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.

Quantification of Low-Attenuation Plaque Burden from Coronary CT Angiography: A Head-to-Head Comparison with Near-Infrared Spectroscopy Intravascular US.

Purpose: To determine the association between low-attenuation plaque (LAP) burden at coronary CT angiography (CCTA) and plaque morphology determined with near-infrared spectroscopy intravascular US (NIRS-IVUS) and to compare the discriminative ability for NIRS-IVUS-verified high-risk plaques (HRPs) between LAP burden and visual assessment of LAP. Materials and Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study included consecutive patients who underwent CCTA before NIRS-IVUS between October 2019 and October 2022 at two facilities. LAPs were visually identified as having a central focal area of less than 30 HU using the pixel lens technique. LAP burden was calculated as the volume of voxels with less than 30 HU divided by vessel volume. HRPs were defined as plaques with one of the following NIRS-IVUS-derived high-risk features: maximum 4-mm lipid core burden index greater than 400 (lipid-rich plaque), an echolucent zone (intraplaque hemorrhage), or echo attenuation (cholesterol clefts). Multivariable analysis was performed to evaluate NIRS-IVUS-derived parameters associated with LAP burden. The discriminative ability for NIRS-IVUS-verified HRPs was compared using receiver operating characteristic analysis. Results: In total, 273 plaques in 141 patients (median age, 72 years; IQR, 63-78 years; 106 males) were analyzed. All the NIRS-IVUS-derived high-risk features were independently linked to LAP burden (P < .01 for all). LAP burden increased with the number of high-risk features (P < .001) and had better discriminative ability for HRPs than plaque attenuation by visual assessment (area under the receiver operating characteristic curve, 0.93 vs 0.89; P = .02). Conclusion: Quantification of LAP burden improved HRP assessment compared with visual assessment. LAP burden was associated with the accumulation of HRP morphology.Keywords: Coronary CT Angiography, Intraplaque Hemorrhage, Lipid-Rich Plaque, Low Attenuation Plaque, Near-Infrared Spectroscopy Intravascular Ultrasound Supplemental material is available for this article. See also the commentary by Ferencik in this issue.© RSNA, 2023.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

OBJECTIVE: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS: The final population comprised 743 patients (mean age 65  ±â€¯ 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION: Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial.

BACKGROUND: Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA). METHODS: This post hoc analysis of the PACIFIC trial included 208 patients with suspected coronary artery disease (CAD) who underwent [15O]H2O PET and CCTA. Low-attenuation plaque (LAP, < 30HU), non-calcified plaque (NCP), and PCAT attenuation were measured by CCTA. RESULTS: In 582 vessels, 211 (36.3%) had impaired per-vessel hyperemic MBF (≤ 2.30 mL/min/g). In multivariable analysis, LAP burden was independently and consistently associated with impaired hyperemic MBF (P = 0.016); over NCP burden (P = 0.997). Addition of LAP burden improved predictive performance for impaired hyperemic MBF from a model with CAD severity and calcified plaque burden (P < 0.001). There was no correlation between PCAT attenuation and hyperemic MBF (r = - 0.11), and PCAT attenuation was not associated with impaired hyperemic MBF in univariable or multivariable analysis of all vessels (P > 0.1). CONCLUSION: In patients with stable CAD, LAP burden was independently associated with impaired hyperemic MBF and a stronger predictor of impaired hyperemic MBF than NCP burden. There was no association between PCAT attenuation and hyperemic MBF.

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve.

BACKGROUND: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). METHODS: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 â€‹± â€‹8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. RESULTS: The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p â€‹< â€‹0.001). Women had a smaller burden of all plaque subtypes (all p â€‹< â€‹0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p â€‹= â€‹0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p â€‹= â€‹0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p â€‹= â€‹0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p â€‹= â€‹0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. CONCLUSIONS: In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.

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.

Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.

BACKGROUND: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET). METHODS: This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT). RESULTS: One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16). CONCLUSIONS: An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

Quantifying sodium [18F]fluoride uptake in abdominal aortic aneurysms.

