Abstract

Recently, in vivo trans-conjunctiva optical coherence tomography (OCT) imaging of the lacrimal passage was demonstrated using a turbid commercial eye drop as an extrinsic contrast agent. However, static OCT images are not sufficient to unambiguously delineate the lumen boundary to render 3D lumen images of the lacrimal passage by segmentation. The turbid eye drop is expected to include small particles that flow and undergo Brownian motion and can be used as an extrinsic contrast agent for dynamic OCT. We conducted dynamic OCT measurements of the lacrimal passage using a swept source OCT system. Firstly, characterization of the dynamic OCT properties of the eye drop was performed. For improved delineation of the lumen boundary, we calculated the sum of the squared differences of intensities with two different normalization parameters. By making composite color images from OCT images and these two dynamic OCT images, we could execute unambiguous segmentation of the lumen of the lacrimal passage. Three-dimensional volumetric images of parts of the lacrimal passage, i.e., lacrimal canaliculus and lacrimal punctum, are demonstrated.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (1)

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

2017 (1)

2016 (2)

C. W. Merkle and V. J. Srinivasan, “Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography,” Neuroimage 125, 350–362 (2016).
[Crossref] [PubMed]

C. W. Merkle, C. Leahy, and V. J. Srinivasan, “Dynamic contrast optical coherence tomography images transit time and quantifies microvascular plasma volume and flow in the retina and choriocapillaris,” Biomed. Opt. Express 7(10), 4289–4312 (2016).
[Crossref] [PubMed]

2012 (1)

2011 (2)

E. Jonathan, J. Enfield, and M. J. Leahy, “Correlation mapping method for generating microcirculation morphology from optical coherence tomography (OCT) intensity images,” J. Biophotonics 4(9), 583–587 (2011).
[PubMed]

J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
[Crossref] [PubMed]

2010 (2)

2009 (1)

C. Drew, T. E. Milner, and C. G. Rylander, “Mechanical tissue optical clearing devices: evaluation of enhanced light penetration in skin using optical coherence tomography,” J. Biomed. Opt. 14(6), 064019 (2009).
[Crossref] [PubMed]

2008 (1)

2007 (1)

2005 (1)

Akagi, T.

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

An, L.

Barton, J.

Cable, A.

Chen, C. L.

Drew, C.

C. Drew, T. E. Milner, and C. G. Rylander, “Mechanical tissue optical clearing devices: evaluation of enhanced light penetration in skin using optical coherence tomography,” J. Biomed. Opt. 14(6), 064019 (2009).
[Crossref] [PubMed]

Enfield, J.

E. Jonathan, J. Enfield, and M. J. Leahy, “Correlation mapping method for generating microcirculation morphology from optical coherence tomography (OCT) intensity images,” J. Biophotonics 4(9), 583–587 (2011).
[PubMed]

J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
[Crossref] [PubMed]

Fingler, J.

Fraser, S. E.

Fujimoto, J. G.

Fujimoto, M.

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

Hornegger, J.

Huang, D.

Jarvi, M.

Jia, Y.

Jiang, J.

Jonathan, E.

J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
[Crossref] [PubMed]

E. Jonathan, J. Enfield, and M. J. Leahy, “Correlation mapping method for generating microcirculation morphology from optical coherence tomography (OCT) intensity images,” J. Biophotonics 4(9), 583–587 (2011).
[PubMed]

Khurana, M.

Kraus, M. F.

Leahy, C.

Leahy, M.

Leahy, M. J.

E. Jonathan, J. Enfield, and M. J. Leahy, “Correlation mapping method for generating microcirculation morphology from optical coherence tomography (OCT) intensity images,” J. Biophotonics 4(9), 583–587 (2011).
[PubMed]

Lee, K.

Leung, M. K.

Leung, M. K. K.

Liu, J. J.

Mariampillai, A.

Merkle, C. W.

C. W. Merkle and V. J. Srinivasan, “Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography,” Neuroimage 125, 350–362 (2016).
[Crossref] [PubMed]

C. W. Merkle, C. Leahy, and V. J. Srinivasan, “Dynamic contrast optical coherence tomography images transit time and quantifies microvascular plasma volume and flow in the retina and choriocapillaris,” Biomed. Opt. Express 7(10), 4289–4312 (2016).
[Crossref] [PubMed]

Milner, T. E.

