Combined intravascular ultrasound-optical coherence tomography (IVUS-OCT) enables more accurate coronary plaque tissue classification compared to single modality systems. Automated solutions are needed to that take advantage of information from both modalities to speed such analysis. This study aimed to train and validate a deep learning (DL) model for tissue classification in combined IVUS-OCT images. Coronary segments from 8 arteries from cadaveric human hearts were studied with the Novasight Hybrid imaging catheter. IVUS-OCT images were matched with histological sections and tissue types annotated. These regions of interest were used train and test a DL-classifier for plaque composition (949 matched histological and IVUS-OCT frames from 8 patients for training, 306 frames from 2 patients for testing). The accuracy of the classifier for regional classification was 78.8% suggesting that the trained DL-model is capable of accurate tissue type classification in combined IVUS-OCT images.
Lipid composition of atherosclerotic plaques is considered to be one of the primary indicators of plaque vulnerability. Therefore, a specific diagnostic or imaging modality that can sensitively evaluate plaques’ necrotic core is highly desirable in atherosclerosis imaging. In this regard, intravascular photoacoustic (IVPA) imaging is an emerging plaque detection technique that provides lipid-specific chemical information from an arterial wall with great optical contrast and long acoustic penetration depth. Within the near-infrared window, a 1210-𝑛𝑚 optical source is usually chosen for IVPA applications as lipids exhibit a strong absorption peak at that wavelength due to the second overtone of the C-H bond vibration within the lipid molecules. However, other arterial tissues also show some degree of absorption near 1210 𝑛𝑚 and thus generate undesirably interfering PA signals. In this study, a theory of the novel Frequency-Domain Differential Photoacoustic Radar (DPAR) modality is introduced as an interference-free detection technique for accurate and reliable evaluation of vulnerable plaques. By assuming two low-power continuous-wave (CW) optical sources at ~ 1210 𝑛𝑚 and ~ 970 𝑛𝑚 in a differential manner, DPAR theory and the corresponding simulation study suggest a unique imaging modality that can efficiently suppress any undesirable absorptions and system noise, while dramatically improving PA sensitivity and specificity toward cholesterol contents of atherosclerotic plaques.
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