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.

Semi-automatic standardized analysis method to objectively evaluate near-infrared fluorescent dyes in image-guided surgery.

Significance: Near-infrared fluorescence imaging still lacks a standardized, objective method to evaluate fluorescent dye efficacy in oncological surgical applications. This results in difficulties in translation between preclinical to clinical studies with fluorescent dyes and in the reproduction of results between studies, which in turn hampers further clinical translation of novel fluorescent dyes. Aim: Our aim is to develop and evaluate a semi-automatic standardized method to objectively assess fluorescent signals in resected tissue. Approach: A standardized imaging procedure was designed and quantitative analysis methods were developed to evaluate non-targeted and tumor-targeted fluorescent dyes. The developed analysis methods included manual selection of region of interest (ROI) on white light images, automated fluorescence signal ROI selection, and automatic quantitative image analysis. The proposed analysis method was then compared with a conventional analysis method, where fluorescence signal ROIs were manually selected on fluorescence images. Dice similarity coefficients and intraclass correlation coefficients were calculated to determine the inter- and intraobserver variabilities of the ROI selections and the determined signal- and tumor-to-background ratios. Results: The proposed non-targeted fluorescent dyes analysis method showed statistically significantly improved variabilities after application on indocyanine green specimens. For specimens with the targeted dye SGM-101, the variability of the background ROI selection was statistically significantly improved by implementing the proposed method. Conclusion: Semi-automatic methods for standardized quantitative analysis of fluorescence images were successfully developed and showed promising results to further improve the reproducibility and standardization of clinical studies evaluating fluorescent dyes.

Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds.

INTRODUCTION: The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation. The aim of this study was to determine thresholds of plaque thickness differences that define whether there is plaque progression and/or regression. These thresholds depend on the contrast-to-noise ratio (CNR). METHODS: Plaque thickness differences between two scans acquired at the same moment in time should always be zero. The negative and positive differences in plaque contour delineation in these scans were used along with the CNR in order to create calibration graphs on which a linear regression analysis was performed. This analysis was conducted on a cohort of 50 patients referred for a CCTA due to chest complaints. A total of 300 coronary vessels were analyzed. First, plaque contours were semi-automatically determined for all major epicardial coronary vessels. Second, manual drawings of seven regions of interest (ROIs) per scan were used to quantify the scan quality based on the CNR for each vessel. RESULTS: A linear regression analysis was performed on the CNR and negative and positive plaque contour delineation differences. Accounting for the standard error of the estimate, the linear regression analysis revealed that above 1.009 - 0.002 × CNR there is an increase in plaque thickness (progression), and below - 1.638 + 0.012 × CNR there is a decrease in plaque thickness (regression). CONCLUSION: This study demonstrates the feasibility of developing vessel-specific, quality-based thresholds for visualizing local plaque thickness differences evaluated by serial CCTA. These thresholds have the potential to facilitate the early detection of atherosclerosis progression.

Intraprocedural assessment of ablation margins using computed tomography co-registration in hepatocellular carcinoma treatment with percutaneous ablation: IAMCOMPLETE study.

PURPOSE: The primary objective of this study was to determine the feasibility of ablation margin quantification using a standardized scanning protocol during thermal ablation (TA) of hepatocellular carcinoma (HCC), and a rigid registration algorithm. Secondary objectives were to determine the inter- and intra-observer variability of tumor segmentation and quantification of the minimal ablation margin (MAM). MATERIALS AND METHODS: Twenty patients who underwent thermal ablation for HCC were included. There were thirteen men and seven women with a mean age of 67.1 ± 10.8 (standard deviation [SD]) years (age range: 49.1-81.1 years). All patients underwent contrast-enhanced computed tomography examination under general anesthesia directly before and after TA, with preoxygenated breath hold. Contrast-enhanced computed tomography examinations were analyzed by radiologists using rigid registration software. Registration was deemed feasible when accurate rigid co-registration could be obtained. Inter- and intra-observer rates of tumor segmentation and MAM quantification were calculated. MAM values were correlated with local tumor progression (LTP) after one year of follow-up. RESULTS: Co-registration of pre- and post-ablation images was feasible in 16 out of 20 patients (80%) and 26 out of 31 tumors (84%). Mean Dice similarity coefficient for inter- and intra-observer variability of tumor segmentation were 0.815 and 0.830, respectively. Mean MAM was 0.63 ± 3.589 (SD) mm (range: -6.26-6.65 mm). LTP occurred in four out of 20 patients (20%). The mean MAM value for patients who developed LTP was -4.00 mm, as compared to 0.727 mm for patients who did not develop LTP. CONCLUSION: Ablation margin quantification is feasible using a standardized contrast-enhanced computed tomography protocol. Interpretation of MAM was hampered by the occurrence of tissue shrinkage during TA. Further validation in a larger cohort should lead to meaningful cut-off values for technical success of TA.

