Intravascular Ultrasound Imaging Research Articles

Angioprotein-like 3 and atheroma progression in statin-treated type 2 diabetic patients with coronary artery disease: the pre-specified analysis from the OPTIMAL randomized controlled trial

Abstract Introduction Despite guideline-recommended control of LDL-C with a statin, type 2 diabetic patients more likely cause cardiovascular events. This indicates the need to identify additional therapeutic targets in those patients. Angioprotein-like 3 (ANGPTL3) is an inhibitor of lipoprotein and endothelial lipase, which increases the levels of LDL-C and other atherogenic lipoproteins. These properties suggest ANGPTL3 as a potential driver associated with diabetic atherosclerosis. The OPTIMAL randomized controlled trial (jRCT1052180152) compared the efficacy of continuous glucose monitoring-guided versus HbA1c-guided glycemic control on coronary atherosclerosis in statin-treated type 2 diabetic patients by using serial intravascular ultrasound (IVUS) imaging. This pre-specified analysis of the OPTIMAL study was designed to evaluate whether ANGPTL3 affects progression of coronary atheroma in statin-treated type 2 diabetic patients. Purpose To elucidate the association of serial change in ANGPTL3 with the degree of atheroma progression in statin-treated type 2 diabetic patients. Methods 59 patients (59 lesions) with serial ANGPTL3 levels at baseline and week 48 were included into the current analysis. Clinical demographics and IVUS-derived measure were compared in patients with and without any increase in ANGPTL3. Results All of study subjects received a statin, and over 60% of them were treated with high-intensity statin. Under these lipid-lowering therapies, any increase in ANGPTL3 levels at week 48 was observed in 52.5% of study participants. Patients with any increase in ANGPTL3 were more likely to have a history of hypertension (90.0% vs. 64.3%, p=0.02), whereas there was no significant difference in on-treatment LDL-C levels between the two groups (1.8 ± 0.4 vs. 1.6 ± 0.6 mmol/L, p=0.25) (Table). On IVUS imaging analysis, baseline percent atheroma volume (PAV) did not differ in those with and without any increase in ANGPTL3. However, patients with any increase in ANGPTL3 presented a greater progression of PAV (0.08 ± 0.28 vs. -0.74 ± 0.29, p=0.04). There was a trend toward a lower frequency of PAV regression in those exhibiting any increase in ANGPTL3 (48.4% vs. 57.1%), but this comparison did not meet statistical significance (p=0.50) (Figure). Multivariate analysis adjusting clinical characteristics demonstrated that any increase in ANGPTL3 level was not independently associated with PAV progression (β=0.16, 95%CI=-0.01 – 0.18, p=0.27). Conclusion Over 50% of statin-treated type 2 diabetic patients with coronary atherosclerosis showed any increase in ANGPTL3 levels at week 48. Serial IVUS imaging revealed that patients with any increase in ANGPTL3 more likely presented a greater atheroma progression. However, this relationship did not exist after adjusting clinical characteristics. Our findings suggest that ANGPTL3 may not be a pivotal driver associated with atheroma progression in statin-treated type 2 diabetic patients.

Association between stent implantation results and target vessel failure after IVUS-Guided PCI: A FLAVOUR substudy

