Intravascular Ultrasound Imaging Research Articles

Improved real-time segmentation of Intravascular Ultrasound images using coordinate-aware fully convolutional networks.

Segmentation of Intravascular Ultrasound (IVUS) images into Lumen and Media (interior and exterior) artery vessel walls is highly clinically relevant in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. When fused with position data, such segmentations also play a key role in reconstructing 3D representations of arteries. Automated segmentation in real-time is known to be a difficult image analysis problem, primarily due to artefacts commonly present in IVUS ultrasound images such as shadows, guide-wire effects, and side-branches. An additional challenge is the limited amount of expert labelled IVUS data, which limits the application of many well-performing deep learning models from other domains. To exploit the circular layered structure of the artery in B-Mode images, we propose a multi-class fully convolutional semantic segmentation network based on a minimal U-Net architecture augmented with learned translation dependence in the polar domain. The coordinate awareness in the multi-class segmentation allows the model to exploit relative spatial context about the interior and exterior vessel walls which are simply separable in polar coordinates. After training on 109 expert-labelled examples, our model significantly outperforms the state-of-the art in terms of mean Jaccard Measure (0.91 vs. 0.89) and Hausdorff distance (0.32 mm vs. 0.48 mm) on Media segmentation, and reaches equivalent performance on Lumen segmentation when evaluated on a standard publicly available dataset of 326 IVUS B-Mode images captured by 20 Mhz ultrasound probes. Using an order of magnitude fewer trainable parameters than the previous state-of-the-art, our model runs over 50 times faster and is able to execute in only 3 ms on a common GPU, achieving both leading accuracy and practical real-time performance.

Intravascular Ultrasound Imaging and Endovascular Treatment of Left Ventricle Assist Device (HM3) Outflow Graft Obstruction

Intravascular Ultrasound Imaging and Endovascular Treatment of Left Ventricle Assist Device (HM3) Outflow Graft Obstruction

Greater Plaque Volume and Cholesterol Content Explain an Increased Incidence of Non-Culprit Events in Diabetic Patients: A Lipid Rich Plaque Substudy

The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time.IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard.The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754–1.061) with similar results in frames portraying calcific plaques or side branches.The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.

Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images

AimsThe aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. Methods and resultsIVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard.The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754–1.061) with similar results in frames portraying calcific plaques or side branches. ConclusionsThe developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.

Assessment with clinical data of a coupled bio-hemodynamics numerical model to predict leukocyte adhesion in coronary arteries

Numerical simulations of coupled hemodynamics and leukocyte transport and adhesion inside coronary arteries have been performed. Realistic artery geometries have been obtained for a set of four patients from intravascular ultrasound and angiography images. The numerical model computes unsteady three-dimensional blood hemodynamics and leukocyte concentration in the blood. Wall-shear stress dependent leukocyte adhesion is also computed through agent-based modeling rules, fully coupled to the hemodynamics and leukocyte transport. Numerical results have a good correlation with clinical data. Regions where high adhesion is predicted by the simulations coincide to a good approximation with artery segments presenting plaque increase, as documented by clinical data from baseline and six-month follow-up exam of the same artery. In addition, it is observed that the artery geometry and, in particular, the tortuosity of the centerline are a primary factor in determining the spatial distribution of wall-shear stress, and of the resulting leukocyte adhesion patterns. Although further work is required to overcome the limitations of the present model and ultimately quantify plaque growth in the simulations, these results are encouraging towards establishing a predictive methodology for atherosclerosis progress.

Capsule networks for segmentation of small intravascular ultrasound image datasets

PurposeIntravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks.MethodsWe systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters.ResultsContrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance.ConclusionCapsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.

Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow.

Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist's visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making.

Mechanically Rotating Intravascular Ultrasound (IVUS) Transducer: A Review.

Intravascular ultrasound (IVUS) is a valuable imaging modality for the diagnosis of atherosclerosis. It provides useful clinical information, such as lumen size, vessel wall thickness, and plaque composition, by providing a cross-sectional vascular image. For several decades, IVUS has made remarkable progress in improving the accuracy of diagnosing cardiovascular disease that remains the leading cause of death globally. As the quality of IVUS images mainly depends on the performance of the IVUS transducer, various IVUS transducers have been developed. Therefore, in this review, recently developed mechanically rotating IVUS transducers, especially ones exploiting piezoelectric ceramics or single crystals, are discussed. In addition, this review addresses the history and technical challenges in the development of IVUS transducers and the prospects of next-generation IVUS transducers.

