Dataset Information

Contrast-free optical coherence tomography:Systematic evaluation of non-contrast media for intravascular assessment.

ABSTRACT: BACKGROUND:Coronary revascularization using imaging guidance is rapidly becoming the standard of care. Intravascular optical coherence tomography uses near-infrared light to obtain high resolution intravascular images. Standard optical coherence tomography imaging technique employs iodinated contrast dye to achieve the required blood clearance during acquisition. We sought to systematically evaluate the technical performance of saline as an alternative to iodinated contrast for intravascular optical coherence tomography assessment. METHODS AND RESULTS:We performed bench top optical coherence tomography analysis on nylon tubing with sequential contrast/saline dilutions to empirically derive adjustment coefficients. We then applied these coefficients in vivo in an established rabbit abdominal stenting model with both saline and contrast optical coherence tomography imaging. In this model, we assessed the impact of saline on both quantitative and qualitative vessel assessment. Nylon tubing assessment demonstrated a linear relationship between saline and contrast for both area and diameter. We then derived adjustment coefficients, allowing for accurate calculation of area and diameter when converting saline into both contrast and reference dimensions. In vivo studies confirmed reduced area with saline versus contrast [7.43 (5.67-8.36) mm2 versus 8.2 (6.34-9.39) mm2, p = 0.001] and diameter [3.08 mm versus 3.23 mm, p = 0.001]. Following correction, a strong relationship was achieved in vivo between saline and contrast in both area and diameter without compromising image quality, artefact, or strut assessment. CONCLUSION:Saline generates reduced dimensions compared to contrast during intravascular optical coherence tomography imaging. The relationship across physiologic coronary diameters is linear and can be corrected with high fidelity. Saline does not adversely impact image quality, artefact, or strut assessment.

SUBMITTER: Simard T

PROVIDER: S-EPMC7446899 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Contrast-free optical coherence tomography:Systematic evaluation of non-contrast media for intravascular assessment.

Simard Trevor T Motazedian Pouya P Majeed Kamran K Sarathy Kiran K Jung Richard G RG Feder Joshua J Ramirez F Daniel FD Di Santo Pietro P Marbach Jeffrey J Dhaliwal Shan S Short Spencer S Labinaz Alisha A Schultz Carl C Russo Juan J JJ So Derek D Chong Aun-Yeong AY Le May Michel M Hibbert Benjamin B

PloS one 20200820 8

<h4>Background</h4>Coronary revascularization using imaging guidance is rapidly becoming the standard of care. Intravascular optical coherence tomography uses near-infrared light to obtain high resolution intravascular images. Standard optical coherence tomography imaging technique employs iodinated contrast dye to achieve the required blood clearance during acquisition. We sought to systematically evaluate the technical performance of saline as an alternative to iodinated contrast for intravasc ...[more]

PMID: 32817672

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Project description:Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Dataset Information

Contrast-free optical coherence tomography:Systematic evaluation of non-contrast media for intravascular assessment.

Publications

Contrast-free optical coherence tomography:Systematic evaluation of non-contrast media for intravascular assessment.

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