Intravascular Imaging Techniques in Cardiovascular Interventions

Intravascular imaging has transformed the diagnosis and treatment of coronary artery disease by enabling direct visualisation of vessel lumen, plaque burden and arterial wall microstructure. Intravascular ultrasound (IVUS) employs high-frequency sound waves to generate cross-sectional images of the artery, delineating lumen size, plaque composition and vessel remodelling. Optical coherence tomography (OCT) and its intracoronary adaptation, intravascular OCT (IVOCT), exploit near-infrared light to achieve micrometre-scale resolution, revealing fibrous cap thickness, lipid pools and microcalcifications. Complementary modalities such as near-infrared spectroscopy (NIRS) further characterise lipid-rich cores, while computational fluid dynamics models derive wall shear stress and plaque structural stress to predict vulnerability. Together, these techniques guide percutaneous coronary interventions by informing stent sizing, optimising device placement and reducing adverse events. Recent advances in machine learning and automated segmentation tools have enhanced the speed and reproducibility of image analysis, paving the way for real-time decision support. Globally, intravascular imaging is now integral to complex lesion management, with implications for personalised therapy and improved clinical outcomes in acute coronary syndromes and stable disease alike.

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Intravascular Imaging Techniques in Cardiovascular Interventions publication trend

The graph below shows the total number of articles in intravascular imaging techniques in cardiovascular interventions across all publications each year (not limited to Nature Index journals).

Technical terms

Intravascular ultrasound (IVUS): High-frequency soundwave imaging modality for cross-sectional views of vessel lumen and wall.
Optical coherence tomography (OCT): Near-infrared laser-based technique providing high-resolution images of tissue microstructure.
Intravascular optical coherence tomography (IVOCT): Intracoronary application of OCT for detailed visualisation of plaque composition.
Segmentation: Computational process of delineating anatomical structures within medical images.
Convolutional neural network (CNN): Deep learning algorithm designed to recognise patterns and features in image data.

References

1. POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation. Medical Image Analysis (2023).
2. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography.. Biomedical Optics Express (2017).
3. Comparison of intravascular ultrasound guided versus angiography guided drug eluting stent implantation: a systematic review and meta-analysis. BMC Cardiovascular Disorders (2015).
4. Impact of combined plaque structural stress and wall shear stress on coronary plaque progression, regression, and changes in composition. European Heart Journal (2019).