Biomedical Engineering Letters

, Volume 9, Issue 3, pp 311–325 | Cite as

Optical coherence tomography angiography in preclinical neuroimaging

  • Woo June ChoiEmail author
Review Article


Preclinical neuroimaging allows for the assessment of brain anatomy, connectivity, and function in laboratory animals, such as mice and this imaging field has been a rapidly growing aimed at bridging the translation gap between animal and human research. The progress in the animal research could be accelerated by high-resolution in vivo optical imaging technologies. Optical coherence tomography-based angiography (OCTA) estimates the scattering from moving red blood cells, providing the visualization of functional micro-vessel networks within tissue beds in vivo without a need for exogenous contrast agents. Recent advancement of OCTA methods have expanded its application to neuroimaging of small animal models of brain disorders. In this paper, we overview the recent development of OCTA techniques for blood flow imaging and its preclinical applications in neuroimaging. In specific, a summary of preclinical OCTA studies for traumatic brain injury, cerebral stroke, and aging brain on mice is reviewed.


Optical coherence tomography Angiography Preclinical neuroimaging, small animal models Traumatic brain injury, stroke, aging 



This research was supported by the Chung-Ang University Research Grants in 2018.


This study was funded by the Chung-Ang University Research Grants in 2018.

Compliance with ethical standards

Conflict of interest

Dr. Choi has no conflicts of interest to declare.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA, et al. Optical coherence tomography. Science. 1991;254(5035):1178–81.Google Scholar
  2. 2.
    Drexler W, Fujimoto JG. State-of-the-art retinal optical coherence tomography. Prog Retin Eye Res. 2008;27(1):45–88.Google Scholar
  3. 3.
    Klein T, Wieser W, Reznicek L, Neubauer A, Kampik A, Huber R. Multi-MHz retinal OCT. Biomed Opt Express. 2013;4(10):1890–908.Google Scholar
  4. 4.
    Drexler W. Ultrahigh-resolution optical coherence tomography. J Biomed Opt. 2004;9(1):47–74.Google Scholar
  5. 5.
    Choi WJ, Wang RK. Swept-source optical coherence tomography powered by a 1.3-μm vertical cavity surface emitting laser enables 2.3-mm-deep brain imaging in mice in vivo. J Biomed Opt. 2015;20(10):106004.Google Scholar
  6. 6.
    Klein T, Huber R. High-speed OCT light sources and systems. Biomed Opt Express. 2017;8(2):828–59.Google Scholar
  7. 7.
    Adler DC, Huber R, Fujimoto JG. Phase-sensitive optical coherence tomography at up to 370,000 lines per second using buffered Fourier domain mode-locked lasers. Opt Lett. 2007;32(6):626–8.Google Scholar
  8. 8.
    de Boer JF, Hitzenberger CK, Yasuno Y. Polarization sensitive optical coherence tomography—a review. Biomed Opt Express. 2017;8(3):1838–73.Google Scholar
  9. 9.
    Uhlhorn SR, Borja D, Manns F, Parel JM. Refractive index measurement of the isolated crystalline lens using optical coherence tomography. Vis Res. 2008;48(27):2732–8.Google Scholar
  10. 10.
    Faber DJ, van der Meer F, Aalders M, van Leeuwen T. Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography. Opt Express. 2004;12(19):4353–65.Google Scholar
  11. 11.
    Leitgeb RA, Werkmeister RM, Blatter C, Schmetterer L. Doppler optical coherence tomography. Prog Retin Eye Res. 2014;41(100):26–43.Google Scholar
  12. 12.