Reverse redistribution-like change on dipyridamole-stress 99mTc-tetrofosmin imaging in a patient with angiographically mild coronary artery stenosis

A 50-year-old man with a 1-month history of chest pain presented to our hospital. Transthoracic echocardiography showed normal left ventricular wall motion. Coronary computed tomography angiography revealed a mild stenosis with low-density non-calcified plaque in the proximal left anterior descending coronary artery (LAD) (Figure 1A, yellow arrows); therefore, the patient underwent a 1-day protocol of dipyridamole stress and rest 99mTc-tetrofosmin myocardial perfusion imaging (MPI). Baseline electrocardiography was normal; however, he developed angina 8 minutes after the start of .56 mg·kg−1 (.14 mg·kg−1·min−1 for 4 minutes) of intravenous dipyridamole infusion and his electrocardiogram showed ST-segment elevation in the precordial leads (Figure 2). After intravenous aminophylline with sublingual nitroglycerin was given, the ST-segment elevation was gradually resolved. Stress MPI showed no perfusion defect (Figure 1B) with abnormal wall motion in the anterior and septal walls on gated MPI (Supplementary Material). He had recurrent angina after the stress MPI. Rest MPI at 2 hours after the stress test showed reverse redistribution-like reduced uptake in the LAD territory (Figure 1B) with normal left ventricular wall motion on gated MPI (Supplementary Material). As with the coronary computed tomography, invasive coronary angiography via the right radial artery demonstrated the mild stenosis in the proximal LAD (Figure 3, yellow arrow). Intravascular ultrasound and optical coherence tomography images showed coronary plaque with neovascularization (Figure 3, red arrows) and small thrombi (Figure 3, white arrows) at the minimum lumen area site, indicating the increased vulnerability of the coronary plaque.1 Percutaneous coronary intervention (PCI) with a drug-eluting stent (4.0 × 33 mm) was successfully performed (Figure 3, red arrows). On the day following the PCI, he underwent cardiopulmonary exercise testing and did not present with chest pain at peak exercise (VO2 at peak was 22.5 mL·kg−1·min−1 [6.4 METs]). Open in a separate window Figure 1 (A) Coronary computed tomography angiography revealed a mild stenosis (yellow arrows) with low-density non-calcified plaque in the proximal left anterior descending coronary artery (LAD). (B) Dipyridamole stress and rest 99mTc-tetrofosmin myocardial perfusion imaging (MPI). Stress MPI (300 MBq of 99mTc-tetrofosmin) showed no perfusion defect, whereas rest MPI (900 MBq of 99mTc-tetrofosmin) 2 hours after the stress test showed reduced uptake (reverse redistribution) in the LAD territory

Rapid Progression of Coronary Atherosclerosis in Patients Taking an Oral Antitumor, Multikinase Receptor Inhibitor.

Rapid Progression of Coronary Atherosclerosis in Patients Taking an Oral Antitumor, Multikinase Receptor Inhibitor.

Fully automatic segmentation of intima/adventitia of the vessel using Bezier curve from intravascular ultrasound

Although medical image segmentation field is regarded as one of most established fields, still fully automatic segmentation to extract target object with high accuracy from intravascular ultrasound (IVUS) is very active area of research. In this paper, we propose a fully automatic morphological approach using Bezier curve in interpolating the boundaries of intima/adventitia of the vessel from IVUS with careful binarization algorithms. In experiment with 800 IVUS images, the proposed method is as good as fuzzy C-means based approach in comparison with human expert’s result with 84.4% satisfaction and better than other morphological method in all performance indices of curve fitting with 97.02% in accuracy and 58.19% in precision.

Association of quantitative flow ratio-derived microcirculatory indices with anatomical-functional discordance in intermediate coronary lesions.

