Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images

Liu, Shengnan; Sotomi, Yohei; Eggermont, Jeroen; Nakazawa, Gaku; Torii, Sho; Ijichi, Takeshi; Onuma, Yoshinobu; Serruys, Patrick W.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

doi:10.1117/1.jbo.22.9.096004

Cited by 49 publications

(56 citation statements)

References 47 publications

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“…Based on a tissue optics interpretation of the qualitative classification, lipid-rich/necrotic tissue can be inferred to have strong attenuation. The attenuation (and backscattering, when provided) values reported by Xu et al, 8 van Soest et al, 16 and Liu et al 40 are consistent with this pattern; whereas, the contrast measured by van der Meer et al 7,78 appears to be inverted. A possible explanation of this difference may lie in the selection of fitting regions, which in the case of van der Meer et al appears to exclude the signal-rich proximal areas in attenuating tissues, causing them to derive data from the slowly varying multiple-scattering background.…”

Section: Cardiologysupporting

confidence: 52%

“…(6) requires that the values of the OCT signal in the last pixels in the image should be negligible, meeting the first assumption above. In the original formulation 38 and subsequent applications 40 of the depth-resolved attenuation coefficient analysis, the CPSF and sensitivity roll-off in depth were not taken into account. Smith et al 41 introduced these corrections, including for noise.…”

Section: Depth-resolved Methodsmentioning

confidence: 99%

“…The former becomes more problematic for pixels toward the end of the A-scan and when prominent multiple-scattering background is present, which can be eliminated by carefully choosing a cutoff constant. 40,42 The latter assumption does not hold in the case of absorption or for scatterers with a strongly structured angular scattering cross section, such as Mie scatterers. Since μ OCT is directly proportional to I½i in Eq.…”

Section: Depth-resolved Methodsmentioning

confidence: 99%

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Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

et al. 2020

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Significance: Optical coherence tomography (OCT) provides cross-sectional and volumetric images of backscattering from biological tissue that reveal the tissue morphology. The strength of the scattering, characterized by an attenuation coefficient, represents an alternative and complementary tissue optical property, which can be characterized by parametric imaging of the OCT attenuation coefficient. Over the last 15 years, a multitude of studies have been reported seeking to advance methods to determine the OCT attenuation coefficient and developing them toward clinical applications. Aim: Our review provides an overview of the main models and methods, their assumptions and applicability, together with a survey of preclinical and clinical demonstrations and their translation potential. Results: The use of the attenuation coefficient, particularly when presented in the form of parametric en face images, is shown to be applicable in various medical fields. Most studies show the promise of the OCT attenuation coefficient in differentiating between tissues of clinical interest but vary widely in approach. Conclusions: As a future step, a consensus on the model and method used for the determination of the attenuation coefficient is an important precursor to large-scale studies. With our review, we hope to provide a basis for discussion toward establishing this consensus.

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Section: Cardiologysupporting

confidence: 52%

Section: Depth-resolved Methodsmentioning

confidence: 99%

Section: Depth-resolved Methodsmentioning

confidence: 99%

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Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

et al. 2020

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“…Also, calcified plaque regions have been quantified as they can increase the risk of stenosis [11], [12]. In general, the backscattering and attenuation coefficient of the OCT signal have been used frequently for identification of atherosclerotic tissue [13]- [17]. Also, automatic classification methods using texture and optical properties as features have been proposed for plaque detection and segmentation [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

Gessert

Lutz

Heyder

et al. 2019

IEEE Trans. Med. Imaging

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Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.

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“…The backscatter term is estimated to be related to the backscatter coefficient. More information about the estimation can be referred to the technical paper [7]. The advantage of this method is its fast pixel-wise estimation, which requires no predefined delineation and is more flexible for post analysis.…”

Section: Computer Image Analysis Of Thrombusmentioning

confidence: 99%

Comparison of visual assessment and computer image analysis of intercoronary thrombus type by optical coherence tomography in clinical patients

Kaivosoja¹,

Liu²,

Dijkstra³

et al. 2018

Interventional-Cardiology

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Objectives: Analysis of intracoronary thrombus type by optical coherence tomography (OCT) imaging is highly subjective. We aimed to compare a newly developed image analysis method to subjective visual classification of thrombus type identified by OCT. Methods: Thirty patients with acute ST elevation myocardial infarction were included. Thrombus type visually classified by two independent readers was compared with analysis using QCU-CMS software. Results: Repeatability of the computer-based measurements was good. By using a ROC, area under curve values for discrimination of white and red thrombi were 0.92 (95% confidence intervals (CI) 0.83-1.00) for median attenuation, 0.96 (95% CI 0.89-1.00) for mean backscatter and 0.96 (95% CI 0.89-1.00) for mean grayscale intensity. Median attenuation of 0.57 mm-1 (sensitivity 100%, specificity 71%), mean backscatter of 5.35 (sensitivity 92%, specificity 94%) and mean grayscale intensity of 120.1 (sensitivity 85%, specificity 100%) were identified as the best cutoff values to differentiate between red and white thrombi. Conclusions: Attenuation, backscatter and grayscale intensity of thrombi in OCT images differentiated red and white thrombi with high sensitivity and specificity. Measurement of these continuous parameters can be used as a less user-dependent method to characterize in vivo thrombi. The clinical significance of these findings needs to be tested in further studies.

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Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images

Cited by 49 publications

References 47 publications

Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

Comparison of visual assessment and computer image analysis of intercoronary thrombus type by optical coherence tomography in clinical patients

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