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Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved. OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of

We aimed to evaluate the association of epicardial adipose tissue (EAT) with coronary CT angiography (CCTA) plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus.Data of 353 patients (62.9 ± 10.4 years, 62% male), who underwent CCTA as part of their clinical workup for the evaluation of suspected or known CAD, were retrospectively analyzed. EAT volume and plaque parameters from CCTA were compared in patients with diabetes (n = 63) and without diabetes (n = 290). Follow-up was performed to record adverse cardiovascular events. The predictive value to detect adverse cardiovascular events was assessed using concordance indices (CIs) and multivariable Cox proportional hazards analysis.In total, 33 events occurred after a median follow-up of 5.1 years. In patients with diabetes, EAT volume and plaque parameters were significantly higher than in patients without diabetes (all p 0.05). A multivariable model demonstrated an incrementally improved C-index of 0.84 (95%CI 0.80-0.88) over the Framingham risk score and single measures alone. In multivariable Cox regression analysis EAT volume (Hazard ratio[HR] 1.21, p = 0.022), obstructive CAD (HR 1.18, p = 0.042), and ≥2 high-risk plaque features (HR 2.13, p = 0.031) were associated with events in patients with diabetes and obstructive CAD (HR 1.88, p = 0.017), and Agatston calcium score (HR 1.009, p = 0.039) in patients without diabetes.EAT, as a biomarker of inflammation, and plaque parameters, as an extent of atherosclerotic CAD, are higher in patients with diabetes and are associated with increased adverse cardiovascular outcomes. These parameters may help identify patients at high risk with need for more aggressive therapeutic and preventive care.

The aim of this study was to evaluate strategies to reduce contrast media volumes for coronary computed tomography (CT) angiography on a clinical first-generation dual-source photon-counting detector (PCD)-CT system using a dynamic circulation phantom.Coronary CT angiograph is an established method for the assessment of coronary artery disease that relies on the administration of iodinated contrast media. Reduction of contrast media volumes while maintaining diagnostic image quality is desirable. In this study, a dynamic phantom containing a 3-dimensional-printed model of the thoracic aorta and coronary arteries was evaluated using a clinical contrast injection protocol with stepwise reduced contrast agent concentrations (100%, 75%, 50%, 40%, 30%, and 20% contrast media content of the same 50 mL bolus, resulting in iodine delivery rates of 1.5, 1.1, 0.7, 0.6, 0.4 and 0.3 gl/s) on a first-generation, dual-source PCD-CT. Polychromatic images (T3D) and virtual monoenergetic images were reconstructed in the range of 40 to 70 keV in 5-keV steps. Attenuation and noise were measured in the coronary arteries and background material and the contrast-to-noise ratio (CNR) were calculated. Attenuation of 350 HU and a CNR of the reference protocol at 70 keV were regarded as sufficient for simulation of diagnostic purposes. Vessel sharpness and noise power spectra were analyzed for the aforementioned reconstructions.The standard clinical contrast protocol (bolus with 100% contrast) yielded diagnostic coronary artery attenuation for all tested reconstructions (398 HU). A 50% reduction in contrast media concentration demonstrated sufficient attenuation of the coronary arteries at 40 to 55 keV (366 HU). Virtual monoenergetic image reconstructions of 40 to 45 and 40 keV allowed satisfactory attenuation of the coronary arteries for contrast concentrations of 40% and 30% of the original protocol. A reduction of contrast agent concentration to 20% of the initial concentration provided insufficient attenuation in the target vessels for all reconstructions. The highest CNR was found for virtual monoenergetic reconstructions at 40 keV for all contrast media injection protocols, yielding a sufficient CNR at a 50% reduction of contrast agent concentration.Using virtual monoenergetic image reconstructions at 40 keV on a dual-source PCD-CT system, contrast media concentration could be reduced by 50% to obtain diagnostic attenuation and objective image quality for coronary CT angiography in a dynamic vessel phantom. These initial feasibility study results have to be validated in clinical studies.

