Quantification of collagen fiber organization using three-dimensional Fourier transform-second-harmonic generation imaging

doi:10.1364/OE.20.021821

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Quantification of collagen fiber organization using three-dimensional Fourier transform-second-harmonic generation imaging

Tung Yuen Lau, Raghu Ambekar, and Kimani C. Toussaint »View Author Affiliations

Optics Express, Vol. 20, Issue 19, pp. 21821-21832 (2012)
http://dx.doi.org/10.1364/OE.20.021821

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– Abstract
1. Introduction
2. Methods
3. Results and discussion
4. Conclusion
– References and links

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Abstract

We present three-dimensional Fourier transform-second-harmonic generation (3D FT-SHG) imaging, a generalization of the previously reported two-dimensional FT-SHG, to quantify collagen fiber organization from 3D image stacks of biological tissues. The current implementation calculates 3D preferred orientation of a region of interest, and classifies regions of interest based on orientation anisotropy and average voxel intensity. Presented are some example applications of the technique which reveal the layered structure of collagen fibers in porcine sclera, and estimates the cut angle of porcine tendon tissues. This technique shows promising potential for studying biological tissues that contain fibrillar structures in 3D.

1. Introduction

Second-harmonic generation (SHG) microscopy is an increasing popular field due to its ability to generate high contrast images of fibrillar collagen-based biological tissues without the need for staining [1

R. Ambekar, M. R. Mehta, S. Leithem, and K. C. Toussaint, “Fourier transform-second-harmonic generation imaging of collagen fibers in biological tissues,” in Biomedical Optics (Optical Society of America, 2010), p. BSuD63.

–5

P. Stoller, B.-M. Kim, A. M. Rubenchik, K. M. Reiser, and L. B. Da Silva, “Polarization-dependent optical second-harmonic imaging of a rat-tail tendon,” J. Biomed. Opt. 7(2), 205–214 (2002). [CrossRef] [PubMed]

]. Existing in the form of fibrous proteins, collagen is an important structural material that accounts for approximately 25% of the total protein mass in the human body [6

B. Alberts, A. Johnson, L. Julian, R. Martin, R. Keith, and W. Peter, Molecular Biology of the Cell (Garland Science, 2007).

]. Being intrinsically non-centrosymmetric, specific types of collagen fibers emit SHG signal when illuminated by a high intensity optical field (e.g., such as from a femtosecond-pulsed laser source) [2

L. Loew, A. Millard, P. J. Campagnola, W. Mohler, and A. Lewis, “Second harmonic imaging microscopy,” Microsc. Microanal. 9, 170–171 (2003).

, 7

P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003). [CrossRef] [PubMed]

]; the emitted signal is at half the wavelength of the incident optical beam. In addition, since the excitation is confined to a sub-femtoliter focal volume, SHG microscopy permits 3D imaging [8

G. Cox, E. Kable, A. Jones, I. Fraser, F. Manconi, and M. D. Gorrell, “3-dimensional imaging of collagen using second harmonic generation,” J. Struct. Biol. 141(1), 53–62 (2003). [CrossRef] [PubMed]

–11

A. M. Pena, A. Fabre, D. Débarre, J. Marchal-Somme, B. Crestani, J. L. Martin, E. Beaurepaire, and M. C. Schanne-Klein, “Three-dimensional investigation and scoring of extracellular matrix remodeling during lung fibrosis using multiphoton microscopy,” Microsc. Res. Tech. 70(2), 162–170 (2007). [CrossRef] [PubMed]

]. As a result, this technique has become an important aspect of both qualitative and quantitative studies of collagen-based tissues [7

L. Loew, A. Millard, P. J. Campagnola, W. Mohler, and A. Lewis, “Second harmonic imaging microscopy,” Microsc. Microanal. 9, 170–171 (2003).

–14

P. J. Campagnola, M. A. Brewer, V. Ajeti, P. Keely, K. Eliceiri, M. Patankar, and K. Tilbury, “SHG imaging of cancer,” in Biomedical Optics(Optical Society of America, 2012), p. BSu4B.1.

Recent studies that utilize SHG microscopy have shown that unwanted alterations in collagen fiber architecture are often associated with diseases or injuries [4

M. Sivaguru, S. Durgam, R. Ambekar, D. Luedtke, G. Fried, A. Stewart, and K. C. Toussaint Jr., “Quantitative analysis of collagen fiber organization in injured tendons using Fourier transform-second harmonic generation imaging,” Opt. Express 18(24), 24983–24993 (2010). [CrossRef] [PubMed]

, 11

, 13

T. Hompland, A. Erikson, M. Lindgren, T. Lindmo, and C. de Lange Davies, “Second-harmonic generation in collagen as a potential cancer diagnostic parameter,” J. Biomed. Opt. 13(5), 054050 (2008). [CrossRef] [PubMed]

, 15

T. Abraham and J. Hogg, “Extracellular matrix remodeling of lung alveolar walls in three dimensional space identified using second harmonic generation and multiphoton excitation fluorescence,” J. Struct. Biol. 171(2), 189–196 (2010). [CrossRef] [PubMed]

–17

T. L. Sun, Y. Liu, M. C. Sung, H. C. Chen, C. H. Yang, V. Hovhannisyan, W. C. Lin, Y. M. Jeng, W. L. Chen, L. L. Chiou, G. T. Huang, K. H. Kim, P. T. C. So, Y. F. Chen, H. S. Lee, and C. Y. Dong, “Ex vivo imaging and quantification of liver fibrosis using second-harmonic generation microscopy,” J. Biomed. Opt. 15(3), 036002–036006 (2010). [CrossRef] [PubMed]

]. For instance, reduced collagen content and increased proportion of nonlamellar collagen structure are observed from bone tissues that suffer from osteogenesis imperfecta, a structural disorder that leads to brittleness and reduction in bone strength [16

M. J. Silva, M. D. Brodt, B. Wopenka, S. Thomopoulos, D. Williams, M. H. M. Wassen, M. Ko, N. Kusano, and R. A. Bank, “Decreased collagen organization and content are associated with reduced strength of demineralized and intact bone in the SAMP6 mouse,” J. Bone Miner. Res. 21(1), 78–88 (2006). [CrossRef] [PubMed]