BACKGROUND: Aortic microcalcification activity is a recently described method of measuring aortic sodium [18F]fluoride uptake in the thoracic aorta on positron emission tomography. In this study, we aimed to compare and to modify this method for use within the infrarenal aorta of patients with abdominal aortic aneurysms. METHODS: Twenty-five patients with abdominal aortic aneurysms underwent an sodium [18F]fluoride positron emission tomography and computed tomography scan. Maximum and mean tissue-to-background ratios (TBR) and abdominal aortic microcalcification activity were determined following application of a thresholding and variable radius method to correct for vertebral sodium [18F]fluoride signal spill-over and the nonlinear changes in aortic diameter, respectively. Agreement between the methods, and repeatability of these approaches were assessed. RESULTS: The aortic microcalcification activity method was much quicker to perform than the TBR method (14 versus 40 min, p < 0.001). There was moderate-to-good agreement between TBR and aortic microcalcification activity measurements for maximum (interclass correlation co-efficient, 0.67) and mean (interclass correlation co-efficient, 0.88) values. These correlations sequentially improved with the application of thresholding (intraclass correlation coefficient 0.93, 95% confidence interval 0.89-0.95) and variable diameter (intraclass correlation coefficient 0.97, 95% confidence interval 0.94-0.99) techniques. The optimised method had good intra-observer (mean 1.57 ± 0.42, bias 0.08, co-efficient of repeatability 0.36 and limits of agreement - 0.43 to 0.43) and inter-observer (mean 1.57 ± 0.42, bias 0.08, co-efficient of repeatability 0.47 and limits of agreement - 0.53 to 0.53) repeatability. CONCLUSIONS: Aortic microcalcification activity is a quick and simple method which demonstrates good intra-observer and inter-observer repeatabilities and provides measures of sodium [18F]fluoride uptake that are comparable to established methods.

Vascular biomechanics and molecular disease activity in the thoracic aorta: a novel imaging method.

AIMS: The influence haemodynamics have on vessel wall pathobiology in aortic disease is incomplete. This aim of this study was to develop a repeatable method for assessing the relationship between aortic wall shear stress (WSS) and disease activity by fusing 4D flow cardiovascular magnetic resonance (CMR) with hybrid positron emission tomography (PET). METHODS AND RESULTS: As part of an ongoing clinical trial, patients with bicuspid aortic valve (BAV) were prospectively imaged with both 18F-sodium fluoride (18F-NaF) PET, a marker of calcification activity, and 4D flow CMR. We developed novel software allowing accurate 3D co-registration and high-resolution comparison of aortic peak systolic WSS and 18F-NaF PET uptake (maximum tissue-to-background ratio). Intra-observer repeatability of both measurements was determined using Bland-Altman plots and intra-class correlation coefficients (ICCs). The relationship between localized WSS and 18F-NaF uptake was analysed using linear mixed-effect models. Twenty-three patients with BAV (median age 50 [44-55] years, 22% female) were included. Intra-observer repeatability for WSS (ICC = 0.92) and 18F-NaF (ICC = 0.91) measurements obtained within 1.4 ± 0.6 cm2 regions of interest was excellent. On multivariable analysis, 18F-NaF PET uptake was independently and negatively associated with WSS as well as diastolic blood pressure (both P < 0.05), adjusted for age. CONCLUSION: Fused assessment of WSS and 18F-NaF PET uptake is feasible and repeatable, demonstrating a clear association between these two factors. This high spatial resolution approach has major potential to advance our understanding of the relationship between vascular haemodynamics and disease activity.

Anatomical validation of automatic respiratory motion correction for coronary 18F-sodium fluoride positron emission tomography by expert measurements from four-dimensional computed tomography.