Previous studies have shown positive effects of intensive low-density lipoprotein (LDL)-lowering therapy on atheroma volume using invasive intravascular ultrasound. This study describes the changes in coronary plaque composition on coronary computed tomography angiography in patients treated with proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors.In this prospective study, coronary plaques were analyzed using third-generation dual-source computed tomography before and after 1 year of PCSK9-inhibitor treatment. Plaque markers included total plaque volume (TPV), calcified plaque volume (CPV), noncalcified plaque volume (NCPV), lumen volume and vessel volume (VV), minimal luminal area (MLA), minimal lumen diameter (MLD), corrected coronary opacification, eccentricity, remodeling index, and functional plaque parameters. Primary endpoint was defined as change in TPV; the secondary endpoint was TPV or CPV regression or nominal change in plaque parameters.We analyzed 74 coronary plaques in 23 patients (60±9 y, 65% male). After 1 year of PCSK9-inhibitor treatment, LDL was reduced from 148 to 66 mg/dL ( P0.0001). Significant changes were found for VV (196 to 215 mm 3 , P =0.0340), MLA (3.1 to 2.6 mm 2 , P =0.0413), and MLD (1.7 to 1.4 mm, P =0.0048). TPV, CPV, NCPV, lumen volume, and functional plaque parameters did not change significantly ( P0.05).Coronary artery plaque analysis by coronary computed tomography angiography highlights that LDL lowering therapy affects plaque composition. The primary endpoint of TPV change was not reached; however, VV, MLA, and MLD changed significantly.

Objectives: The aim of this study was to evaluate the effect of timing for post-interventional CT imaging on the rate of re-intervention and all-cause mortality in patients with endovascular treatment of type B aortic dissections (TBAD). Material and methods: Data on 70 patients with endovascular repair of aortic dissection during a three-year period from a single institution retrospectively were collected. Study participants were stratified based on those who had a postoperative CTA in the first 30 days after index intervention (early) vs. those who did not (late). The re-intervention and all-cause mortality rates between the two groups were investigated using Kaplan-Meier and Cox regression analysis. Results: During a median follow-up time of 230 days, the primary endpoint (additional operation) was reached in 24/70 patients (34.3%) with no statistically significant difference between the early and late CTA group (log-rank-test: P = 0.886). All-cause mortality was present in 14/70 (20%) patients, with no statistically significant difference between both groups (log-rank-test: P = 0.440). Additionally, both groups had no significant differences in time to additional operation and death. Cox regression analysis revealed the presence of a chronic TBAD and underlying connective tissue disease as relevant risk factors for the need for an additional operation and obesity as a protective and renal failure as a negative factor for all-cause mortality. Conclusion: CTA surveillance within 30 days of the index operation did not significantly modify mortality or rate of re-intervention after endovascular treatment for TBAD. Surveillance recommendations should be tailored to individualized factors.

This study sought to determine whether machine learning (ML) can be used to better identify the risk factors and establish the prediction models for the prevalence and severity of coronary artery calcification (CAC) in nondialysis chronic kidney disease (CKD) patients and compare the performance of distinctive ML models with conventional logistic regression (LR) model.In all, 3701 Chinese nondialysis CKD patients undergoing noncontrast cardiac computed tomography (CT) scanning were enrolled from November 2013 to December 2017. CAC score derived from the cardiac CT was calculated with the calcium scoring software and was used to assess and stratify the prevalence and severity of CAC. Four ML models (LR, random forest, support vector machine, and k-nearest neighbor) and the corresponding feature ranks were conducted. The model that incorporated the independent predictors was shown as the receiver-operating characteristic (ROC) curve. Area under the curve (AUC) was used to present the prediction value. ML model performance was compared with the traditional LR model using pairwise comparisons of AUCs.Of the 3701 patients, 943 (25.5%) patients had CAC. Of the 943 patients with CAC, 764 patients (20.6%) and 179 patients (4.8%) had an Agatston CAC score of 1 to 300 and ≥300, respectively. The primary cohort and the independent validation cohort comprised 2957 patients and 744 patients, respectively. For the prevalence of CAC, the AUCs of ML models were from 0.78 to 0.82 in the training data set and the internal validation cohort. For the severity of CAC, the AUCs of the 4 ML models were from 0.67 to 0.70 in the training data set and from 0.53 to 0.70 in the internal validation cohort. For the prevalence of CAC, the AUC was 0.80 (95% confidence interval [CI]: 0.77-0.83) for ML (LR) versus 0.80 (95% CI: 0.77-0.83) for the traditional LR model ( P =0.2533). For the severity of CAC, the AUC was 0.70 (95% CI: 0.63-0.77) for ML (LR) versus 0.70 (95% CI: 0.63-0.77) for traditional LR model ( P =0.982).This study constructed prediction models for the presence and severity of CAC based on Agatston scores derived from noncontrast cardiac CT scanning in nondialysis CKD patients using ML, and showed ML LR had the best performance.

