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Intravascular ultrasound and optical coherence tomography are used with increasing frequency for the care of coronary patients and in research studies. These imaging tools can identify culprit lesions in acute coronary syndromes, assess coronary stenosis severity, guide percutaneous coronary intervention (PCI), and detect vulnerable plaques and patients. However, they have significant limitations that have stimulated the development of multimodality intracoronary imaging catheters, which provide improvements in assessing vessel wall pathology and guiding PCI. Prototypes combining 2 or even 3 imaging probes with complementary attributes have been developed, and several multimodality systems have already been used in patients, with near-infrared spectroscopy intravascular ultrasound-based studies showing promising results for the identification of high-risk plaques. Moreover, postmortem histology studies have documented that hybrid imaging catheters can enable more accurate characterization of plaque morphology than standalone imaging. This review describes the evolution in the field of hybrid intracoronary imaging; presents the available multimodality catheters; and discusses their potential role in PCI guidance, vulnerable plaque detection, and the assessment of endovascular devices and emerging pharmacotherapies targeting atherosclerosis.

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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.

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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.

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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.

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Objectives: Use of intracoronary imaging (ICI) in cases of stent thrombosis (ST) is recommended and tailored treatment appears reasonable. Nevertheless, data supporting such a strategy are lacking. The aim of this study was to evaluate the clinical impact of ICI in the management of ST. Methods: The unadjusted study population was consecutive patients with definite ST presenting in a single tertiary cardiac centre and undergoing percutaneous coronary intervention (PCI). The presumed major mechanism of ST was assigned according to the real-time ICI interpretation by the PCI operator. Propensity score matching was performed with regard to ICI use to form the adjusted population and Kaplan-Meier analysis was applied to compare survival free of cardiac death (CD) or target lesion revascularization (TLR). Results: The unadjusted population included 130 ST patients, with the majority presenting with ST-elevation myocardial infarction (STEMI) (88%) and very late ST (86%). ICI was performed in 45 patients, of whom optical coherence tomography (OCT) was performed in 30 cases. When the individual ST mechanisms were viewed as groups, there was an interaction observed between type of treatment (stent vs. non-stent) and ST mechanism, with non-stent treatment being more prevalent in cases of underexpansion, malapposition, in-stent restenosis and mechanism uncertainty. After application of matching, two groups of 30 patients were formed. ICI-guided management resulted in better survival free of CD-TLR at 2 years (93% vs. 73%, p = 0.037). Conclusions: Intracoronary imaging guidance during PCI for ST had a direct impact on management (stent vs. non-stent) and resulted in a lower event rate at mid-term follow-up when propensity matched analysis was applied.

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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.

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BACKGROUND: Computed tomography cardiac angiography (CTCA) is recommended for the evaluation of patients with prior coronary artery bypass graft (CABG) surgery. The BYPASS-CTCA study demonstrated that CTCA prior to invasive coronary angiography (ICA) in CABG patients leads to significant reductions in procedure time and contrast-induced nephropathy (CIN), alongside improved patient satisfaction. However, whether CTCA information was used to facilitate selective graft cannulation at ICA was not protocol mandated. In this post-hoc analysis we investigated the influence of CTCA facilitated selective graft assessment on angiographic parameters and study endpoints. METHODS: BYPASS-CTCA was a randomized controlled trial in which patients with previous CABG referred for ICA were randomized to undergo CTCA prior to ICA, or ICA alone. In this post-hoc analysis we assessed the impact of selective ICA (grafts not invasively cannulated based on the CTCA result) following CTCA versus non-selective ICA (imaging all grafts irrespective of CTCA findings). The primary endpoints were ICA procedural duration, incidence of CIN, and patient satisfaction post-ICA. Secondary endpoints included the incidence of procedural complications and 1-year major adverse cardiac events. RESULTS: In the CTCA cohort (n â€‹= â€‹343), 214 (62.4%) patients had selective coronary angiography performed, whereas 129 (37.6%) patients had non-selective ICA. Procedure times were significantly reduced in the selective CTCA â€‹+ â€‹ICA group compared to the non-selective CTCA â€‹+ â€‹ICA group (-5.82min, 95% CI -7.99 to -3.65, p â€‹< â€‹0.001) along with reduction of CIN (1.5% vs 5.8%, OR 0.26, 95% CI 0.10 to 0.98). No difference was seen in patient satisfaction with the ICA, however procedural complications (0.9% vs 4.7%, OR 0.21, 95% CI 0.09-0.87) and 1-year major adverse cardiac events (13.1% vs 20.9%, HR 0.55, 95% CI 0.32-0.96) were significantly lower in the selective group. CONCLUSIONS: In patients with prior CABG, CTCA guided selective angiographic assessment of bypass grafts is associated with improved procedural parameters, lower complication rates and better 12-month outcomes. Taken in addition to the main findings of the BYPASS-CTCA trial, these results suggest a synergistic approach between CTCA and ICA should be considered in this patient group. REGISTRATION: ClinicalTrials.gov, NCT03736018.