C. Drew, T. E. Milner, and C. G. Rylander, “Mechanical tissue optical clearing devices: evaluation of enhanced light penetration in skin using optical coherence tomography,” J. Biomed. Opt. 14(6), 064019 (2009).
[Crossref] [PubMed]

Moriyama, E. H.

Munce, N. R.

Ogino, K.

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

Potsaid, B.

Qin, J.

Rylander, C. G.

C. Drew, T. E. Milner, and C. G. Rylander, “Mechanical tissue optical clearing devices: evaluation of enhanced light penetration in skin using optical coherence tomography,” J. Biomed. Opt. 14(6), 064019 (2009).
[Crossref] [PubMed]

Schwartz, D.

Srinivasan, V. J.

C. W. Merkle, C. Leahy, and V. J. Srinivasan, “Dynamic contrast optical coherence tomography images transit time and quantifies microvascular plasma volume and flow in the retina and choriocapillaris,” Biomed. Opt. Express 7(10), 4289–4312 (2016).
[Crossref] [PubMed]

C. W. Merkle and V. J. Srinivasan, “Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography,” Neuroimage 125, 350–362 (2016).
[Crossref] [PubMed]

Standish, B. A.

Stromski, S.

Subhash, H.

Tan, O.

Tokayer, J.

Uji, A.

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

Vitkin, A.

Vitkin, I. A.

Wang, R. K.

Wang, Y.

Wilson, B. C.

Yang, C.

Yang, V. X. D.

Yoshimura, N.

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

Biomed. Opt. Express (3)

J. Biomed. Opt. (1)

C. Drew, T. E. Milner, and C. G. Rylander, “Mechanical tissue optical clearing devices: evaluation of enhanced light penetration in skin using optical coherence tomography,” J. Biomed. Opt. 14(6), 064019 (2009).
[Crossref] [PubMed]

J. Biophotonics (1)

E. Jonathan, J. Enfield, and M. J. Leahy, “Correlation mapping method for generating microcirculation morphology from optical coherence tomography (OCT) intensity images,” J. Biophotonics 4(9), 583–587 (2011).
[PubMed]

Neuroimage (1)

C. W. Merkle and V. J. Srinivasan, “Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography,” Neuroimage 125, 350–362 (2016).
[Crossref] [PubMed]

Opt. Express (4)

Opt. Lett. (2)

Sci. Rep. (1)

M. Fujimoto, A. Uji, K. Ogino, T. Akagi, and N. Yoshimura, “Lacrimal canaliculus imaging using optical coherence tomography dacryography,” Sci. Rep. 8(1), 9808 (2018).
[Crossref] [PubMed]

Other (4)

M. J. Ali, “Lacrimal Pathologies and Optical Coherence Tomography,” Atlas of Lacrimal Drainage Disorders (Springer, 2018), pp. 187–195.

“American National Standard for the Safe Use of Lasers, ANSI Z136.1-2000,” Laser Institute of America, Orlando, Florida, (2000).

ImageJ, https://imagej.nih.gov/ij/index.html

J. K. Kanski, “Lacrimal Drainage System,” Clinical Ophthalmology: A Systematic Approach (Butterworth Heinemann) (2007), pp. 151–164.

Supplementary Material (2)

NameDescription
» Visualization 1       The 3D volume image of lacrimal canaliculus (LC) under the conjunctiva. The scanning area id 5.0 mm x 5.0 mm.
» Visualization 2       The 3D volume image of lacrimal canaliculus under the conjunctiva. The scanning area is 2.5 mm x 2.5 mm.

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Figures (13)