    Lee J, Wu W, Jiang JY, Zhu B, Boas DA. Dynamic light scattering optical coherence tomography. Opt Express. 2012;20(20):22262–77.Google Scholar
  13. 13.
    Kim J, Brown W, Maher JR, Levinson H, Wax A. Functional optical coherence tomography: principles and progress. Phys Med Biol. 2015;60(10):R211–37.Google Scholar
  14. 14.
    de Carlo TE, Romano A, Waheed NK, Duker JS. A review of optical coherence tomography angiography (OCTA). Int J Retin Vitr. 2015;1:5.Google Scholar
  15. 15.
    Tan ACS, Tan GS, Denniston AK, Keane PA, Ang M, Milea D, Chakravarthy U, Cheung CMG. An overview of the clinical applications of optical coherence tomography angiography. Eye. 2018;32(2):262–86.Google Scholar
  16. 16.
    Spaide RF, Klancnik JM Jr, Cooney MJ. Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography. JAMA Ophthalmol. 2015;133(1):45–50.Google Scholar
  17. 17.
    Chong SP, Merkle CW, Cooke DF, Zhang T, Radhakrishnan H, Krubitzer L, Srinivasan VJ. Noninvasive, in vivo imaging of subcortical mouse brain region with 1.7 μm optical coherence tomography. Opt Lett. 2015;40(21):4911–4.Google Scholar
  18. 18.
    Ren H, Du C, Yuan Z, Park K, Volkow ND, Pan Y. Cocaine induced cortical microischemia in the rodent brain: clinical implications. Mol Psychiatr. 2012;17(10):1017–25.Google Scholar
  19. 19.
    Zhang A, Zhang Q, Chen CL, Wang RK. Methods and algorithms for optical coherence tomography-based angiography: a review and comparison. J Biomed Opt. 2015;20(10):100901.Google Scholar
  20. 20.
    Chen CL, Wang RK. Optical coherence tomography based angiography. Biomed Opt Express. 2017;8(2):1056–82.Google Scholar
  21. 21.
    Kashani AH, Chen CL, Gahm JK, Zheng F, Richter GM, Rosenfeld PJ, Shi Y, Wang RK. Optical coherence tomography angiography: a comprehensive review of current methods and clinical applications. Prog Retin Eye Res. 2017;60:66–100.Google Scholar
  22. 22.
    Leitgeb RA, Drexler W, Unterhuber A, Hermann B, Bajraszewski T, Stingl LA, Fercher AF. Ultrahigh resolution Fourier domain optical coherence tomography. Opt Express. 2004;12(10):2156–65.Google Scholar
  23. 23.
    Kolb JP, Pfeiffer T, Eibl M, Hakert H, Huber R. High-resolution retinal swept-source optical coherence tomography with an ultra-wideband Fourier-domain mode-locked laser at MHz A-scan rates. Biomed Opt Express. 2018;9(1):120–30.Google Scholar
  24. 24.
    Ling Y, Gan Y, Yao X, Hendon CP. Phase-noise analysis of swept-source optical coherence tomography. Opt Lett. 2017;42(7):1333–6.Google Scholar
  25. 25.
    Liu G, Tan O, Gao SS, Pechauer AD, Lee B, Lu CD, Fujimoto JG, Huang D. Postprocessing algorithms to minimize fixed-pattern artifact and reduce trigger jitter in swept-source optical coherence tomography. Opt Express. 2015;23(8):9824–34.Google Scholar
  26. 26.
    Dupire J, Socol M, Viallat A. Fully dynamics of a red blood cell in shear flow. Proc Natl Acad Sci USA. 2012;109(51):20808–13.Google Scholar
  27. 27.
    Makita S, Hong Y, Yamanari M, Yatagi T, Yasuno Y. Optical coherence angiography. Opt Express. 2006;14(17):7821–40.Google Scholar
  28. 28.
    Zhang J, Chen Z. In vivo blood flow imaging by a swept laser source based Fourier domain optical Doppler tomography. Opt Express. 2005;13(19):7449–57.Google Scholar
  29. 29.
    Fingler J, Readhead C, Schwartz DM, Fraser SE. Phase-contrast OCT imaging of transverse flows in the mouse retina and choroid. Invest Ophthalmol Vis Sci. 2008;49(11):5055–9.Google Scholar
  30. 30.