, 18

R. Ambekar, M. Chittenden, I. Jasiuk, and K. C. Toussaint Jr., “Quantitative second-harmonic generation microscopy for imaging porcine cortical bone: comparison to SEM and its potential to investigate age-related changes,” Bone 50(3), 643–650 (2012). [CrossRef] [PubMed]

–20

S. M. Weis, J. L. Emery, K. D. Becker, D. J. McBride Jr, J. H. Omens, and A. D. McCulloch, “Myocardial mechanics and collagen structure in the osteogenesis imperfecta murine (oim),” Circ. Res. 87(8), 663–669 (2000). [CrossRef] [PubMed]

]. Another representative example is the observation of highly ordered, yet unevenly-distributed collagen fibers in malignant tissues using SHG microscopy [14

P. J. Campagnola, M. A. Brewer, V. Ajeti, P. Keely, K. Eliceiri, M. Patankar, and K. Tilbury, “SHG imaging of cancer,” in Biomedical Optics(Optical Society of America, 2012), p. BSu4B.1.

, 21

O. Nadiarnykh, R. B. LaComb, M. A. Brewer, and P. J. Campagnola, “Alterations of the extracellular matrix in ovarian cancer studied by second harmonic generation imaging microscopy,” BMC Cancer 10(1), 94 (2010). [CrossRef] [PubMed]

, 22

C. Thrasivoulou, G. Virich, T. Krenacs, I. Korom, and D. L. Becker, “Optical delineation of human malignant melanoma using second harmonic imaging of collagen,” Biomed. Opt. Express 2(5), 1282–1295 (2011). [CrossRef] [PubMed]

]. Such spatial alterations may provide cues to cell migration and invasion during cancer progression [22

, 23

D. Barkan, J. E. Green, and A. F. Chambers, “Extracellular matrix: a gatekeeper in the transition from dormancy to metastatic growth,” Eur. J. Cancer 46(7), 1181–1188 (2010). [CrossRef] [PubMed]

]. In addition, collagen fiber organization is also associated with the structural role and function of the biological tissue [4

, 10

P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J. 82(1), 493–508 (2002). [CrossRef] [PubMed]

, 16

, 24

R. Ambekar, K. C. Toussaint Jr, and A. Wagoner Johnson, “The effect of keratoconus on the structural, mechanical, and optical properties of the cornea,” J. Mech. Behav. Biomed. Mater. 4(3), 223–236 (2011). [CrossRef] [PubMed]

–27

M. F. Young, “Bone matrix proteins: their function, regulation, and relationship to osteoporosis,” Osteoporosis Int. 14, 35–42 (2003).

]. For example, collagen fibers in cartilage form a flexible network of fibers that provides both support and cushioning [28

K. Brockbank, W. MacLellan, J. Xie, S. Hamm-Alvarez, Z. Chen, and K. Schenke-Layland, “Quantitative second harmonic generation imaging of cartilage damage,” Cell Tissue Banking 9, 299–307 (2008).

–30

E. Werkmeister, N. de Isla, P. Netter, J. F. Stoltz, and D. Dumas, “Collagenous extracellular matrix of cartilage submitted to mechanical forces studied by second harmonic generation microscopy,” Photochem. Photobiol. 86(2), 302–310 (2010). [CrossRef] [PubMed]

]. Moreover, mechanical properties of cornea were shown to be dependent on the orientation of the embedded collagen-fiber lamellae [9

N. Morishige, Y. Takagi, T. Chikama, A. Takahara, and T. Nishida, “Three-dimensional analysis of collagen lamellae in the anterior stroma of the human cornea visualized by second harmonic generation imaging microscopy,” Invest. Ophthalmol. Vis. Sci. 52(2), 911–915 (2011). [CrossRef] [PubMed]

, 24

, 31

C. Boote, S. Dennis, Y. Huang, A. J. Quantock, and K. M. Meek, “Lamellar orientation in human cornea in relation to mechanical properties,” J. Struct. Biol. 149(1), 1–6 (2005). [CrossRef] [PubMed]

–33

M. Winkler, D. Chai, S. Kriling, C. J. Nien, D. J. Brown, B. Jester, T. Juhasz, and J. V. Jester, “Nonlinear optical macroscopic assessment of 3-D corneal collagen organization and axial biomechanics,” Invest. Ophthalmol. Vis. Sci. 52(12), 8818–8827 (2011). [CrossRef] [PubMed]

]. Therefore, quantification of SHG images of collagen fibers would be useful for both disease assessment and tissue engineering.

In order to evaluate the fibrillar structure of biological tissues, researchers have developed different metrics to quantify SHG images [3

R. A. Rao, M. R. Mehta, and K. C. Toussaint Jr., “Fourier transform-second-harmonic generation imaging of biological tissues,” Opt. Express 17(17), 14534–14542 (2009). [CrossRef] [PubMed]

, 31

, 34

T. Abraham, D. Kayra, B. McManus, and A. Scott, “Quantitative assessment of forward and backward second harmonic three dimensional images of collagen type I matrix remodeling in a stimulated cellular environment,” J. Struct. Biol. (2012), http://dx.doi.org/10.1016/j.jsb.2012.05.004. [CrossRef] [PubMed]

]. To assess spatial changes in fiber hierarchy, parameters such as fiber orientation, spacing, and thickness are often employed [3

R. A. Rao, M. R. Mehta, and K. C. Toussaint Jr., “Fourier transform-second-harmonic generation imaging of biological tissues,” Opt. Express 17(17), 14534–14542 (2009). [CrossRef] [PubMed]

, 12

X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012). [CrossRef] [PubMed]

, 33

]. Such parameters can simply be obtained through inspection of the SHG images in real space [9

, 35

J. Wu, S. L. Voytik-Harbin, D. L. Filmer, C. M. Hoffman, B. Yuan, C.-S. Chiang, J. Sturgis, and J. P. Robinson, “Modeling ECM fiber formation: structure information extracted by analysis of 2D and 3D image sets,” in Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing IX (SPIE, San Jose, CA, USA, 2002), pp. 52–56.