Fig. 1
Fig. 1 Photograph of eye and schematic depiction of lacrimal drainage system. To image the lacrimal canaliculus (LC) in the conjunctiva, the inferior eyelid is flipped to expose it. (a): lacrimal gland, (b): superior lacrimal punctum (LP), (c): inferior LP, (d): superior LC, (e): inferior LC, (f): common canaliculus, (g): lacrimal sac, (h): nasolacrimal duct.
Fig. 2
Fig. 2 (a) Experimental set up for SS-OCT system. CP1 and CP2: couplers, CRR and CRS: circulators, PCR and PCS: polarization controllers, DAQ: data acquisition board, D/A converter: digital-to-analog converter, CLR and CLS: collimator, OLR and OLS: lens, BPR: balanced photo receiver, GM: galvano mirror. (b) Direction of illuminating beam out of the OCT system is tilted about 45 degrees from the horizontal to focus on the conjunctiva of the volunteer with the objective lens (OLS). The volunteer flips his eyelid with his finger to expose the conjunctiva.
Fig. 3
Fig. 3 (a) B-scan was made with 200 A-scans in the lateral x-direction. Four B-scans were acquired as a set at the same location. 200 of such B-scan sets were acquired in the lateral y-direction to complete a volume scan. (b) Data processing flow.
Fig. 4
Fig. 4 OCT images of rebamipide ophthalmic suspension UD2% (ROS) and intralipid in the respective ampules. (a) Photograph of ampule containing liquid, where the red line indicates location of B-scan. (b) OCT B-scan image of ROS along the red line in (a). (c) OCT B-scan image of intralipid along the red line in (a). (d) A-scan profile of ROS along the red dotted line in (b). (e) A-scan profile of ROS along the red dotted line in (c).
Fig. 5
Fig. 5 The dependence of averaged normalized intensity difference (ANID) on B-frame time interval: (a) ROS and (b) intralipid.
Fig. 6
Fig. 6 M-scan images of OCT as functions of A-scan interval: (a) ROS, and (b) intralipid. OCT images of the ampule wall are nearly constant, while those of ROS and intralipid change. The change is slow for ROS compared with that for intralipid. The averaged normalized intensity differences (ANIDs) are plotted as functions of A-scan interval for (c) ROS and (d) intralipid.
Fig. 7
Fig. 7 Comparison of D-OCT A-scan profiles calculated using STSD by selecting different α values: α = 0.5 (green curve), and α = 1 (red curve).
Fig. 8
Fig. 8 (a): Photograph of the eyelid and LP showing the OCT scanning area (white rectangle representing an area of 2.5 mm×2.5 mm), (b): OCT image of the LC before using ROS, (c): OCT image of the LC after using ROS. White arrows in (b) and (c) indicate the LP and LC. Scale bars in (b) and (c) are 1 mm. The gray scale bars indicate OCT signal power in dB.
Fig. 9
Fig. 9 (a), (b) and (c): 2D images of the LC after applying ROS, (d-I) and (d-II) are profiles of A-scans corresponding to the white dashed lines I and II in (a), (b) and (c). (a): OCT, (b): STSD (α = 0.5), (c): STSD (α = 1). Red arrows in the 2D images indicate the LC location, corresponding to the peaks pointed to with red arrows in (d). Scale bars in (a), (b) and (c) are 1 mm.
Fig. 10
Fig. 10 (a), (b), and (c): 2D images around the LC after using ROS, (d-I), (d-II): the profiles of A-scans corresponding to the white dashed lines in (a) to (c). (a): OCT, (b): STSD (α = 0.5), (c): STSD (α = 1). Red arrows in (a) to (c) indicate the area corresponding to the red arrows in (d-I) and (d-II). Scale bars in (a), (b) and (c) are 1 mm.
Fig. 11
Fig. 11 En face maximum intensity projection (MIP) view image of the LC after applying ROS. The scanning areas of (a) to (d) are 5.0 mm×5.0 mm and (e) to (h) are 2.5 mm×2.5 mm, respectively. (a) and (e) are the OCT images, (b) and (f) are the STSD images with α = 0.5, (c) and (g) are the STSD images with α = 1, and (d) and (h) are the color-composite images with OCT as red, STSD α = 0.5 as green, and STSD α = 1 as blue. The red arrows in the images show the LP location in each image. Yellow ovals in (c) and (g) indicate the ROS spreading within the LC. Scale bars in (a) and (e) are 1 mm.
Fig. 12
Fig. 12 An example of color-composited LC image. (a): OCT image as red, (b): STSD (α = 0.5) image as green, (c): STSD (α = 1) image as blue, (d): color-composited image of (a), (b), and (c), (d): segmented and binarized LC image.
Fig. 13
Fig. 13 The snapshots of 3D volume images of the LC under the conjunctiva (Visualization 1 and Visualization 2). The scanning area of (a) is 5.0 mm×5.0 mm and that of (b) is 2.5 mm×2.5 mm, respectively. The white arrows indicate the LP location. Scale bars are 1 mm.

Equations (2)

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D α ( i,j,k )= 2 N( N1 ) n>m=1 N ( I i,j,k ( n ) I i,j,k ( m ) ) 2 ( I i,j,k 2 ( n )+ I i,j,k 2 ( m ) ) α
ANID( n )= ( I i,j,k ( n ) I i,j,k ( 0 ) ) 2 I i,j,k 2 ( n )+ I i,j,k 2 ( 0 )

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