Progress of quantitative intravascular optical coherence tomography_Journal of Biomedical Engineering

Authors：

YANG Fei ,  SUN Zheng

Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei 071003, P.R.China;

Corresponding author：

Keywords：

Intravascular optical coherence tomography; quantitative imaging; optical parameter; elastic parameter; hemodynamic paramete

DOI：

10.7507/1001-5515.201906061

Video：

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Intravascular optical coherence tomography (IVOCT) has emerged as a high-resolution and minimal-invasive imaging technique that provides high-speed visualization of coronary arterial vessel walls and clearly displays the vessel lumen and lesions under the intima. However, morphological gray-scale images cannot provide enough information about the tissue components to accurately characterize the plaque tissues including calcified, fibrous, lipidic and mixed plaques. Quantitative IVOCT (qIVOCT) is necessary to provide the physiological contrast mechanisms and obtain the characteristic parameters of tissues with clinical diagnostic value. In this paper, the progress of qIVOCT is reviewed. The current methods for quantitatively measuring optical, elastic and hemodynamic parameters of vessel wall and plaque tissues using IVOCT gray-scale images and raw backscattered signals are introduced and potential development is forecast.

Citation： YANG Fei, SUN Zheng. Progress of quantitative intravascular optical coherence tomography. Journal of Biomedical Engineering, 2020, 37(2): 358-364. doi: 10.7507/1001-5515.201906061 Copy

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2.	Yuan W, Kut C, Liang W, et al. Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection. Scientific Reports, 2017, 7: 44909.
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4.	Gnanadesigan M, Hussain A S, White S, et al. Optical coherence tomography attenuation imaging for lipid core detection: an ex-vivo validation study. Int J Cardiovasc Imaging, 2017, 33(1): 5-11.
5.	Sun P, Li Q, Li H, et al. Depth-resolved physiological response of retina to transcorneal electrical stimulation measured with optical coherence tomography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(5): 905-915.
6.	Abdolmanafi A, Duong L, Dahdah N, et al. Characterization of coronary artery pathological formations from OCT imaging using deep learning. Biomed Opt Express, 2018, 9(10): 4936-4960.
7.	Liu S, Sotomi Y, Eggermont J, et al. Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images. J Biomed Opt, 2017, 22(9): 096004.
8.	Smith G T, Dwork N, O'Connor D, et al. Automated,depth-resolved estimation of the attenuation coefficient from optical coherence tomography data. IEEE Trans Med Imaging, 2015, 34(12): 2592-2602.
9.	董昕, 陈思佳, 李军. 基于压缩感知和散斑相关法的散射介质成像方法研究. 激光生物学报, 2019, 28(1): 64-71.
10.	Nicholas D, Smith G T, Theodore L, et al. Automatically determining the confocal parameters from OCT B-scans for quantification of the attenuation coefficients. IEEE Trans Med Imaging, 2019, 38(1): 261-268.
11.	Stefan S, Jeong K S, Polucha C, et al. Determination of confocal profile and curved focal plane for OCT mapping of the attenuation coefficient. Biomed Opt Express, 2018, 9(10): 5084-5099.