]. However, such approaches are subject to error in estimation of fiber location, shape, and orientation [35

]. Furthermore, this becomes extremely time-consuming when performed on complex images [35

]. In comparison, the computational approach may provide more objective measurements and faster results.

In the fields of fiber engineering and pattern recognition, image processing techniques, such as the Hough transform [36

B. Pourdeyhimi and H. S. Kim, “Measuring fiber orientation in nonwovens: the hough transform,” Text. Res. J. 72(9), 803–809 (2002). [CrossRef]

], Fourier analysis [1

, 3

R. A. Rao, M. R. Mehta, and K. C. Toussaint Jr., “Fourier transform-second-harmonic generation imaging of biological tissues,” Opt. Express 17(17), 14534–14542 (2009). [CrossRef] [PubMed]

, 37

A. A. A. Jaddi, H. S. Kim, and B. Pourdeyhimi, “Measurement of fiber orientation in nonwovens optical Fourier transform,” Inter. Nonwovens J. 10, 10–16 (2001).

, 38

B. Josso, D. R. Burton, and M. J. Lalor, “Texture orientation and anisotropy calculation by Fourier transform and principal component analysis,” Mech. Sys. Signal Process. 19(5), 1152–1161 (2005). [CrossRef]

], and principle component analysis [38

–40

W. Yi and S. Marshall, “Principal component analysis in application to object orientation,” Geo-Spat. Inf. Sci. 3, 76–78 (2000).

] are widely employed to extract spatial information from 2D images. Such methods are useful for analyzing images globally, and the computational cost is invariant to the complexity of the image. In contrast, bottom-up techniques such as fiber tracing and reconstruction [41

S. Mori and P. C. van Zijl, “Fiber tracking: principles and strategies - a technical review,” NMR Biomed. 15(7-8), 468–480 (2002). [CrossRef] [PubMed]

–43

J. Wu, B. Rajwa, D. L. Filmer, C. M. Hoffmann, B. Yuan, C. S. Chiang, J. Sturgis, and J. P. Robinson, “Analysis of orientations of collagen fibers by novel fiber-tracking software,” Microsc. Microanal. 9(6), 574–580 (2003). [CrossRef] [PubMed]

] target individual fibers and are often applied to diffusion tensor imaging (DIT) to determine the dimensions, location, and orientation of nerve fibers and muscle fibers in 3D [42

N. Toussaint, M. Sermesant, C. T. Stoeck, S. Kozerke, and P. G. Batchelor, “In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images,” Med. Image Comput. Comput. Assist Interv. 13(Pt 1), 418–425 (2010). [PubMed]

, 44

C. C. Van Donkelaar, L. J. G. Kretzers, P. H. M. Bovendeerd, L. M. A. Lataster, K. Nicolay, J. D. Janssen, and M. R. Drost, “Diffusion tensor imaging in biomechanical studies of skeletal muscle function,” J. Anat. 194(1), 79–88 (1999). [CrossRef] [PubMed]

–48

H. J. Park, M. Kubicki, C. F. Westin, I. F. Talos, A. Brun, S. Peiper, R. Kikinis, F. A. Jolesz, R. W. McCarley, and M. E. Shenton, “Method for combining information from white matter fiber tracking and gray matter parcellation,” AJNR Am. J. Neuroradiol. 25(8), 1318–1324 (2004). [PubMed]

]. It has also been applied to analyze the 3D orientation and branching of collagen fibers in collagen gels and cornea, respectively [33

, 35

, 43

]. However, the performance and computational cost of bottom-up methods strongly depends on the complexity of the image (fiber population, density, and length) [45

O. Friman, G. Farnebäck, and C. F. Westin, “A Bayesian approach for stochastic white matter tractography,” IEEE Trans. Med. Imaging 25(8), 965–978 (2006). [CrossRef] [PubMed]

]; bottom-up methods are not feasible for application to, for example, dense fibrillar tissues. Therefore, to study the overall organization of collagen fibers in biological tissues, methods such as the spatial Fourier transform offer more utility.

Our previous studies in Fourier transform-second harmonic generation (FT-SHG) imaging utilizes 2D spatial Fourier analysis to quantify the structural organization of collagen fibers using parameters such as, fiber orientation, spacing, and thickness [1

, 4

, 18

, 24

, 49

R. Ambekar, T.-Y. Lau, M. Walsh, R. Bhargava, and K. C. Toussaint, “Quantifying collagen structure in breast biopsies using second-harmonic generation imaging,” Biomed. Opt. Express 3(9), 2021–2035 (2012). [CrossRef]

, 50

R. A. Rao, M. R. Mehta, S. Leithem, and K. C. Toussaint Jr., “Quantitative analysis of forward and backward second-harmonic images of collagen fibers using Fourier transform second-harmonic-generation microscopy,” Opt. Lett. 34(24), 3779–3781 (2009). [CrossRef] [PubMed]

]. Additionally, our technique labels regions of interest (ROI) in an image based on the orientation anisotropy and the average pixel intensity [4

, 18

]. In addition, recent studies have utilized polarization-SHG microscopy to calculate collagen orientation at the molecular scale [49

, 51

I. Amat-Roldan, S. Psilodimitrakopoulos, P. Loza-Alvarez, and D. Artigas, “Fast image analysis in polarization SHG microscopy,” Opt. Express 18(16), 17209–17219 (2010). [CrossRef] [PubMed]

–54

P. J. Su, W. L. Chen, T. H. Li, C. K. Chou, T. H. Chen, Y. Y. Ho, C. H. Huang, S. J. Chang, Y. Y. Huang, H. S. Lee, and C. Y. Dong, “The discrimination of type I and type II collagen and the label-free imaging of engineered cartilage tissue,” Biomaterials 31(36), 9415–9421 (2010). [CrossRef] [PubMed]