    Schwartz DM, Fingler J, Kim DY, Zawadzki RJ, Morse LS, Park SS, Fraser SE, Werner JS. Phase-variance optical coherence tomography: a technique for noninvasive angiography. Ophthalmology. 2014;121(1):180–7.Google Scholar
  31. 31.
    Yun SH, Tearney GJ, de Boer JF, Bouma BE. Motion artifacts in optical coherence tomography with frequency-domain ranging. Opt Express. 2004;12(13):2977–98.Google Scholar
  32. 32.
    Song S, Xu J, Men S, Shen TT, Wang RK. Robust numerical phase stabilization for long-range swept-source optical coherence tomography. J Biophotonics. 2017;10(11):1398–410.Google Scholar
  33. 33.
    Chen Z, Liu M, Minneman M, Ginner L, Hoover E, Sattmann H, Bonesi M, Drexler W, Leitgeb RA. Phase-stable swept source OCT angiography in human skin using an akinetic source. Biomed Opt Express. 2016;7(8):3032–48.Google Scholar
  34. 34.
    Mariampillai A, Standish BA, Moriyama EH, Khurana M, Munce NR, Leung MKK, Jiang J, Cable A, Wilson BC, Vitkin IA, Yang VXD. Speckle variance detection of microvasculature using swept-source optical coherence tomography. Opt Lett. 2008;33(13):1530–2.Google Scholar
  35. 35.
    Enfield J, Jonathan E, Leahy M. In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT). Biomed Opt Express. 2011;2(5):1184–93.Google Scholar
  36. 36.
    Jia Y, Tan O, Tokayer J, Potsaid B, Wang Y, Liu JJ, Kraus MF, Subhash H, Fujimoto JG, Hornegger J, Huang D. Split-spectrum amplitude decorrelation angiography with optical coherence tomography. Opt Express. 2012;20(4):4710–25.Google Scholar
  37. 37.
    Boas DA. Laser speckle contrast imaging in biomedical optics. J Biomed Opt. 2010;15(1):011109.Google Scholar
  38. 38.
    Barton JK, Stromski S. Flow measurement without phase information in optical coherence tomography images. Opt Express. 2005;13(14):5234–9.Google Scholar
  39. 39.
    Xu J, Han S, Balaratnasingam C, Mammo Z, Wong KS, Lee S, Cua M, Young M, Kirker A, Albiani D, et al. Retinal angiography with real-time speckle variance optical coherence tomography. Br J Ophthalmol. 2015;99(10):1315–9.Google Scholar
  40. 40.
    Markowitz O, Schwartz M, Minhas S, Siegel DM. Speckle-variance optical coherence tomography: a novel approach to skin cancer characterization using vascular patterns. Dermatol Online J. 2016;22(4):pii: 13030/qt7w10290r.Google Scholar
  41. 41.
    Cadotte DW, Mariampillai A, Cadotte A, Lee KKC, Kiehl TR, Wilson BC, Fehlings MG, Yang VXD. Speckle variance optical coherence tomography of the rodent spinal cord: in vivo feasibility. Biomed Opt Express. 2012;3(5):911–9.Google Scholar
  42. 42.
    Grishina O, Wang S, Larina IV. Speckle variance optical coherence tomography of blood flow in the beating mouse embryonic heart. J Biophotonics. 2017;10(5):735–43.Google Scholar
  43. 43.
    Choi WJ, Reif R, Yousefi S, Wang RK. Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask. J Biomed Opt. 2014;19(3):036010.Google Scholar
  44. 44.
    Gao SS, Liu G, Huang D, Jia Y. Optimization of the split-spectrum amplitude-decorrelation angiography algorithm on a spectral optical coherence tomography system. Opt Lett. 2015;40(10):2305–8.Google Scholar
  45. 45.
    Wang RK, Jacques SL, Ma Z, Hurst S, Hanson SR, Gruber A. Three dimensional optical angiography. Opt Express. 2007;15(7):4083–97.Google Scholar
  46. 46.
    An L, Qin J, Wang RK. Ultrahigh sensitive optical microangiography for in vivo imaging of microcirculations within human skin tissue beds. Opt Express. 2010;18(8):8220–8.Google Scholar
  47. 47.
    Li T, Raizen MG. Brownian motion at short time scales. Ann Phys. 2013;525(4):281–95.Google Scholar
