Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography

Yu Gan; Christine P. Fleming

doi:10.1364/BOE.4.002150

Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography

Yu Gan and Christine P. Fleming

Biomedical Optics Express
Vol. 4,
Issue 10,
pp. 2150-2165
(2013)
•https://doi.org/10.1364/BOE.4.002150

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Yu Gan and Christine P. Fleming, "Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography," Biomed. Opt. Express 4, 2150-2165 (2013)

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Related Topics
Table of Contents Category
- Image Processing
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Image processing (100.0100)
- Optical coherence tomography (110.4500)
- Tissue characterization (170.6935)

About this Article
History
- Original Manuscript: August 2, 2013
- Revised Manuscript: September 5, 2013
- Manuscript Accepted: September 9, 2013
- Published: September 13, 2013

Accessible

Open Access

Abstract

Abnormal changes in orientation of myofibers are associated with various cardiac diseases such as arrhythmia, irregular contraction, and cardiomyopathy. To extract fiber information, we present a method of quantifying fiber orientation and reconstructing three-dimensional tractography of myofibers using optical coherence tomography (OCT). A gradient based algorithm was developed to quantify fiber orientation in three dimensions and particle filtering technique was employed to track myofibers. Prior to image processing, three-dimensional image data set were acquired from all cardiac chambers and ventricular septum of swine hearts using OCT system without optical clearing. The algorithm was validated through rotation test and comparison with manual measurements. The experimental results demonstrate that we are able to visualize three-dimensional fiber tractography in myocardium tissues.

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References

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2013 (1)

C. Fan and G. Yao, “Imaging myocardial fiber orientation using polarization sensitive optical coherence tomography,” Biomed. Opt. Express 4(3), 460–465 (2013).
[Crossref] [PubMed]

2012 (6)

H.-C. Park, C. Song, M. Kang, Y. Jeong, and K.-H. Jeong, “Forward imaging OCT endoscopic catheter based on MEMS lens scanning,” Opt. Lett. 37(13), 2673–2675 (2012).
[Crossref] [PubMed]

C. J. Goergen, H. Radhakrishnan, S. Sakadžić, E. T. Mandeville, E. H. Lo, D. E. Sosnovik, and V. J. Srinivasan, “Optical coherence tractography using intrinsic contrast,” Opt. Lett. 37(18), 3882–3884 (2012).
[Crossref] [PubMed]

C. Mekkaoui, S. Nielles-Vallespin, P. Gatehouse, M. Jackowski, D. Firmin, and D. Sosnovik, “Diffusion MRI tractography of the human heart In Vivo at end-diastole and end-systole,” J. Cardiovasc. Magn. Reson. 14(Suppl 1), O49 (2012).
[Crossref]

M. Pluijmert, W. Kroon, A. C. Rossi, P. H. M. Bovendeerd, and T. Delhaas, “Why SIT Works: Normal Function Despite Typical Myofiber Pattern in Situs Inversus Totalis (SIT) Hearts Derived by Shear-Induced Myofiber Reorientation,” PLOS Comput. Biol. 8(7), e1002611 (2012).
[Crossref] [PubMed]

W. N. Lee, M. Pernot, M. Couade, E. Messas, P. Bruneval, A. Bel, A. A. Hagège, M. Fink, and M. Tanter, “Mapping Myocardial Fiber Orientation Using Echocardiography-Based Shear Wave Imaging,” IEEE Trans. Med. Imaging 31(3), 554–562 (2012).
[Crossref] [PubMed]

C. M. Ambrosi, V. V. Fedorov, R. B. Schuessler, A. M. Rollins, and I. R. Efimov, “Quantification of fiber orientation in the canine atrial pacemaker complex using optical coherence tomography,” J. Biomed. Opt. 17(7), 071309 (2012).
[Crossref] [PubMed]

2011 (2)

H. Wang, W. Kang, T. Carrigan, A. Bishop, N. Rosenthal, M. Arruda, and A. M. Rollins, “In vivo intracardiac optical coherence tomography imaging through percutaneous access: toward image-guided radio-frequency ablation,” J. Biomed. Opt. 16(11), 110505 (2011).
[Crossref] [PubMed]

B. R. Klyen, T. Shavlakadze, H. G. Radley-Crabb, M. D. Grounds, and D. D. Sampson, “Identification of muscle necrosis in the mdx mouse model of Duchenne muscular dystrophy using three-dimensional optical coherence tomography,” J. Biomed. Opt. 16(7), 076013 (2011).
[Crossref] [PubMed]

