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.

翻译：在冠状动脉光学相干断层扫描（OCT）中，血液残留物与气泡会导致衰减伪影，从而可能遮蔽关键的血管结构。这些伪影的存在及其严重程度可能需要重新采集图像，延长手术时间并增加造影剂用量。准确检测此类伪影可指导针对性重采，减少为获得具有诊断价值图像所需的重复扫描次数。然而，这些伪影高度异质化的外观给受影响图像区域的自动检测带来了挑战。为实现基于严重程度的血液残留物与气泡所致衰减伪影的自动检测，我们提出一种卷积神经网络，可将衰减线（A-line）分类为三类：无伪影、轻度伪影与重度伪影。我们的模型从笛卡尔坐标系和极坐标系下的OCT图像中提取并融合特征，其中图像的每一列代表一条A线。本方法在OCT帧中检测衰减伪影的存在，对轻度与重度伪影的F分数分别达到0.77和0.94。完整OCT扫描的推理时间约为6秒。实验表明，同时分析笛卡尔与极坐标系下的图像表征优于仅分析极坐标系，提示这两种表征包含互补特征。本研究为冠状动脉OCT成像中的自动化伪影评估与图像采集指导奠定了基础。