]. The applications of 2D FT-SHG to assess injuries in horse tendon and to explore age-related growth in porcine cortical bone have shown promising potential to utilize this technique for important biomedical problems [4

, 18

]. Still, there are many problems in biology that would benefit from the analysis of the spatial organization of collagen fibers in 3D. For example, recent research on collagenous fibrosis has shown that pathological changes due to fibrosis progression are depicted more precisely through 3D analysis [9

, 11

, 17

, 55

N. Morishige, N. Yamada, S. Teranishi, T.-i. Chikama, T. Nishida, and A. Takahara, “Detection of subepithelial fibrosis associated with corneal stromal edema by second harmonic generation imaging microscopy,” Invest. Ophthalmol. Vis. Sci. 50(7), 3145–3150 (2009). [CrossRef] [PubMed]

]. Moreover, significant differences between the 3D structures of collagen fibers in normal and malignant ovary tissues were observed and discussed qualitatively using SHG microscopy [14

P. J. Campagnola, M. A. Brewer, V. Ajeti, P. Keely, K. Eliceiri, M. Patankar, and K. Tilbury, “SHG imaging of cancer,” in Biomedical Optics(Optical Society of America, 2012), p. BSu4B.1.

, 21

In this paper, we generalize our previous method to quantify collagen fiber organization in 3D. In section 2 we describe our methodology, including sample preparation, image acquisition, image processing, and Fourier orientation analysis. A comparative analysis of 2D and 3D FT-SHG, as well as application to example tissue specimens are provided in section 3. We conclude in section 4 with a summary and a brief discussion of the potential future outlook of our technique.

2. Methods

2.1 Sample preparation

Porcine tissue samples of sclera and tendon were obtained from a local slaughter house after euthanasia, and stored immediately in 10% formalin. Smaller tissue sections were cut using surgical knife and embedded in OCT blocks overnight at −80°C. The tendon tissue samples were cut at an arbitrary angle φ with respect to the longitudinal direction to artificially generate 3D structures (Fig. 1 ). Regions of porcine sclera near the optical nerve were cut in 35-µm-thick sections, with the Leica CM3050 cryostat, since we expect collagen fibers to be organized in various orientations in this region. Tissue slices were soaked in 1x PBS to remove excess OCT, and mounted onto glass slides using a permanent aqueous mounting media.

Fig. 1 The porcine tendon tissue was cut along the yellow dashed line at an arbitrary angle φ with respect to the longitudinal direction (red line).

2.2 Image acquisition

Each image stack was acquired by using the Zeiss LSM 710 confocal microscope. The light source was a tunable Ti:Sapphire laser (Spectra-Physics Mai-Tai eHP DeepSee) that produces 70 femtosecond-duration pulses spectrally centered at 780 nm at 80-MHz repetition rate. A half-wave plate was used to generate circularly polarized light to ensure SHG emission from collagen fibers at all orientations. A short-pass 760-nm dichroic beam splitter was placed inside the microscope to reflect the beam towards the sample. A 1.2 NA water immersion objective was used to focus the beam onto the sample. Backscattered SHG signal was collected through the same objective, and projected onto a non-descanned detector. In order to avoid uncertainty due to interpolation between consecutive images, each image stack was taken with an axial step size that is similar to the size of each pixel on the lateral plane. For sclera, the scanning step between images is 100 nm, and the x-y pixel size is 104 nm. For tendon, the z-step-size is 140 nm and the x-y pixel size is 139 nm.

2.3 Imaging processing

The Canny edge detection algorithm was adapted for use in 3D image stacks in order to extract surfaces of collagen fibers in the samples [56

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). [CrossRef] [PubMed]

]. Canny’s algorithm consists of the following four steps: 1. Smoothing, 2. Calculation of the intensity gradient, 3. Hysteresis thresholding, and 4. Non-maximum suppression [56

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). [CrossRef] [PubMed]

]. Smoothing helps mitigate the effect of Poisson noise by slightly blurring the image stack using a 3D Gaussian filter [35

, 56

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). [CrossRef] [PubMed]

]. Since SHG signals are emitted only at places where collagen fibers are present, the detected signal intensity should change significantly across the surfaces of collagen fibers. Therefore, by calculating of the intensity gradient of each voxel, we can estimate the locations of the surfaces. Using this information, one may define a threshold value, where any voxel with a gradient value above the threshold is a surface voxel. However, such criteria may lead to discontinuous surfaces. Therefore, Canny’s algorithm applies hysteresis thresholding to improve the continuity of surfaces while maintaining a good detection of surfaces [56

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). [CrossRef] [PubMed]

]. Hysteresis thresholding utilizes two threshold values. The higher threshold value is used to choose voxels that are more likely to correspond to surfaces. The lower threshold value is then used to extend those surfaces by tracing less pronounced surfaces around them. Finally, non-maxima suppression was performed to refine the estimated surface to a thickness of 1 voxel.

2.4 3D orientation analysis

In order to solve for the orientation of collagen fibers in 3D space, a 3D grid was first created to divide the image stack into a set of ROIs. The overall orientation of collagen fibers within each ROI was calculated using the filter bank method [57

R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40(4), 882–893 (1992). [CrossRef]

–59

D. A. Forsyth and J. Ponce, Computer Vision: A modern approach (Prentice Hall, 2011).

]. The filter bank consists of various 3D orientation filters constructed in the Fourier space. The 3D Fourier-transform of each ROI is then compared with the filter banks, and the orientation filter that corresponds to the maximum correlation is the best estimate of orientation for the test object. In order to decrease the computational time, we first calculate the coarse orientation with limited number of lines, and then iteratively add filter banks around the coarse orientation to calculate the precise orientation using the coarse-to-fine searching technique [59

D. A. Forsyth and J. Ponce, Computer Vision: A modern approach (Prentice Hall, 2011).

]. Subsequently, orientations of fibers were stored as 3D vectors to avoid ambiguity due to the gimbal lock problem [60

J. Vince, Mathematics for computer graphics (Springer, 2010).

] that occurs in the spherical coordinate system.