Abstract Background Despite substantial advancements in coronary stent systems and implantation techniques, target vessel failure (TVF) remains a clinical issue to be addressed. Purpose We used deep learning algorithms for automatic stent analysis of intravascular ultrasound (IVUS) data to identify stent implantation characteristics associated with TVF. Methods In patients from the IVUS-guided group of the FLAVOUR trial who had undergone successful stenting, validated deep learning algorithms were applied to IVUS images to delineate plaque and stent strut for the quantification of stent expansion, apposition, and residual plaque burden. Stent overexpansion was defined as stent area exceeding 120% of the hypothetical healthy lumen area. Murray law-based quantitative flow ratio (μQFR) was also assessed from angiographic images. The primary endpoint was TVF at 2 years, defined as a composite of cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization. Results A total of 459 vessels in 441 patients were included, with TVF events occurred in 15 vessels (3.3%) during the 2-year follow-up. Stent analysis was performed in 687,379 stent struts from 70,369 IVUS image frames of the interrogated vessels with the assistance of specific deep learning algorithms. On vessel-level multivariate analysis adjusting for procedural covariates, stent overexpansion length >5 mm (hazard ratio, 4.57; 95% confidence interval [CI], 1.31 to 15.92; P=0.017), residual plaque burden at proximal stent edge >50% (hazard ratio, 3.34; 95% CI, 1.19 to 9.33; P=0.022), and post-PCI μQFR <0.90 (hazard ratio, 5.09; 95% CI, 1.62 to 15.97; P=0.005) were more likely to be present in vessels with TVF events than in those without. Conclusions Besides the established indexes of post-procedural residual plaque burden and low μQFR, deep learning-powered IVUS stent overexpansion was significantly associated with increased risk of TVF.Suboptimal stenting results and TVF

Lesion-level changes induced by low-density lipoprotein cholesterol lowering therapy in patients with acute myocardial infarction: the pre-specified PACMAN-AMI lesion-level analysis

Abstract Background Previous intracoronary imaging studies investigated atherosclerotic changes induced by lipid-lowering therapy in extensive coronary segments, based on anatomical landmarks and irrespective of the baseline atheroma burden. The effects of lipid-lowering therapy focusing on coronary lesions with advanced atherosclerotic features and at presumably higher risk for future events remains largely unknown. Purpose To provide a lesion-level analysis reporting changes in high-risk atherosclerotic features and plaque phenotypes induced by alirocumab versus placebo in addition to high-intensity statin therapy in nonculprit coronary lesions of patients admitted with acute myocardial infarction (AMI). Methods In this pre-specified lesion-level analysis of the PACMAN-AMI trial, coronary lesions were identified as segments with plaque burden ≥40% defined by intravascular ultrasound (IVUS). IVUS, near-infrared spectroscopy (NIRS) and optical coherence tomography (OCT) images at baseline and 52-week follow-up were manually matched and lesion-level imaging outcome measures were calculated by analysts blinded to treatment allocation and patient clinical data. The evolution of high-risk plaque phenotypes as defined by each imaging modality (IVUS-defined plaque burden ≥70%, NIRS-defined maximum LCBI 4 mm ≥400, OCT-defined thin-cap fibroatheroma <65µm) was investigated. Results Overall, 591 lesions with available baseline and follow-up imaging were included. Of these, 287 were identified in 118 patients (214 vessels) in the alirocumab arm, and 304 lesions were identified in 127 patients (239 vessels) in the placebo arm. At lesion-level, mean change in percent atheroma volume (PAV) by IVUS was -4.86% with alirocumab vs -2.78% with placebo (difference, -2.02 [-3.00 to -1.05], p<.001), substantially greater than that at vessel-level (Figure 1). At the minimum lumen area (MLA) site, mean change in PAV was -10.14% with alirocumab vs -6.70% with placebo (difference, -3.36 (-4.98 to -1.75, p<0.001). MLA increased by 0.15 mm2 with alirocumab and decreased by 0.07 mm2 in the placebo arm (difference, +0.21, 0.01 to 0.41, p=0.036), whereas arterial remodeling was similar between arms (-0.73±1.61 in alirocumab vs -0.63±1.31 in placebo group, p=0.482). Among lesions with high-risk plaque phenotypes at baseline, those in the alirocumab arm more frequently showed a more stable and less lipid-rich plaque phenotype at follow-up than those in the placebo arm (Figure 2). Conclusions At lesion-level, intense lipid-lowering therapy with alirocumab in addition to high-intensity statin therapy induced extensive PAV regression, substantially greater than described in previous vessel-level analyses. Compared to statin therapy alone, alirocumab treatment was associated with a greater enlargement of the lesion MLA, similar negative arterial remodeling, and a more frequent transition of high-risk plaque phenotypes into more stable, less lipid-rich plaque phenotypes.Figure 1.Figure 2