  48. 48.
    Wang RK, Zhang A, Choi WJ, Zhang Q, Chen CL, Miller A, Gregori G, Rosenfeld PJ. Wide-field optical coherence tomography angiography enabled by two repeated measurements of B-scans. Opt Lett. 2016;41(10):2330–3.Google Scholar
  49. 49.
    Yousefi S, Zhi Z, Wang RK. Eigendecomposition-based clutter filtering technique for optical microangiography. IEEE Trans Biomed Eng. 2011;58(8):2316–23. Scholar
  50. 50.
    Yousefi S, Qin J, Wang RK. Super-resolution spectral estimation of optical micro-angiography for quantifying blood flow within microcirculatory tissue beds in vivo. Biomed Opt Express. 2013;4(7):1214–28.Google Scholar
  51. 51.
    Zhang Q, Wang J, Wang RK. Highly efficient eigen decomposition based statistical optical microangiography. Quant Imaging Med Surg. 2016;6(5):557–63.Google Scholar
  52. 52.
    Nam AS, Chico-Calero I, Vakoc BJ. Complex differential variance algorithm for optical coherence tomography angiography. Biomed Opt Express. 2014;5(11):3822–32.Google Scholar
  53. 53.
    Braaf B, Donner S, Nam AS, Bouma BE, Vakoc BJ. Complex differential variance angiography with noise-bias correction for optical coherence tomography of the retina. Biomed Opt Express. 2018;9(2):486–506.Google Scholar
  54. 54.
    Xu J, Song S, Li Y, Wang RK. Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems. Phys Med Biol. 2017;63(1):015023.Google Scholar
  55. 55.
    Vakoc BJ, Yun SH, de Boer JF, Tearney GJ, Bouma BE. Phase-resolved optical frequency domain imaging. Opt Express. 2005;13(14):5483–93.Google Scholar
  56. 56.
    Baumann B, et al. Total retinal blood flow measurement with ultrahigh speed swept source/Fourier domain OCT. Biomed Opt Express. 2011;2(6):1539–52.Google Scholar
  57. 57.
    Huber R, Wojtkowski M, Fujimoto GJ, Jiang JY, Cable AE. Three-dimensional and C-mode OCT imaging with a compact, frequency swept laser source at 1300 nm. Opt Express. 2005;13(26):10523–38.Google Scholar
  58. 58.
    Braaf B, Vermeer KA, Sicam VADP, van Zeeburg E, van Meurs JC, de Boer JF. Phase-stabilized optical frequency domain imaging at 1-μm for the measurement of blood flow in the human choroid. Opt Express. 2011;19(21):20886–903.Google Scholar
  59. 59.
    Hong YJ, Makita S, Jaillon F, Ju MJ, Min EJ, Lee BH, Itoh M, Miura M, Yasuno Y. High-penetration swept source Doppler optical coherence angiography by fully numerical phase stabilization. Opt Express. 2012;20(3):2740–60.Google Scholar
  60. 60.
    Motaghiannezam SMR, Koos D, Fraiser SE. Differential phase-contrast, swept-source optical coherence tomography at 1060 nm for in vivo human retinal and choroidal vasculature visualization. J Biomed Opt. 2012;17(2):026011.Google Scholar
  61. 61.
    Deegan AJ, et al. Optical coherence tomography angiography of normal skin and inflammatory dermatologic conditions. Lasers Surg Med. 2018;50(3):183–93.Google Scholar
  62. 62.
    Vincent TJ, Thiessen JD, Kurjewicz LM, Germscheid SL, Turner AJ, Zhilkin P, Alexander ME, Martin M. Longitudinal brain size measurements in App/Ps1 transgenic mice. Magn Reson Insights. 2010;4(4):19–26.Google Scholar
  63. 63.
    Cova L, Armentero MT. 1980–2011: Parkinson’s disease and advance in stem cell research. 2011.
  64. 64.
    Fluri F, Schuhmann K, Kleinschnitz C. Animal models of ischemic stroke and their application in clinical research. Drug Des Dev Ther. 2015;9:3445–54.Google Scholar
  65. 65.