If the average voxel intensity or the orientation anisotropy of a ROI is too low, calculation for the preferred orientation for such ROI becomes unnecessary [1

, 4

, 18

]. A dark threshold determines the minimal average voxel intensity required for orientation analysis [1

, 4

, 18

]. If the average voxel intensity of a ROI is lower than the dark threshold, the preferred orientation will not be calculated for that particular ROI. Since the coarse-to-fine approach was used for the orientation analysis, orientation anisotropy of each ROI was determined during the first iteration. If many orientation filters give a similar correlation value, there is no unique orientation, and the ROI is labeled as isotropic [1

, 4

, 18

]. Consequently, the calculation for finer results will not be carried out for that ROI.

3. Results and discussion

3.1 Test objects

Different computer test objects were generated to aid evaluation of accuracy and speed of our method using Matlab. Figures 2(a) and 2(b) are examples of the cylindrical test objects (64 x 64 x 64) that were used to assess accuracy of our method. In order to mimic the signal intensity of real objects, voxel intensities of the test objects follow a Gaussian distribution. Intensities are highest along the axis of the cylinder and diminish radially outwards. The green cylindrical shells shown in Figs. 2(a) and 2(b) represent locations where the voxel intensities are at 50% of the maximum voxel intensity of the test objects. According to the figures, the calculated orientation (red line) follows the test object (green cylinder) closely. The test objects were generated at 1° interval in both θ and φ directions of the spherical coordinate system from 0° through 360°. After a total number of 129,600 tests, we obtained a maximum error of 0.25° with a runtime of approximately 2.5 sec per test object. For a 256 x 256 x 96 test object with spirally curved surfaces shown in Fig. 2(c)., the runtime was approximately 1.5 min on a desktop computer with a 3.4 GHz processor and 8 GB of RAM. Similar tests were performed on bigger test objects with sparse or dense features. In the case of a 512 x 512 x 512 sparse object with less than 15% of the total volume occupied, the runtime was approximately 5 min. Moreover, a dense object of the same size with more than 80% of total volume occupied took approximately 30 min.

Fig. 2 Cylindrical test objects (green) oriented in 3D space and their calculated preferred orientation (red line) oriented at (a) θ = 5.00°, φ = 50.00°, and (b) θ = 9.50°, φ = 1.50°. (c) A test object with spirally curved surfaces (gray) and their calculated preferred orientations within each voxel (red arrows).

3.2 Application to an image stack

Figure 3(a) shows an SHG image stack of porcine tendon tissue, whereby the center portion [Fig. 3(b)] is the ROI chosen for analysis. It was first divided into smaller ROIs. The preferred orientation (red arrows) of collagen fibers was calculated and stored as a 3D vector in Cartesian coordinates for each ROI that has anisotropically oriented collagen fibers [Fig. 3(c)]. ROIs with no preferred fiber orientations were labeled as isotropic or dark based on their orientation anisotropy and average voxel intensity, respectively. The spatial distribution of the labeled regions can be seen in Fig. 3(d).

Fig. 3 Illustrative example of analyzing a region of interest of (a) an image stack (142 μm x 142 μm x 2.24 μm). (b)The center portion (54 μm x 54 μm x 2.24 μm) is analyzed based on (c) fiber orientation, and (d) region labels. Based on the calculation, three histograms are generated to show the distributions of (e) the orientations with respect to z-axis (φ), (f) the lateral orientations along the xy-plane (θ), and (g) the numbers of different regions.

In order to facilitate analysis, the preferred orientations were subsequently converted to θ and φ of the spherical coordinate system. The overall orientation of the center portion of the image stack was displayed as histograms of θ and φ in Figs. 3(e) and 3(f), respectively. Based on the orientation analysis, collagen fibers at the center portion have an orientation of ~58.2° in the transverse plane and an inclination angle of ~71.7°. In addition to the preferred orientations, the center portion was analyzed based on the distribution of different labeled regions. The number of each type of labeled region was summarized in the histogram in Fig. 3(g). As a densely packed fibrillar tissue, the majority of the ROIs were labeled as anisotropic or isotropic regions. Moreover, 84% of all ROIs were labeled as anisotropic. Accompanying a relatively small spread in the overall orientation, it indicates that collagen fibers in this region are well aligned.

3.3 Porcine sclera tissue

3D FT-SHG analysis was performed on the image stack of porcine sclera tissue near the optic nerve [Fig. 4(a) ]. A 3D model of the image stacks [Fig. 4(b)] was generated using an open-source 3D rendering software: Volview [61

Kitware, “Volview,” http://www.kitware.com/opensource/volview.html.

]. The analysis shows that collagen fibers in porcine sclera tissue are primarily arranged in layers [Fig. 4(c)]. A video of this 3D collagen fiber arrangement is provided in Media 1. Moreover, the 3D quiver plot of the porcine sclera tissues [Fig. 4(c)] closely resembles the 3D models [Fig. 4(b)]. The histogram of φ [Fig. 4(d)] shows a single peak centered at ~87.5°. Such consistency in φ indicates that the porcine sclera consists of parallel sheets of collagen fibers. By observation, collagen fibers are aligned along similar preferred orientations within each layer [Fig. 4(c)]. However, fibers in different layers possess different orientations, which lead to multiple peaks in Fig. 4(e). A similar trend in collagen fiber distribution is also observed from Fig. 4(f) whereby collagen fibers are arranged in discrete sheets in porcine sclera. A detailed view of the distribution of regions is given in the video in Media 2. Moreover, the statistics of labeled regions show that a large portion of the tissue was not occupied by collagen fibers [Fig. 4(g)]; more than 50% of the total volume is labeled as dark regions [Fig. 4(g)].

Fig. 4 (a) An image from a 102.4 μm x 102.4 μm x 16.2 μm image stack of porcine sclera tissue. (b) 3D model of the image stack. (c) 3D quiver plot shows fiber orientations of anisotropic ROI's (Media 1). Histograms showing (d) orientations of collagen fibers with respect to the z-axis, (e) orientations of collagen fibers on the xy-plane. (f) 3D plot of labeled regions shows location of anisotropic, isotropic, and dark regions (Media 2), and a histogram of the (g) numbers of labeled regions in the analyzed image.