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.

Computational Modeling of Thermal Ablation Zones in the Liver: A Systematic Review.

PURPOSE: This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy. METHODS: A systematic literature search was performed in the MEDLINE and Web of Science databases. Characteristics of the computational model and validation method of the included articles were retrieved. RESULTS: The literature search identified 780 articles, of which 35 were included. A total of 19 articles focused on simulating radiofrequency ablation (RFA) zones, and 16 focused on microwave ablation (MWA) zones. Out of the 16 articles simulating MWA, only 2 used in vivo experiments to validate their simulations. Out of the 19 articles simulating RFA, 10 articles used in vivo validation. Dice similarity coefficients describing the overlap between in vivo experiments and simulated RFA zones varied between 0.418 and 0.728, with mean surface deviations varying between 1.1 mm and 8.67 mm. CONCLUSION: Computational models to simulate ablation zones of MWA and RFA show considerable heterogeneity in model type and validation methods. It is currently unknown which model is most accurate and best suitable for use in clinical practice.

A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion.

Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that enables an accurate diagnosis of plaque erosion using coronary computed tomography angiography (CTA). A total of 532 CTA scans from 395 patients were used to develop a DL model: 426 CTA scans from 316 patients for training and internal validation, and 106 separate scans from 79 patients for validation. Momentum Distillation-enhanced Composite Transformer Attention (MD-CTA), a novel DL model that can effectively process the entire set of CTA scans to diagnose plaque erosion, was developed. The novel DL model, compared to the convolution neural network, showed significantly improved AUC (0.899 [0.841-0.957] vs. 0.724 [0.622-0.826]), sensitivity (87.1 [70.2-96.4] vs. 71.0 [52.0-85.8]), and specificity (85.3 [75.3-92.4] vs. 68.0 [56.2-78.3]), respectively, for the patient-level prediction. Similar results were obtained at the slice-level prediction AUC (0.897 [0.890-0.904] vs. 0.757 [0.744-0.770]), sensitivity (82.2 [79.8-84.3] vs. 68.9 [66.2-71.6]), and specificity (80.1 [79.1-81.0] vs. 67.3 [66.3-68.4]), respectively. This newly developed DL model enables an accurate CT diagnosis of plaque erosion, which might enable cardiologists to provide tailored therapy without invasive procedures.Clinical Trial Registration: http://www.clinicaltrials.gov , NCT04523194.

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).

Preprocedural physiological assessment of coronary disease patterns to predict haemodynamic outcomes post-PCI.