Implications of calcified nodule and adjacent calcification severity for target lesion revascularization after percutaneous coronary intervention: multi-center intravascular ultrasound imaging study

Abstract Background Calcified nodule (CN) is a distinct plaque phenotype, characterized as protruding nodular calcification penetrating lumen surface with the attached thrombus. Recent studies revealed that a risk of repeat revascularization after PCI was substantially elevated in patients with coronary artery disease (CAD) attributable to CN. Furthermore, pathohistological studies have elucidated mechanistic insights into the formulation and progression of CN, suggesting that it is formulated by the fracture of sheet calcification at its adjacent segment. Purpose To elucidate the association of calcified plaque volume at CN and adjacent lesions with TLR risks in CAD patients receiving PCI by using IVUS imaging. Methods This multicenter study analyzed IVUS imaging at culprit segment was conducted in 204 CAD patients attributable to de novo CN receiving PCI. The calc grade was assigned at each 1-mm cross-sectional frame of CN and culprit segment, respectively (no calc=0, 1-89°=1, 90-179°=2, 180-269°=3, 270-360°=4). Calcium Volume Index (CVI) at both CN and adjacent zone were calculated (Figure; Calcification volume index at CN zone = the sum of calcium scores at CN・averaged lesion slices of CN / CN lesion slices, Calcification volume index at adjacent zone = the sum of calcium scores at adjacent zone・averaged lesion slices of adjacent zone / adjacent zone slices). Predictors of TLR were analyzed by multivariate Cox hazard model. Results CN was successfully treated by DES (82%) and DCB (18%). 34% and 8.3% of subjects required rotablator and orbital atherectomy, respectively. Consequently, final lumen area after PCI was 7.2 ± 0.4 mm2. During the 2.8-year observational period (interquartile range: 2.4 to 3.2 years), TLR was required in 63 patients (30.9%) of study population. CVI at CN and adjacent zone were 9.9 ± 0.4 and 7.0 ± 0.7, respectively. On multivariate analysis, hemodialysis, final lumen area, CVI at both CN and adjacent zone predicted TLR (Figure). Recurrent TLR (two or more TLRs) was found in 23.3% patients who suffered from TLR. Of note, even among patients who required TLR, CVIs at both zones were significantly higher in patients with recurrent TLR than those without (Figure). Conclusion CAD subjects caused by CN exhibited a considerably high-risk TLRs. Our findings underscore that calcification severity at both CN and adjacent segment predicted TLR and is associated with repeated TLR.methodsResults

Coronary artery plaque characteristics in smokers assessed by intracoronary multi-modality imaging: a NIRS-IVUS guided PROSPECT II substudy

