Dataset Information

Direct Comparison of Virtual-Histology Intravascular Ultrasound and Optical Coherence Tomography Imaging for Identification of Thin-Cap Fibroatheroma.

ABSTRACT: BACKGROUND:Although rupture of thin-cap fibroatheroma (TCFA) underlies most myocardial infarctions, reliable TCFA identification remains challenging. Virtual-histology intravascular ultrasound (VH-IVUS) and optical coherence tomography (OCT) can assess tissue composition and classify plaques. However, direct comparisons between VH-IVUS and OCT are lacking and it remains unknown whether combining these modalities improves TCFA identification. METHODS AND RESULTS:Two hundred fifty-eight regions-of-interest were obtained from autopsied human hearts, with plaque composition and classification assessed by histology and compared with coregistered ex vivo VH-IVUS and OCT. Sixty-seven regions-of-interest were classified as fibroatheroma on histology, with 22 meeting criteria for TCFA. On VH-IVUS, plaque (10.91±4.82 versus 8.42±4.57 mm(2); P=0.01) and necrotic core areas (1.59±0.99 versus 1.03±0.85 mm(2); P=0.02) were increased in TCFA versus other fibroatheroma. On OCT, although minimal fibrous cap thickness was similar (71.8±44.1 ?m versus 72.6±32.4; P=0.30), the number of continuous frames with fibrous cap thickness ?85 ?m was higher in TCFA (6.5 [1.75-11.0] versus 2.0 [0.0-7.0]; P=0.03). Maximum lipid arc on OCT was an excellent discriminator of fibroatheroma (area under the curve, 0.92; 95% confidence interval, 0.87-0.97) and TCFA (area under the curve, 0.86; 95% confidence interval, 0.81-0.92), with lipid arc ?80° the optimal cut-off value. Using existing criteria, the sensitivity, specificity, and diagnostic accuracy for TCFA identification was 63.6%, 78.1%, and 76.5% for VH-IVUS and 72.7%, 79.8%, and 79.0% for OCT. Combining VH-defined fibroatheroma and fibrous cap thickness ?85 ?m over 3 continuous frames improved TCFA identification, with diagnostic accuracy of 89.0%. CONCLUSIONS:Both VH-IVUS and OCT can reliably identify TCFA, although OCT accuracy may be improved using lipid arc ?80° and fibrous cap thickness ?85 ?m over 3 continuous frames. Combined VH-IVUS/OCT imaging markedly improved TCFA identification.

SUBMITTER: Brown AJ

PROVIDER: S-EPMC4596008 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Direct Comparison of Virtual-Histology Intravascular Ultrasound and Optical Coherence Tomography Imaging for Identification of Thin-Cap Fibroatheroma.

Brown Adam J AJ Obaid Daniel R DR Costopoulos Charis C Parker Richard A RA Calvert Patrick A PA Teng Zhongzhao Z Hoole Stephen P SP West Nick E J NE Goddard Martin M Bennett Martin R MR

Circulation. Cardiovascular imaging 20151001 10

<h4>Background</h4>Although rupture of thin-cap fibroatheroma (TCFA) underlies most myocardial infarctions, reliable TCFA identification remains challenging. Virtual-histology intravascular ultrasound (VH-IVUS) and optical coherence tomography (OCT) can assess tissue composition and classify plaques. However, direct comparisons between VH-IVUS and OCT are lacking and it remains unknown whether combining these modalities improves TCFA identification.<h4>Methods and results</h4>Two hundred fifty-e ...[more]

PMID: 26429760

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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.

Project description:Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Dataset Information

Direct Comparison of Virtual-Histology Intravascular Ultrasound and Optical Coherence Tomography Imaging for Identification of Thin-Cap Fibroatheroma.

Publications

Direct Comparison of Virtual-Histology Intravascular Ultrasound and Optical Coherence Tomography Imaging for Identification of Thin-Cap Fibroatheroma.

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