Implications of coronary calcification on the assessment of plaque pathology: a comparison of computed tomography and multimodality intravascular imaging.

OBJECTIVES: This study aimed to investigate the impact of calcific (Ca) on the efficacy of coronary computed coronary angiography (CTA) in evaluating plaque burden (PB) and composition with near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) serving as the reference standard. MATERIALS AND METHODS: Sixty-four patients (186 vessels) were recruited and underwent CTA and 3-vessel NIRS-IVUS imaging (NCT03556644). Expert analysts matched and annotated NIRS-IVUS and CTA frames, identifying lumen and vessel wall borders. Tissue distribution was estimated using NIRS chemograms and the arc of Ca on IVUS, while in CTA Hounsfield unit cut-offs were utilized to establish plaque composition. Plaque distribution plots were compared at segment-, lesion-, and cross-sectional-levels. RESULTS: Segment- and lesion-level analysis showed no effect of Ca on the correlation of NIRS-IVUS and CTA estimations. However, at the cross-sectional level, Ca influenced the agreement between NIRS-IVUS and CTA for the lipid and Ca components (p-heterogeneity < 0.001). Proportional odds model analysis revealed that Ca had an impact on the per cent atheroma volume quantification on CTA compared to NIRS-IVUS at the segment level (p-interaction < 0.001). At lesion level, Ca affected differences between the modalities for maximum PB, remodelling index, and Ca burden (p-interaction < 0.001, 0.029, and 0.002, respectively). Cross-sectional-level modelling demonstrated Ca's effect on differences between modalities for all studied variables (p-interaction ≤ 0.002). CONCLUSION: Ca burden influences agreement between NIRS-IVUS and CTA at the cross-sectional level and causes discrepancies between the predictions for per cent atheroma volume at the segment level and maximum PB, remodelling index, and Ca burden at lesion-level analysis. CLINICAL RELEVANCE STATEMENT: Coronary calcification affects the quantification of lumen and plaque dimensions and the characterization of plaque composition coronary CTA. This should be considered in the analysis and interpretation of CTAs performed in patients with extensive Ca burden. KEY POINTS: Coronary CT Angiography is limited in assessing coronary plaques by resolution and blooming artefacts. Agreement between dual-source CT angiography and NIRS-IVUS is affected by a Ca burden for the per cent atheroma volume. Advanced CT imaging systems that eliminate blooming artefacts enable more accurate quantification of coronary artery disease and characterisation of plaque morphology.

Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease.

Background: Coronary artery calcium score (CACS) is an established marker of coronary artery disease (CAD) and has been extensively used to stratify risk in asymptomatic individuals. However, the value of CACS in predicting plaque morphology in patients with advanced CAD is less established. The present analysis aims to assess the association between CACS and plaque characteristics detected by near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) imaging in patients with obstructive CAD. Methods: Seventy patients with obstructive CAD underwent coronary computed tomography angiography (CTA) and 3-vessel NIRS-IVUS imaging were included in the present analysis. The CTA data were used to measure the CACS in the entire coronary tree and the segments assessed by NIRS-IVUS, and these estimations were associated with the NIRS-IVUS measurements at a patient and segment level. Results: In total, 65 patients (188 segments) completed the study protocol and were included in the analysis. A weak correlation was noted between the CACS, percent atheroma volume (r = 0.271, P = .002), and the calcific burden measured by NIRS-IVUS (r = 0.648, P < .001) at patient-level analysis. Conversely, there was no association between the CACS and the lipid content, or the incidence of high-risk plaques detected by NIRS. Linear regression analysis at the segment level demonstrated an association between the CACS and the total atheroma volume (coefficient, 0.087; 95% CI, 0.024-0.149; P = .008) and the calcific burden (coefficient, 0.117; 95% CI, 0.048-0.186; P = .001), but there was no association between the lipid content or the incidence of high-risk lesions. Conclusions: In patients with obstructive CAD, the CACS is not associated with the lipid content or plaque phenotypes. These findings indicate that the CACS may have a limited value for screening or stratifying cardiovascular risk in symptomatic patients with a high probability of CAD.

Advanced CT Imaging for the Assessment of Calcific Coronary Artery Disease and PCI Planning.

Vascular calcification is a hallmark of atherosclerosis and adds considerable challenges for percutaneous coronary intervention (PCI). This review underscores the critical role of coronary computed tomography (CT) angiography in assessing and quantifying vascular calcification for optimal PCI planning. Severe calcification significantly impacts procedural outcomes, necessitating accurate preprocedural evaluation. We describe the potential of coronary CT for calcium assessment and how CT may enhance precision in device selection and procedural strategy. These advancements, along with the ongoing Precise Procedural and PCI Plan study, represent a transformative shift toward personalized PCI interventions, ultimately improving patient outcomes in the challenging landscape of calcified coronary lesions.

Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.

There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology. METHODS: Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods. RESULTS: For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method. CONCLUSION: The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging.

BACKGROUND: Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard. METHODS: Fifty-one participants (150 vessels) with chronic coronary syndrome who underwent CCTA and 3-vessel NIRS-IVUS were included. CCTA analysis was performed by an expert and an automated edge detection method and their estimations were compared to NIRS-IVUS at a segment-, lesion-, and frame-level. RESULTS: Segment-level analysis demonstrated a similar performance of the two CCTA analyses (conventional and automatic) with large biases and limits of agreement compared to NIRS-IVUS estimations for the total atheroma (ICC: 0.55 vs 0.25, mean difference:192 (-102-487) vs 243 (-132-617) and percent atheroma volume (ICC: 0.30 vs 0.12, mean difference: 12.8 (-5.91-31.6) vs 20.0 (0.79-39.2). Lesion-level analysis showed that the experts were able to detect more accurately lesions than the automated method (68.2 â€‹% and 60.7 â€‹%) however both analyses had poor reliability in assessing the minimal lumen area (ICC 0.44 vs 0.36) and the maximum plaque burden (ICC 0.33 vs 0.33) when NIRS-IVUS was used as the reference standard. CONCLUSIONS: Conventional and automated CCTA analyses had similar performance in assessing coronary artery pathology using NIRS-IVUS as a reference standard. Therefore, automated segmentation can be used to expedite CCTA analysis and enhance its applications in clinical practice.

Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention.

The role of coronary CT angiography for the diagnosis and risk stratification of coronary artery disease is well established. However, its potential beyond the diagnostic phase remains to be determined. The current review focuses on the insights that coronary CT angiography can provide when planning and performing percutaneous coronary interventions. We describe a novel approach incorporating anatomical and functional pre-procedural planning enhanced by artificial intelligence, computational physiology and online 3D CT guidance for percutaneous coronary interventions. This strategy allows the individualisation of patient selection, optimisation of the revascularisation strategy and effective use of resources.

Reference Values for Inward Displacement in the Normal Left Ventricle: A Novel Method of Regional Left Ventricular Function Assessment.

BACKGROUND: Regional functional left ventricular (LV) assessment using current imaging techniques remains limited. Inward displacement (InD) has been developed as a novel technique to assess regional LV function via measurement of the regional displacement of the LV endocardial border across each of the 17 LV segments. Currently, normal ranges for InD are not available for clinical use. The aim of this study was to validate the normal reference limits of InD in healthy adults across all LV segments. METHODS: InD was analyzed in 120 healthy subjects with a normal LV ejection fraction, using the three standard long-axis views obtained during cardiac MRI that quantified the degree of inward endocardial wall motion towards the true LV center of contraction. For all LV segments, InD was measured in mm and expressed as a percentage of the theoretical degree of maximal segment contraction towards the true LV centerline. The arithmetic average InD was obtained for each of the 17 segments. The LV was divided into three regions, obtaining average InD at the LV base (segments 1-6), mid-cavity (segments 7-12) and apex (segments 13-17). RESULTS: Average InD was 33.4 ± 4.3%. InD was higher in basal and mid-cavity LV segments (32.8 ± 4.1% and 38.1 ± 5.8%) compared to apical LV segments (28.6 ± 7.7%). Interobserver variability correlations for InD were strong (R = 0.80, p < 0.0001). CONCLUSIONS: We provide clinically meaningful reference ranges for InD in subjects with normal LV function, which will emerge as an important screening and assessment imaging tool for a range of HFrEF therapies.

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

Aims: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Methods and results: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. Conclusions: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography.

Background: Plaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and results: The coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84-0.97). The mean percentage difference for plaque volumes was -0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p < 0.001) and total low attenuation plaque volume (rho = 0.29, p < 0.001). Conclusions: We have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.

Pericoronary Adipose Tissue Attenuation in Patients With Acute Coronary Syndrome Versus Stable Coronary Artery Disease.