Abstract Background Smoking is a risk factor for coronary artery disease and myocardial infarction. Purpose Among patients with myocardial infarction, we hypothesized smokers would have increased prevalence of nonculprit high-risk plaque characteristics as assessed by intracoronary multimodality imaged when compared to nonsmokers. Method This is a prespecified post-hoc analysis of the PROSPECT II (Providing Regional Observations to Study Predictors of Events in the Coronary Tree). Altogether 898 patients with acute myocardial infarction with or without ST-segment elevation who after successful percutaneous coronary intervention had near-infrared spectroscopy and intravascular ultrasound imaging performed. Data on lesion location, plaque burden, and lipid content were collected. Current, former, and nonsmokers were examined regarding the presence of high-risk plaques in both culprit and nonculprit lesions as well as plaque location. High-risk plaque characteristics were defined as a maximum plaque burden (maxPB) of ≥70% and a maximum 4 mm lipid core burden index (maxLCBI4mm) of ≥324.7. Results Out of 886 patients with known smoking history, 32% were smokers, 32% former, and 37% nonsmokers. Smokers were younger (median age: smokers 59.0; former 65.5; nonsmokers 64.0 years, p<0.001) with fewer co-morbidities and presented more often with STEMI (% presenting with STEMI: smokers 22.2%; former 18.9%; nonsmokers 19.4%, p=0.006). LDL was similar but CRP was higher in smokers (median µg/mL: smokers 4.0; former 3.1; nonsmokers 2.9, p=0.004). In multivariate models of nonculprit lesion morphology smoking history was not associated with maxPB or maxLCBI4mm but was associated with presence of calcified nodules (OR: 2.88 (95% CI 1.46–5.69) p=0.002 [smokers vs nonsmokers]). Calcified nodules were also associated with number of years smoked (OR: 1.02 (95% CI 1.01–1.03) p=0.004). Smoking was not associated with presence of ≥1 lesions with MaxLCBI4mm, ≥ 324.7, maxPB >=70% or the combination of both. Smoking was not associated with presence of plaque rupture or thrombus. Right coronary artery culprit lesions were more common in smokers and former smokers (smokers 29.9%; former 30.9%; nonsmokers 21.6%; OR: 1.12 (95% CI 1.04–1.21) p<0.001 [smokers vs nonsmokers]). Conclusion In this prospective intracoronary multimodality imaging study of patients with recent acute myocardial infarction, smoking was not associated with a higher rate of high-risk nonculprit plaques based on lipid content by NIRS or PB by IVUS when compared to former or nonsmokers. However, smoking was associated with higher presence of IVUS-detected calcified nodules, higher CRP-levels, and the right coronary artery as it angiographically was a more common site for culprit lesions. These results indicate that the elevated risk in smokers is unrelated to vulnerable plaque morphology defined by high PB and lipid content, and instead possibly related to inflammation, calcification, or hemodynamic changes.Table 1

Evaluation of non-target lesions in femoropopliteal disease using near-infrared spectroscopy intravascular ultrasound imaging

Abstract Aim This study aims to assess the plaque morphology of non-target lesions, especially lipid-core-containing plaque (LCP), and compare it to target lesions using near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) in patients with femoropopliteal disease. Methods We conducted a single-center prospective observational study on 14 patients who underwent endovascular therapy for femoropopliteal disease between July 2022 and November 2023. NIRS-IVUS assessment was performed on the whole femoropopliteal arterial segment. Forty-one LCP lesions > maximum lipid-core burden index in any 4-mm region (max LCBI4mm) 100 were detected by NIRS-IVUS. We evaluated patient and lesion characteristics. In addition, the LCP lesions were divided into two groups, the target lesion group (n=18) and the non-target lesion group (n=23), and compared. Results Patient characteristics were notable for advanced age (76.8±6.6 years), high incidence of male (78.7%), hypertension (100%), dyslipidemia (78.6%), diabetes (64.3%), statin use (71.4%). Lipid profiles were as follows: LDL-cholesterol 85.9±26.7 mg/dl, HDL-cholesterol 48.9±12.7 mg/dl, triglyceride 138.9±80.3 mg/dl, lipoprotein(a) 19.6±16.3 mg/dl. Ten participants had LCPs in the non-target lesion (71.4%). For IVUS findings, the target lesion group had significantly longer lesion length (34.6±11.9 mm vs. 22.6±8.7 mm, p=0.001), smaller minimum lumen area (7.0±2.8 mm2 vs. 14.8±5.4 mm2, p<0.001), and larger plaque burden (78.0±5.0 % vs. 53.0±12.0 %, p<0.001) than those in the non-target lesion group. No significant differences were observed in the prevalence of calcification (target lesion group vs. non-target lesion group: 83.3 % vs. 91.3 %, p=0.64) and the extent of calcification (p=0.76). For NIRS findings, the target lesion group contained a significantly smaller proportion of LCP in the lesion length (25.9±15.7 % vs. 50.6±29.2 %, p=0.002) than the non-target lesion group. On the other hand, there were no significant differences in the value of max LCBI4mm (284.4±153.4 vs. 289.5±113.1, p=0.90) and the length of LCP lesion (9.8±9.7 mm vs. 10.7±6.9 mm, p=0.74) and distribution of LCP (p=0.08) between both groups. In addition, the number of LCPs in the target femoropopliteal artery was positively correlated with max LCBI4mm in the target femoropopliteal artery (r=0.671, p=0.008) Conclusion NIRS-IVUS findings in this study demonstrated that the non-target lesions contained LCPs to the same degree as the target lesions.