BACKGROUND: Even with intracoronary imaging-guided stent optimisation, suboptimal haemodynamic outcomes post-percutaneous coronary intervention (PCI) can be related to residual lesions in non-stented segments. Preprocedural assessment of pathophysiological coronary artery disease (CAD) patterns could help predict the physiological response to PCI. AIMS: The aim of this study was to assess the relationship between preprocedural pathophysiological haemodynamic patterns and intracoronary imaging findings, as well as their association with physiological outcomes immediately post-PCI. METHODS: Data from 206 patients with chronic coronary syndrome enrolled in the ASET-JAPAN study were analysed. Pathophysiological CAD patterns were characterised using Murray law-based quantitative flow ratio (µQFR)-derived indices acquired from pre-PCI angiograms. The diffuseness of CAD was defined by the pullback pressure gradient (PPG) index. Intracoronary imaging in stented segments after stent optimisation was also analysed. RESULTS: In the multivariable analysis, diffuse disease - defined by the pre-PCI µQFR-PPG index - was an independent factor for predicting a post-PCI µQFR <0.91 (per 0.1 decrease of PPG index, odds ratio 1.57, 95% confidence interval: 1.07-2.34; p=0.022), whereas the stent expansion index (EI) was not associated with a suboptimal post-PCI µQFR. Among vessels with an EI ≥80% and post-PCI µQFR <0.91, 84.0% of those vessels had a diffuse pattern preprocedure. There was no significant difference in EI between vessels with diffuse disease and those with focal disease. The average plaque burden in the stented segment was significantly larger in vessels with a preprocedural diffuse CAD pattern. CONCLUSIONS: A physiological diffuse pattern preprocedure was an independent factor in predicting unfavourable immediate haemodynamic outcomes post-PCI, even after stent optimisation using intracoronary imaging. Preprocedural assessment of CAD patterns could identify patients who are likely to exhibit superior immediate haemodynamic outcomes following PCI.

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks.

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.

A Prospective Randomized Trial Comparing Sirolimus-Coated Balloon With Paclitaxel-Coated Balloon in De Novo Small Vessels.

BACKGROUND: There are no data comparing sirolimus-coated balloons (SCBs [MagicTouch, Concept Medical]) to paclitaxel-coated balloons (PCBs [SeQuent Please Neo, B. Braun]) for the treatment of de novo small vessel disease (SVD). OBJECTIVES: This study sought to compare quantitative coronary angiographic outcomes at 6 months after treatment of de novo SVD with a PCB or SCB. METHODS: This prospective, multicenter, noninferiority trial randomized 121 patients (129 SVD lesions) to treatment with an SCB or PCB, with balloon sizing determined using optical coherence tomography. The primary endpoint was noninferiority for the 6-month angiographic net lumen gain. RESULTS: Angiographic follow-up was completed in 109 (90.1%) patients in the per-protocol analysis. The mean ± SD angiographic net gains were 0.25 ± 0.40 mm with SCBs vs 0.48 ± 0.37 mm with PCBs, resulting in SCBs failing to meet the 0.30 mm criterion for noninferiority (Pnoninferiority = 0.173), with an absolute difference of -0.23 mm (95% CI: -0.37 to -0.09) secondary to a smaller late loss (0.00 ± 0.32 mm vs 0.32 ± 0.47 mm; P < 0.001) and more frequent late lumen enlargement (53.7% vs 30.0%; OR: 2.60; 95% CI: 1.22-5.67; P = 0.014) with PCBs. Binary restenosis rates were 32.8% and 12.5% following treatment with SCBs and PCBs, respectively (OR: 3.41; 95% CI: 1.36-9.44; P = 0.012). The mean angiography-derived fractional flow ratio at follow-up was 0.86 ± 0.15 following treatment with SCBs and 0.91 ± 0.09 following PCBs (P = 0.026); a fractional flow ratio ≤0.80 occurred in 13 and 5 vessels after treatment with SCBs and PCBs, respectively. CONCLUSIONS: The SCB MagicTouch failed to demonstrate noninferiority for angiographic net lumen gain at 6 months compared to the PCB SeQuent Please Neo.

Angiography and optical coherence tomography derived shear stress: are they equivalent in my opinion?

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.

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation.

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.

Plaque burden is associated with minimal intimal coverage following drug-eluting stent implantation in an adult familial hypercholesterolemia swine model.