Background: Non-contrast chest CTs (NCCT) are performed routinely for coronary artery calcium (CAC) scoring and lung cancer screening. However, a large amount of noncoronary and nonpulmonary data from these scans remain unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction for cardiovascular outcomes. Methods: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent a routine non-ECG gated NCCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. Findings: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 mL/m 2 . AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p

We aimed to validate and test a prototype algorithm for automated dual-energy computed tomography (DECT)-based myocardial extracellular volume (ECV) assessment in patients with various cardiomyopathies.This retrospective study included healthy subjects (n=9; 61±10 y) and patients with cardiomyopathy (n=109, including a validation cohort n=60; 68±9 y; and a test cohort n=49; 69±11 y), who had previously undergone cardiac DECT. Myocardial ECV was calculated using a prototype-based fully automated algorithm and compared with manual assessment. Receiver-operating characteristic analysis was performed to test the algorithm's ability to distinguish healthy subjects and patients with cardiomyopathy.The fully automated method led to a significant reduction of postprocessing time compared with manual assessment (2.2±0.4 min and 9.4±0.7 min, respectively, P0.001). There was no significant difference in ECV between the automated and manual methods ( P =0.088). The automated method showed moderate correlation and agreement with the manual technique ( r =0.68, intraclass correlation coefficient=0.66). ECV was significantly higher in patients with cardiomyopathy compared with healthy subjects, regardless of the method used ( P0.001). In the test cohort, the automated method yielded an area under the curve of 0.98 for identifying patients with cardiomyopathies.Automated ECV estimation based on DECT showed moderate agreement with the manual method and matched with previously reported ECV values for healthy volunteers and patients with cardiomyopathy. The automatically derived ECV demonstrated an excellent diagnostic performance to discriminate between healthy and diseased myocardium, suggesting that it could be an effective initial screening tool while significantly reducing the time of assessment.

Acute myocarditis (AM) and hypertensive heart disease (HHD) have different pathophysiological backgrounds, thus potentially showing distinct patterns of altered myocardial deformation. Therefore, CMR left ventricular (LV) feature tracking (FT)- based strain parameters were indexed to myocardial mass index (LVMi) in order to evaluate potential additional value in the differentiation among AM, HHD, and healthy volunteers (HV) compared to non-indexed conventional strain.Patients with AM (n = 43) and HHD (n = 28) underwent CMR at 3T. 61 HV served as controls. Cine imaging-based FT-strain analysis was performed and natural strain (nStrain) values were evaluated for gender and age specific differences in HV. Strain parameters were indexed to LVMi yielding ratio Strain (rStrain). These were evaluated for their discriminatory accuracy compared to nStrain values.There were significant differences in nStrain between genders (p0.05), but not between age groups in HV. Circumferential strains differentiated best between HV and AM, reaching an area under the curve (AUC) of 0.86 (female) and 0.81 (male), yielding 93 (72) % sensitivity and 55 (75) % specificity. In discriminating between HV and HHD as well as AM and HHD, longitudinal strains outperformed all other parameters with AUCs of 1.00 (female)/ 0.92 (male) and 0.90 (female)/ 0.74 (male), respectively. Sensitivity and specificity levels of 100 %/ 100 % (female) and 91 %/ 72 % (male) for HV versus AM as well as 82 %/ 71 % (female) and 91%/ 57 % (male) for AM versus HHD could be demonstrated. The usage of rStrains significantly increased the AUC for circumferential and radial strains in male patients.rStrain provided additional value in the differentiation of diseases with increased LVM. As rStrain is derived from standard native cine imaging, such parameters can be time efficiently and reliably calculated, giving them the potential to be a powerful addition to the currently developing multiparametric native diagnostic approaches.

The aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector-computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions.Although CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector-computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1-10, 11-100, 101-400, and400) was compared using Cohen κ .Phantom studies demonstrated strong agreement between CACS PureCalcium and CACS TNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], -39.8/26.6), whereas CACS VNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, -87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACS PureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P0.001, r = 0.97, ICC = 0.98; mean bias, -113.5; LoA, -470.2/243.2). In comparison, CACS VNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P0.001, r = 0.97, ICC = 0.54; mean bias, -551.6; LoA, -2037.5/934.4). CACS PureCalcium showed superior agreement of CACS classification ( κ = 0.88) than CACS VNC ( κ = 0.60).The accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.

We present a patient with history of systemic lupus erythematosus who presented with acute chest pain. Electrocardiography, invasive coronary angiography, and cardiac MRI were performed during the course of her evaluation. Invasive coronary angiography demonstrated obstructive disease in the diagonal system and cardiovascular MRI confirmed an anterior infarct consistent with the electrocardiographic findings. However, MRI also revealed focal inferoseptal hypoperfusion inconsistent with electrocardiographic and angiographic findings. Rather, these findings indicate the presence of concurrent microvascular coronary artery disease, which has a high prevalence among women with autoimmune disease.