12.	Wang S, Singh M, Tran T T, et al. Biomechanical assessment of myocardial infarction using optical coherence elastography. Biomed Opt Express, 2018, 9(2): 728.
13.	Qu Y, Ma T, He Y, et al. Miniature probe for mapping mechanical properties of vascular lesions using acoustic radiation force optical coherence elastography, 2017: 4731.
14.	Kennedy B F, Kennedy K M, Oldenburg A L, et al. Optical coherence elastography//Drexler W, Fujimoto J G. Optical Coherence Tomography, Springer International Publishing Switzerland, 2015: 1007-1054.
15.	Zaitsev V Y, Matveyev A L, Matveev L A, et al. Optical coherence elastography for strain dynamics measurements in laser correction of cornea shape. J Biophotonics, 2017, 10(11): 1450-1463.
16.	Chau A H, Chan R C, Shishkov M, et al. Mechanical analysis of atherosclerotic plaques based on optical coherence tomography. Ann Biomed Eng, 2004, 32(11): 1494-1503.
17.	Chan R C, Chau A H, Karl W C, et al. OCT-based arterial elastography: robust estimation exploiting tissue biomechanics. Opt Express, 2004, 12(19): 4558-4572.
18.	Khalil A S, Chan R C, Chau A H, et al. Tissue elasticity estimation with optical coherence elastography: toward mechanical characterization of in vivo Soft tissue. Ann Biomed Eng, 2005, 33(11): 1631-1639.
19.	Elahi S, Gu S, Thrane L, et al. Complex regression Doppler optical coherence tomography. J Biomed Opt, 2018, 23(4): 1-8.
20.	Shin P, Choi W J, Joo J Y, et al. Quantitative hemodynamic analysis of cerebral blood flow and neurovascular coupling using optical coherence tomography angiography. Journal of Cerebral Blood Flow & Metabolism, 2019, 39(10): 1983-1994.
21.	Jing J C, Chen J J, Chou L, et al. Visualization and detection of ciliary beating pattern and frequency in the upper airway using phase resolved doppler optical coherence tomography. Sci Rep, 2017, 7(1): 8522.
22.	钱婕. 基于相位解析多普勒光相干层析成像的流速测量研究. 江苏, 2017.
23.	潘柳华, 张向阳, 李中梁, 等. 基于光声-光学相干层析成像的血流测量技术. 中国激光, 2018, 45(6): 224-230.
24.	孟婕. 多普勒光学相干层析成像方法与应用研究. 浙江, 2010.
25.	杜宜纲, 刘德杰, 沈莹莹, 等. 血管壁面剪切应力的测量及其临床研究进展. 中国生物医学工程学报, 2018, 37(5): 593-605.
26.	Athanasiou L, Nezami F R, Galon M Z, et al. Optimized computer-aided segmentation and 3D REconstruction using intracoronary optical coherence tomography. IEEE J Biomed Health Inform, 2018, 22(4): 1168-1176.
27.	Migliori S, Chiastra C, Bologna M, et al. A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images. Medical Engineering and Physics, 2017, 47: 105-116.
28.	Leiknes T, Fortunato L, Qamar A, et al. In-situ assessment of biofilm formation in submerged membrane system using optical coherence tomography and computational fluid dynamics. Journal of Membrane Science, 2017, 521: 84-94.
29.	Kelsey L, Schultz C, Miller K, et al. The effects of geometric variation from OCT-derived 3D reconstructions on wall shear stress in a patient-specific coronary artery//Wittek A, Joldes G, Nielsen P M F, et al. Computational Biomechanics for Medicine: From Algorithms to Models and Applications. Springer, 2017: 1-13.
30.	房兴锐, 吴剑胜, 郭攸胜, 等. 冠脉造影术、FFR 技术及 FD-OCT 评估的冠脉临界病变患者预后的比较. 心血管康复医学杂志, 2018, 27(3): 293-296.
31.	Matsuo Y, Higashioka D, Ino Y, et al. Association of hemodynamic severity with plaque vulnerability and complexity of coronary artery stenosis:a combined optical coherence tomography and fractional flow reserve study. JACC Cardiovasc Imaging, 2019, 12(6): 1103-1105.