Discrepancy between coronary lesion severity and functional significance has always been a relevant issue in the management of patients undergoing coronary angiography and/or revascularization. We sought to investigate the relationship between quantitative flow ratio (QFR)-derived microcirculatory indices and anatomical-functional mismatch/reverse mismatch in intermediate coronary lesions. Intravascular ultrasound (IVUS) imaging and QFR were analyzed in 117 de novo intermediate coronary lesions. Lesions with QFR ≤ 0.8 were considered hemodynamically significant. Anatomical significance of the lesions was defined according to the best cutoff value of combined IVUS parameters for predicting QFR ≤ 0.8. QFR-derived microcirculatory indices including contrast-flow QFR minus fixed-flow QFR (cQFR-fQFR), hyperemic flow velocity and angiography-derived index of microcirculatory resistance (IMRangio) were calculated. The best cutoff values of IVUS parameters for predicting QFR ≤ 0.8 were minimum lumen area (MLA) 3.1mm2 and plaque burden (PB) 70%, with area under the curve of 0.635 and 0.703, respectively. The total discordance rate of lesion functional significance between IVUS and QFR assessments was 26.5%, with 21 lesions (17.9%) being classified as mismatch (MLA ≤ 3.1mm2 and PB ≥ 70% and QFR > 0.8) and 10 lesions (8.5%) as reverse-mismatch (MLA > 3.1 mm2 or PB < 70% and QFR ≤ 0.8). At multivariate analysis, IMRangio was identified as an independent predictor of mismatch (OR1.675, 95%CI:1.176-2.386, P = 0.004), whereas hyperemic flow velocity was identified as an independent predictor of reverse-mismatch (OR 1.233, 95%CI:1.073-1.416, P = 0.003). In intermediate coronary lesions, although MLA 3.1mm2 and PB 70% determined by IVUS are predictive of QFR-defined functional significance, the discordance rate remains substantial. QFR-derived microcirculatory indices are independently associated with anatomical-functional discordance between IVUS and QFR assessments.

Recent Advances in Transducers for Intravascular Ultrasound (IVUS) Imaging.

As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology and other associated blood and vessel properties can be precisely assessed in IVUS imaging. Ultrasound transducer, as the key component of an IVUS system, is critical in determining the IVUS imaging performance. In recent years, a wide range of achievements in ultrasound transducers have been reported for IVUS imaging applications. Herein, a comprehensive review is given on recent advances in ultrasound transducers for IVUS imaging. Firstly, a fundamental understanding of IVUS imaging principle, evaluation parameters and IVUS catheter are summarized. Secondly, three different types of ultrasound transducers (piezoelectric ultrasound transducer, piezoelectric micromachined ultrasound transducer and capacitive micromachined ultrasound transducer) for IVUS imaging are presented. Particularly, the recent advances in piezoelectric ultrasound transducer for IVUS imaging are extensively examined according to their different working mechanisms, configurations and materials adopted. Thirdly, IVUS-based multimodality intravascular imaging of atherosclerotic plaque is discussed. Finally, summary and perspectives on the future studies are highlighted for IVUS imaging applications.

Target Lesion Lipid Content Detected by Near-Infrared Spectroscopy After Stenting and the Risk of Subsequent Target Lesion Failure

After percutaneous coronary intervention (PCI), it is unknown whether retained lipid content in the stented segment increases the risk of target lesion failure (TLF). This study evaluated the association between retained lipid content in the stented segment detected by post-PCI intracoronary near-infrared spectroscopy and TLF. Approach and Results: After the performance of PCI, post-PCI near-infrared spectroscopy-intravascular ultrasound images were acquired and analyzed by an independent core laboratory for retained lipid content in the stented segment, quantified by the maximum lipid core burden index in 4 mm (maxLCBI4mm). The primary outcome was TLF during follow-up, defined as a composite of cardiovascular death, target vessel myocardial infarction, and clinically driven target lesion revascularization. Among 202 patients with 209 target lesions treated by PCI and followed for 3.5±1.4 years, baseline post-PCI near-infrared spectroscopy-intravascular ultrasound images revealed a significantly greater maxLCBI4mm in stented lesions with (297 [211, 401]) versus without (119 [9, 258]) TLF during follow-up (P=0.006). By multivariate logistic regression, maxLCBI4mm in the stented segment was independently associated with subsequent TLF (odds ratio, 1.6 [95% CI, 1.2-2.1] for every 100-unit increase, P=0.004). By receiver-operating characteristic analysis, the optimal residual maxLCBI4mm threshold in the stented segment for subsequent TLF was 200. Stented lesions with a residual maxLCBI4mm >200 had significantly greater TLF during follow-up than stented lesions with a maxLCBI4mm ≤200 (15.0% versus 3.1%, P=0.002). Retained lipid content detected by near-infrared spectroscopy in the stented segment after PCI was associated with an increased risk of subsequent TLF.