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or r2, and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; r2 = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner (P = .15), sex (P = .051), and section thickness (P = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

Objectives: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Background: Whether APCs differ based on lesion severity and diabetic status is unknown. Methods: We retrospectively evaluated 303 subjects from the CREDENCE trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (>50% stenosed) or non-obstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low density noncalcified plaque (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP) and percent atheroma volume (PAV; plaque volume normalized for vessel volume). The relationship between APCs, stenosis severity and diabetic status was assessed. Results: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the diabetic cohort, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (p=0.004). Patients with diabetes and nonobstructive disease had greater PV (p=0.02), PAV (p=0.02), NCP (p=0.03), PAV NCP (p=0.02), diseased vessels (p=0.03), and max stenosis (p=0.02) than nondiabetic patients with nonobstructive disease. APCs were similar between diabetics with non-obstructive disease and non-diabetic patients with obstructive disease. Diabetic status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and non-obstructive lesions. Conclusions: Patients with diabetes and non-obstructive stenosis had an association to similar APCs as patients without diabetes who have obstructive stenosis. Among patients with non-obstructive disease, patients with diabetes had more total PV and NCP.

Background Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort. Methods Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments. Results ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]). Conclusion In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.

Four-dimensional (4D) flow MRI allows for the quantification of complex flow patterns; however, its clinical use is limited by its inherently long acquisition time. Compressed sensing (CS) is an acceleration technique that provides substantial reduction in acquisition time.To compare intracardiac flow measurements between conventional and CS-based highly accelerated 4D flow acquisitions.Prospective.Fifty healthy volunteers (28.0 ± 7.1 years, 24 males).Whole heart time-resolved 3D gradient echo with three-directional velocity encoding (4D flow) with conventional parallel imaging (factor 3) as well as CS (factor 7.7) acceleration at 3 T.4D flow MRI data were postprocessed by applying a valve tracking algorithm. Acquisition times, flow volumes (mL/cycle) and diastolic function parameters (ratio of early to late diastolic left ventricular peak velocities [E/A] and ratio of early mitral inflow velocity to mitral annular early diastolic velocity [E/e']) were quantified by two readers.Paired-samples t-test and Wilcoxon rank sum test to compare measurements. Pearson correlation coefficient (r), Bland-Altman-analysis (BA) and intraclass correlation coefficient (ICC) to evaluate agreement between techniques and readers. A P value 0.05 was considered statistically significant.A significant improvement in acquisition time was observed using CS vs. conventional accelerated acquisition (6.7 ± 1.3 vs. 12.0 ± 1.3 min). Net forward flow measurements for all valves showed good correlation (r 0.81) and agreement (ICCs 0.89) between conventional and CS acceleration, with 3.3%-8.3% underestimation by the CS technique. Evaluation of diastolic function showed 3.2%-17.6% error: E/A 2.2 [1.9-2.4] (conventional) vs. 2.3 [2.0-2.6] (CS), BA bias 0.08 [-0.81-0.96], ICC 0.82; and E/e' 4.6 [3.9-5.4] (conventional) vs. 3.8 [3.4-4.3] (CS), BA bias -0.90 [-2.31-0.50], ICC 0.89.Analysis of intracardiac flow patterns and evaluation of diastolic function using a highly accelerated 4D flow sequence prototype is feasible, but it shows underestimation of flow measurements by approximately 10%.2 TECHNICAL EFFICACY: Stage 1.

Intracranial aneurysms (IAs) are increasingly found in clinical practice due to widely used advanced imaging examinations. However, the mechanism of development, growth, rupture, and recurrence of IAs remains unknown. Rupture of IAs results in subarachnoid hemorrhage, which is associated with high morbidity and mortality. Assessment of intra-aneurysmal hemodynamics using computational fluid dynamics (CFD) holds great promise in the management of IAs. Hemodynamic factors have a critical role in the formation, progression, and recurrence of aneurysms, thus having great potential to guide the decision-making in clinical practice. This review describes current evidence of CFD-based hemodynamics in assessing the formation, growth, rupture, and recurrence of aneurysms. The challenges and future directions of the clinical implementations of CFD are briefly discussed.