3.4 Porcine tendon tissue

Porcine tendon tissue was sliced at an arbitrary angle [Fig. 5(a) ], and subsequently imaged and analyzed. Again, a 3D model of the image stacks [Fig. 5(b)] was generated using Volview [61

Kitware, “Volview,” http://www.kitware.com/opensource/volview.html.

]. The Matlab generated 3D quiver plot [Fig. 5(c)] also resembles the shape of the 3D model. A video of this is provided in Media 3. The calculated preference orientations with respect to the z-axis, φ, are not centered at 90 degrees [Fig. 5(d)]; rather, it indicates that there are collagen fibers oriented across the image stacks. The mean φ value of the tissue slice centers at 36.8 degrees [Fig. 5(d)]. This may be indicative of the cut angle with respect to longitudinal axis (see Fig. 1). Such finding demonstrates the potential of utilizing 3D FT-SHG imaging as a tool for determining the cut angle for tissues. Moreover, our analysis shows that collagen fibers are aligned along similar preferred orientations within each fascicle as seen from Fig. 5(c). The histogram of θ displays a major peak value at ~122.6° [Fig. 5(e)]; it supports the fact that collagen fibers are algined in similar orientation within each fascicle, as well as between different fascicles [62

N. Maffulli, P. Renstrom, and W. B. Leadbetter, Tendon Injuries: Basic Science and Clinical Medicine (Springer, 2005).

]. The analysis on the distribution of labeled regions reveals that porcine tendon tissues are densely packed with collagen fibers [Fig. 5(f)] indicated by an insignificant number of dark regions [Fig. 5(g)]. A detailed view of the distribution of regions is given in the video in Media 4.

Fig. 5 (a) An image from a 142 μm x 142 μm x 21.5 μm image stack of porcine tendon tissue. (b) 3D model of the image stack. (c) 3D quiver plot shows fiber orientations of anisotropic ROI's (Media 3). Histograms showing (d) orientations of collagen fibers with respect to the z-axis, (e) orientations of collagen fibers on the xy-plane. (f) 3D plot of labeled regions shows location of anisotropic, isotropic, and dark regions (Media 4), and a histogram of the (g) numbers of labeled regions in the analyzed image.

3.5 2D FT-SHG versus 3D FT-SHG

Figure 6 is a comparative analysis of 2D FT-SHG and 3D FT-SHG as applied to porcine tendon sample sliced at an arbitary angle. We observe from both methods similar trends of preferred fiber orientation [Figs. 6(a) and 6(b)]. In addition, relatively small spreads (spread = 15.6° for 2D FT-SHG and 9.4° for 3D FT-SHG) in the preferred orientation of collagen fibers are observed from Figs. 6(c) and 6(d). For the region highlighted in red, the 2D approach failed to compute the preferred orientation accurately. Even for some areas in the region the 2D approach failed to compute fiber orientation that were not restricted to the transverse plane. Since collagen fibers are held together as bundles (facicles) in tendon, they appear as individual disks on the cross-section image. In contrast, since the 3D analysis considers the entire image stack, disks on the cross-section images will stack up and form cylinders in 3D space. Therefore, the 3D method can resolve such uncertainty that occurs in 2D analysis. Similarly to section 3.4, 3D FT-SHG shows that collagen fibers are oriented obliquely to the transverse plane as the histogram of φ was not centered 90⁰ [Fig. 6(e)]; it shows that the sample could be sliced at 67.9⁰ with respect to the longitudinal direction (see Fig. 1). Similar population distributions of anisotropic, isotropic, and dark regions [Figs. 6(f) and 6(g)] are obtained from both methods. Consistent with our findings from the previous section, porcine tendon is a densely packed fibrillar collagenous tissue as indicated by the presence of only few dark regions [Figs. 6(f) and 6(g)].

Fig. 6 (a), (b) Overlaid plots of the quiver plot of calculated orientation and the analyzed image of a porcine tendon tissue sample. Histograms showing (c), (d) orientations of collagen fibers on the xy-plane, (e) orientations of collagen fibers with respect to the z-axis, and (f, g) numbers of labeled regions in the analyzed image.

4. Conclusion

We presented 3D FT-SHG imaging as an approach to quantify the spatial structure of collagen-based tissues in 3D. As an extension of 2D FT-SHG, our presented technique calculates preferred orientation of fibers in each ROI in 3D, as well as the orientation anisotropy and average voxel intensity. The application of 3D FT-SHG on known test objects showed that 3D orientation can be solved accurately and relatively quickly by combining 3D spatial Fourier analysis and the coarse-to-fine searching technique. The application of 3D FT-SHG to porcine sclera showed that collagen fibers are primarily arranged in 2D layers, whereby the fibers in each layer are aligned along different orientations. Moreover, the application to obliquely sliced porcine tendon not only showed that fibers are oriented consistently within fascicles, but also estimated the value of the cut angle. The sample applications demonstrated that 3D FT-SHG can effectively evaluate collagen fiber spatial organization in 3D. We believe that this technique will be useful for studying biological tissues such as bone, cornea, and lung tissues, where collagen fibers are typically organized in 3D [11

, 15

, 26

S. Viguet-Carrin, P. Garnero, and P. D. Delmas, “The role of collagen in bone strength,” Osteoporosis Int. 17(3), 319–336 (2006). [CrossRef] [PubMed]

, 27

M. F. Young, “Bone matrix proteins: their function, regulation, and relationship to osteoporosis,” Osteoporosis Int. 14, 35–42 (2003).

, 33

Acknowledgments

This work was supported by the National Academies Keck Futures Initiatives (NAS NAKFI MN A002309601). We thank Larry Schook and Laurie Rund (Department of Biomedical Sciences, UIUC) for providing tissue specimens for experiments. We also thank the reviewers as well as Brian Roxworthy and Santosh Tripathi for their meticulous reading of this manuscript.

