KEYWORDS: Shrinkage, Optical coherence tomography, Finite element methods, Arteries, Data processing, Ultrasonography, Spectroscopy, Near infrared spectroscopy, In vivo imaging, Computer simulations
Coronary artery plaque structural stress (PSS) is associated with plaque vulnerability and is quantifiable in vivo with optical coherence tomography (OCT) and near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) but the accuracy of these is unclear. This study explored the performance of the two modalities in measuring PSS using histology as reference standard. NIRS-IVUS and OCT images obtained in vessels under physiological pressure require transformation to a zero-pressure condition to estimate PSS. Two methods were examined to achieve this – uniform and non-uniform shrinkage (which may to be superior for eccentric plaques) followed by PSS computation which was compared to histology-derived PSS. NIRS-IVUS and OCT imaging were conducted ex vivo in cadaveric human coronaries prior to histological analysis. In 93 pairs of NIRS-IVUS-histology and 88 pairs of OCT-histology sections, the correlation between the PSS estimated by histology and NIRS-IVUS using the uniform shrinkage approach was higher than that derived by OCT. Non-uniform shrinkage resulted in a numerically lower correlation but no significant difference by Bland-Altman analysis compared to uniform shrinkage.
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.
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