Automated Coronary Plaque Characterization With Intravascular Optical Coherence Tomography and Smart-Algorithm Approach

Baruah, Vikram; Zahedivash, Aydin; Hoyt, Taylor; McElroy, Austin; Vela, Deborah; Buja, L. Maximilian; Cabe, Andrew; Oglesby, Meagan; Estrada, Arnold D.; Antonik, Piotr; Milner, Thomas E.; Feldman, Marc D.

doi:10.1016/j.jcmg.2020.02.022

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Further advancements could also assist in differentiating between healthy re-endothelization or fibrin drug eluting stent coverage, improving the ability to stratify risk of late stent thrombosis [ 222 ]. Combining this ability to accurately segment pathological borders and extract molecular information, reminiscent of an advanced virtual histology IVUS/OCT [ 223 , 224 ], presents opportunities to reverse engineer tissue constitutive models and adapt structural simulations to patient-specific conditions, currently a major limitation in the field of biomechanics [ 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 ]. However, there is still a need for further evidence to determine which multi-modal imaging technique can provide the strongest incremental benefits and risk stratification to improve both clinical outcomes and simulation capability.…”

Section: Discussionmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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Section: Discussionmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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“…Ultimately, a structured feedback-based curriculum may benefit those seeking to improve their capacity to interpret and apply intracoronary imaging to guide diagnostic and therapeutic decision-making in the cardiac catheterization laboratory. This is particularly important as the marked advantages of OCT become increasingly apparent commensurate with the growth of complex interventional cases and wider adoption of device technology [ 1 , 10 ].…”

Section: Discussionmentioning

confidence: 99%

Immersive educational curriculum on intracoronary optical coherence tomography image analysis among naïve readers

et al. 2022

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Background Optical coherence tomography (OCT) is an intravascular imaging modality for analysing coronary vessels. Image interpretation remains an obstacle for novice readers due to technical artefacts and uncertainty in tissue characterization. Despite an expanding clinical and research role for OCT, few training efforts exist, and there is an absence of a national standardized educational curriculum. We sought to determine whether an interactive, feedback-based OCT curriculum improved image interpretation among naive readers. Methods Naive OCT readers completed both a Standard curriculum, comprised of self-directed didactics and consensus statements, and an Augmented curriculum, which provided real-time digital feedback of feature identification and measurements. Modules were separated by a minimum one-week washout period. After each module, and blinded to the exam answers, subjects completed an identical expert-designed 413-item exam to assess technical knowledge and ability to identify and measure vessel features. Performances were compared using Exact Wilcoxon signed-rank tests. Results Among the 7 included subjects were 3 medical students, 3 internal medicine residents, and 1 cardiovascular medicine fellow with no prior OCT experience. The technical knowledge score (maximum 13) was significantly higher with the Augmented compared with the Standard curriculum (median 11 vs. 7, p = 0.03). After undergoing the Augmented curriculum, all 7 subjects were able to identify features of plaque rupture (Standard curriculum: 5/7 subjects, p = 0.5) and macrophages (Standard curriculum: 6/7 subjects, p = 0.99), differentiate the components between red and white thrombus (Standard curriculum: 6/7 subjects, p = 0.99), and characterize lipid plaque by attenuation, signal, homogeneity, and borders (Standard curriculum: 5/7 subjects, p = 0.5). Performances on the remaining exam portions did not differ between curricula. Conclusions The need for standardized, effective training in OCT image interpretation is increasingly essential as the intravascular imaging modality becomes widely utilized among interventional cardiologists and trainees. A novel interactive OCT curriculum enhanced naive readers’ technical knowledge and may supplement traditional self-learning in refining analytic skills.

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“…Further advancements could also assist in differentiating between healthy re-endothelisation or fibrin drug eluting stent coverage, improving the ability to stratify risk of late stent thrombosis [208]. Combining this ability to accurately segment pathological borders and extract molecular information, reminiscent of an advanced virtual histology IVUS/OCT [209,210], presents opportunities to reverse engineer tissue constitutive models and adapt structural simulations to patient-specific conditions, currently a major limitation in the field of biomechanics [211][212][213][214][215][216][217][218][219][220]. However, there is still a need for further evidence to determine which multi-modal imaging technique can provide the strongest incremental benefits and risk stratification to improve both clinical outcomes and simulation capability.…”

Section: Figure 14mentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Carpenter¹,

Ghayesh²,

Zander³

et al. 2022

Preprint

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualisation of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarises advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the three-dimensional reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; plaque characteristics and subtypes; artery layers; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

show abstract

Automated Coronary Plaque Characterization With Intravascular Optical Coherence Tomography and Smart-Algorithm Approach

Cited by 7 publications

References 3 publications

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Immersive educational curriculum on intracoronary optical coherence tomography image analysis among naïve readers

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

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