OBJECTIVES The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments. METHODS A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance. RESULTS AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence. CONCLUSION In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Background Left-to-right (L-R) shunts are characterized by a pathological connection between high- and low-pressure systems, leading to a mixing of oxygen-rich blood with low oxygenated blood. They are typically diagnosed by phase-contrast cardiac magnetic resonance imaging (MRI) which requires extensive planning. T2 is sensitive to blood oxygenation and may be able to detect oxygenation differences between the left (LV) and right ventricles (RV) caused by L-R shunts. Purpose To test the feasibility of routine T2 mapping to detect L-R shunts. Study type Retrospective. Population Patients with known L-R shunts (N = 27), patients with RV disease without L-R shunts (N = 21), and healthy volunteers (HV; N = 52). Field strength/sequence 1.5 and 3 T/balanced steady-state free-precession (bSSFP) sequence (cine imaging), T2-prepared bSSFP sequence (T2 mapping), and velocity sensitized gradient echo sequence (phase-contrast MRI). Assessment Aortic (Qs) and pulmonary (Qp) flow was measured by phase-contrast imaging, and the Qp/Qs ratio was calculated as a measure of shunt severity. T2 maps were used to measure T2 in the RV and LV and the RV/LV T2 ratio was calculated. Cine imaging was used to calculate RV end-diastolic volume index (RV-EDVi). Statistical tests Wilcoxon test, paired t-tests, Spearmen correlation coefficient, receiver operating curve (ROC) analysis. Significance level P Results The Qp/Qs and T2 ratios in L-R shunt patients (1.84 ± 0.84 and 0.89 ± 0.07) were significantly higher compared to those in patients with RV disease (1.01 ± 0.03 and 0.72 ± 0.10) and in HV (1.04 ± 0.04 and 0.71 ± 0.09). A T2 ratio of >0.78 showed a sensitivity, specificity, and negative predictive value of 100%, 73.9%, and 100%, respectively, for the detection of L-R shunts. The T2 ratio was strongly correlated with the severity of the shunt (r = 0.83). Data conclusion RV/LV T2 ratio is an imaging biomarker that may be able to detect or rule-out L-R shunts. Such a diagnostic tool may prevent unnecessary phase-contrast acquisitions in cases with RV dilatation of unknown etiology. Level of evidence 3 TECHNICAL EFFICACY: Stage 2.

Aims To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. Methods and results We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen’s Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. Conclusion Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

PURPOSE: To evaluate the value of using left ventricular (LV) long-axis shortening (LAS) derived from coronary CT angiography (CCTA) to predict mortality in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS: Patients with severe AS who underwent CCTA for preprocedural TAVR planning between September 2014 and December 2019 were included in this retrospective study. CCTA covered the whole cardiac cycle in 10% increments. Image series reconstructed at end systole and end diastole were used to measure LV-LAS. All-cause mortality within 24 months of follow-up after TAVR was recorded. Cox regression analysis was performed, and hazard ratios (HRs) are presented with 95% CIs. The C index was used to evaluate model performance, and the likelihood ratio χ(2) test was performed to compare nested models. RESULTS: The study included 175 patients (median age, 79 years [IQR, 73–85 years]; 92 men). The mortality rate was 22% (38 of 175). When adjusting for predictive clinical confounders, it was found that LV-LAS could be used independently to predict mortality (adjusted HR, 2.83 [95% CI: 1.13, 7.07]; P = .03). In another model using the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), LV-LAS remained significant (adjusted HR, 3.38 [95 CI: 1.48, 7.72]; P = .004), and its use improved the predictive value of the STS-PROM, increasing the STS-PROM C index from 0.64 to 0.71 (χ(2) = 29.9 vs 19.7, P = .001). In a subanalysis of patients with a normal LV ejection fraction (LVEF), the significance of LV-LAS persisted (adjusted HR, 3.98 [95 CI: 1.56, 10.17]; P = .004). CONCLUSION: LV-LAS can be used independently to predict mortality in patients undergoing TAVR, including those with a normal LVEF. Keywords: CT Angiography, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Outcomes Analysis, Cardiomyopathies, Left Ventricle, Aortic Valve Supplemental material is available for this article. © RSNA, 2022 See also the commentary by Everett and Leipsic in this issue.

To evaluate the potential clinical benefit of the superior spatial resolution of 3D prototype thin-slab stack-of-stars (tsSOS) quiescent-interval slice-selective (QISS) MRA over standard 2D-QISS MRA for the detection peripheral artery disease (PAD), using computed tomography angiography (CTA) as reference. Twenty-three patients (70 ± 8 years, 18 men) with PAD who had previously undergone run-off CTA were prospectively enrolled. Patients underwent non-contrast MRA using 2D-QISS and tsSOS-QISS at 1.5 T. Eighteen arterial segments were evaluated for subjective and objective image quality (normalized signal-to-noise, nSNR), vessel sharpness, and area under the curve (AUC) for > 50% stenosis detection. Overall subjective image quality ratings for the entire run-off were not different between tsSOS-QISS and 2D-QISS (3 [3; 4] vs 4 [3; 4], respectively; P = 0.813). Sharpness of primary branch vessels demonstrated improved image quality using tsSOS-QISS compared with 2D-QISS (4 [3; 4] vs 3 [2; 3], P = 0.008). Objective image quality measures were not different between 2D-QISS and tsSOS-QISS (nSNR 5.0 ± 1.9 vs 4.2 ± 1.8; P = 0.132). AUCs for significant stenosis detection by tsSOS-QISS and 2D-QISS were 0.877 and 0.856, respectively (P = 0.336). The prototype 3D tsSOS-QISS technique provides similar accuracy in patients with PAD to a standard commercially available 2D-QISS technique, indicating that the use of relatively thick slices does not limit the diagnostic performance of 2D-QISS. However, subjective image quality for branch vessel depiction is improved using the 3D approach.