2010 (4)

C. P. Fleming, K. J. Quan, H. Wang, G. Amit, and A. M. Rollins, “In vitro characterization of cardiac radiofrequency ablation lesions using optical coherence tomography,” Opt. Express 18(3), 3079–3092 (2010).
[Crossref] [PubMed]

C. P. Fleming, K. J. Quan, and A. M. Rollins, “Toward guidance of epicardial cardiac radiofrequency ablation therapy using optical coherence tomography,” J. Biomed. Opt. 15(4), 041510 (2010).
[Crossref] [PubMed]

M.-R. Tsai, Y.-W. Chiu, M. T. Lo, and C.-K. Sun, “Second-harmonic generation imaging of collagen fibers in myocardium for atrial fibrillation diagnosis,” J. Biomed. Opt. 15(2), 026002 (2010).
[Crossref] [PubMed]

T. B. Leergaard, N. S. White, A. de Crespigny, I. Bolstad, H. D’Arceuil, J. G. Bjaalie, and A. M. Dale, “Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain,” PLoS ONE 5(1), e8595 (2010).
[Crossref]

2009 (4)

D. E. Sosnovik, R. Wang, G. Dai, T. G. Reese, and V. J. Wedeen, “Diffusion MR tractography of the heart,” J. Cardiovasc. Magn. Reson. 11(1), 47 (2009).
[Crossref] [PubMed]

C. M. Ambrosi, N. Moazami, A. M. Rollins, and I. R. Efimov, “Virtual histology of the human heart using optical coherence tomography,” J. Biomed. Opt. 14(5), 054002 (2009).
[Crossref] [PubMed]

F. Zhang, E. R. Hancock, C. Goodlett, and G. Gerig, “Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling,” Med. Image Anal. 13(1), 5–18 (2009).
[Crossref] [PubMed]

M. Gargesha, M. W. Jenkins, D. L. Wilson, and A. M. Rollins, “High temporal resolution OCT using image-based retrospective gating,” Opt. Express 17(13), 10786–10799 (2009).
[Crossref] [PubMed]

2008 (3)

W. J. Hucker, C. M. Ripplinger, C. P. Fleming, V. V. Fedorov, A. M. Rollins, and I. R. Efimov, “Bimodal biophotonic imaging of the structure-function relationship in cardiac tissue,” J. Biomed. Opt. 13(5), 054012 (2008).
[Crossref] [PubMed]

C. P. Fleming, C. M. Ripplinger, B. Webb, I. R. Efimov, and A. M. Rollins, “Quantification of cardiac fiber orientation using optical coherence tomography,” J. Biomed. Opt. 13(3), 030505 (2008).
[Crossref] [PubMed]

A. J. Pope, G. B. Sands, B. H. Smaill, and I. J. LeGrice, “Three-dimensional transmural organization of perimysial collagen in the heart,” Am. J. Physiol. Heart Circ. Physiol. 295(3), H1243–H1252 (2008).
[Crossref] [PubMed]

2006 (2)

M.-T. Wu, W.-Y. I. Tseng, M.-Y. M. Su, C.-P. Liu, K.-R. Chiou, V. J. Wedeen, T. G. Reese, and C.-F. Yang, “Diffusion Tensor Magnetic Resonance Imaging Mapping the Fiber Architecture Remodeling in Human Myocardium After Infarction: Correlation With Viability and Wall Motion,” Circulation 114(10), 1036–1045 (2006).
[Crossref] [PubMed]

W.-Y. I. Tseng, J. Dou, T. G. Reese, and V. J. Wedeen, “Imaging myocardial fiber disarray and intramural strain hypokinesis in hypertrophic cardiomyopathy with MRI,” J. Magn. Reson. Imaging 23(1), 1–8 (2006).
[Crossref] [PubMed]

2005 (2)

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]

K. Tobita, J. B. Garrison, L. J. Liu, J. P. Tinney, and B. B. Keller, “Three-dimensional myofiber architecture of the embryonic left ventricle during normal development and altered mechanical loads,” Anat. Rec. A Discov. Mol. Cell. Evol. Biol. 283A(1), 193–201 (2005).
[Crossref] [PubMed]

2004 (1)

A. G. Kléber and Y. Rudy, “Basic Mechanisms of Cardiac Impulse Propagation and Associated Arrhythmias,” Physiol. Rev. 84(2), 431–488 (2004).
[Crossref] [PubMed]

2002 (1)