Safety and efficacy of coronary drug-eluting stents (DES) are often preclinically tested using healthy or minimally diseased swine. These generally show significant fibrotic neointima at follow-up, while in patients, incomplete healing is often observed. The aim of this study was to investigate neointima responses to DES in swine with significant coronary atherosclerosis. Adult familial hypercholesterolemic swine (n = 6) received a high fat diet to develop atherosclerosis. Serial OCT was performed before, directly after, and 28 days after DES implantation (n = 14 stents). Lumen, stent and plaque area, uncovered struts, neointima thickness and neointima type were analyzed for each frame and averaged per stent. Histology was performed to show differences in coronary atherosclerosis. A range of plaque size and severity was found, from healthy segments to lipid-rich plaques. Accordingly, neointima responses ranged from uncovered struts, to minimal neointima, to fibrotic neointima. Lower plaque burden resulted in a fibrotic neointima at follow-up, reminiscent of minimally diseased swine coronary models. In contrast, higher plaque burden resulted in minimal neointima and more uncovered struts at follow-up, similarly to patients' responses. The presence of lipid-rich plaques resulted in more uncovered struts, which underscores the importance of advanced disease when performing safety and efficacy testing of DES.

Ablation margin quantification after thermal ablation of malignant liver tumors: How to optimize the procedure? A systematic review of the available evidence.

Introduction: To minimize the risk of local tumor progression after thermal ablation of liver malignancies, complete tumor ablation with sufficient ablation margins is a prerequisite. This has resulted in ablation margin quantification to become a rapidly evolving field. The aim of this systematic review is to give an overview of the available literature with respect to clinical studies and technical aspects potentially influencing the interpretation and evaluation of ablation margins. Methods: The Medline database was reviewed for studies on radiofrequency and microwave ablation of liver cancer, ablation margins, image processing and tissue shrinkage. Studies included in this systematic review were analyzed for qualitative and quantitative assessment methods of ablation margins, segmentation and co-registration methods, and the potential influence of tissue shrinkage occurring during thermal ablation. Results: 75 articles were included of which 58 were clinical studies. In most clinical studies the aimed minimal ablation margin (MAM) was ≥ 5 mm. In 10/31 studies, MAM quantification was performed in 3D rather than in three orthogonal image planes. Segmentations were performed either semi-automatically or manually. Rigid and non-rigid co-registration algorithms were used about as often. Tissue shrinkage rates ranged from 7% to 74%. Conclusions: There is a high variability in ablation margin quantification methods. Prospectively obtained data and a validated robust workflow are needed to better understand the clinical value. Interpretation of quantified ablation margins may be influenced by tissue shrinkage, as this may cause underestimation.

Microvascular lung vessels obstructive thromboinflammatory syndrome in patients with COVID-19: Insights from lung intravascular optical coherence tomography.

Background: Microvascular lung vessels obstructive thromboinflammatory syndrome has been proposed as a possible mechanism of respiratory failure in COVID-19 patients. However, it has only been observed in post-mortem studies and has never been documented in vivo, probably because of a lack of CT scan sensitivity in small pulmonary arteries. The aim of the present study was to assess the safety, tolerability, and diagnostic value of optical coherence tomography (OCT) for the assessment of patients with COVID-19 pneumonia for pulmonary microvascular thromboinflammatory syndrome. Methods: The COVID-OCT trial was a multicenter, open-label, prospective, interventional clinical study. Two cohorts of patients were included in the study and underwent pulmonary OCT evaluation. Cohort A consisted of patients with COVID-19 with a negative CT scan for pulmonary thrombosis and elevated thromboinflammatory markers (D-dimer > 10,000 ng/mL or 5,000 < D-dimer < 10,000 ng/mL and one of: C-reactive Protein > 100 mg/dL, IL-6 > 6 pg/mL, or ferritin > 900 ng/L). Cohort B consisted of patients with COVID-19 and a CT scan positive for pulmonary thrombosis. The primary endpoints of the study were: (i) to evaluate the overall safety of OCT investigation in patients with COVID-19 pneumonia, and (ii) to report on the potential value of OCT as a novel diagnostic tool for the diagnosis of microvascular pulmonary thrombosis in COVID-19 patients. Results: A total of 13 patients were enrolled. The mean number of OCT runs performed in each patient was 6.1 ± 2.0, both in ground glass and healthy lung areas, achieving a good evaluation of the distal pulmonary arteries. Overall, OCT runs identified microvascular thrombosis in 8 patients (61.5%): 5 cases of red thrombus, 1 case of white thrombus, and 2 cases of mixed thrombus. In Cohort A, the minimal lumen area was 3.5 ± 4.6 mm2, with stenosis of 60.9 ± 35.9% of the area, and the mean length of thrombus-containing lesions was 5.4 ± 3.0 mm. In Cohort B, the percentage area obstruction was 92.6 ± 2.6, and the mean thrombus-containing lesion length was 14.1 ± 13.9 mm. No peri-procedural complications occurred in any of the 13 patients. Conclusion: OCT appears to be a safe and accurate method of evaluating the distal pulmonary arteries in hospitalized COVID-19 patients. Here, it enabled the first in vivo documentation of distal pulmonary arterial thrombosis in patients with elevated thromboinflammatory markers, even when their CT angiogram was negative for pulmonary thrombosis. Clinical trial registration: ClinicalTrial.gov, identifier NCT04410549.