Adventitia segmentation in intravascular ultrasound images based on improved Snake algorithm

Segmentation of coronary artery intravascular ultrasound (IVUS) images is the first step in assessing blood vessel morphology and detecting possible atherosclerotic lesions. To overcome the disadvantage of manual acquisition of initial contour in traditional Snake algorithm and construction of external force only from gradient information, an improved Snake algorithm is proposed for automatic outer membrane segmentation in intravascular ultrasound images. Firstly, we design a basic segmentation algorithm to automatically extract the approximate contour of the first frame, and this is iterated as the initial contour of the Snake algorithm. Next, the extended structure tensor is combined with the Snake algorithm to make it part of the external force of the Snake algorithm, and then the curve is evolved to complete the segmentation. To verify the effectiveness of the proposed method, this paper not only shows the experimental results qualitatively, but also quantitatively measures the RMSE and RDD indices. Comparative experiments show that the proposed method not only has higher accuracy, but also has better timeliness.

Effects of the PCSK9 antibody alirocumab on coronary atherosclerosis in patients with acute myocardial infarction: a serial, multivessel, intravascular ultrasound, near-infrared spectroscopy and optical coherence tomography imaging study–Rationale and design of the PACMAN-AMI trial

The risk for cardiovascular adverse events after acute myocardial infarction (AMI) remains high despite potent medical treatment including low-density lipoprotein cholesterol (LDL-C) lowering with statins. Proprotein convertase subtilisin/kexin type 9 (PCSK9) antibodies substantially reduce LDL-C when added to statin. Alirocumab, a monoclonal antibody to PCSK9, reduces major adverse cardiovascular events after AMI. The effects of alirocumab on coronary atherosclerosis including plaque burden, plaque composition and fibrous cap thickness in patients presenting with AMI remains unknown. To determine the effect of LDL-C lowering with alirocumab on top of high-intensity statin therapy on intravascular ultrasound (IVUS)-derived percent atheroma volume (PAV), near-infrared spectroscopy (NIRS)-derived maximum lipid core burden index within 4 mm (maxLCBI4 mm) and optical coherence tomography (OCT)-derived fibrous cap thickness (FCT) in patients with AMI. In this multicenter, double-blind, placebo-controlled trial, 300 patients with AMI (ST-elevation or non-ST-elevation myocardial infarction) were randomly assigned to receive either biweekly subcutaneous alirocumab (150 mg) or placebo beginning <24 hours after the acute event as add-on therapy to rosuvastatin 20 mg. Patients undergo serial IVUS, NIRS and OCT in the two non-infarct related arteries at baseline (at the time of treatment of the culprit lesion) and at 52 weeks. The primary endpoint, change in IVUS-derived PAV, and the powered secondary endpoints, change in NIRS-derived maxLCBI4 mm, and OCT-derived minimal FCT, will be assessed 52 weeks post randomization. The PACMAN-AMI trial will determine the effect of alirocumab on top of high-intensity statin therapy on high-risk coronary plaque characteristics as assessed by serial, multimodality intracoronary imaging in patients presenting with AMI. NCT03067844.