Background To assess the prognostic implications of cardiac magnetic resonance imaging (CMR) in patients with clinical suspicion of myocardial infarction with non-obstructed coronary arteries (MINOCA). Methods A total of 145 patients (58 ± 15 years, 97 men) were retrospectively enrolled in this single-center, longitudinal observational study. All patients underwent CMR including cine, edema-sensitive, and late gadolinium enhancement acquisitions, within a median of 3 days after cardiac catheterization. Follow-up was performed by medical records chart review and phone interviews; the median follow-up time was 4.2 years. The primary endpoint was defined as a combination of death, stroke, new onset of congestive heart failure, recurrent hospitalization, or the need for an invasive cardiac procedure. Results In 143 (98.6%) cases, CMR revealed the following cardiac pathologies: myocarditis (n = 48, 33.1%), structural cardiomyopathies (n = 40, 27.6%), “true” myocardial infarction (n = 22, 15.1%), hypertensive heart disease (n = 19, 13.1%), and Tako-Tsubo cardiomyopathy (n = 14, 9.7%). Only two patients (1.4%) had a normal CMR examination. There were significant prognostic differences between different etiologies, e.g. myocarditis and Tako-Tsubo cardiomyopathy had a more favorable prognosis then structural cardiomyopathies. Age, end-diastolic volume index and time-to-CMR showed significant association with the primary endpoint in multi-variate Cox regression. Conclusions CMR performed early after the onset of clinical symptoms allows discrimination between acute myocardial injury from “true” MINOCA in patients presenting with chest pain and elevated cardiac biomarkers, thereby helping to identify the underlying pathology in suspected MINOCA and allowing risk stratification based on the established diagnosis. Furthermore, CMR parameters allow for improved prediction of adverse events compared to clinical and laboratory parameters.

Computed tomography angiography (CTA) of the aorta and the iliofemoral arteries is crucial for preprocedural planning of transcatheter aortic valve implantation (TAVI) in patients with severe aortic stenosis (AS), because it provides details on a variety of aspects required for heart team decision-making. In addition to providing relevant diagnostic information on the degree of aortic valve calcification, CTA allows for a customized choice of the transcatheter heart valve system and the TAVI access route. Furthermore, current guidelines recommend the exclusion of relevant coronary artery disease (CAD) prior to TAVI. The feasibility of coronary artery assessment with CTA in patients scheduled for TAVI has been established previously, and accumulating data support its value. In addition, fractional flow reserve determined from CTA (CT–FFR) and machine learning-based CT–FFR were recently shown to improve its diagnostic yield for this purpose. However, the utilization of CTA for coronary artery evaluation remains limited in this specific population of patients due to the relatively high risk of CAD coexistence with severe AS. Therefore, the current diagnostic work-up prior to TAVI routinely includes invasive catheter coronary angiography at most centers. In this article, the authors address technological prerequisites and CT protocol considerations, discuss pitfalls, review the current literature regarding combined CTA coronary artery assessment and preprocedural TAVI evaluation, and provide an overview of unanswered questions and future research goals within the field.

To investigate the utility of coronary CT angiography–derived fractional flow reserve (FFRCT) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE). This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death. FFRCT and plaque parameters were analyzed on a per-vessel and per-patient basis. Univariable and multivariable COX regression analysis determined predictors of MACE. The prognostic value of FFRCT and plaque progression were assessed in nested models. Two hundred eighty-four patients (median age, 61 years (interquartile range, 54–70); 202 males) were evaluated. MACE was observed in 45 patients (15.8%, 45/284). By Cox multivariable regression modeling, vessel-specific FFRCT ≤ 0.80 was associated with a 2.4-fold increased risk of MACE (HR (95% CI): 2.4 (1.3–4.4); p = 0.005) and plaque progression was associated with a 9-fold increased risk of MACE (HR (95% CI): 9 (3.5–23); p < 0.001) after adjusting for clinical and imaging risk factors. FFRCT and plaque progression improved the prediction of events over coronary artery calcium (CAC) score and high-risk plaques (HRP) in the receiver operating characteristics analysis (area under the curve: 0.70 to 0.86; p = 0.002). Fractional flow reserve and plaque progression assessed by serial coronary CT angiography predicted the risk of future MACE. • Vessel-specific CT angiography–derived fractional flow reserve (FFR CT ) ≤ 0.80 and plaque progression improved the prediction of events over current risk factors. • Major adverse cardiovascular events (MACE) significantly increased with the presence of plaque progression at follow-up stratified by the FFR CT change group.