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE, Trans. Sig. Pro 50(2), 174–188 (2002).
[Crossref]

2000 (2)

D. F. Scollan, A. Holmes, J. Zhang, and R. L. Winslow, “Reconstruction of Cardiac Ventricular Geometry and Fiber Orientation Using Magnetic Resonance Imaging,” Ann. Biomed. Eng. 28(8), 934–944 (2000).
[Crossref] [PubMed]

P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000).
[Crossref] [PubMed]

1998 (3)

A. T. Armstrong, P. F. Binkley, P. B. Baker, P. D. Myerowitz, and C. V. Leier, “Quantitative investigation of cardiomyocyte hypertrophy and myocardial fibrosis over 6 years after cardiac transplantation,” J. Am. Coll. Cardiol. 32(3), 704–710 (1998).
[Crossref] [PubMed]

W. J. Karlon, J. W. Covell, A. D. McCulloch, J. J. Hunter, and J. H. Omens, “Automated measurement of myofiber disarray in transgenic mice with ventricular expression of ras,” Anat. Rec. 252(4), 612–625 (1998).
[Crossref] [PubMed]

P. A. Doevendans, M. J. Daemen, E. D. de Muinck, and J. F. Smits, “Cardiovascular phenotyping in mice,” Cardiovasc. Res. 39(1), 34–49 (1998).
[Crossref] [PubMed]

1992 (1)

P. J. Hunter, P. M. Nielsen, B. H. Smaill, I. J. LeGrice, and I. W. Hunter, “An anatomical heart model with applications to myocardial activation and ventricular mechanics,” Crit. Rev. Biomed. Eng. 20(5-6), 403–426 (1992).
[PubMed]

1969 (2)

D. D. J. Streeter, H. M. Spotnitz, D. P. Patel, J. J. Ross, and E. H. Sonnenblick, “Fiber orientation in the canine left ventricle during diastole and systole,” Circ. Res. 24(3), 339–347 (1969).
[Crossref] [PubMed]

D. D. Streeter, H. M. Spotnitz, D. P. Patel, J. Ross, and E. H. Sonnenblick, “Fiber orientation in the canine left ventricle during diastole and systole,” Circ. Res. 24(3), 339–347 (1969).
[Crossref] [PubMed]

1963 (1)

S. Konno and S. Sakakibara, “ENdo-myocardial biopsy,” Dis. Chest 44(4), 345–350 (1963).
[Crossref] [PubMed]

Aldroubi, A.

P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000).
[Crossref] [PubMed]

Ambrosi, C. M.

C. M. Ambrosi, N. Moazami, A. M. Rollins, and I. R. Efimov, “Virtual histology of the human heart using optical coherence tomography,” J. Biomed. Opt. 14(5), 054002 (2009).
[Crossref] [PubMed]

Amit, G.

Armstrong, A. T.

Arruda, M.

C. P. Fleming, N. Rosenthal, A. M. Rollins, and M. Arruda, “First in vivo Real-Time Imaging of Endocardial RF Ablation by Optical Coherence Tomography,” J. In. Card. R. M2 (2011).

Arulampalam, M.

Baker, P. B.

Basser, P. J.

P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000).
[Crossref] [PubMed]

Beg, M. F.

Bel, A.

Binkley, P. F.

Bishop, A.

Bjaalie, J. G.

Bolstad, I.

Bovendeerd, P. H. M.

Bruneval, P.

Carrigan, T.

Chiou, K.-R.

Chiu, Y.-W.

Clapp, T.

Couade, M.

Covell, J. W.

D’Arceuil, H.

Daemen, M. J.

P. A. Doevendans, M. J. Daemen, E. D. de Muinck, and J. F. Smits, “Cardiovascular phenotyping in mice,” Cardiovasc. Res. 39(1), 34–49 (1998).
[Crossref] [PubMed]

Dai, G.

D. E. Sosnovik, R. Wang, G. Dai, T. G. Reese, and V. J. Wedeen, “Diffusion MR tractography of the heart,” J. Cardiovasc. Magn. Reson. 11(1), 47 (2009).
[Crossref] [PubMed]

Dale, A. M.

de Crespigny, A.

de Muinck, E. D.

P. A. Doevendans, M. J. Daemen, E. D. de Muinck, and J. F. Smits, “Cardiovascular phenotyping in mice,” Cardiovasc. Res. 39(1), 34–49 (1998).
[Crossref] [PubMed]

Delhaas, T.

Doevendans, P. A.