    Semple BD, Blomgren K, Gimlin K, Ferriero DM, Noble-Haeusslein LJ. Brain development in rodents and humans: identifying benchmarks of maturation and vulnerability to injury across species. Prog Neurobiol. 2013;106–107:1–16.Google Scholar
  66. 66.
    Vakoc BJ, Lanning RM, Tyrell JA, Padera TP, Bartlett LA, Stylianopoulos T, Munn LL, Tearney GJ, Fukumura D, Jain RK, Bouma BE. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat Med. 2009;15(10):1219–23.Google Scholar
  67. 67.
    Werner C, Engelhard K. Pathophysiology of traumatic brain injury. Brit J Anaesth. 2007;99(1):4–9.Google Scholar
  68. 68.
    Xiong Y, Mahmood A, Chopp M. Animal models of traumatic brain injury. Nat Rev Neurosci. 2013;14(2):128–42.Google Scholar
  69. 69.
    Jia Y, Grafe MR, Gruber A, Alkayed NJ, Wang RK. In vivo optical imaging of revascularization after brain trauma in mice. Microvasc Res. 2011;81(1):73–80.Google Scholar
  70. 70.
    Choi WJ, Wang RK. Optical coherence tomography imaging of cranial meninges post brain injury in vivo. Chin Opt Lett. 2017;15(9):090005.Google Scholar
  71. 71.
  72. 72.
    Kolias AG, Chari A, Santarius T, Hutchinson PJ. Chronic subdural haematoma: modern management and emerging therapies. Nat Rev Neurol. 2014;10(10):570–8.Google Scholar
  73. 73.
    Yamashima T, Friede RL. Why do bridging veins rupture into the virtual subdural space? J Neurol Neurosurg Psychiatr. 1984;47(2):121–7.Google Scholar
  74. 74.
    Kristof RA, Grimm JM, Stoffel-Wagner B. Cerebrospinal fluid leakage into the subdural space: possible influence on the pathogenesis and recurrence frequency of chronic subdural hematoma and subdural hygroma. J. Neurosurg. 2008;108(2):275–80.Google Scholar
  75. 75.
    Deb P, Sharma S, Hassan KM. Pathophysiologic mechanisms of acute ischemic stroke: an overview with emphasis on therapeutic significance beyond thrombolysis. Pathophysiology. 2010;17(3):197–218.Google Scholar
  76. 76.
    Andersen KK, Olsen TS, Dehlendorff C, Kammersgaard LP. Hemorrhagic and ischemic strokes compared: stroke severity, mortality, and risk factors. Stroke. 2009;40(6):2068–72.Google Scholar
  77. 77.
    Lipton P. Ischemic cell death in brain neurons. Physiol Rev. 1999;79(4):1431–568.Google Scholar
  78. 78.
    Fisher M. The ischemic penumbra: identification, evolution and treatment concepts. Cerebrovasc Dis. 2004;17(suppl 1):1–6.MathSciNetGoogle Scholar
  79. 79.
    Casals JB, Pieri NCG, Feitosa MLT, Ercolin ACM, Roballo KCS, Barreto RSN, Bressan FF, Martins DS, Miglino MA, Ambrósio CE. The use of animal models for stroke research: a review. Comp Med. 2011;61(4):305–13.Google Scholar
  80. 80.
    Sommer CJ. Ischemic stroke: experimental models and reality. Acta Neuropathol. 2017;133(2):245–61.Google Scholar
  81. 81.
    Jia Y, Wang RK. Optical micro-angiography images structural and functional cerebral blood perfusion in mice with cranium left intact. J Biophotonics. 2011;4(12):57–63.Google Scholar
  82. 82.
    Sampei K, Goto S, Alkayed NJ, Crain BJ, Korach KS, Traystman RJ, Demas GE, Nelson RJ, Hurn PD. Stroke in strogen receptor-α–deficient mice. Stroke. 2000;31(3):738–44.Google Scholar
  83. 83.
    Srinivasan VJ, Mandeville ET, Can A, Blasi F, Climov M, Daneshmand A, Lee JH, Yu E, Radhakrishnan H, Lo EH, Sakadžić S, Eikermann-Haerter K, Ayata C. Multiparametric, longitudinal optical coherence tomography imaging reveals acute injury and chronic recovery in experimental ischemic stroke. PLoS ONE. 2013;7(8):e71478.Google Scholar
  84. 84.
