Intravascular Ultrasound Imaging Research Articles

A new agent for contrast-enhanced intravascular ultrasound imaging in vitro: polybutylcyanoacrylate nanoparticles with drug-carrying capacity

This study prepared and evaluated polymeric polybutylcyanoacrylate (PBCA) nanoparticles (NPs) that can be used as a new agent for contrast-enhanced intravascular ultrasound (IVUS) imaging with drug delivery capacity. The nanoformulation was successfully developed using suspension polymerisation and characterised in terms of size, size distribution, zeta potential, morphology, stability, toxicity effects, imaging effects and drug release study. The results showed that the nanoparticles were round and hollow, with a particle diameter of 215.8 ± 25.3 nm and a zeta potential of −22.2 ± 4.1 mV. In vitro experiments, the nanoparticles were safe, non-toxic, and stable in nature with the capacity to carry and release drug (ant-miR-126). Moreover, the nanoparticles can match the high-frequency probe of commercially IVUS as a contrast agent to improve the resolution of imaging (the mean echo intensity ratio in the vascular wall increased significantly from 10.89 ± 1.10 at baseline, to 24.51 ± 1.91 during injection and 43.70 ± 0.88 after injection, respectively p < .0001). Overall, a new nano agent with drug-carrying capacity was prepared, which can be used in combination with IVUS for simultaneous diagnosis and targeted therapy of coronary atherosclerosis.

Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images

Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features. This way limits the generalization of these methods. The existing semantic segmentation methods integrate low-level and high-level features to enhance generalization performance. But these methods also introduce additional interference, which is harmful to solving structural intrinsic distractions. Distraction cue methods attempt to address structural intrinsic distractions by removing interference from the features through a unique decoder. However, they tend to overlook the problem of inter-structural distractions. In this paper, we propose distraction-aware hierarchical learning (DHL) for vascular structure segmentation in IVUS images. Inspired by distraction cue methods for removing interference in a decoder, the DHL is designed as a hierarchical decoder that gradually removes structure-dependent distractions. The DHL includes global perception process, distraction perception process and structural perception process. The global perception process and distraction perception process remove structural intrinsic distractions then the structural perception process removes inter-structural distractions. In the global perception process, the DHL searches for the coarse structural region of the vascular structures on the slice of IVUS sequence. In the distraction perception process, the DHL progressively refines the coarse structural region of the vascular structures to remove structural distractions. In the structural perception process, the DHL detects regions of inter-structural distractions in fused structure features then separates them. Extensive experiments on 361 subjects show that the DHL is effective (e.g., the average Dice is greater than 0.95), and superior to ten state-of-the-art IVUS vascular structure segmentation methods.

Intravascular ultrasound imaging of pulmonary artery with high-altitude pulmonary hypertension.

Intravascular ultrasound imaging of pulmonary artery with high-altitude pulmonary hypertension.

Finite element method simulation, design, and fabrication of micromachined high-frequency piezoelectric composite with hexagonal pillars for intravascular ultrasound imaging

Finite element method simulation, design, and fabrication of micromachined high-frequency piezoelectric composite with hexagonal pillars for intravascular ultrasound imaging

Long-term outcome of percutaneous endovascular stenting in external iliac artery endofibrosis.

External iliac artery endofibrosis (EIAE) is a rare vascular disease which has been traditionally seen in avid cyclists. The conventional approach has been surgery, although no high-quality evidence suggests superiority of surgery over percutaneous endovascular intervention. There are limited data on the efficacy of stenting in EIAE. Over a 14-year period, we treated 10 patients (13 limbs) with EIAE with stents. These patients had declined surgery. The mean follow up was 8.4 ± 3.3 years. There were eight women. Five patients were competitive runners, three were cyclists, and two were triathletes. The mean age was 40.7 ± 2.9 years and body mass index was 19.46 ± 1.6. Intravascular ultrasound (IVUS) was used in eight limbs. Procedural success was achieved in all. The recurrence of symptoms occurred in three patients at a mean of 9.3 ± 2.1 months postindex intervention. The other seven patients remained symptom free. IVUS revealed a pathognomonic finding which we termed 'perfect circle appearance'. It results from symmetric or asymmetric hypertrophy of one or more layers of the arterial wall leading to negative remodeling, which creates a distinct echo dense structure contrasting itself from the luminal blood's echoluscent appearance. It is identical to IVUS images of diffuse venous stenosis with important implications in the treatment technique. We conclude that stenting in EIAE is safe and effective with a good long-term outcome. It can be an alternative to surgery, particularly in those patients who refuse a surgical approach. The IVUS image is pathognomonic and 'sine qua non' of EIAE.