BACKGROUND: Pericoronary adipose tissue (PCAT) attenuation has been associated with coronary inflammation and can be evaluated with coronary computed tomography angiography. The aims of this study were to compare the PCAT attenuation across precursors of culprit and nonculprit lesions of patients with acute coronary syndrome versus stable coronary artery disease (CAD). METHODS: In this case-control study, patients with suspected CAD who underwent coronary computed tomography angiography were included. Patients who developed an acute coronary syndrome within 2 years after the coronary computed tomography angiography scan were identified, and patients with stable CAD (defined as any coronary plaque ≥30% luminal diameter stenosis) were 1:2 propensity score matched for age, sex, and cardiac risk factors. The mean PCAT attenuation was analyzed at lesion level and compared between precursors of culprit lesions, nonculprit lesions, and stable coronary plaques. RESULTS: In total, 198 patients (age 62±10 years, 65% male) were selected, including 66 patients who developed an acute coronary syndrome and 132 propensity matched patients with stable CAD. Overall, 765 coronary lesions were analyzed (culprit lesion precursors: n=66; nonculprit lesion precursors: n=207; and stable lesions: n=492). Culprit lesion precursors had larger total plaque volume, fibro-fatty plaque volume, and low-attenuation plaque volume compared to nonculprit and stable lesions. The mean PCAT attenuation was significantly higher across culprit lesion precursors compared to nonculprit and stable lesions (-63.8±9.7 Hounsfield units versus -68.8±10.6 Hounsfield units versus -69.6±10.6 Hounsfield units, respectively; P<0.001), whereas the mean PCAT attenuation around nonculprit and stable lesions was not significantly different (P=0.99). CONCLUSIONS: The mean PCAT attenuation is significantly increased across culprit lesion precursors in patients with acute coronary syndrome, compared to nonculprit lesions of these patients and to lesions of patients with stable CAD, which may suggest a higher intensity of inflammation. PCAT attenuation on coronary computed tomography angiography may be a novel marker to identify high-risk plaques.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound.

BACKGROUND: Advances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. METHODS: Matched cross-sections (n â€‹= â€‹7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 â€‹mm and 0.50 â€‹mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. RESULTS: Segment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 â€‹mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p â€‹< â€‹0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p â€‹< â€‹0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64-0.67; p â€‹= â€‹0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p â€‹< â€‹0.001 and plaque burden ICC: 0.45, RC: 32.0, p â€‹< â€‹0.001). CONCLUSION: CCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. CLINICALTRIALS: gov (NCT03556644).

Vessel and sex differences in pericoronary adipose tissue attenuation obtained with coronary CT in individuals without coronary atherosclerosis.

Pericoronary adipose tissue (PCAT) attenuation, derived from coronary computed tomography angiography (CCTA), is associated with coronary artery inflammation. Values for PCAT attenuation in men and women without atherosclerosis on CCTA are lacking. The aim of the current study was to assess the mean PCAT attenuation in individuals without coronary artery atherosclerosis on CCTA. Data on PCAT attenuation in men and women without coronary artery atherosclerosis on CCTA were included in this retrospective analysis. The PCAT attenuation was analyzed from the proximal part of the right coronary artery (RCA), the left anterior descending artery (LAD), and the left circumflex artery (LCx). For patient level analyses the mean PCAT attenuation was defined as the mean of the three coronary arteries. In 109 individuals (mean age 45 ± 13 years; 44% men), 320 coronary arteries were analyzed. The mean PCAT attenuation of the overall population was - 64.4 ± 8.0 HU. The mean PCAT attenuation was significantly lower in the LAD compared with the LCx and RCA (- 67.8 ± 7.8 HU vs - 62.6 ± 6.8 HU vs - 63.6 ± 7.9 HU, respectively, p < 0.001). In addition, the mean PCAT attenuation was significantly higher in men vs. women in all three coronary arteries (LAD: - 65.7 ± 7.6 HU vs - 69.4 ± 7.6 HU, p = 0.014; LCx: - 60.6 ± 7.4 HU vs - 64.3 ± 5.9 HU, p = 0.008; RCA: -61.7 ± 7.9 HU vs - 65.0 ± 7.7 HU, p = 0.029, respectively). The current study provides mean PCAT attenuation values, derived from individuals without CAD. Moreover, the mean PCAT attenuation is lower in women vs. men. Furthermore, the mean PCAT attenuation is significantly lower in the LAD vs LCx and RCA.

Association Between Changes in Perivascular Adipose Tissue Density and Plaque Progression.