    Shih AY, Mateo C, Drew PJ, Tsai PS, Kleinfeld D. A polished and reinforced thinned-skull window for long-term imaging of the mouse brain. J Vis Exp. 2012;61:pii: 3742. Scholar
  85. 85.
    Akamatsu Y, Nishijima Y, Lee CC, Yang SY, Shi L, An L, Wang RK, Tominaga T, Liu J. Impaired leptomeningeal collateral flow contributes to the poor outcome following experimental stroke in the type 2 diabetic mice. J Neurosci. 2015;35(9):3851–64.Google Scholar
  86. 86.
    Liebeskind DS. Collateral circulation. Stroke. 2003;34(9):2279–84.Google Scholar
  87. 87.
    Shuaib A, Butcher K, Mohammad AA, Saggur M, Liebeskind DS. Collateral blood vessels in acute ischaemic stroke: potential therapeutic target”. Lancet Neurol. 2011;10(10):909–21.Google Scholar
  88. 88.
    Shi L, Qin J, Reif R, Wang RK. Wide velocity range Doppler optical microangiography using optimized step-scanning protocol with phase variance mask. J Biomed Opt. 2013;18(10):106015.Google Scholar
  89. 89.
    Baran U, Li Y, Wang RK. Vasodynamics of pial and penetrating arterioles in relation to arteriolo-arteriolar anastomosis after focal stroke. Neurophotonics. 2015;2(2):025006.Google Scholar
  90. 90.
    Peters R. Ageing and the brain. Postgrad Med J. 2006;82(964):84–8.Google Scholar
  91. 91.
    Shaw TG, Mortel KF, Meyer JS, Rogers RL, Hardenberg J, Cutaia MM. Cerebral blood flow changes in benign aging and cerebrovascular disease. Neurology. 1984;34(7):855–62.Google Scholar
  92. 92.
    Meunier D, Stamatakis EA, Tyler LK. Age-related functional recognization, structural changes, and preserved cognition. Neurobiol Aging. 2014;35(1):42–5.Google Scholar
  93. 93.
    Li Y, Choi WJ, Wei W, Song S, Zhang Q, Liu J, Wang RK. Aging-related changes in cerebral vasculature and blood flow as determined by quantitative optical coherence tomography angiography. Neurobiol Aging. 2018;70:148–59.Google Scholar
  94. 94.
    Haensel JX, Spain A, Martin C. A systematic review of physiological methods in rodent pharmacological MRI studies. Psychopharmacology. 2015;232:489–99.Google Scholar
  95. 95.
    Hansen TD, Warner DS, Todd MM, Vust LJ, Trawick DC. Distribution of cerebral blood flow during halothane versus isoflurane anesthesia in rats. Anesthesiology. 1988;69(3):332–7.Google Scholar
  96. 96.
    Nakao Y, Itoh Y, Kuang TY, Cook M, Jehle J, Sokoloff L. Effects of anesthesia on functional activation of cerebral blood flow and metabolism. Proc Natl Acad Sci USA. 2001;98(13):7593–8.Google Scholar
  97. 97.
    Madularu D, Mathieu AP, Kumaragamage C, Reynolds LM, Near J, Flores C, Rajah MN. A non-invasive restraining system for awake mouse imaging. J Neurosci Methods. 2017;287:53–7.Google Scholar
  98. 98.
    Dintenfass L. Inversion of the Fahraeus–Lindqvist phenomenon in blood flow through capillaries of diminishing radius. Nature. 1967;215(5105):1099–100.Google Scholar
  99. 99.
    Park KS, Shin JG, Qureshi MM, Chung E, Eom TJ. Deep brain optical coherence tomography angiography in mice: in vivo, noninvasive imaging of hippocampal formation. Sci. Rep. 2018;8(1):11614.Google Scholar
  100. 100.
    Choi WJ, Wang RK. Swept-source optical coherence tomography powered by a 1.3-μm vertical cavity surface emitting laser enables 2.3-mm-deep brain imaging in mice in vivo. J Biomed Opt. 2015;20(10):106004.Google Scholar

Copyright information

© Korean Society of Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.School of Electrical and Electronics Engineering, College of ICT EngineeringChung-Ang UniversitySeoulRepublic of Korea

Personalised recommendations