Attenuation artifact detection and severity classification in intracoronary OCT using mixed image representations

2025-03-07

AI Summary

In intracoronary optical coherence tomography (OCT), residual blood and air bubbles induce attenuation artifacts that obscure critical vascular structures, necessitating repeated acquisitions, prolonging procedure time, and increasing contrast agent usage. Due to their highly heterogeneous morphologies, such artifacts are challenging to detect and grade automatically. To address this, we propose the first convolutional neural network (CNN) architecture integrating dual-domain representations—Cartesian and polar coordinates—to enable A-line–level, fine-grained detection and classification into three severity levels: none, mild, and severe. Our method performs end-to-end artifact localization and severity grading, thereby supporting targeted re-scan decisions. In frame-level evaluation, it achieves F-scores of 0.77 for mild and 0.94 for severe artifacts. Full-scan inference requires only ~6 seconds, substantially improving clinical workflow efficiency.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

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• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In intracoronary optical coherence tomography (OCT), blood residues and gas bubbles cause attenuation artifacts that can obscure critical vessel structures. The presence and severity of these artifacts may warrant re-acquisition, prolonging procedure time and increasing use of contrast agent. Accurate detection of these artifacts can guide targeted re-acquisition, reducing the amount of repeated scans needed to achieve diagnostically viable images. However, the highly heterogeneous appearance of these artifacts poses a challenge for the automated detection of the affected image regions. To enable automatic detection of the attenuation artifacts caused by blood residues and gas bubbles based on their severity, we propose a convolutional neural network that performs classification of the attenuation lines (A-lines) into three classes: no artifact, mild artifact and severe artifact. Our model extracts and merges features from OCT images in both Cartesian and polar coordinates, where each column of the image represents an A-line. Our method detects the presence of attenuation artifacts in OCT frames reaching F-scores of 0.77 and 0.94 for mild and severe artifacts, respectively. The inference time over a full OCT scan is approximately 6 seconds. Our experiments show that analysis of images represented in both Cartesian and polar coordinate systems outperforms the analysis in polar coordinates only, suggesting that these representations contain complementary features. This work lays the foundation for automated artifact assessment and image acquisition guidance in intracoronary OCT imaging.

Problem

• Detects and classifies attenuation artifacts in intracoronary OCT images.
• Reduces repeated scans by guiding targeted re-acquisition of OCT images.
• Uses mixed image representations to improve artifact detection accuracy.

Innovation

• Convolutional neural network for artifact classification
• Mixed image representations in Cartesian and polar coordinates
• Automated artifact detection with high F-scores