P. A. Doevendans, M. J. Daemen, E. D. de Muinck, and J. F. Smits, “Cardiovascular phenotyping in mice,” Cardiovasc. Res. 39(1), 34–49 (1998).
[Crossref] [PubMed]

Dou, J.

Duda, J.

P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000).
[Crossref] [PubMed]

Efimov, I. R.

C. M. Ambrosi, N. Moazami, A. M. Rollins, and I. R. Efimov, “Virtual histology of the human heart using optical coherence tomography,” J. Biomed. Opt. 14(5), 054002 (2009).
[Crossref] [PubMed]

Fan, C.

C. Fan and G. Yao, “Imaging myocardial fiber orientation using polarization sensitive optical coherence tomography,” Biomed. Opt. Express 4(3), 460–465 (2013).
[Crossref] [PubMed]

Fedorov, V. V.

Fink, M.

Firmin, D.

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Fig. 1 Flowchart of the automated algorithm for quantification of fiber orientation in three dimensions. Components of preprocessing, two dimensional fiber orientation algorithm and interpolation are used to quantify fiber orientation in en face plane. Edge detection block and plane fitting block are used to back project the fiber orientation from two dimensions to three dimensions.

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Fig. 2 (a) Schematic of quantifying fiber orientation in three dimensions (green line). Two planes are produced: one is parallel to surface plane and the other is perpendicular to en face plane with a projection in en face plane as the red line. The intersection of the two planes is the fiber orientation in three dimensions. (b) Angles in three dimensions. The red line represents the fiber. Polar angle ϕ is the angle of fiber with respect to z axis. Azimuth angle θ is the angle of the projection of fiber in en face plane with respect to x axis. (c) Visualization of rotation experiment for azimuth angle. Sample is placed in a based under OCT system. The base rotates Δθ’ in en face plane and remains unchanged in polar angle. (d) Visualization of rotation experiment for polar angle. Sample is placed in a based under OCT system. The base rotates Δϕ’ in polar angle and the azimuth angle is kept unchanged.

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Fig. 3 Quantification of 3D fiber orientation from three swine hearts: (a) ventricular septum (Heart I); (b) left atrium (Heart II); (c) left ventricle (Heart III). Light gray shows endocardial surface. Two representative en face OCT images are shown, with 3D fiber orientation overlayed with orientation encoded in color.

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Fig. 4 Flow diagram of tractography using particle filter method. Sequential clusters of particles are generated, weighted in each iteration. The coordinates of particles in each cluster are weighted summed to estimate the trace of myofibers in OCT image. Neff is a metrics to evaluate whether most of particles are with low weights. Ns is a threshold to determine whether a resampling process is needed.

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Fig. 5 Fiber tracking in en face plane within a right ventricle (Heart IV). a) OCT image in en face plane, with orientation overlayed in coded color. b) Propagation of particles for tracking fiber trace. The fiber trace starts at an anchor denoted as blue dot. The colored lines show the traces how particles propagate from one step to another. The white dots are the estimated trace of fiber. c) Tractography results for multiple fibers. All fibers start at anchors located at the boundary of image. The white lines represent the fiber trace.

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Fig. 6 Fiber orientation results in the rotation experiment of azimuth angle as described in Fig. 2(c). The data was obtained from right ventricle of Heart IV. The reference OCT image and fiber orientations before rotation is shown in (a). During the rotation, we imaged and processed the image when the rotations are (b) 30° (c) 60° (d) 90°. The measured mean value and standard deviation of azimuth angle and polar angle are computed and displayed on the top of each image. The measured change on polar angle, Δϕ’, was within 5°. The change of azimuth angle, Δθ’, matches the rotation in our experiment with respect to reference angle.

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Fig. 7 Fiber orientation results in the rotation experiment of polar angle as described in Fig. 2(d). The data was obtained from right ventricle of Heart IV. The reference OCT image and fiber orientations before rotation are shown in (a). During the rotation, we imaged and processed the image when the rotations are (b) 30° (c) 60° (d) 90°. The measured mean value and standard deviation of azimuth angle and polar angle are computed and displayed on the top of each image. Light gray shows endocardial surface. The azimuth angle, Δθ’, remains changes little. The change of polar angle, Δϕ’, matches the rotation in our experiment with respect to reference angle.

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Fig. 8 Comparison of manual fiber orientation measurements of azimuth angle, θ, vs computed θ based on automated algorithm. The samples are from left atrium (blue circle) and left ventricle (red triangle) of Heart II over an area of 1mm × 1mm area at an increased depth. The depth was increased from epicardial side to endocardial side in an increment of 25 µm. The results from automated algorithm match the manual measurements.

