Attenuation artifact detection and severity classification in intracoronary OCT using mixed image representations
This work addresses the challenge of automated artifact detection in intracoronary OCT imaging to reduce procedure time and contrast agent use, representing an incremental improvement in a domain-specific application.
The paper tackled the problem of detecting and classifying attenuation artifacts caused by blood residues and gas bubbles in intracoronary OCT images, which can obscure vessel structures and lead to unnecessary re-acquisitions. Their method achieved F-scores of 0.77 for mild artifacts and 0.94 for severe artifacts, with an inference time of about 6 seconds per scan.
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.