To determine the functional connectome change pattern based on subregions of the hippocampus in young APOEe4 carriers during a 3-year follow-up. All the participants (n = 213) were tested for resting-state functional MRI, neuropsychological scales, and APOE genotype. The age- and sex-matched APOE e4/e3 (23.9 ± 3.2 years old, 6 female/7 male) carriers and APOE e3/e3 (22.9 ± 1.6 years old, 7 female/12 male) carriers were finally followed up. The hippocampus and its anterior/middle/posterior subregion-based functional connectivity (FC) patterns were compared between APOEe4 and APOEe3 groups by a two-sample t-test at baseline and mixed-effect analysis at follow-up. The effective connectivity (EC) patterns among the altered regions of interaction effect were examined in the APOEe4 groups. APOEe4 carries displayed saliently enhanced FC in the right anterior/middle hippocampus and core regions of the default mode network (DMN) (P < 0.05 by Gaussian Random Fields (GRF) correction). However, the APOEe4-by-time interaction was evident in the middle/posterior hippocampus with connection to the lateral temporal lobe and anterior cingulate cortex (ACC) (P < 0.05 by GRF correction). Moreover, the APOEe4 group at follow-up showed increased EC separately from both the left middle hippocampus and lateral temporal lobe to the left posterior hippocampus, and its changes of FC/EC significantly correlated with altered memory function. The posterior hippocampus might be especially vulnerable to early modulation in young APOEe4 carriers. Its connection with the lateral temporal lobe, rather than with DMN, might be the early compensative mechanism of memory function regulation influenced by APOE e4 in the young adults.

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has adversely affected diagnosis and treatment of noncommunicable diseases. Its effects on delivery of diagnostic care for cardiovascular disease, which remains the leading cause of death worldwide, have not been quantified. OBJECTIVES The study sought to assess COVID-19`s impact on global cardiovascular diagnostic procedural volumes and safety practices. METHODS The International Atomic Energy Agency conducted a worldwide survey assessing alterations in cardiovascular procedure volumes and safety practices resulting from COVID-19. Noninvasive and invasive cardiac testing volumes were obtained from participating sites for March and April 2020 and compared with those from March 2019. Availability of personal protective equipment and pandemic-related testing practice changes were ascertained. RESULTS Surveys were submitted from 909 inpatient and outpatient centers performing cardiac diagnostic procedures, in 108 countries. Procedure volumes decreased 42% from March 2019 to March 2020, and 64% from March 2019 to April 2020. Transthoradc echocardiography decreased by 59%, transesophageat echocardiography 76%, and stress tests 78%, which varied between stress modalities. Coronary angiography (invasive or computed tomography) decreased 55% (p < 0.001 for each procedure). hi multivariable regression, significantly greater reduction in procedures occurred for centers in countries with lower gross domestic product. Location in a low-income and lower-middle-income country was associated with an additional 22% reduction in cardiac procedures and less availability of personal protective equipment and teteheatth. CONCLUSIONS COVID-19 was associated with a significant and abrupt reduction in cardiovascular diagnostic testing across the globe, especially affecting the world's economically challenged. Further study of cardiovascular outcomes and COVID-19-related changes in care delivery is warranted. (C) 2021 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.

BACKGROUND Fat deposition in the liver and the skeletal muscle are linked to cardiovascular risk factors. Fat content in tissues can be estimated by measuring attenuation on noncontrast computed tomography (CT). Quantifying intramyocardial fat content is of interest as it may be related to myocardial dysfunction or development of heart failure. We hypothesized that myocardial fat content would correlate with severity of obesity, liver fat, and components of the metabolic syndrome. METHODS We measured attenuation values on 121 noncontrast CT scans from the spleen, liver, erector spinae muscle, and myocardial septum. A chart review was performed for patient demographics and clinical characteristics. We tested for correlations between attenuation values in each of the tissues and various clinical parameters. RESULTS We studied 78 females and 43 males, with a mean age of 54.5±11.2 years. Weak, but significant inverse Spearman correlation between body mass index and attenuation values were found in the liver (ρ=-0.228, P=0.012), spleen (ρ=-0.225, P=0.017), and erector spinae muscle (ρ=-0.211, P=0.022) but not in the myocardial septum (ρ=0.012, P=0.897). Mean attenuation in the nonobese group versus obese group (body mass index >30 kg/m2) were 41.1±5.0 versus 42.3±6.9 (P=0.270) in myocardial septum, 56.1±8.7 versus 51.7±10.9 (P=0.016) in the liver, 43.9±8.9 versus 40.1±10.4 (P=0.043) in the spleen, and 41.7±8.3 versus 39.0±8.8 (P=0.087) in the erector spinae muscle. CONCLUSIONS Although CT is a theoretically appealing modality to assess fat content of the myocardium, we did not find a relationship between myocardial CT attenuation and obesity, or other cardiovascular risk factors. These findings suggest that the degree of myocardial fat accumulation in obesity or metabolic syndrome is too small to be detected with this modality.