Bioresorbable stent unloading during percutaneous coronary intervention: Early detection and management

In this letter, we comment on a recent case report by Sun et al in the World Journal of Cardiology . The report describes the successful management of a rare complication: The unloading or detachment of a bioresorbable stent (BRS) during percutaneous coronary intervention (PCI) in a male patient. The unloading of BRS was detected via angiography and intravascular ultrasound (IVUS) imaging of the left coronary artery and left anterior descending artery. Although this case is interesting, the authors’ report lacked crucial details. Specifically, insufficient information about the type of BRS used, potential causes of BRS unloading, or whether optical coherence tomography (OCT) imaging for coronary arteries was performed before, during, or after PCI. The OCT imaging of coronary arteries before PCI can potentially prevent BRS unloading due to its higher resolution compared to IVUS. In addition, despite detecting myocardial bridging during the PCI, the authors did not provide any details regarding this variation. Here we discuss the various types of BRS, the importance of OCT in PCI, and the clinical relevance of myocardial bridging.

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaque rupture. Manual classification is time-consuming and labor-intensive. Several machine learning-based classification approaches have been proposed and evaluated in recent years. In the current study, we develop a novel pipeline composed of serial classifiers for distinguishing IVUS images into five categories: normal, calcified plaque, attenuated plaque, fibrous plaque, and echolucent plaque. The cascades comprise densely connected classification models and machine learning classifiers at different stages. Over 100,000 IVUS frames of five different lesion types were collected and labeled from 471 patients for model training and evaluation. The overall accuracy of the proposed classifier is 0.877, indicating that the proposed framework has the capacity to identify the nature and category of coronary plaques in IVUS images. Further, it may provide real-time assistance on plaque identification and facilitate clinical decision-making in routine practice.

TCT-204 Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images

TCT-204 Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images

Clinical outcomes of percutaneous coronary intervention for severely calcified lesions: comparison between the morphologies of severely calcified coronary lesions.

Existing studies evaluating the comparison of clinical outcome of percutaneous coronary intervention (PCI) for severe calcified coronary lesions are limited, and the clinical outcomes of PCI for different morphologies of calcified lesions are controversial. Overall, consecutive 576 lesions with severe calcification that were treated with PCI from 2010 to 2021 at Nagoya Heart Center were investigated. All lesions were assessed using invasive coronary angiogram (CAG) or computed tomography-CAG at 12months after DES implantation. We divided the patients into three groups based on the results of intravascular ultrasound (IVUS) imaging (concentric calcified lesion [CC] n = 273, eccentric calcified lesion [EC] n = 217, calcified nodule [CN] n = 86). The clinical and angiographic outcomes of each group were investigated retrospectively to compare the prognosis between the three groups and identify predictive factors for the device-oriented composite end points (DoCE). There were no differences in patient characteristics among the three groups, except that there were significantly more patients on dialysis in the CN group. The incidence of DoCE was significantly higher in the CN group than in the other groups (CC; 18.3% vs. EC; 23.5% vs. CN; 36.0%; Log-Rank test; p = 0.001). Cox regression analysis showed that the independent predictors of DoCE were CN, insulin use, hemodialysis, right coronary artery lesions, and calcium cracks. The incidence of DoCE was significantly higher in the CN group. Calcium cracks are crucial for improving outcomes in severely calcified lesions, being key predictors of DoCE.

Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.

Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%.

Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact.Using deep learning models, we extracted geometrical parameters and maximal attenuation (or calcium) burden index (ABI-max or CBI-max) from the intravascular ultrasound (IVUS) images of nonculprit vessels in 1115 patients. The endpoints included cardiac death, myocardial infarction, and target vessel revascularization of nonculprit vessel.Cardiac death occurred in 27 (2.4 %) patients at 3 years and 39 (3.5 %) patients at 5 years. At 5 years, the cardiac death-free survival rate was significantly lower with ABP-max ≥11.37 % vs. < 11.37 % (90.0 % vs. 98.7 %), CBI-max ≥13.40 % vs. < 13.40 % (92.8 % vs. 98.4 %), and percent atheroma volume ≥ 51.35 % vs. < 51.35 % (94.0 % vs. 97.7) (all log-rank p < 0.001). The independent predictors of 5-year cardiovascular mortality were age (hazard ratio [HR] 1.21), female sex (HR 0.33), history of heart failure (HR 6.06), chronic kidney disease (HR 18.28), ABI-max (HR 1.04), and CBI-max (HR 1.05). The independent determinants of 5-year target vessel revascularization of nonculprit vessel were fractional flow reserve (HR 0.95 per 0.01 increase), minimal lumen area (HR 0.63), and plaque burden (HR 1.15).In patients with nonculprit coronary artery lesions, a large burden of attenuated or calcified plaques predicted cardiac mortality, while IVUS geometry was associated with repeat revascularization. Thus, deep learning–based IVUS analysis of the whole target vessel may help clinicians identify high-risk lesions.

Does Artificial Intelligence Change How We Design User Interfaces? A Case Study from Representatives in the Medical Device Industry

Intravascular ultrasound (IVUS) is an imaging technique that allows interventional cardiologists (ICs) to capture and visually display images of blood vessels during Percutaneous Coronary Interventions (PCI). IVUS images allow ICs to: (i) evaluate the type and characteristics of lesions present, (ii) assess the appropriate intervention, (iii) determine the size of the stent or balloon to be used if they were needed, and (iv) examine if the intervention was successful. The size of the stent or balloon is determined by identifying, and then measuring, the diameters of vessel or lumen borders at the treatment locations. Identification of vessel borders is a manual time-consuming process that is rigorous to accomplish. Alternatively, a machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed. This industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.

Lesion-Level Effects of LDL-C–Lowering Therapy in Patients With Acute Myocardial Infarction

Previous studies investigated atherosclerotic changes induced by lipid-lowering therapy in extensive coronary segments irrespective of baseline disease burden (a vessel-level approach). To investigate the effects of lipid-lowering therapy on coronary lesions with advanced atherosclerotic plaque features and presumably higher risk for future events. The PACMAN-AMI randomized clinical trial (enrollment: May 2017 to October 2020; final follow-up: October 2021) randomized patients with acute myocardial infarction to receive alirocumab or placebo in addition to high-intensity statin therapy. In this post hoc lesion-level analysis, nonculprit lesions were identified as segments with plaque burden 40% or greater defined by intravascular ultrasound (IVUS). IVUS, near-infrared spectroscopy, and optical coherence tomography images at baseline and the 52-week follow-up were manually matched by readers blinded to treatment allocation. Data for this study were analyzed from October 2022 to November 2023. Alirocumab or placebo in addition to high-intensity statin therapy. Lesion-level imaging outcome measures, including high-risk plaque characteristics and phenotypes. Of the 245 patients in whom lesions were found, 118 were in the alirocumab group (mean [SD] age, 58.2 [10.0] years; 101 [85.6%] male and 17 [14.4%] female) and 127 in the placebo group (mean [SD] age, 57.7 [8.8] years; 104 [81.9%] male and 23 [18.1%] female). Overall, 591 lesions were included: 287 lesions (118 patients, 214 vessels) in the alirocumab group and 304 lesions (127 patients, 239 vessels) in the placebo group. Lesion-level mean change in percent atheroma volume (PAV) was -4.86% with alirocumab vs -2.78% with placebo (difference, -2.02; 95% CI, -3.00 to -1.05; P < .001). At the minimum lumen area (MLA) site, mean change in PAV was -10.14% with alirocumab vs -6.70% with placebo (difference, -3.36; 95% CI, -4.98 to -1.75; P < .001). MLA increased by 0.15 mm2 with alirocumab and decreased by 0.07 mm2 with placebo (difference, 0.21; 95% CI, 0.01 to 0.41; P = .04). Among 122 lipid-rich lesions, 34 of 55 (61.8%) in the alirocumab arm and 27 of 67 (41.8%) in the placebo arm showed a less lipid-rich plaque phenotype at follow-up (P = .03). Among 63 lesions with thin-cap fibroatheroma at baseline, 8 of 26 (30.8%) in the alirocumab arm and 3 of 37 (8.1%) in the placebo arm showed a fibrous/fibrocalcific plaque phenotype at follow-up (P = .02). At the lesion level, very intensive lipid-lowering therapy induced substantially greater PAV regression than described in previous vessel-level analyses. Compared with statin therapy alone, alirocumab treatment was associated with greater enlargement of the lesion MLA and more frequent transition of presumably high-risk plaque phenotypes into more stable, less lipid-rich plaque phenotypes. ClinicalTrials.gov Identifier: NCT03067844.