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BACKGROUND: Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear. OBJECTIVES: To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up. METHODS AND RESULTS: We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 µm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 µm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups. RESULTS: In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001. CONCLUSION: In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices.

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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.

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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).

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Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts' estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

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BACKGROUND: Patients with previous coronary artery bypass grafting often require invasive coronary angiography (ICA). However, for these patients, the procedure is technically more challenging and has a higher risk of complications. Observational studies suggest that computed tomography cardiac angiography (CTCA) may facilitate ICA in this group, but this has not been tested in a randomized controlled trial. METHODS: This study was a single-center, open-label randomized controlled trial assessing the benefit of adjunctive CTCA in patients with previous coronary artery bypass grafting referred for ICA. Patients were randomized 1:1 to undergo CTCA before ICA or ICA alone. The co-primary end points were procedural duration of the ICA (defined as the interval between local anesthesia administration for obtaining vascular access and removal of the last catheter), patient satisfaction after ICA using a validated questionnaire, and the incidence of contrast-induced nephropathy. Linear regression was used for procedural duration and patient satisfaction score; contrast-induced nephropathy was analyzed using logistic regression. We applied the Bonferroni correction, with P<0.017 considered significant and 98.33% CIs presented. Secondary end points included incidence of procedural complications and 1-year major adverse cardiac events. RESULTS: Over 3 years, 688 patients were randomized with a median follow-up of 1.0 years. The mean age was 69.8±10.4 years, 108 (15.7%) were women, 402 (58.4%) were White, and there was a high burden of comorbidity (85.3% hypertension and 53.8% diabetes). The median time from coronary artery bypass grafting to angiography was 12.0 years, and there were a median of 3 (interquartile range, 2 to 3) grafts per participant. Procedure duration of the ICA was significantly shorter in the CTCA+ICA group (CTCA+ICA, 18.6±9.5 minutes versus ICA alone, 39.5±16.9 minutes [98.33% CI, -23.5 to -18.4]; P<0.001), alongside improved mean ICA satisfaction scores (1=very good to 5=very poor; -1.1 difference [98.33% CI, -1.2 to -0.9]; P<0.001), and reduced incidence of contrast-induced nephropathy (3.4% versus 27.9%; odds ratio, 0.09 [98.33% CI, 0.04-0.2]; P<0.001). Procedural complications (2.3% versus 10.8%; odds ratio, 0.2 [95% CI, 0.1-0.4]; P<0.001) and 1-year major adverse cardiac events (16.0% versus 29.4%; hazard ratio, 0.4 [95% CI, 0.3-0.6]; P<0.001) were also lower in the CTCA+ICA group. CONCLUSIONS: For patients with previous coronary artery bypass grafting, CTCA before ICA leads to reductions in procedure time and contrast-induced nephropathy, with improved patient satisfaction. CTCA before ICA should be considered in this group of patients. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03736018.

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Advances in image reconstruction using either single or multimodality imaging data provide increasingly accurate three-dimensional (3D) patient's arterial models for shear stress evaluation using computational fluid dynamics (CFD). We aim to evaluate the impacts on endothelial shear stress (ESS) derived from a simple image reconstruction using 3D-quantitative coronary angiography (3D-QCA) versus a multimodality reconstruction method using optical coherence tomography (OCT) in patients' vessels treated with bioresorbable scaffolds. Seven vessels at baseline and five-year follow-up of seven patients from a previous CFD investigation were retrospectively selected for a head-to-head comparison of angiography-derived versus OCT-derived ESS. 3D-QCA significantly underestimated the minimum stent area [MSA] (-2.38mm2) and the stent length (-1.46 mm) compared to OCT-fusion method reconstructions. After carefully co-registering the region of interest for all cases with a sophisticated statistical method, the difference in MSA measurements as well as the inability of angiography to visualise the strut footprint in the lumen surface have translated to higher angiography-derived ESS than OCT-derived ESS (1.76 Pa or 1.52 times for the overlapping segment). The difference in ESS widened with a more restricted region of interest (1.97 Pa or 1.63 times within the scaffold segment). Angiography and OCT offer two distinctive methods of ESS calculation. Angiography-derived ESS tends to overestimate the ESS compared to OCT-derived ESS. Further investigations into ESS analysis resolution play a vital role in adopting OCT-derived ESS.

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Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.

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