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Updated: Apr 18, 2026
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Identification and quantification of macrophage presence in coronary atherosclerotic plaques by optical coherence
Luca Di Vito1, Manuela Agozzino2, Valeria Marco3
1Interventional Cardiology Unit, San Giovanni Addolorata Hospital, Via dell'Amba Aradam 8, 00184 Rome, Italy CLI Foundation, Rome, Italy.
Aims:
Vulnerable plaques are characterized by a high macrophage content. We investigated the optical coherence tomography (OCT) capability of identifying coronary plaque macrophage presence using tissue property indexes.
Methods And Results:
Fifteen epicardial coronary arteries were imaged by OCT and subsequently analysed by histology. Correlating OCT-histological sections were identified and regions of interest (ROIs) were selected on both atherosclerotic plaques and normal appearing vessel tracts. OCT-derived tissue property indexes named normalized standard deviation (NSD), signal attenuation, and granulometry index were applied on ROIs to identify inflamed ROIs defined as a macrophage percentage >10 by histology. Forty-three paired samples (OCT frame and histology section) were considered suitable as ROIs for analysis. Eleven out of 43 ROIs were considered inflamed and the remaining 32 ROIs were non-inflamed on the basis of histological count of macrophage percentage. All OCT-derived tissue property indexes were positively correlated with macrophage percentage (P = 0.0001 for all). Receiver operating characteristic curve analysis showed that NSD, granulometry index, and signal attenuation had a significant area under the curve (area = 0.906, 0.804, and 0.793, respectively). A two-step algorithm requiring to first apply NSD with a cut-off value of 0.0570 followed by granulometry index was able to identify an inflamed ROI with a sensitivity of 100% and a specificity of 96.8%.
Conclusion:
OCT was able to identify and quantify macrophage presence in coronary artery specimens using tissue property indexes. NSD and granulometry index showed the highest accuracy in identifying a significant plaque inflammation, especially if used together in a two-step algorithm.
Insights
Optical coherence tomography (OCT) can identify macrophage presence in coronary artery plaques. Tissue property indexes, particularly normalized standard deviation (NSD) and granulometry index, accurately detect plaque inflammation.
Area of Science:
- Cardiovascular Imaging
- Histopathology
- Biomedical Optics
Background:
- Vulnerable atherosclerotic plaques are characterized by high macrophage content, a key indicator of inflammation.
- Accurate identification of macrophage presence is crucial for assessing plaque vulnerability and guiding treatment strategies.
Purpose of the Study:
- To evaluate the capability of optical coherence tomography (OCT) in identifying coronary plaque macrophage presence.
- To assess the utility of OCT-derived tissue property indexes for quantifying macrophage content and detecting inflammation.
Main Methods:
- Fifteen human coronary arteries were imaged using OCT and analyzed via histology.
- Regions of interest (ROIs) in atherosclerotic plaques and normal vessel segments were analyzed.
- OCT-derived tissue property indexes, including normalized standard deviation (NSD), signal attenuation, and granulometry index, were correlated with histological macrophage percentage.
Main Results:
- All OCT-derived tissue property indexes showed a significant positive correlation with macrophage percentage (P = 0.0001).
- Receiver operating characteristic curve analysis demonstrated significant areas under the curve for NSD (0.906), granulometry index (0.804), and signal attenuation (0.793).
- A two-step algorithm using NSD and granulometry index achieved 100% sensitivity and 96.8% specificity in identifying inflamed ROIs.
Conclusions:
- OCT, utilizing tissue property indexes, can effectively identify and quantify macrophage presence in coronary artery specimens.
- NSD and granulometry index are highly accurate in detecting significant plaque inflammation, especially when combined in a sequential algorithm.
