BVS Dynamic Learning: A Novel Approach to Bioresorbable Vascular Scaffold Strut Detection in OCT Images

Bioresorbable vascular scaffolds (BVS) represent the next evolution in minimally invasive vascular interventions, offering new possibilities for both patients and clinicians. However, the polymeric composition of BVS presents unique imaging challenges. Optical coherence tomography (OCT), a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology, making it a valuable tool for BVS assessment. Traditionally, BVS strut segmentation in OCT images was performed manually by experts, a process that is time-consuming and impractical for large datasets. While automated methods exist for metallic stents, they are not applicable to BVS. This article delves into a novel BVS strut detection method in intracoronary OCT images, leveraging K-means clustering for automated segmentation.

The Promise and Challenges of Bioresorbable Vascular Scaffolds

Coronary artery disease, driven by occlusive plaque burden, has a profound global impact. Addressing arterial stenoses primarily involves the insertion of metallic stents or BVS. These devices expand and maintain the constricted region, restoring blood flow. The shift from invasive bypass surgery to minimally invasive interventions necessitates a comprehensive understanding of the effects of different device types on cardiovascular function.

Metallic stents, while effective, can lead to long-term issues due to their inflexibility, hindering natural arterial remodeling and vasomotion. BVS, on the other hand, offer the potential for early restoration of endothelial function, normal vasomotion, and natural remodeling of the coronary artery. Despite these advantages, there have been concerns regarding excess thrombosis and myocardial infarction associated with BVS. Given the relative novelty of BVS technology, there is a need for computational methodologies to analyze their quantitative effects.

Optical Coherence Tomography: A High-Resolution Imaging Modality

Optical coherence tomography (OCT) is an intracoronary imaging technique that provides high-resolution images of coronary arteries. OCT is increasingly favored over intravascular ultrasound for coronary imaging due to its superior resolution, which is achieved by using reflected light with a shorter wavelength compared to the acoustic waves used in ultrasound. OCT enables detailed morphological assessment of the vessel wall, luminal border, and features associated with increased vulnerability. As a result, OCT imaging is the primary tool used by clinicians to detect the lumen, characterize plaque, and detect devices in vivo. The high resolution of OCT images allows for visualization of both BVS struts and metallic stents.

While several methods exist for detecting metallic stent struts, there is limited research on BVS strut detection using OCT images. Existing approaches may require different methodologies for malapposed and embedded struts, rely on percentile-based thresholds that are patient-specific, and are susceptible to artifacts like blood in the lumen. These limitations hinder the effectiveness of such methods, as many patients exhibit a combination of malapposed and embedded stents, as well as blood artifacts in the lumen.

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A Novel K-Means Clustering-Based Method for BVS Strut Detection

To overcome the limitations of existing methods, a new approach based on K-means clustering is proposed. This method is designed to work for both malapposed and embedded devices, automatically detect them, and remove artifacts from the lumen area.

Image Preprocessing

The raw OCT image consists of a series of A-lines stored in polar coordinates. To accurately represent the image, it is converted into Cartesian coordinates using a transformation. A bilateral filter is then applied to reduce image noise while preserving edges. This filter replaces the intensity value of each pixel with a weighted average of neighboring pixels, taking into account both Euclidean distances and intensity differences. A 3 × 3 kernel window with a σ =1.0 is used in both Gaussian kernel functions to ensure the total image energy is preserved.

Before searching for BVS struts, the imaging catheter, protective sheath, and guide wire are removed. The start of the catheter is identified by finding the first pixel in the first and last column with an intensity above 0.8 units. The lower border of the protective sheath is located approximately 40 pixels below this point. An edge detection method is used to detect the outer boundary of the protective sheath, involving walking along the gradient of the sheath boundary.

K-Means Clustering and Segmentation

To automatically segment the image, a K-means algorithm (with k = 3 clusters) is employed. K-means is an unsupervised machine-learning algorithm used to solve clustering problems. The algorithm aims to minimize the within-cluster sum of squares, where μi is the mean intensity of all points in cluster Si. This algorithm is applied to each OCT frame, classifying points into noise (two clusters) and the black background (one cluster). Experiments with different cluster values (k = 2, 3, and 4) revealed that k = 3 yields the highest positive predictive value. The clustered image is then converted into a binary image by setting the cluster with the minimum intensity to 0 and all other clusters to 1.

Strut Isolation and Outline Detection

Once the start and end points of the strut core are identified along each A-line, that region is marked as 1, and all other pixels in that column are set to 0. This produces a binary image with candidate BVS strut core segments. To eliminate false positives, features with inappropriate aspect ratios or those falling below a certain area threshold are removed. The perimeter of the candidate segments is then outlined to produce closed polygons in polar coordinates. Finally, these polygons are converted back to Cartesian coordinates to obtain the final result.

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Validation and Performance Evaluation

The proposed plaque characterization method was validated using anonymized OCT examinations from 7 patients, provided by the Hospital Universitario Marqués de Valdecilla, Santander, Spain. The images were acquired using a Frequency Domain (FD - OCT) OCT equipment (LightLab Imaging, Inc) with a 6 Fr FD-OCT catheter (C7 Dragonfly). Automated contrast injection was performed to optimize image quality. An expert manually marked the BVS struts on 658 images, serving as the gold standard for the study. The methodology was then used to identify strut boundaries and count the number of struts found. Broken struts and low-quality images were excluded from the validation study.

Validation Metrics

The following validation metrics were calculated: Pearson Correlation Coefficient, true positive rate (TPR), positive predictive value (PPV), false negative rate (FNR), false discovery rate (FDR), and F1 score. Regression analysis and Bland-Altman analysis were also performed. The Bland-Altman analysis revealed a mean of −0.32, indicating no significant bias.

Results

The methodology achieved a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and an F1 score of 0.92. A correlation graph between the BVS struts annotated by experts and those detected by the proposed methodology yielded an R2 of 0.89, with a slope of 0.85 and a y-intercept of 1.

Advantages and Implications

The presented method offers a fully automated approach to detect BVS struts in OCT images. It effectively removes imaging artifacts, employs a K-means algorithm for automatic image thresholding, and outlines BVS struts based on the thresholded image. Unlike existing methods that rely on simplified artifact removal techniques and percentile-based thresholding, this methodology provides a robust and automated solution applicable to both malapposed and embedded struts. This is particularly important as many patients have a combination of both. The method has a time complexity of fewer than 3 seconds per frame on an i7 4 GB RAM machine.

The accurate localization of BVS struts is a clinically relevant parameter. By utilizing these strut locations, clinicians can determine strut diameters from the lumen and monitor strut movement over time. Temporal tracking is crucial for assessing BVS efficacy, as certain strut orientations may increase the risk of thrombosis. Furthermore, the proposed BVS strut detection method can serve as a foundation for researchers studying hemodynamics and rheology within stented coronary arteries.

The method can be used to detect BVS struts in OCT frames and then translated in 3D using well-known 3D OCT reconstruction algorithms. Building patient-specific 3D models will provide clinicians with accurate quantitative metrics to assess patient cardiovascular function post BVS implantation.

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