BACKGROUND: The association between the change in vessel inflammation, as quantified by perivascular adipose tissue (PVAT) density, and the progression of coronary atherosclerosis remains to be determined. OBJECTIVES: The purpose of this study was to explore the association between the change in PVAT density and the progression of total and compositional plaque volume (PV). METHODS: Patients were selected from a prospective multinational registry. Patients who underwent serial coronary computed tomography angiography studies with ≥2-year intervals and were scanned with the same tube voltage at baseline and follow-up were included. Total and compositional PV and PVAT density at baseline and follow-up were quantitatively analyzed for every lesion. Multivariate linear regression models using cluster analyses were constructed. RESULTS: A total of 1,476 lesions were identified from 474 enrolled patients (mean age 61.2 ± 9.3 years; 65.0% men). The mean PVAT density was -74.1 ± 11.5 HU, and total PV was 48.1 ± 83.5 mm3 (19.2 ± 44.8 mm3 of calcified PV and 28.9 ± 51.0 mm3 of noncalcified PV). On multivariate analysis (adjusted for clinical risk factors, medication use, change in lipid levels, total PV at baseline, luminal HU attenuation, location of lesions, and tube voltage), the increase in PVAT density was positively associated with the progression of total PV (estimate = 0.275 [95% CI: 0.004-0.545]; P = 0.047), driven by the association with fibrous PV (estimate = 0.245 [95% CI: 0.070-0.420]; P = 0.006). Calcified PV progression was not associated with the increase in PVAT density (P > 0.050). CONCLUSIONS: Increase in vessel inflammation represented by PVAT density is independently associated with the progression of the lipid component of coronary atherosclerotic plaques. (Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging [PARADIGM]; NCT02803411).

Abnormal Diastolic Hemodynamic Forces: A Link Between Right Ventricular Wall Motion, Intracardiac Flow, and Pulmonary Regurgitation in Repaired Tetralogy of Fallot.

Background and Objective: The effect of chronic pulmonary regurgitation (PR) on right ventricular (RV) dysfunction in repaired Tetralogy of Fallot (RTOF) patients is well recognized by cardiac magnetic resonance (CMR). However, the link between RV wall motion, intracardiac flow and PR has not been established. Hemodynamic force (HDF) represents the global force exchanged between intracardiac blood volume and endocardium, measurable by 4D flow or by a novel mathematical model of wall motion. In our study, we used this novel methodology to derive HDF in a cohort of RTOF patients, exclusively using routine CMR imaging. Methods: RTOF patients and controls with CMR imaging were retrospectively included. Three-dimensional (3D) models of RV were segmented, including RV outflow tract (RVOT). Feature-tracking software (QStrain 2.0, Medis Medical Imaging Systems, Leiden, Netherlands) captured endocardial contours from long/short-axis cine and used to reconstruct RV wall motion. A global HDF vector was computed from the moving surface, then decomposed into amplitude/impulse of three directional components based on reference (Apical-to-Basal, Septal-to-Free Wall and Diaphragm-to-RVOT direction). HDF were compared and correlated against CMR and exercise stress test parameters. A subset of RTOF patients had 4D flow that was used to derive vorticity (for correlation) and HDF (for comparison against cine method). Results: 68 RTOF patients and 20 controls were included. RTOF patients had increased diastolic HDF amplitude in all three directions (p<0.05). PR% correlated with Diaphragm-RVOT HDF amplitude/impulse (r = 0.578, p<0.0001, r = 0.508, p < 0.0001, respectively). RV ejection fraction modestly correlated with global HDF amplitude (r = 0.2916, p = 0.031). VO2-max correlated with Septal-to-Free Wall HDF impulse (r = 0.536, p = 0.007). Diaphragm-to-RVOT HDF correlated with RVOT vorticity (r = 0.4997, p = 0.001). There was no significant measurement bias between Cine-derived HDF and 4D flow-derived HDF by Bland-Altman analysis. Conclusion: RTOF patients have abnormal diastolic HDF that is correlated to PR, RV function, exercise capacity and vorticity. HDF can be derived from conventional cine, and is a potential link between RV wall motion and intracardiac flow from PR in RTOF patients.

An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data.

BACKGROUND: Wall shear stress (WSS) estimated in 3D-quantitative coronary angiography (QCA) models appears to provide useful prognostic information and identifies high-risk patients and lesions. However, conventional computational fluid dynamics (CFD) analysis is cumbersome limiting its application in the clinical arena. This report introduces a user-friendly software that allows real-time WSS computation and examines its reproducibility and accuracy in assessing WSS distribution against conventional CFD analysis. METHODS: From a registry of 414 patients with borderline negative fractional flow reserve (0.81-0.85), 100 lesions were randomly selected. 3D-QCA and CFD analysis were performed using the conventional approach and the novel CAAS Workstation WSS software, and QCA as well as WSS estimations of the two approaches were compared. The reproducibility of the two methodologies was evaluated in a subgroup of 50 lesions. RESULTS: A good agreement was noted between the conventional approach and the novel software for 3D-QCA metrics (ICC range: 0.73-0-93) and maximum WSS at the lesion site (ICC: 0.88). Both methodologies had a high reproducibility in assessing lesion severity (ICC range: 0.83-0.97 for the conventional approach; 0.84-0.96 for the CAAS Workstation WSS software) and WSS distribution (ICC: 0.85-0.89 and 0.83-0.87, respectively). Simulation time was significantly shorter using the CAAS Workstation WSS software compared to the conventional approach (4.13 ± 0.59 min vs 23.14 ± 2.56 min, p < 0.001). CONCLUSION: CAAS Workstation WSS software is fast, reproducible, and accurate in assessing WSS distribution. Therefore, this software is expected to enable the broad use of WSS metrics in the clinical arena to identify high-risk lesions and vulnerable patients.

