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.

Apixaban versus warfarin in evaluation of progression of atherosclerotic and calcified plaques (prospective randomized trial).

Warfarin has been showed to increase vascular calcification. Apixaban, a direct factor Xa inhibitor, has no interaction with vitamin K and its effect on coronary plaques is unknown. We randomized and compared warfarin and apixaban on progression of coronary atherosclerotic plaques measured by coronary computed tomographic angiography in 66 subjects with non-valvular atrial fibrillation over the period of one-year follow up. There was significant higher total, calcified and low attenuation plaque volume in the group randomized to warfarin as compared to apixaban (all P < .05). Greater volume of total (ß2 = 28.54; P = .03), low attenuation plaque (ß2 = 3.58; P = .02) and calcified (ß2 = 14.10; P = .005) plaque progression was observed in the VKA_group.

Aged Garlic Extract Reduces Low Attenuation Plaque in Coronary Arteries of Patients with Metabolic Syndrome in a Prospective Randomized Double-Blind Study.

BACKGROUND: Although several previous studies have demonstrated that aged garlic extract (AGE) inhibits the progression of coronary artery calcification, its effect on noncalcified plaque (NCP) has been unclear. OBJECTIVE: This study investigated whether AGE reduces coronary plaque volume measured by cardiac computed tomography angiography (CCTA) in patients with metabolic syndrome (MetS). METHODS: Fifty-five patients with MetS (mean ± SD age: 58.7 ± 6.7 y; 71% men) were prospectively assigned to consume 2400 mg AGE/d (27 patients) or placebo (28 patients) orally. Both groups underwent CCTA at baseline and follow-up 354 ± 41 d apart. Coronary plaque volume, including total plaque volume (TPV), dense calcium (DC), NCP, and low-attenuation plaque (LAP), were measured based upon predefined intensity cutoff values. Multivariable linear regression analysis, adjusted for age, gender, number of risk factors, hyperlipidemia medications, history of coronary artery disease, scan interval time, and baseline %TPV, was performed to examine whether AGE affected each plaque change. RESULTS: The %LAP change was significantly reduced in the AGE group compared with the placebo group (-1.5% ± 2.3% compared with 0.2% ± 2.0%, P = 0.0049). In contrast, no difference was observed in %TPV change (0.3% ± 3.3% compared with 1.6% ± 3.0%, P = 0.13), %NCP change (0.2% ± 3.3% compared with 1.4% ± 2.9%, P = 0.14), and %DC change (0.2% ± 1.4%, compared with 0.2% ± 1.7%, P = 0.99). Multivariable linear regression analysis found a beneficial effect of AGE on %LAP regression (ß: -1.61; 95% CI: -2.79, -0.43; P = 0.008). CONCLUSIONS: This study indicates that the %LAP change was significantly greater in the AGE group than in the placebo group. Further studies are needed to evaluate whether AGE has the ability to stabilize vulnerable plaque and decrease adverse cardiovascular events. This trial was registered at clinicaltrials.gov as NCT01534910.

Clinical Feasibility of 3D Automated Coronary Atherosclerotic Plaque Quantification Algorithm on Coronary Computed Tomography Angiography: Comparison with Intravascular Ultrasound.

OBJECTIVE: To evaluate the diagnostic performance of automated coronary atherosclerotic plaque quantification (QCT) by different users (expert/non-expert/automatic). METHODS: One hundred fifty coronary artery segments from 142 patients who underwent coronary computed tomography angiography (CCTA) and intravascular ultrasound (IVUS) were analyzed. Minimal lumen area (MLA), maximal lumen area stenosis percentage (%AS), mean plaque burden percentage (%PB), and plaque volume were measured semi-automatically by expert, non-expert, and fully automatic QCT analyses, and then compared to IVUS. RESULTS: Between IVUS and expert QCT analysis, the correlation coefficients (r) for the MLA, %AS, %PB, and plaque volume were excellent: 0.89 (p < 0.001), 0.84 (p < 0.001), 0.91 (p < 0.001), and 0.94 (p < 0.001), respectively. There were no significant differences in the mean parameters (all p values >0.05) except %AS (p = 0.01). The automatic QCT analysis showed comparable performance to non-expert QCT analysis, showing correlation coefficients (r) of the MLA (0.80 vs. 0.82), %AS (0.82 vs. 0.80), %PB (0.84 vs. 0.73), and plaque volume (0.84 vs. 0.79) when they were compared to IVUS, respectively. CONCLUSION: Fully automatic QCT analysis showed clinical utility compared with IVUS, as well as a compelling performance when compared with semiautomatic analyses. KEY POINTS: • Coronary CTA enables the assessment of coronary atherosclerotic plaque. • High-risk plaque characteristics and overall plaque burden can predict future cardiac events. • Coronary atherosclerotic plaque quantification is currently unfeasible in practice. • Quantitative computed tomography coronary plaque analysis software (QCT) enables feasible plaque quantification. • Fully automatic QCT analysis shows excellent performance.