BACKGROUND: Respiratory motion correction is of importance in studies of coronary plaques employing 18 F-NaF; however, the validation of motion correction techniques mainly relies on indirect measures such as test-retest repeatability assessments. In this study, we aim to compare and, thus, validate the respiratory motion vector fields obtained from the positron emission tomography (PET) images directly to the respiratory motion observed during four-dimensional cine-computed tomography (CT) by an expert observer. PURPOSE: To investigate the accuracy of the motion correction employed in a software (FusionQuant) used for evaluation of 18 F-NaF PET studies by comparing the respiratory motion of the coronary plaques observed in PET to the respiratory motion observed in 4D cine-CT images. METHODS: This study included 23 patients who undertook thoracic PET scans for the assessment of coronary plaques using 18 F-sodium fluoride (18 F-NaF). All patients underwent a 5-s cine-CT (4D-CT), a coronary CT angiography (CTA), and 18 F-NaF PET. The 4D-CT and PET scan were reconstructed into 10 phases. Respiratory motion was estimated for the non-contrast visible coronary plaques using diffeomorphic registrations (PET) and compared to respiratory motion observed on 4D-CT. We report the PET motion vector fields obtained in the three principal axes in addition to the 3D motion. Statistical differences were examined using paired t-tests. Signal-to-noise ratios (SNR) are reported for the single-phase images (end-expiratory phase) and for the motion-corrected image-series (employing the motion vector fields extracted during the diffeomorphic registrations). RESULTS: In total, 19 coronary plaques were identified in 16 patients. No statistical differences were observed for the maximum respiratory motion observed in x, y, and the 3D motion fields (magnitude and direction) between the CT and PET (X direction: 4D CT = 2.5 ± 1.5 mm, PET = 2.4 ± 3.2 mm; Y direction: 4D CT = 2.3 ± 1.9 mm, PET = 0.7 ± 2.9 mm, 3D motion: 4D CT = 6.6 ± 3.1 mm, PET = 5.7 ± 2.6 mm, all p ≥ 0.05). Significant differences in respiratory motion were observed in the systems' Z direction: 4D CT = 4.9 ± 3.4 mm, PET = 2.3 ± 3.2 mm, p = 0.04. Significantly improved SNR is reported for the motion corrected images compared to the end-expiratory phase images (end-expiratory phase = 6.8±4.8, motion corrected = 12.2±4.5, p = 0.001). CONCLUSION: Similar respiratory motion was observed in two directions and 3D for coronary plaques on 4D CT as detected by automatic respiratory motion correction of coronary PET using FusionQuant. The respiratory motion correction technique significantly improved the SNR in the images.

Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography.

OBJECTIVES: The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis. BACKGROUND: It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events. METHODS: A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes. RESULTS: Culprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both Ptrend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004). CONCLUSIONS: Culprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.

Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.

Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results (P < 0.001), but not compared with readers interpreting with DL results (P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.

Pericoronary Adipose Tissue Attenuation, Low-Attenuation Plaque Burden, and 5-Year Risk of Myocardial Infarction.

BACKGROUND: Pericoronary adipose tissue (PCAT) attenuation and low-attenuation noncalcified plaque (LAP) burden can both predict outcomes. OBJECTIVES: This study sought to assess the relative and additive values of PCAT attenuation and LAP to predict future risk of myocardial infarction. METHODS: In a post hoc analysis of the multicenter SCOT-HEART (Scottish Computed Tomography of the Heart) trial, the authors investigated the relationships between the future risk of fatal or nonfatal myocardial infarction and PCAT attenuation measured from coronary computed tomography angiography (CTA) using multivariable Cox regression models including plaque burden, obstructive coronary disease, and cardiac risk score (incorporating age, sex, diabetes, smoking, hypertension, hyperlipidemia, and family history). RESULTS: In 1,697 evaluable participants (age: 58 ± 10 years), there were 37 myocardial infarctions after a median follow-up of 4.7 years. Mean PCAT was -76 ± 8 HU and median LAP burden was 4.20% (IQR: 0%-6.86%). PCAT attenuation of the right coronary artery (RCA) was predictive of myocardial infarction (HR: 1.55; P = 0.017, per 1 SD increment) with an optimum threshold of -70.5 HU (HR: 2.45; P = 0.01). In multivariable analysis, adding PCAT-RCA of ≥-70.5 HU to an LAP burden of >4% (the optimum threshold for future myocardial infarction; HR: 4.87; P < 0.0001) led to improved prediction of future myocardial infarction (HR: 11.7; P < 0.0001). LAP burden showed higher area under the curve compared to PCAT attenuation for the prediction of myocardial infarction (AUC = 0.71 [95% CI: 0.62-0.80] vs AUC = 0.64 [95% CI: 0.54-0.74]; P < 0.001), with increased area under the curve when the 2 metrics are combined (AUC = 0.75 [95% CI: 0.65-0.85]; P = 0.037). CONCLUSION: Coronary CTA-defined LAP burden and PCAT attenuation have marked and complementary predictive value for the risk of fatal or nonfatal myocardial infarction.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0-5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70-16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07-5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99-1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction. FUNDING: National Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.