Age-specific sex differences in intravascular ultrasound based coronary atherosclerotic plaque characteristics

Insights in age- and sex-specific coronary atherosclerotic plaque characteristics may contribute to a better understanding of coronary artery disease and, ultimately, to its prevention and treatment. In 307 women and 406 men aged 20 to 90 years undergoing intravascular ultrasound imaging, sex-based differences in coronary atherosclerotic plaque characteristics were mainly present in younger patients, while these differences were less pronounced at advanced age.

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets. In the test set, the Dice similarity coefficients (DSC) were 0.966 ± 0.025 and 0.982 ± 0.017 for the lumen and EEM, respectively. Even at sites of extensive attenuation, the frame-level performance was excellent (DSCs > 0.96 for the lumen and EEM). The model (vs. the expert) showed a better temporal consistency for contouring the EEM. The agreement between the model- vs. the expert-derived cross-sectional and volumetric measurements was excellent in the independent retrospective cohort (all, intra-class coefficients > 0.94). The model-derived percent atheroma volume > 52.5% (area under curve 0.70, sensitivity 71% and specificity 67%) and plaque burden at the minimal lumen area site (area under curve 0.72, sensitivity 72% and specificity 66%) best predicted 3-year cardiac death and nonculprit-related target vessel revascularization, respectively. In the stented segment, the DSCs > 0.96 for contouring lumen and EEM were achieved. Applied to the 60-MHz IVUS images, the DSCs were > 0.97. In the external cohort with 45-MHz IVUS, the DSCs were > 0.96. The deep learning model accurately delineated vascular geometry, which may be cost-saving and support clinical decision-making.

High Frequency Ultrasound Transducer Based on Sm-Doped Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 Ceramic for Intravascular Ultrasound Imaging.

Sm-doped Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 (PMN-0.28PT) ceramic has been reported to exhibit very large piezoelectric response (d33~1300 pC/N) that can be comparable with PMN-0.30PT single crystal. Based on the Sm-doped PMN-0.28PT ceramics, a high frequency ultrasound transducer with the center frequency above 30 MHz has been designed and fabricated for intravascular ultrasound imaging, and the performance of the transducer was investigated via ultrasound pulse-echo tests. Further, for a porcine vessel wall, the 2D and 3D ultrasound images were constructed using signal acquisition and processing from the fabricated high-frequency transducer. The obtained details of the vessel wall by the IVUS transducer indicate that Sm-doped PMN-0.28PT ceramic is a promising candidate for high frequency transducers.

Is it possible to regress atherosclerotic lesions with long-term treatment with lipoprotein apheresis?

