Intravascular OCT Imaging Artifacts

Phipps, Jennifer E.; Hoyt, Taylor; Halaney, David L.; Mancuso, James J.; Milner, Thomas E.; Feldman, Marc D.

doi:10.1007/978-3-319-10801-8_5

Cited by 4 publications

(12 citation statements)

References 18 publications

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“…Artifacts are common with OCT imaging. They may adversely affect the image quality that limits identification of the true tissue structures or sometimes be misinterpreted as pathologies that leads to unnecessary interventions (12).…”

Section: Artifacts In Oct Imagingmentioning

confidence: 99%

“…It appears as smearing or geometric distortion in the image that conceals the structures in that part of the circumference and limits accurate cross-sectional measurements. It is often due to tortuosity or acute bends in the artery, tight stenosis, heavy calcification, small guiding catheter, guiding catheter with multiple curves, excessive tightening of the hemostatic valve or defective imaging catheter (Figure 5A) (2,12).…”

Section: Nonuniform Rotational Distortion (Nurd)mentioning

confidence: 99%

“…However, when the vessel is large in size and in vessel curvature, the catheter alignment is non-coaxial and the resulting image appears elliptical. This makes the measurements inaccurate (Figure 5B) (2,12).…”

Section: Obliquitymentioning

confidence: 99%

“…While ultrasound waves do no penetrate calcium and is an important cause of shadowing during IVUS imaging, calcium allows light photons to pass through and does not cause shadowing on OCT imaging. This allows the estimation of calcium thickness with OCT imaging (2,12).…”

Section: Shadow Artifactmentioning

confidence: 99%

“…During OCT imaging, multiple reflections may happen within the facets of the imaging catheter that produce circular lines around the catheter (Figure 5C) or with stent struts (Figure 5D) that result in reverberations. Multiple reflection artifacts should not be mistaken for additional interfaces in the vessel or additional layer of stent struts (2,12).…”

Section: Multiple Reflections and Reverberationsmentioning

confidence: 99%

See 4 more Smart Citations

Optical coherence tomography: fundamentals and clinical utility

Subban

Raffel²

2020

Cardiovasc Diagn Ther

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Although coronary angiography is the standard method employed to assess the severity of coronary artery disease and to guide treatment strategies, it provides only 2D image of the intravascular lesions. In contrast, intravascular imaging modalities such as optical coherence tomography (OCT) produce cross-sectional images of the coronary arteries at a far greater spatial resolution, capable of accurately determining vessel size as well as plaque morphology, eliminating many of the disadvantages inherent to angiography. This review will discuss the role of OCT in the catherization laboratory for the assessment and management of coronary disease.

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Section: Artifacts In Oct Imagingmentioning

confidence: 99%

Section: Nonuniform Rotational Distortion (Nurd)mentioning

confidence: 99%

Section: Obliquitymentioning

confidence: 99%

Section: Shadow Artifactmentioning

confidence: 99%

Section: Multiple Reflections and Reverberationsmentioning

confidence: 99%

See 3 more Smart Citations

Optical coherence tomography: fundamentals and clinical utility

Subban

Raffel²

2020

Cardiovasc Diagn Ther

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Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning

Shi

Xin

et al. 2023

Journal of Biophotonics

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Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning‐based methods usually require well‐annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta‐learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi‐level gradient strategy to train a meta‐learner for capturing the shared meta‐knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw‐type network and a contrast consistency loss were designed to better learn the meta‐knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state‐of‐art performance.

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