Intra-Vascular Ultra Sound Image Classification System for the Diagnosis of Coronary Artery Disease Using Adaptive Wavelet Transform with Support Vector Machine

In this study, an efficient Decision Support System (DSS) is presented to classify coronary artery disease using Intra-Vascular Ultra Sound (IVUS) images. IVUS images are commonly used to diagnose coronary artery diseases. Wavelet transform is a multiresolution texture analysis tool which is applied to various image analysis and classification systems. Unlike the wavelet transform, Empirical Wavelet Transform (EWT) is a dependent decomposition approach that provides superior temporal and frequency information. Hence, EWT is considered as a feature extraction approach in this study. Before extracting EWT features, an adaptive non-linear speckle reducing filter; Lee filter is used to remove the IVUS images’ noises. The accumulated energies of EWT sub-bands are computed and fed to four Support Vector Machine (SVM) for coronary plague classification into five different classes; normal, calcium, fibrous, necrotic (thrombus) and soft plague (fibro-fatty). A total number of 400 IVUS images and their corresponding labeling are obtained from Shifa hospitals, Tirunelveli, Tamilnadu, India. Results prove that the classification of coronary plague is done with higher accuracy by using the EWT-SVM approach.

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.

Prediction of Coronary Stent Underexpansion by Pre-Procedural IntravascularUltrasound-Based DeepLearning.

The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)-based models for predicting the occurrence of stent underexpansion. Although post-stenting IVUS has been used to optimize percutaneous coronary intervention, there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment. A total of 618 coronary lesions in 618 patients undergoing percutaneous coronary intervention were randomized into training and test sets in a 5:1 ratio. Following the coregistration of pre- and post-stenting IVUS images, the pre-procedural images and clinical information (stent diameter, length, and inflation pressure; balloon diameter; and maximal balloon pressure) were used to develop a regression model using a convolutional neural network to predict post-stenting stent area. To separate the frames with from those without the occurrence of underexpansion (stent area<5.5mm2), binary classification models (XGBoost) were developed. Overall, the frequency of stent underexpansion was 15% (5,209 of 34,736 frames). At the frame level, stent areas predicted by the pre-procedural IVUS-based regression model significantly correlated with those measured on post-stenting IVUS (r=0.802). To predict stent underexpansion, maximal accuracy of 94% (area under the curve=0.94) was achieved when the convolutional neural network- and mask image-derived features were used for the classification model. At the lesion level, there were significant correlations between predicted and measured minimal stent area (r=0.832) and between predicted and measured total stent volume (r=0.958). Deep-learning algorithms accurately predicted incomplete stent expansion. A data-driven approach may assist clinicians in making treatment decisions to avoid stent underexpansion as a preventable cause of stent failure.

Is the effect of atorvastatin 60 mg on stabilization of lipid-rich plaque equivalent to that of rosuvastatin 10 mg? A serial optical coherence tomography combined with intravascular ultrasound imaging.

This study aimed to compare the effect of atorvastatin 60 (AT60) mg to that of rosuvastatin 10 (RT10) mg on the morphological changes in lipid-rich plaques (LRPs) and plaque volume, using serial optical coherence tomography (OCT) and intravascular ultrasound imaging (IVUS). Intensive lipid lowering therapy by statin provides more clinical benefit compared to that of moderate lipid lowering therapy. Fifty patients who underwent OCT and IVUS at baseline, 6, and 12 months were grouped by statin therapy into the AT60 mg (n = 27) and RT10 mg (n = 23) groups. The relationships between absolute and percentage changes in biomarkers and fibrous cap thickness (FCT) during follow-up were investigated using a simple regression analysis. At 6 months, the mean low-density lipoprotein cholesterol level reduced from 113.5 to 65.5 mg/dl (AT60 mg group) and 100.2 to 72.2 mg/dl (RT10 mg groups). A continuous increase in FCT from baseline to 12 months was observed in both groups (p < .001, p < .001, respectively). Mean lipid arc significantly decreased in both AT60 mg (189.0 ± 55.9°, 170.9 ± 60.2°, 155.6 ± 50.6°, p < .001) and RT10 mg (160.0 ± 45.6°, 151.2 ± 48.5°, 141.1 ± 52.9°, p = .010) groups. Plaque burden did not change significantly in both groups. Lipid-lowering therapy effect with AT60 mg was equivalent to that of RT10 mg in terms of change in plaque morphology. AT60 mg showed more intensive low-density lipid cholesterol level reduction compared to RT10 mg while RT10 mg was effective in increasing the high-density lipid cholesterol level. Both statin therapies could effectively stabilize LRPs.