrow and splenic uptake, expressed as the standardised uptake value (SUV). RESULTS: Twenty-two individuals with type 2 diabetes (mean age 63.2±6.4 years, 82% male, LDL-cholesterol 3.42±0.81 mmol/l, HbA1c 55±12 mmol/mol [7.2%±3.2%]) completed both 68Ga-DOTATATE PET/CT scans. The maximum TBR was -31% (95% CI -50, -12) lower in the coronary arteries, and bone marrow and splenic 68Ga-DOTATATE uptake was also significantly lower post statin treatment, with a mean percentage reduction of -15% (95% CI -27, -4) and -17% (95% CI -32, -2), respectively. CONCLUSIONS/INTERPRETATION: 68Ga-DOTATATE uptake across the cardio-haematopoietic axis was lower after statin therapy in individuals with type 2 diabetes. Therefore, 68Ga-DOTATATE is promising as a metric for vascular and haematopoietic inflammation in intervention studies using anti-inflammatory therapeutics in individuals with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05730634.

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

PURPOSE: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables. METHODS: Consecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM). RESULTS: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1). CONCLUSION: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk 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.

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.

Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks.

BACKGROUND: Coronary 18F-sodium-fluoride (18F-NaF) positron emission tomography (PET) showed promise in imaging coronary artery disease activity. Currently image processing remains subjective due to the need for manual registration of PET and computed tomography (CT) angiography data. We aimed to develop a novel fully automated method to register coronary 18F-NaF PET to CT angiography using pseudo-CT generated by generative adversarial networks (GAN). METHODS: A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS: Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 seconds per patient. Difference in displacement motion vectors between GAN-based and observer-based registration in the x-, y-, and z-directions was 0.8 ± 3.0, 0.7 ± 3.0, and 1.7 ± 3.9 mm, respectively. TBRmax had a coefficient of repeatability (CR) of 0.31, mean bias of 0.03 and narrow limits of agreement (LOA) (95% LOA: - 0.29 to 0.33). SUVmax had CR of 0.26, mean bias of 0 and narrow LOA (95% LOA: - 0.26 to 0.26). CONCLUSION: Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.