Successful healing of aneurysmal false lumen using a second-generation drug-eluting stent in spontaneous coronary artery dissection: a case report

BackgroundAccording to 2023 ESC Guideline, conservative medical management is generally recommended for the treatment of spontaneous coronary artery dissection (SCAD) except for patients with signs of ongoing myocardial ischemia. However, in some cases, invasive treatment (coronary artery bypass graft surgery or percutaneous coronary intervention (PCI)) is performed because of the progression of aneurysm in SCAD. Although there is no established strategy for the management of coronary aneurysm in SCAD, we report a case of successful healing of aneurysmal false lumen (AFL) using a second-generation drug-eluting stent (DES) in SCAD.Case presentationA 44-year-old woman without any cardiovascular risk factors was transferred to our hospital due to inferior myocardial infarction. Coronary angiography (CAG) showed multiple SCADs in the coronary artery. We performed PCI to the distal right coronary artery (RCA) because the RCA showed severe stenosis (99%) with bradycardia. Six days after the first PCI, SCAD relapsed in the mid left anterior descending artery (LAD). Furthermore, AFL was observed by intravascular ultrasound imaging. To avoid enlargement of the AFL and progression of the dissection toward the proximal site of the LAD, we performed PCI to the mid LAD to seal the entry tear of the dissection using a second-generation DES. CAG revealed that the AFL in the mid LAD completely diminished at 1 year after PCI.ConclusionsThe implantation of a second-generation DES might be one of therapeutic options for sealing AFL in SCAD patients.

Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline’s plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.

Diagnostic performance of intravascular ultrasound-based fractional flow reserve in evaluating of intermediate left main stenosis.

The recently introduced ultrasonic flow ratio (UFR), is a novel fast computational method to derive fractional flow reserve (FFR) from intravascular ultrasound (IVUS) images. In the present study, we evaluate the diagnostic performance of UFR in patients with intermediate left main (LM) stenosis. This is a prospective, single center study enrolling consecutive patients with presence of intermediated LM lesions (diameter stenosis of 30%-80% by visual estimation) underwent IVUS and FFR measurement. An independent core laboratory assessed offline UFR and IVUS-derived minimal lumen area (MLA) in a blinded fashion. Both UFR and FFR were successfully achieved in 41 LM patients (mean age, 62.0 ± 9.9 years, 46.3% diabetes). An acceptable correlation between UFR and FFR was identified (r = 0.688, P < 0.0001), with an absolute numerical difference of 0.03 (standard difference: 0.01). The area under the curve (AUC) in diagnosis of physiologically significant coronary stenosis for UFR was 0.94 (95% CI: 0.87-1.01), which was significantly higher than angiographic identified stenosis > 50% (AUC = 0.66, P < 0.001) and numerically higher than IVUS-derived MLA (AUC = 0.82; P = 0.09). Patient level diagnostic accuracy, sensitivity and specificity for UFR to identify FFR ≤ 0.80 was 82.9% (95% CI: 70.2-95.7), 93.1% (95% CI: 82.2-100.0), 58.3% (95% CI: 26.3-90.4), respectively. In patients with intermediate LM diseases, UFR was proved to be associated with acceptable correlation and high accuracy with pressure wire-based FFR as standard reference. The present study supports the use of UFR for functional evaluation of intermediate LM stenosis.

Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease.

Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

Intravascular ultrasound use in peripheral arterial and deep venous interventions: Multidisciplinary expert opinion from SCAI/AVF/AVLS/SIR/SVM/SVS

Percutaneous revascularization is the primary strategy for treating lower extremity venous and arterial disease. Angiography is limited by its ability to accurately size vessels, precisely determine the degree of stenosis and length of lesions, characterize lesion morphology, or correctly diagnose postintervention complications. These limitations are overcome with use of intravascular ultrasound (IVUS). IVUS has demonstrated the ability to improve outcomes following percutaneous coronary intervention, and there is increasing evidence to support its benefits in the setting of peripheral vascular intervention. At this stage in its evolution, there remains a need to standardize the use and approach to peripheral vascular IVUS imaging. This manuscript represents considerations and consensus perspectives that emerged from a roundtable discussion including 15 physicians with expertise in interventional cardiology, interventional radiology, and vascular surgery, representing 6 cardiovascular specialty societies, held on February 3, 2023. The roundtable’s aims were to assess the current state of lower extremity revascularization, identify knowledge gaps and need for evidence, and determine how IVUS can improve care and outcomes for patients with peripheral arterial and deep venous pathology.