Bypass Grafting and Native Coronary Artery Disease Activity.

OBJECTIVES: The aim of this study was to describe the potential of 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) to identify graft vasculopathy and to investigate the influence of coronary artery bypass graft (CABG) surgery on native coronary artery disease activity and progression. BACKGROUND: As well as developing graft vasculopathy, CABGs have been proposed to accelerate native coronary atherosclerosis. METHODS: Patients with established coronary artery disease underwent baseline 18F-NaF PET, coronary artery calcium scoring, coronary computed tomographic angiography, and 1-year repeat coronary artery calcium scoring. Whole-vessel coronary microcalcification activity (CMA) on 18F-NaF PET and change in calcium scores were quantified in patients with and without CABG surgery. RESULTS: Among 293 participants (mean age 65 ± 9 years, 84% men), 48 (16%) underwent CABG surgery 2.7 years [IQR: 1.4-10.4 years] previously. Although all arterial and the majority (120 of 128 [94%]) of vein grafts showed no 18F-NaF uptake, 8 saphenous vein grafts in 7 subjects had detectable CMA. Bypassed native coronary arteries had 3 times higher CMA values (2.1 [IQR: 0.4-7.5] vs 0.6 [IQR: 0-2.7]; P < 0.001) and greater progression of 1-year calcium scores (118 Agatston unit [IQR: 48-194 Agatston unit] vs 69 [IQR: 21-142 Agatston unit]; P = 0.01) compared with patients who had not undergone CABG, an effect confined largely to native coronary plaques proximal to the graft anastomosis. In sensitivity analysis, bypassed native coronary arteries had higher CMA (2.0 [IQR: 0.4-7.5] vs 0.8 [IQR: 0.3-3.2]; P < 0.001) and faster disease progression (24% [IQR: 16%-43%] vs 8% [IQR: 0%-24%]; P = 0.002) than matched patients (n = 48) with comparable burdens of coronary artery disease and cardiovascular comorbidities in the absence of bypass grafting. CONCLUSIONS: Native coronary arteries that have been bypassed demonstrate increased disease activity and more rapid disease progression than nonbypassed arteries, an observation that appears independent of baseline atherosclerotic plaque burden. Microcalcification activity is not a dominant feature of graft vasculopathy.

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction.

BACKGROUND: Current 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements. MATERIALS AND METHODS: Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity. RESULTS: Compared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46). CONCLUSION: 3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Observer repeatability and interscan reproducibility of 18F-sodium fluoride coronary microcalcification activity.

BACKGROUND: We aimed to establish the observer repeatability and interscan reproducibility of coronary 18F-sodium-fluoride positron emission tomography (PET) uptake using a novel semi-automated approach, coronary microcalcification activity (CMA). METHODS: Patients with multivessel coronary artery disease underwent repeated hybrid PET and computed tomography angiography (CTA) imaging (PET/CTA). CMA was defined as the integrated standardized uptake values (SUV) in the entire coronary tree exceeding 2 standard deviations above the background SUV. Coefficients of repeatability between the same observer (intraobserver repeatability), between 2 observers (interobserver repeatability) and coefficient of reproducibility between 2 scans (interscan reproducibility), were determined at vessel and patient level. RESULTS: In 19 patients, CMA was assessed twice in 43 coronary vessels on two PET/CT scans performed 12 ± 5 days apart. There was excellent intraclass correlation for intraobserver and interobserver repeatability as well as interscan reproducibility (all ≥ 0.991). There was 100% intraobserver, interobserver and interscan agreement for the presence (CMA > 0) or absence (CMA = 0) of coronary18F-NaF uptake. Mean CMA was 3.12 ± 0.62 with coefficients of repeatability of ≤ 10% for all measures: intraobserver 0.24 and 0.22, interobserver 0.30 and 0.29 and interscan 0.33 and 0.32 at a per-vessel and per-patient level, respectively. CONCLUSIONS: CMA is a repeatable and reproducible global measure of coronary atherosclerotic activity.