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OCIS Codes
(100.2960) Image processing : Image analysis
(100.6950) Image processing : Tomographic image processing
(180.4315) Microscopy : Nonlinear microscopy

ToC Category:
Image Processing

History
Original Manuscript: July 19, 2012
Revised Manuscript: September 3, 2012
Manuscript Accepted: September 4, 2012
Published: September 7, 2012

Virtual Issues
Vol. 7, Iss. 11 Virtual Journal for Biomedical Optics

Citation
Tung Yuen Lau, Raghu Ambekar, and Kimani C. Toussaint, "Quantification of collagen fiber organization using three-dimensional Fourier transform-second-harmonic generation imaging," Opt. Express 20, 21821-21832 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-20-19-21821

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References

R. Ambekar, M. R. Mehta, S. Leithem, and K. C. Toussaint, “Fourier transform-second-harmonic generation imaging of collagen fibers in biological tissues,” in Biomedical Optics (Optical Society of America, 2010), p. BSuD63.
L. Loew, A. Millard, P. J. Campagnola, W. Mohler, and A. Lewis, “Second harmonic imaging microscopy,” Microsc. Microanal. 9, 170–171 (2003).
R. A. Rao, M. R. Mehta, and K. C. Toussaint., “Fourier transform-second-harmonic generation imaging of biological tissues,” Opt. Express 17(17), 14534–14542 (2009). [CrossRef] [PubMed]
M. Sivaguru, S. Durgam, R. Ambekar, D. Luedtke, G. Fried, A. Stewart, and K. C. Toussaint., “Quantitative analysis of collagen fiber organization in injured tendons using Fourier transform-second harmonic generation imaging,” Opt. Express 18(24), 24983–24993 (2010). [CrossRef] [PubMed]
P. Stoller, B.-M. Kim, A. M. Rubenchik, K. M. Reiser, and L. B. Da Silva, “Polarization-dependent optical second-harmonic imaging of a rat-tail tendon,” J. Biomed. Opt. 7(2), 205–214 (2002). [CrossRef] [PubMed]
B. Alberts, A. Johnson, L. Julian, R. Martin, R. Keith, and W. Peter, Molecular Biology of the Cell (Garland Science, 2007).
P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003). [CrossRef] [PubMed]
G. Cox, E. Kable, A. Jones, I. Fraser, F. Manconi, and M. D. Gorrell, “3-dimensional imaging of collagen using second harmonic generation,” J. Struct. Biol. 141(1), 53–62 (2003). [CrossRef] [PubMed]
N. Morishige, Y. Takagi, T. Chikama, A. Takahara, and T. Nishida, “Three-dimensional analysis of collagen lamellae in the anterior stroma of the human cornea visualized by second harmonic generation imaging microscopy,” Invest. Ophthalmol. Vis. Sci. 52(2), 911–915 (2011). [CrossRef] [PubMed]
P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J. 82(1), 493–508 (2002). [CrossRef] [PubMed]
A. M. Pena, A. Fabre, D. Débarre, J. Marchal-Somme, B. Crestani, J. L. Martin, E. Beaurepaire, and M. C. Schanne-Klein, “Three-dimensional investigation and scoring of extracellular matrix remodeling during lung fibrosis using multiphoton microscopy,” Microsc. Res. Tech. 70(2), 162–170 (2007). [CrossRef] [PubMed]
X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012). [CrossRef] [PubMed]
T. Hompland, A. Erikson, M. Lindgren, T. Lindmo, and C. de Lange Davies, “Second-harmonic generation in collagen as a potential cancer diagnostic parameter,” J. Biomed. Opt. 13(5), 054050 (2008). [CrossRef] [PubMed]
P. J. Campagnola, M. A. Brewer, V. Ajeti, P. Keely, K. Eliceiri, M. Patankar, and K. Tilbury, “SHG imaging of cancer,” in Biomedical Optics(Optical Society of America, 2012), p. BSu4B.1.
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Canny, J.
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AJNR Am. J. Neuroradiol.

H. J. Park, M. Kubicki, C. F. Westin, I. F. Talos, A. Brun, S. Peiper, R. Kikinis, F. A. Jolesz, R. W. McCarley, and M. E. Shenton, “Method for combining information from white matter fiber tracking and gray matter parcellation,” AJNR Am. J. Neuroradiol. 25(8), 1318–1324 (2004). [PubMed]

Ann. N. Y. Acad. Sci.

P. Helm, M. F. Beg, M. I. Miller, and R. L. Winslow, “Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging,” Ann. N. Y. Acad. Sci. 1047(1), 296–307 (2005). [CrossRef] [PubMed]

Appl. Phys. Lett.

W. L. Chen, T. H. Li, P. J. Su, C. K. Chou, P. T. Fwu, S. J. Lin, D. Kim, P. T. C. So, and C. Y. Dong, “Second harmonic generation chi tensor microscopy for tissue imaging,” Appl. Phys. Lett. 94, 3 (2009).

Arthritis Res. Ther.

J. Caetano-Lopes, A. M. Nery, H. Canhão, J. Duarte, R. Cascão, A. Rodrigues, I. P. Perpétuo, S. Abdulghani, P. M. Amaral, S. Sakaguchi, Y. T. Konttinen, L. Graça, M. F. Vaz, and J. E. Fonseca, “Chronic arthritis leads to disturbances in the bone collagen network,” Arthritis Res. Ther. 12(1), R9 (2010). [CrossRef] [PubMed]

Biomaterials

P. J. Su, W. L. Chen, T. H. Li, C. K. Chou, T. H. Chen, Y. Y. Ho, C. H. Huang, S. J. Chang, Y. Y. Huang, H. S. Lee, and C. Y. Dong, “The discrimination of type I and type II collagen and the label-free imaging of engineered cartilage tissue,” Biomaterials 31(36), 9415–9421 (2010). [CrossRef] [PubMed]

Biomed. Opt. Express

Biophys. J.