An optical coherence tomography and endothelial shear stress study of a novel bioresorbable bypass graft.

Endothelial shear stress (ESS) plays a key role in the clinical outcomes in native and stented segments; however, their implications in bypass grafts and especially in a synthetic biorestorative coronary artery bypass graft are yet unclear. This report aims to examine the interplay between ESS and the morphological alterations of a biorestorative coronary bypass graft in an animal model. Computational fluid dynamics (CFD) simulation derived from the fusion of angiography and optical coherence tomography (OCT) imaging was used to reconstruct data on the luminal anatomy of a bioresorbable coronary bypass graft with an endoluminal "flap" identified during OCT acquisition. The "flap" compromised the smooth lumen surface and considerably disturbed the local flow, leading to abnormally low ESS and high oscillatory shear stress (OSI) in the vicinity of the "flap". In the presence of the catheter, the flow is more stable (median OSI 0.02384 versus 0.02635, p < 0.0001; maximum OSI 0.4612 versus 0.4837). Conversely, OSI increased as the catheter was withdrawn which can potentially cause back-and-forth motions of the "flap", triggering tissue fatigue failure. CFD analysis in this report provided sophisticated physiological information that complements the anatomic assessment from imaging enabling a complete understanding of biorestorative graft pathophysiology.

Early healing after treatment of coronary lesions by thin strut everolimus, or thicker strut biolimus eluting bioabsorbable polymer stents: The SORT-OUT VIII OCT study.

AIMS: Early healing after drug-eluting stent (DES) implantation may reduce the risk of stent thrombosis. The aim of this study was to compare patterns of early healing after implantation of the thin strut everolimus-eluting Synergy DES (Boston Scientific) or the biolimus-eluting Biomatix Neoflex DES (Biosensors). METHODS AND RESULTS: A total of 160 patients with the chronic or acute coronary syndrome were randomized 1:1 to Synergy or Biomatrix DES. Optical coherence tomography (OCT) was performed at baseline and at either 1- or 3-month follow-up. The primary endpoint was a coronary stent healing index (CSHI), a weighted index of strut coverage, neointimal hyperplasia, malapposition, and extrastent lumen. A total of 133 cases had OCT follow-up and 119 qualified for matched OCT analysis. The median CSHI score did neither differ significantly between the groups at 1 month: Synergy 8.0 (interquartile range [IQR]: 3.0; 14.0) versus Biomatrix 8.5 (IQR: 4.0; 15.0) (p = 0.47) nor at 3 months: Synergy 6.5 (IQR: 2.0; 13.0) versus Biomatrix 6.0 (IQR: 4.0; 11.0) (p = 0.83). Strut coverage was 84.6% (IQR: 72.0; 97.9) for Synergy versus 77.6% (IQR: 70.1; 90.3) for Biomatrix (p = 0.15) at 1 month and 90.3% (IQR 79.0; 98.8) (Synergy) versus 83.9% (IQR: 77.5; 92.6) (Biomatrix) (p = 0.068) at 3 months. Pooled 1- and 3-month coverage was 88.6% (IQR: 74.4; 98.4) for Synergy compared with 80.7% (IQR: 73.2; 90.8) for Biomatrix (p = 0.02). CONCLUSIONS: The early healing response after treatment with the Synergy or Biomatrix DES did not differ significantly as determined by a healing index. The Synergy DES showed overall better early stent strut coverage.