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

BackgroundCoronary 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. MethodsFifty-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. ResultsSegment-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 ​% and 60.7 ​%) 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. ConclusionsConventional 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.

Coupled Contour Regression for Efficient Delineation of Lumen and External Elastic Lamina in Intravascular Ultrasound Images.

Automatic delineation of the lumen and vessel contours in intravascular ultrasound (IVUS) images is crucial for the subsequent IVUS-based analysis. Existing methods usually address this task through mask-based segmentation, which cannot effectively handle the anatomical plausibility of the lumen and external elastic lamina (EEL) contours and thus limits their performance. In this article, we propose a contour encoding based method called coupled contour regression network (CCRNet) to directly predict the lumen and EEL contour pairs. The lumen and EEL contours are resampled, coupled, and embedded into a low-dimensional space to learn a compact contour representation. Then, we employ a convolutional network backbone to predict the coupled contour signatures and reconstruct the signatures to the object contours by a linear decoder. Assisted by the implicit anatomical prior of the paired lumen and EEL contours in the signature space and contour decoder, CCRNet has the potential to avoid producing unreasonable results. We evaluated our proposed method on a large IVUS dataset consisting of 7204 cross-sectional frames from 185 pullbacks. The CCRNet can rapidly extract the contours at 100 fps. Without any post-processing, all produced contours are anatomically reasonable in the test 19 pullbacks. The mean Dice similarity coefficients of our CCRNet for the lumen and EEL are 0.940 and 0.958, which are comparable to the mask-based models. In terms of the contour metric Hausdorff distance, our CCRNet achieves 0.258 mm for lumen and 0.268 mm for EEL, which outperforms the mask-based models.

Absolute zero-contrast percutaneous coronary interventions: An intravascular ultrasound-guided case series and literature review

PurposeContrast media (CM)-associated adverse effects including mainly acute kidney injury and hypersensitivity reactions still remain a significant treatment burden to vulnerable patients requiring percutaneous coronary intervention (PCI). The complete omission of CM administration accompanied by intravascular ultrasound (IVUS) guidance may offer an appropriate revascularization treatment. MethodsWe hereby present a case series of four patients with challenging coronary lesions and relative/absolute contraindications to CM use [(hypersensitivity reaction owning to CM, history of contrast-induced acute kidney injury, solitary kidney, or advanced chronic kidney disease (CKD)] who underwent absolute zero-contrast IVUS-guided PCI following a predetermined protocol. ResultsThe initial diagnostic angiography was reviewed for landmark identification, and multiple guidewires created a metallic road map providing additional landmarks and protected side branches. IVUS imaging was performed to determine lesion length, reference segments and landing zones, assess stent expansion/apposition and identify major complications. All procedures were successfully completed without any CM administration, renal function deterioration, pericardial effusion, or stent edge dissection. ConclusionsWe demonstrated the feasibility of absolute zero-contrast IVUS-guided PCI in patients with complex coronary lesions susceptible to CM-associated adverse effects. Since the safety of this strategy is well-documented in patients with CKD and challenging lesions morphology even in acute settings, evidence-based validation of this approach is capable of changing the otherwise conservative treatment of vulnerable patients where even minimum doses of CM may have detrimental effects. Summary for the annotated table of contentsPatients with vulnerable renal function as well as former hypersensitivity reaction to contrast media encounter a significant treatment burden regarding coronary artery disease. Zero-contrast IVUS-guided strategy based on a predetermined protocol provides a feasible alternative approach in patients susceptible to contrast media-associated adverse effects even in the presence of complex coronary lesions.

Comparison of deep learning-based image segmentation methods for intravascular ultrasound on retrospective and large image cohort study