P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J. 82(1), 493–508 (2002). [CrossRef] [PubMed]

BMC Cancer

O. Nadiarnykh, R. B. LaComb, M. A. Brewer, and P. J. Campagnola, “Alterations of the extracellular matrix in ovarian cancer studied by second harmonic generation imaging microscopy,” BMC Cancer 10(1), 94 (2010). [CrossRef] [PubMed]

Bone

R. Ambekar, M. Chittenden, I. Jasiuk, and K. C. Toussaint., “Quantitative second-harmonic generation microscopy for imaging porcine cortical bone: comparison to SEM and its potential to investigate age-related changes,” Bone 50(3), 643–650 (2012). [CrossRef] [PubMed]

Cell Tissue Banking

K. Brockbank, W. MacLellan, J. Xie, S. Hamm-Alvarez, Z. Chen, and K. Schenke-Layland, “Quantitative second harmonic generation imaging of cartilage damage,” Cell Tissue Banking 9, 299–307 (2008).

Circ. Res.

S. M. Weis, J. L. Emery, K. D. Becker, D. J. McBride, J. H. Omens, and A. D. McCulloch, “Myocardial mechanics and collagen structure in the osteogenesis imperfecta murine (oim),” Circ. Res. 87(8), 663–669 (2000). [CrossRef] [PubMed]

Eur. J. Cancer

D. Barkan, J. E. Green, and A. F. Chambers, “Extracellular matrix: a gatekeeper in the transition from dormancy to metastatic growth,” Eur. J. Cancer 46(7), 1181–1188 (2010). [CrossRef] [PubMed]

Geo-Spat. Inf. Sci.

W. Yi and S. Marshall, “Principal component analysis in application to object orientation,” Geo-Spat. Inf. Sci. 3, 76–78 (2000).

IEEE Trans. Image Process.

A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, “Filterbank-based fingerprint matching,” IEEE Trans. Image Process. 9(5), 846–859 (2000). [CrossRef] [PubMed]

IEEE Trans. Med. Imaging

O. Friman, G. Farnebäck, and C. F. Westin, “A Bayesian approach for stochastic white matter tractography,” IEEE Trans. Med. Imaging 25(8), 965–978 (2006). [CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). [CrossRef] [PubMed]

IEEE Trans. Signal Process.

R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40(4), 882–893 (1992). [CrossRef]

Inter. Nonwovens J.

A. A. A. Jaddi, H. S. Kim, and B. Pourdeyhimi, “Measurement of fiber orientation in nonwovens optical Fourier transform,” Inter. Nonwovens J. 10, 10–16 (2001).

Invest. Ophthalmol. Vis. Sci.

N. Morishige, N. Yamada, S. Teranishi, T.-i. Chikama, T. Nishida, and A. Takahara, “Detection of subepithelial fibrosis associated with corneal stromal edema by second harmonic generation imaging microscopy,” Invest. Ophthalmol. Vis. Sci. 50(7), 3145–3150 (2009). [CrossRef] [PubMed]
M. Winkler, D. Chai, S. Kriling, C. J. Nien, D. J. Brown, B. Jester, T. Juhasz, and J. V. Jester, “Nonlinear optical macroscopic assessment of 3-D corneal collagen organization and axial biomechanics,” Invest. Ophthalmol. Vis. Sci. 52(12), 8818–8827 (2011). [CrossRef] [PubMed]
N. Morishige, Y. Takagi, T. Chikama, A. Takahara, and T. Nishida, “Three-dimensional analysis of collagen lamellae in the anterior stroma of the human cornea visualized by second harmonic generation imaging microscopy,” Invest. Ophthalmol. Vis. Sci. 52(2), 911–915 (2011). [CrossRef] [PubMed]

Invest. Ophthalmol. Visual Sci.

Y. Komai and T. Ushiki, “The three-dimensional organization of collagen fibrils in the human cornea and sclera,” Invest. Ophthalmol. Visual Sci. 32(8), 2244–2258 (1991). [PubMed]

J. Anat.

C. C. Van Donkelaar, L. J. G. Kretzers, P. H. M. Bovendeerd, L. M. A. Lataster, K. Nicolay, J. D. Janssen, and M. R. Drost, “Diffusion tensor imaging in biomechanical studies of skeletal muscle function,” J. Anat. 194(1), 79–88 (1999). [CrossRef] [PubMed]

J. Biomed. Opt.

P. Stoller, B.-M. Kim, A. M. Rubenchik, K. M. Reiser, and L. B. Da Silva, “Polarization-dependent optical second-harmonic imaging of a rat-tail tendon,” J. Biomed. Opt. 7(2), 205–214 (2002). [CrossRef] [PubMed]
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J. Bone Miner. Res.

M. J. Silva, M. D. Brodt, B. Wopenka, S. Thomopoulos, D. Williams, M. H. M. Wassen, M. Ko, N. Kusano, and R. A. Bank, “Decreased collagen organization and content are associated with reduced strength of demineralized and intact bone in the SAMP6 mouse,” J. Bone Miner. Res. 21(1), 78–88 (2006). [CrossRef] [PubMed]

J. Mech. Behav. Biomed. Mater.

C. P. Brown, M. A. Houle, M. Chen, A. J. Price, F. Légaré, and H. S. Gill, “Damage initiation and progression in the cartilage surface probed by nonlinear optical microscopy,” J. Mech. Behav. Biomed. Mater. 5(1), 62–70 (2012). [CrossRef] [PubMed]
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Quantification of collagen fiber organization using three-dimensional Fourier transform-second-harmonic generation imaging

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Abstract

1. Introduction

2. Methods

2.1 Sample preparation

2.2 Image acquisition

2.3 Imaging processing

2.4 3D orientation analysis

3. Results and discussion

3.1 Test objects

3.2 Application to an image stack

3.3 Porcine sclera tissue

3.4 Porcine tendon tissue

3.5 2D FT-SHG versus 3D FT-SHG

4. Conclusion

Acknowledgments

References and links

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Nat. Protoc.

NMR Biomed.

Opt. Express

Opt. Lett.

Osteoporosis Int.

Photochem. Photobiol.

Proc. Natl. Acad. Sci. U.S.A.

Text. Res. J.

Other

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