Wall shear stress estimated by 3D-QCA can predict cardiovascular events in lesions with borderline negative fractional flow reserve.

BACKGROUND AND AIMS: There is some evidence of the implications of wall shear stress (WSS) derived from three-dimensional quantitative coronary angiography (3D-QCA) models in predicting adverse cardiovascular events. This study investigates the efficacy of 3D-QCA-derived WSS in detecting lesions with a borderline negative fractional flow reserve (FFR: 0.81-0.85) that progressed and caused events. METHODS: In this retrospective cohort study, we identified 548 patients who had at least one lesion with an FFR 0.81-0.85 and complete follow-up data; 293 lesions (286 patients) with suitable angiographic characteristics were reconstructed using a dedicated 3D-QCA software and included in the analysis. In the reconstructed models blood flow simulation was performed and the value of 3D-QCA variables and WSS distribution in predicting events was examined. The primary endpoint of the study was the composite of cardiac death, target lesion related myocardial infarction or clinically indicated target lesion revascularization. RESULTS: During a median follow-up of 49.4 months, 37 events were reported. Culprit lesions had a greater area stenosis [(AS), 66.1% (59.5-72.3) vs 54.8% (46.5-63.2), p<0.001], smaller minimum lumen area [(MLA), 1.66 mm2 (1.45-2.30) vs 2.10 mm2 (1.69-2.70), p=0.011] and higher maximum WSS [9.0 Pa (5.10-12.46) vs 5.0 Pa (3.37-7.54), p < 0.001] than those that remained quiescent. In multivariable analysis, AS [hazard ratio (HR): 1.06, 95% confidence interval (CI): 1.03-1.10, p=0.001] and maximum WSS (HR: 1.08, 95% CI: 1.02-1.14, p=0.012) were the only independent predictors of the primary endpoint. Lesions with an increased AS (≥58.6%) that were exposed to high WSS (≥7.69Pa) were more likely to progress and cause events (27.8%) than those with a low AS exposed to high WSS (7.4%) or those exposed to low WSS that had increased (12.8%) or low AS (2.7%, p<0.001). CONCLUSIONS: This study for the first time highlights the potential value of 3D-QCA-derived WSS in detecting, among lesions with a borderline negative FFR, those that cause cardiovascular events.

Quantified coronary total plaque volume from computed tomography angiography provides superior 10-year risk stratification.

AIMS: Automated coronary total plaque volume (TPV) quantification derived from coronary computed tomographic angiography (CTA) datasets provide exact and reliable assessment of calcified and non-calcified coronary atherosclerosis burden. The aim of this analysis was to investigate the long-term predictive value of TPV. METHODS AND RESULTS: TPV was quantified in 1577 patients undergoing coronary CTA and cardiovascular events were collected during 10.5 years (interquartile range 6.0-11.4) of follow-up. The study endpoint comprised cardiac death and acute coronary syndrome and occurred in 59 (3.7%) patients. Coronary TPV provided additive prognostic value over clinical risk assessed with the Morise Score and coronary artery disease severity (rise in C-index from 0.744 to 0.769, P = 0.03). A category-based reclassification approach combining the Morise Score and TPV revealed superior risk stratification (categorical net reclassification improvement: 0.48 with 95% CI 0.13-0.68, P < 0.001) and resulted in reclassification of 800 (51%) patients compared with the Morise Score alone. The 10-year risk for the study endpoint was 0.6% (95% CI 0-1.3) for patients classified as low risk (n = 807), 4.8% (95% CI 2.4-7.2) for patients at intermediate risk (n = 400), and 10.3% (95% CI 6.6-13.9) for patients at high risk (n = 370) using the combined reclassification approach. CONCLUSION: Quantification of TPV from coronary CTA permits an improved 10-year cardiovascular risk stratification.