The article presents the up-to-date information on the effect of lipoprotein apheresis (LA) on atherosclerotic lesions. Some studies using modern diagnostic imaging techniques (such as intravascular ultrasound or magnetic resonance imaging) have clearly demonstrated plaque regression. Coronary angiography has also seen reversal or at least slow plaque progression. Plaque regression likely leads to a decrease in the incidence of atherosclerotic cardiovascular events (CVEs). However, this has not yet been fully proven. Recent data indicate that reduction in low-density lipoprotein cholesterol and lipoprotein(a) levels is not a major factor in reducing the incidence of atherosclerotic CVEs in patients undergoing extracorporeal treatment. The most significant risk factors in this case are older age and a greater CVE rate observed before the start of LA, as well as smoking. New studies using modern diagnostic imaging methods in patients receiving LA are necessary.

Evaluation of nontarget lesions in femoropopliteal disease using near-infrared spectroscopy intravascular ultrasound imaging.

In coronary artery disease (CAD), lipid-core-containing plaque (LCP) in nontarget lesions detected using near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) was related to increased major adverse cardiovascular events in patients with CAD. In the endovascular therapy field, few previous studies using NIRS-IVUS revealed the presence of LCPs in severe stenotic lesions of femoropopliteal disease. This study aimed to assess the plaque morphology of nontarget lesions, especially LCPs, and compare it with that of target lesions using NIRS-IVUS in patients with femoropopliteal disease. This single-center prospective observational study included 14 patients who underwent endovascular therapy for FP disease. NIRS-IVUS assessment was performed on the entire FP arterial segment. Forty-one LCP lesions with a maximum lipid-core burden index in any 4-mm region (max LCBI4mm) > 100 were detected using NIRS-IVUS. We evaluated the patient and lesion characteristics. LCP lesions were divided into the target (n = 18) and nontarget (n = 23) lesion groups for comparison. Patient characteristics were notable for advanced age (76.8 ± 6.6 years); high proportion of males (78.7%); and high incidence of hypertension (100%), dyslipidemia (78.6%), diabetes (64.3%). Regarding NIRS findings, the target lesion group exhibited a significantly smaller proportion of LCPs concerning the lesion length (25.9 ± 15.7% vs. 50.6 ± 29.2%, p = 0.002) than the nontarget lesion group. Conversely, there were no significant differences in the value of max LCBI4mm (284.4 ± 153.4 vs. 289.5 ± 113.1, p = 0.90), length of LCP lesion (9.8 ± 9.7 mm vs. 10.7 ± 6.9 mm, p = 0.74), and distribution of LCPs (p = 0.08) between the groups. In addition, the number of LCPs in the target FP artery positively correlated with max LCBI4mm in the target FP artery (r = 0.671, p = 0.008). NIRS-IVUS findings demonstrated the presence of LCPs in nontarget lesions in patients with FP disease. Moreover, the abundance of LCPs in nontarget lesions was similar to that in target lesions in FP disease.

Multi-class plaque segmentation in Intravascular Ultrasound via inter-frame feature fusion and contrast feature extraction

Intravascular Ultrasound (IVUS) is an effective aid in diagnosing cardiovascular diseases, with plaque segmentation in IVUS images being a pivotal step in assessing vascular plaque risk. However, IVUS plaque segmentation encounters two major challenges: (1) the variability in plaque shapes makes it difficult to locate plaques accurately, and (2) distinguishing plaque types is challenging due to the similarity in visual features among different types. To tackle these challenges, we propose a novel framework for IVUS plaque segmentation. Our approach incorporates a segmentation network featuring a dual feature attention(DFA) module and a cross feature fusion(CFF) module. Additionally, we introduce contrastive learning to enhance plaque sensitivity and extract contrast features within our network. We evaluate our method using a private IVUS dataset, achieving the state-of-the-art performance with a Dice score of 84.79% and a 14.17 HD value. Our approach represents significant progress in the field of multi-class IVUS plaque segmentation, making it a more effective tool to assist doctors in diagnosing vascular plaques.