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.
Accurate classification of plaque composition is essential for treatment planning. Deep learning (DL) methods have been introduced for this purpose, to analyze intravascular images and characterize in a fast and subjective manner plaque types. In this study, we compared the efficacy of two DL methods, designed to process data acquired by two intravascular–an optical coherence tomography (OCT) and a near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS)–catheters to assess plaque types using histology as the reference standard. We matched histology, OCT, and NIRS-IVUS images, compared their estimations, and found that the DL method developed for NIRS-IVUS analysis had a better correlation with histology for calcific and lipidic tissue as compared with the OCT-DL method while both methods had a moderate correlation with the estimations of histology for fibrotic tissue. These findings could be attributed to the fact that OCT due to its poor penetration especially in lesions with large plaque burden fails to identify the deep-seated plaque and also to the fact that the NIRS-IVUS-DL method was developed with the use of histology instead of experts’ analysis.
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.
This conference presentation was prepared for the Diagnostic and Therapeutic Applications of Light in Cardiology 2023 conference at SPIE BiOS, SPIE Photonics West 2023.
Advances in image and signal processing and the miniaturisation of the medical devices have enabled the design of hybrid intravascular imaging catheters that have two probes on their tip which allow more complete assessment of plaque morphology physiology and biology than standalone intravascular imaging techniques. Histology studies have highlighted the superiority of hybrid intravascular imaging in assessing lesion characteristics; however the value of these techniques in the clinical arena and research is yet unclear. The aim of this presentation is to summarise the developments in the field, present the advantages and limitations of the existing prototypes and based on the existing evidence discuss the potential value of the established or emerging hybrid intravascular imaging probes in guiding percutaneous coronary intervention, assessing lesion pathology and detecting plaques that are a likely to progress and cause events.
Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson’s correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts’ annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.
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