1. Yonetsu T, Jang I K. Advances in intravascular imaging: new insights into the vulnerable plaque from imaging studies. Korean Circulation Journal, 2018, 48(1): 1-15.
2. Yuan W, Kut C, Liang W, et al. Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection. Scientific Reports, 2017, 7: 44909.
3. Gal-Or O, Dansingani K K, Sebrow D, et al. Inner choroidal flow signal attenuation in pachychoroid disease: optical coherence tomography angiography. Retina-The Journal of Retinal and Vitreous Diseases, 2018, 38(10): 1984-1992.
4. Gnanadesigan M, Hussain A S, White S, et al. Optical coherence tomography attenuation imaging for lipid core detection: an ex-vivo validation study. Int J Cardiovasc Imaging, 2017, 33(1): 5-11.
5. Sun P, Li Q, Li H, et al. Depth-resolved physiological response of retina to transcorneal electrical stimulation measured with optical coherence tomography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(5): 905-915.
6. Abdolmanafi A, Duong L, Dahdah N, et al. Characterization of coronary artery pathological formations from OCT imaging using deep learning. Biomed Opt Express, 2018, 9(10): 4936-4960.
7. Liu S, Sotomi Y, Eggermont J, et al. Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images. J Biomed Opt, 2017, 22(9): 096004.
8. Smith G T, Dwork N, O'Connor D, et al. Automated,depth-resolved estimation of the attenuation coefficient from optical coherence tomography data. IEEE Trans Med Imaging, 2015, 34(12): 2592-2602.
9. 董昕, 陈思佳, 李军. 基于压缩感知和散斑相关法的散射介质成像方法研究. 激光生物学报, 2019, 28(1): 64-71.
10. Nicholas D, Smith G T, Theodore L, et al. Automatically determining the confocal parameters from OCT B-scans for quantification of the attenuation coefficients. IEEE Trans Med Imaging, 2019, 38(1): 261-268.
11. Stefan S, Jeong K S, Polucha C, et al. Determination of confocal profile and curved focal plane for OCT mapping of the attenuation coefficient. Biomed Opt Express, 2018, 9(10): 5084-5099.
12. Wang S, Singh M, Tran T T, et al. Biomechanical assessment of myocardial infarction using optical coherence elastography. Biomed Opt Express, 2018, 9(2): 728.
13. Qu Y, Ma T, He Y, et al. Miniature probe for mapping mechanical properties of vascular lesions using acoustic radiation force optical coherence elastography, 2017: 4731.
14. Kennedy B F, Kennedy K M, Oldenburg A L, et al. Optical coherence elastography//Drexler W, Fujimoto J G. Optical Coherence Tomography, Springer International Publishing Switzerland, 2015: 1007-1054.
15. Zaitsev V Y, Matveyev A L, Matveev L A, et al. Optical coherence elastography for strain dynamics measurements in laser correction of cornea shape. J Biophotonics, 2017, 10(11): 1450-1463.
16. Chau A H, Chan R C, Shishkov M, et al. Mechanical analysis of atherosclerotic plaques based on optical coherence tomography. Ann Biomed Eng, 2004, 32(11): 1494-1503.
17. Chan R C, Chau A H, Karl W C, et al. OCT-based arterial elastography: robust estimation exploiting tissue biomechanics. Opt Express, 2004, 12(19): 4558-4572.
18. Khalil A S, Chan R C, Chau A H, et al. Tissue elasticity estimation with optical coherence elastography: toward mechanical characterization of in vivo Soft tissue. Ann Biomed Eng, 2005, 33(11): 1631-1639.
19. Elahi S, Gu S, Thrane L, et al. Complex regression Doppler optical coherence tomography. J Biomed Opt, 2018, 23(4): 1-8.
20. Shin P, Choi W J, Joo J Y, et al. Quantitative hemodynamic analysis of cerebral blood flow and neurovascular coupling using optical coherence tomography angiography. Journal of Cerebral Blood Flow & Metabolism, 2019, 39(10): 1983-1994.
21. Jing J C, Chen J J, Chou L, et al. Visualization and detection of ciliary beating pattern and frequency in the upper airway using phase resolved doppler optical coherence tomography. Sci Rep, 2017, 7(1): 8522.
22. 钱婕. 基于相位解析多普勒光相干层析成像的流速测量研究. 江苏, 2017.
23. 潘柳华, 张向阳, 李中梁, 等. 基于光声-光学相干层析成像的血流测量技术. 中国激光, 2018, 45(6): 224-230.
24. 孟婕. 多普勒光学相干层析成像方法与应用研究. 浙江, 2010.
25. 杜宜纲, 刘德杰, 沈莹莹, 等. 血管壁面剪切应力的测量及其临床研究进展. 中国生物医学工程学报, 2018, 37(5): 593-605.
26. Athanasiou L, Nezami F R, Galon M Z, et al. Optimized computer-aided segmentation and 3D REconstruction using intracoronary optical coherence tomography. IEEE J Biomed Health Inform, 2018, 22(4): 1168-1176.
27. Migliori S, Chiastra C, Bologna M, et al. A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images. Medical Engineering and Physics, 2017, 47: 105-116.
28. Leiknes T, Fortunato L, Qamar A, et al. In-situ assessment of biofilm formation in submerged membrane system using optical coherence tomography and computational fluid dynamics. Journal of Membrane Science, 2017, 521: 84-94.
29. Kelsey L, Schultz C, Miller K, et al. The effects of geometric variation from OCT-derived 3D reconstructions on wall shear stress in a patient-specific coronary artery//Wittek A, Joldes G, Nielsen P M F, et al. Computational Biomechanics for Medicine: From Algorithms to Models and Applications. Springer, 2017: 1-13.
30. 房兴锐, 吴剑胜, 郭攸胜, 等. 冠脉造影术、FFR 技术及 FD-OCT 评估的冠脉临界病变患者预后的比较. 心血管康复医学杂志, 2018, 27(3): 293-296.
31. Matsuo Y, Higashioka D, Ino Y, et al. Association of hemodynamic severity with plaque vulnerability and complexity of coronary artery stenosis:a combined optical coherence tomography and fractional flow reserve study. JACC Cardiovasc Imaging, 2019, 12(6): 1103-1105.

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