ObjectivesThe aim of this study was to investigate the generalization performance of deep learning segmentation models on a large cohort intravascular ultrasound (IVUS) image dataset over the lumen and external elastic membrane (EEM), and to assess the consistency and accuracy of automated IVUS quantitative measurement parameters.MethodsA total of 11,070 IVUS images from 113 patients and pullbacks were collected and annotated by cardiologists to train and test deep learning segmentation models. A comparison of five state of the art medical image segmentation models was performed by evaluating the segmentation of the lumen and EEM. Dice similarity coefficient (DSC), intersection over union (IoU) and Hausdorff distance (HD) were calculated for the overall and for subsets of different IVUS image categories. Further, the agreement between the IVUS quantitative measurement parameters calculated by automatic segmentation and those calculated by manual segmentation was evaluated. Finally, the segmentation performance of our model was also compared with previous studies.ResultsCENet achieved the best performance in DSC (0.958 for lumen, 0.921 for EEM) and IoU (0.975 for lumen, 0.951 for EEM) among all models, while Res-UNet was the best performer in HD (0.219 for lumen, 0.178 for EEM). The mean intraclass correlation coefficient (ICC) and Bland–Altman plot demonstrated the extremely strong agreement (0.855, 95% CI 0.822–0.887) between model's automatic prediction and manual measurements.ConclusionsDeep learning models based on large cohort image datasets were capable of achieving state of the art (SOTA) results in lumen and EEM segmentation. It can be used for IVUS clinical evaluation and achieve excellent agreement with clinicians on quantitative parameter measurements.

Hemodynamic assessment of pci using ultrasonic flow ratio in multivessel cad

Abstract Background Ultrasonic flow ratio (UFR) is a novel method for fast computation of fractional flow reserve (FFR) from intravascular ultrasound. Recently studies have shown the feasibility of morphofunctional computational methods to reliably estimate coronary physiology stemming from intravascular ultrasound images. Purpose The objective of the study was to evaluate the reasons for suboptimal physiological results after PCI using the UFR. Methods The study population comprised 150 consecutive patients (283 vessels) who underwent intravascular ultrasound for optimization of PCI in three-vessel-disease as mandated in the ongoing MULTIVESSEL TALENT trial (NCT04390672). The UFR analysis of final IVUS pullback was performed at the CORRIB Core Lab by an analyst who was blinded to the clinical data using a prototype software package (IvusPlus prototype, Shanghai Pulse Medical, Shanghai, China). Suboptimal physiological results were defined as post PCI UFR value of &amp;lt;0.91. UFR drop of ≥0.05 (gradient) was taken as a significant drop either intra or outside of the stent. Based on the definition of the HAWKEYE study, Focal lesions were defined as UFR drop of ≥0.05 in 10mm, diffuse lesions were defined as progressive decline of UFR of ≥0.05 without any clear evidence of focal drop, and mixed lesions were defined as the combination of focal and diffuse lesions. Results The median UFR with IQR among 283 vessels was 0.94 (0.91-0.97). Sixty-four (22.61%) of the 283 vessels had a UFR less than 0.91. We measured gradients using the UFR pull back curve intra-stent and outside of stent (Figure 1) in vessels with UFR &amp;lt;0.91. Significant "intra stent gradient", "out of stent gradient", combined "intra stent "and "out of stent" gradients were seen in 28 (44%), 18 (28%), and 10 (16%) vessels respectively. No clearly defined gradients were found in 8 (12%) vessels. Out of the 28 "out of stent" gradients, 20 were diffuse lesions, 5 were focal lesions and 3 was a mixed lesion. Conclusion IVUS has been widely used in optimization of PCI especially in scenarios with complex multi vessel disease. Combination of IVUS and FFR enable the operator to conform to the highest standards in PCI. This study has demonstrated that using UFR it is possible to identify the cause(s) of suboptimal PCI results and thereby to further optimize PCI results.

Minimum stent area, stent expansion and ultrasonic flow ratio post pci: insights from multivessel talent trial