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.

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.

Automated quantitative coronary computed tomography correlates of myocardial ischaemia on gated myocardial perfusion SPECT.

PURPOSE: Automated software tools have permitted more comprehensive, robust and reproducible quantification of coronary stenosis, plaque burden and plaque location of coronary computed tomography angiography (CTA) data. The association between these quantitative CTA (QCT) parameters and the presence of myocardial ischaemia has not been explored. The aim of the present investigation was to evaluate the association between QCT parameters of coronary artery lesions and the presence of myocardial ischaemia on gated myocardial perfusion single-photon emission CT (SPECT). METHODS: Included in the study were 40 patients (mean age 58.2 ± 10.9 years, 27 men) with known or suspected coronary artery disease (CAD) who had undergone multidetector row CTA and gated myocardial perfusion SPECT within 6 months. From the CTA datasets, vessel-based and lesion-based visual analyses were performed. Consecutively, lesion-based QCT was performed to assess plaque length, plaque burden, percentage lumen area stenosis and remodelling index. Subsequently, the presence of myocardial ischaemia was assessed using the summed difference score (SDS ≥2) on gated myocardial perfusion SPECT. RESULTS: Myocardial ischaemia was seen in 25 patients (62.5%) in 37 vascular territories. Quantitatively assessed significant stenosis and quantitatively assessed lesion length were independently associated with myocardial ischaemia (OR 7.72, 95% CI 2.41-24.7, p < 0.001, and OR 1.07, 95% CI 1.00-1.45, p = 0.032, respectively) after correcting for clinical variables and visually assessed significant stenosis. The addition of quantitatively assessed significant stenosis (χ(2) = 20.7) and lesion length (χ(2) = 26.0) to the clinical variables and the visual assessment (χ(2) = 5.9) had incremental value in the association with myocardial ischaemia. CONCLUSION: Coronary lesion length and quantitatively assessed significant stenosis were independently associated with myocardial ischaemia. Both quantitative parameters have incremental value over baseline variables and visually assessed significant stenosis. Potentially, QCT can refine assessment of CAD, which may be of potential use for identification of patients with myocardial ischaemia.

Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification.

AIMS: Previous studies have used semi-automated approaches for coronary plaque quantification on multi-detector row computed tomography (CT), while an automated quantitative approach using a dedicated registration algorithm is currently lacking. Accordingly, the study aimed to demonstrate the feasibility and accuracy of automated coronary plaque quantification on cardiac CT using dedicated software with a novel 3D coregistration algorithm of CT and intravascular ultrasound (IVUS) data sets. METHODS AND RESULTS: Patients who had undergone CT and IVUS were enrolled. Automated lumen and vessel wall contour detection was performed for both imaging modalities. Dedicated automated quantitative software (QCT) with a unique registration algorithm was used to fuse a complete IVUS run with a CT angiography volume using true anatomical markers. At the level of the minimal lumen area (MLA), percentage lumen area stenosis, plaque burden, and degree of remodelling were obtained on CT. Additionally, mean plaque burden was assessed for the whole coronary plaque. At the identical level within the coronary artery, the same variables were derived from IVUS. Fifty-one patients (40 men, 58 ± 11 years, 103 coronary arteries) with 146 lesions were evaluated. Quantitative computed tomography and IVUS showed good correlation for MLA (n = 146, r = 0.75, P < 0.001). At the level of the MLA, both techniques were well-correlated for lumen area stenosis (n = 146, r = 0.79, P < 0.001) and plaque burden (n = 146, r = 0.70, P < 0.001). Mean plaque burden (n = 146, r = 0.64, P < 0.001) and remodelling index (n = 146, r = 0.56, P < 0.001) showed significant correlations between QCT and IVUS. CONCLUSION: Automated quantification of coronary plaque on CT is feasible using dedicated quantitative software with a novel 3D registration algorithm.

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.

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.

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).

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).

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.