In this review, we aim to assess previous radiologic studies in COVID-19 and suggest a pulmonary pathogenesis based on radiologic findings. Although radiologic features are not specific and there is heterogeneity in symptoms and radiologic and clinical manifestation, we suggest that the dominant pattern of computed tomography is consistent with limited pneumonia, followed by interstitial pneumonitis and organizing pneumonia.

Transcatheter tricuspid valve therapies are an emerging field in structural heart interventions due to the rising number of patients with severe tricuspid regurgitation and the high risk for surgical treatment. Computed tomography (CT) allows exact measurements of the annular plane, evaluation of adjacent structures, assessment of the access route, and can also be used to identify optimal fluoroscopic projection planes to enhance periprocedural imaging. This review provides an overview of current transcatheter tricuspid valve repair and replacement therapies and to what extent CT can support these interventions.

OBJECTIVE. The purpose of this study was to prospectively evaluate, using software support, the feasibility and the quantitative and qualitative image quality parameters of a tube voltage-tailored contrast medium (CM) application protocol for patient-specific injection during coronary CT angiography (CCTA). SUBJECTS AND METHODS. In the Voltage-Based Contrast Media Adaptation in Coronary Computed Tomography Angiography (VOLCANIC-CTA) study, a single-center trial, 120 patients referred for CCTA were prospectively assigned to a tube voltage-tailored CM injection protocol. Automated tube voltage levels were selected in 10-kV intervals and ranged from 70 to 130 kV, and the iodine delivery rate (IDR) was adapted to the tube voltage level using dedicated software. The administered CM volume (370 mg I/mL) ranged from 33 mL at 70 kV (IDR, 0.7 g I/s) to 65 mL at 130 kV (IDR, 1.7 g I/s). Attenuation was measured in the aorta and coronary arteries to calculate quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point scales were used to evaluate overall image quality. Radiation metrics were also assessed and compared among the protocols. RESULTS. The mean age of the study patients was 62.5 ± 11.9 (SD) years. Image quality was rated as diagnostic in all patients. Contrast attenuation peaked at 70 kV (p < 0.001), whereas SNR and CNR parameters showed no significant differences between tube voltage levels (p ≥ 0.085). Additionally, no significant differences in subjective image quality parameters were found among the different protocols (p ≥ 0.139). The lowest radiation dose values were observed in the group assigned to the 70-kV protocol, which had a median radiation effective dose of 2.0 mSv (p < 0.001). CONCLUSION. The proposed tube voltage-tailored injection protocol allows individualized scanning of patients undergoing CCTA and significantly reduces CM and radiation dose while maintaining a high diagnostic image quality.

The proton density–weighted, in-phase stack-of-stars (PDIP-SOS) MRI technique provides calcification visualization in peripheral artery disease (PAD). This study sought to investigate the diagnostic accuracy of a combined non-contrast quiescent-interval slice-selective (QISS) MRA and PDIP-SOS MRI protocol for the detection of PAD, in comparison with CTA and digital subtraction angiography (DSA). Twenty-six prospectively enrolled PAD patients (70 ± 8 years) underwent lower extremity CTA and 1.5-T or 3-T PDIP-SOS/QISS MRI prior to DSA. Two readers rated image quality and graded stenosis (≥ 50%) on QISS MRA without/with calcification visualization. Sensitivity, specificity, and area under the curve (AUC) were calculated against DSA. Calcification was quantified and compared between MRI and non-contrast CT (NCCT) using paired t test, Pearson’s correlation, and Bland-Altman analysis. Image quality ratings were significantly higher for CTA compared to those for MRA (4.0 [3.0–4.0] and 3.0 [3.0–4.0]; p = 0.0369). The sensitivity and specificity of QISS MRA, QISS MRA with PDIP-SOS, and CTA for ≥ 50% stenosis detection were 85.4%, 92.2%, and 90.2%, and 90.3%, 93.2%, and 94.2%, respectively, while AUCs were 0.879, 0.928, and 0.923, respectively. A significant increase in AUC was observed when PDIP-SOS was added to the MRA protocol (p = 0.0266). Quantification of calcification showed significant differences between PDIP-SOS and NCCT (80.6 ± 31.2 mm3 vs. 88.0 ± 29.8 mm3; p = 0.0002) with high correlation (r = 0.77, p