Abstract Background Ultrasonic flow ratio (UFR) is a novel method for fast computation of fractional flow reserve (FFR) from intravascular ultrasound. Recently studies have shown the feasibility of morphofunctional computational methods to reliably estimate coronary physiology from intravascular ultrasound images. Purpose To assess minimum stent area and stent expansion and its relation to ultrasonic flow ratio post percutaneous coronary intervention. Methods The study population consisted of 150 consecutive patients (280 vessels) who underwent intravascular ultrasound for optimization of PCI for three vessel disease as mandated in the ongoing MULTIVESSEL TALENT trial (NCT04390672). The UFR analysis was performed at the CORRIB Core Lab by an analyst who was blinded to the clinical data using a prototype software package (IvusPlus prototype, Pulse Medical Imaging Technology, Shanghai, China). The stent expansion and minimum stent area was automatically detected by the software. Results The median and mean UFR of 280 vessels post-PCI was 0.94 (0.91-0.97).and 0.93±0.04, respectively. Twenty three percent (n=115) of the 280 vessels had a UFR less than 0.91.The average minimum stent area (MSA) among the population was 5.82 ± 1.95 mm2. The mean stent expansion among the population was 97% ± 22%. We compared the site reported MSA with the software derived MSA which showed strong correlation (r=0.859; P&amp;lt;0.001). There was no correlation between MSA and UFR (r= -0.13, P=0.81). The stent expansion among patients who had UFR &amp;lt;0.91 when compared to ≥0.91 was significantly lower (82% vs 98%, P&amp;lt;0.001). The delta UFR within the stent was significantly higher in patients who had a UFR&amp;lt;0.94 vs ≥0.94 (0.06 vs 0.02; P&amp;lt;0.01). Delta UFR within the stent was higher when the stent expansion was &amp;lt;80% and &amp;lt;90% when compared to &amp;gt;80% and &amp;gt;90% (0.07 vs 0.03; P&amp;lt;0.01) and (0.06 vs 0.02; P&amp;lt;0.01) respectively. Conclusion Combination of IVUS and FFR enables the operator to conform to highest standards in PCI. This study demonstrates that UFR is not related to the minimum stent area, and patients who have UFR &amp;lt;0.94 have sub-optimal stent expansion.MSA and UFRUFR and Stent expansion

A Backing-Layer-Shared Miniature Dual-Frequency Ultrasound Probe for Intravascular Ultrasound Imaging: In Vitro and Ex Vivo Validations.

Intravascular ultrasound (IVUS) imaging has been extensively utilized to visualize atherosclerotic coronary artery diseases and to guide coronary interventions. To receive ultrasound signals within the vessel wall safely and effectively, miniaturized ultrasound transducers that meet the strict size constraints and have a simple manufacturing procedure are highly demanded. In this work, the first known IVUS probe that employs a backing-layer-shared dual-frequency structure and a single coaxial cable is introduced, featuring a small thickness and easy interconnection procedure. The dual-frequency transducer is designed to have center frequencies of 30 MHz and 80 MHz, and both have an aperture size of 0.5 mm × 0.5 mm. The total thickness of the dual-frequency transducer is less than 700 µm. In vitro phantom imaging and ex vivo porcine coronary artery imaging experiments are conducted. The low-frequency transducer achieves spatial resolutions of 40 µm axially and 321 µm laterally, while the high-frequency transducer exhibits axial and lateral resolutions of 17 µm and 247 µm, respectively. A bandpass filter is utilized to separate the ultrasound images. Combining in vitro phantom imaging analysis with ex vivo imaging validation, a comprehensive demonstration of the promising application of the proposed miniature ultrasound probe is established.

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

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. 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. 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. 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.

Coronary Plaque Characteristics Associated With Major Adverse Cardiovascular Events in Atherosclerotic Patients and Lesions: A Systematic Review and Meta-Analysis

BackgroundThe clinical value of high-risk coronary plaque characteristics (CPCs) to inform intensified medical therapy or revascularization of non–flow-limiting lesions remains uncertain. ObjectivesThe authors performed a systematic review and meta-analysis to study the prognostic impact of CPCs on patient-level and lesion-level major cardiovascular adverse events (MACE). MethodsThirty studies (21 retrospective, 9 prospective) with 30,369 patients evaluating the association of CPCs with MACE were included. CPCs included high plaque burden, low minimal lumen area, thin cap fibroatheroma, high lipid core burden index, low-attenuation plaque, spotty calcification, napkin ring sign, and positive remodeling. ResultsCPCs were evaluated with the use of intracoronary modalities in 9 studies (optical coherence tomography in 4 studies, intravascular ultrasound imaging in 3 studies, and near-infrared spectroscopy intravascular ultrasound imaging in 2 studies) and by means of coronary computed tomographic angiography in 21 studies. CPCs significantly predicted patient-level and lesion-level MACE in both unadjusted and adjusted analyses. For most CPCs, accuracy for MACE was modest to good at the patient level and moderate to good at the lesion level. Plaques with more than 1 CPC had the highest accuracy for lesion-level MACE (AUC: 0.87). Because the MACE rate was low, estimated positive predictive values for lesion-level MACE were modest. Results were mostly consistent across imaging modalities and clinical presentations, and in studies with prevailing hard outcomes. ConclusionsCharacterization of CPCs identifies high-risk atherosclerotic plaques that place lesions and patients at risk for future MACE, albeit with modest sensitivity and positive predictive value (Coronary Plaque Characteristics Associated With Major Adverse Cardiovascular Events Among Atherosclerotic Patients and Lesions; CRD42021251810)