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Fig. 9 Fiber orientation as a function of depth. Three volumes of OCT image data were acquired and processed for each chambers from Heart I to Heart III. The relationship is linearly fitted by least square method. The fiber orientations are quantified in each en face plane. The mean of orientation over an area of 1 mm × 1mm is plotted at different depth from epicardial side. The experiments in different chambers: (a) right atrium, (b) left atrium, (c) right ventricle, (d) ventricular septum and (e) left ventricle. The fiber orientation changes monotonically with depth. “EP” denotes epicardium. “EN” denotes endocardium.

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Fig. 10 The tractography of myofibers in three dimensions. Our quantification and tractography algorithms enable the reconstruction of fiber structure in myocardium tissues. Results are compared with OCT image in (a) ventricular septum of Heart I (Media 1) (b) left atrium of Heart II (Media 2) (c) left ventricle of Heart III (Media 3) in a volume of 2 mm × 2mm × 1mm underneath the surface. The fiber structures are also shown along with fiber OCT image in en face plane. The reconstructed fibers match the streamline in original OCT data in (d) ventricular septum (Media 4) (e) left atrium (Media 5) (f) left ventricle (Media 6).

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Fig. 11 Microstructure of the atria. (a-c) Representative three-dimensional data set of left atrium: (a) X-Y plane showing myofiber organization; (b) X-Z plane; (c) Y-Z plane. Imaging is possible through the entire atrial wall, where the smallest wall thickness is 0.5mm.

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Tables (2)

Table 1 Statistic results of rotation experiments. The polar angle and azimuth angle rotation experiments are described in Fig. 2(c) and Fig. 2(d), respectively. Both polar angle and azimuth angle are measured in each experiment setup. The measurements of change of angle are listed as mean ± standard deviation calculated from Heart I to Heart V.

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Table 2 R Square values at increased depth range in atrium and ventricle of Heart II.

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Equations (9)

Equations on this page are rendered with MathJax. Learn more.

G (i, j) = \sqrt{G_{x}^{2} (i, j) + G_{y}^{2} (i, j)}

Φ (i, j) = a \tan (G_{y} / G_{x})

P {(ω)}^{W} = \tilde{P} {(ω)}^{W} / \sum_{ω = 0}^{179} \tilde{P} {(ω)}^{W}

\tilde{P} {(ω)}^{W} = \sum_{(i, j) \in W} G (i, j) \frac{\exp (2 \cos [2 (ω - Φ (i, j))])}{\exp (2)}

z_{n + 1} = z_{n} + ρ {\vec{v}}_{n}

p ({\vec{v}}_{k + 1}^{m} | z_{0 : k}^{m}) = p ({\vec{v}}_{k + 1}^{m} | z_{k}^{m}) = P {(ω)}^{W}

w_{k + 1}^{m} \propto w_{k}^{m} p (y_{k}^{m} | v_{k}^{m})

w_{k + 1}^{m} = w_{k + 1}^{m} / \sum_{m = 1}^{M} w_{k + 1}^{m}

N_{e f f} = 1 / \sum_{m = 1}^{M} {(w_{k + 1}^{m})}^{2}

		Reference (°)		Measurements (°)	# of samples
Azimuth Angle Rotation	Δθ’	90		89.14 ± 7.11	5
Azimuth Angle Rotation	Δϕ’	0		1.76 ± 1.35	5
Polar Angle Rotation	Δθ’		0	4.96 ± 4.02	5
Polar Angle Rotation	Δϕ’		15	13.99 ± 3.01	5

Left Atrium		Left Ventricle
Depth range (mµ)	R square	Depth range(mµ)	R square
0 to 300	0.7748	0 to 500	0.7778
0 to 1000	0.2160	0 to 1000	0.4154

		Reference (°)		Measurements (°)	# of samples
Azimuth Angle Rotation	Δθ’	90		89.14 ± 7.11	5
Azimuth Angle Rotation	Δϕ’	0		1.76 ± 1.35	5
Polar Angle Rotation	Δθ’		0	4.96 ± 4.02	5
Polar Angle Rotation	Δϕ’		15	13.99 ± 3.01	5

Left Atrium		Left Ventricle
Depth range (mµ)	R square	Depth range(mµ)	R square
0 to 300	0.7748	0 to 500	0.7778
0 to 1000	0.2160	0 to 1000	0.4154

Abstract

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