Quantitative Assessment of Cardiac Hypermetabolism and Perfusion for Diagnosis of Cardiac Sarcoidosis.

BACKGROUND: Quantitative assessment of cardiac hypermetabolism from 18Flourodeoxy glucose (FDG) positron emission tomography (PET) may improve diagnosis of cardiac sarcoidosis (CS). We assessed different approaches for quantification of cardiac hypermetabolism and perfusion in patients with suspected CS. METHODS AND RESULTS: Consecutive patients undergoing 18FDG PET assessment for possible CS between January 2014 and March 2019 were included. Cardiac hypermetabolism was quantified using maximal standardized uptake value (SUVMAX), cardiometabolic activity (CMA) and volume of inflammation, using relative thresholds (1.3× and 1.5× left ventricular blood pool [LVBP] activity), and absolute thresholds (SUVMAX > 2.7 and 4.1). Diagnosis of CS was established using the Japanese Ministry of Health and Wellness criteria. In total, 69 patients were studied, with definite or possible CS in 29(42.0%) patients. CMA above 1.5× LVBP SUVMAX had the highest area under the receiver operating characteristic curve (AUC 0.92). Quantitative parameters using relative thresholds had higher AUC compared to absolute thresholds (p < 0.01). Interobserver variability was low for CMA, with excellent agreement regarding absence of activity (Kappa 0.970). CONCLUSIONS: Quantitation with scan-specific thresholds has superior diagnostic accuracy compared to absolute thresholds. Based on the potential clinical benefit, programs should consider quantification of cardiac hypermetabolism when interpreting 18F-FDG PET studies for CS.

Respiration-averaged CT versus standard CT attenuation map for correction of 18F-sodium fluoride uptake in coronary atherosclerotic lesions on hybrid PET/CT.

BACKGROUND: To evaluate the impact of respiratory-averaged computed tomography attenuation correction (RACTAC) compared to standard single-phase computed tomography attenuation correction (CTAC) map, on the quantitative measures of coronary atherosclerotic lesions of 18F-sodium fluoride (18F-NaF) uptake in hybrid positron emission tomography and computed tomography (PET/CT). METHODS: This study comprised 23 patients who underwent 18F-NaF coronary PET in a hybrid PET/CT system. All patients had a standard single-phase CTAC obtained during free-breathing and a 4D cine-CT scan. From the cine-CT acquisition, RACTAC maps were obtained by averaging all images acquired over 5 seconds. PET reconstructions using either CTAC or RACTAC were compared. The quantitative impact of employing RACTAC was assessed using maximum target-to-background (TBRMAX) and coronary microcalcification activity (CMA). Statistical differences were analyzed using reproducibility coefficients and Bland-Altman plots. RESULTS: In 23 patients, we evaluated 34 coronary lesions using CTAC and RACTAC reconstructions. There was good agreement between CTAC and RACTAC for TBRMAX (median [Interquartile range]): CTAC = 1.65 [1.23 to 2.38], RACTAC = 1.63 [1.23 to 2.33], p = 0.55), with coefficient of reproducibility of 0.18, and CMA: CTAC = 0.10 [0 to 1.0], RACTAC = 0.15 [0 to 1.03], p = 0.55 with coefficient of reproducibility of 0.17 CONCLUSION: Respiratory-averaged and standard single-phase attenuation correction maps provide similar and reproducible methods of quantifying coronary 18F-NaF uptake on PET/CT.