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Real-time multi-functional optical coherence tomography

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Abstract

We demonstrate real-time acquisition, processing, and display of tissue structure, birefringence, and blood flow in a multi-functional optical coherence tomography (MF-OCT) system. This is accomplished by efficient data processing of the phase-resolved inteference patterns without dedicated hardware or extensive modification to the high-speed fiber-based OCT system. The system acquires images of 2048 depth scans per second, covering an area of 5 mm in width×1.2 mm in depth with real-time display updating images in a rolling manner 32 times each second. We present a video of the system display as images from the proximal nail fold of a human volunteer are taken.

©2003 Optical Society of America

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Supplementary Material (2)

Media 1: MOV (2471 KB)     
Media 2: MOV (7718 KB)     

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Figures (7)

Fig. 1.
Fig. 1. Diagram of the OCT system and driving waveforms. The RSOD and polarization modulator are driven by a rounded triangle and a step function, respectively, both at approximately 1 kHz. A phase delay is introduced such that the RSOD galvo response is in phase with the polarization modulator. The system processes the central 80% of the positive and negative sloping regions of the RSOD response, yielding even and odd A-lines. (pol: polarizer, pc: passive polarization controller, pm: electro-optic polarization modulator, oc:optical circulator, RSOD: rapid scanning optical delay line, fpb: fiber polarizing beam splitter, pd: fiber-pigtailed photodiodes, ccd: charge coupled device camera).
Fig. 2.
Fig. 2. Flow diagram of data processing. The main thread begins processing a data chunk as soon as it has been acquired. This entails activating even and odd A-line processing threads to convert the detected interference patterns into Stokes parameters and phase information, as well as updating the intensity image. Once initial processing of the data chunk has been completed, the birefringence and flow threads perform their respective analysis and image updates. The save thread writes raw data to disk once an image is completely acquired.
Fig. 3.
Fig. 3. Line graph demonstrating the relationship between the time scales tj and Tk for parameters of 2048 points sampled at 5 MS/s at a carrier frequency of 625 kHz and q=8.
Fig. 4.
Fig. 4. Birefringence calculation illustrating (a): the surface states, I⃗ 1 and I⃗ 2, in blue and the reflected states, I⃗1 and I⃗2, in green, (b, c) the planes P 1 and P 2 that span all possible rotation axes, and (d): the intersection of the planes resulting in determination of the optic axis A⃗.
Fig 5.
Fig 5. Effect of noise on the calculated rotation angle. For a given axis optic axis A⃗, two pairs of I⃗ 1 and I⃗1 are shown, one with large θ A , I j and the other with small θ A , I j . The cones represent the uncertainty in orientation of the polarization states due to noise. The red areas show the variation in rotation angle θ j due to the uncertainty introduced by noise, demonstrating the increased uncertainty involved in determining θ j when the polarization state of incident light is closely aligned with the optic axis.
Fig. 6.
Fig. 6. Intensity, birefringence, and flow (phase variance) images of the proximal nail fold of a human volunteer (upper, middle, and lower images respectively). The epidermal (a) and dermal (b) areas of the nail fold, cuticle (c), nail plate (d), nail bed (e), and nail matrix (f) are all identifiable in the intensity image. The birefringence image shows the phase retardation of the epidermal-dermal boundary (g) as well as the lower half of the nail plate (h). Small transverse blood vessels in the nail fold are distinguishable in the flow image by their lighter color. Each image is 5 mm×1.2 mm, with all data acquired in 1 s.
Fig. 7.
Fig. 7. (2.5 MB) Video recording of 4 seconds of real-time MF-OCT imaging of the same proximal nail fold. Moving clockwise from the top left corner are the simple user interface, intensity, birefringence, flow images, view of the scanning area, and spectrum. (7.5 MB version).

Equations (15)

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f ( t ) = Re { f e n v ( t ) e i ( s 0 t + ϕ ( t ) ) } = f cos ( t ) cos ( s 0 t ) f sin ( t ) sin ( s 0 t )
0 Δ t f ( t ) e i s 0 t d t = 0 Δ t ( f cos ( t ) cos ( s 0 t ) f sin ( t ) sin ( s 0 t ) ) ( cos ( s 0 t ) i sin ( s 0 t ) ) dt
= 0 Δ t ( f cos ( t ) cos 2 ( s 0 t ) + i f sin ( t ) sin 2 ( s 0 t ) ) d t
f cos ( t ) + i f sin ( t )
cos H n , V n [ T k ] = l = 1 q H n , V n [ T k + l δ t ] cos ( s 0 l δ t )
sin H n , V n [ T k ] = l = 1 q H n , V n [ T k + l δ t ] sin ( s 0 l δ t )
I n [ T k ] = ( cos H n 2 [ T k ] + sin H n 2 [ T k ] + cos V n 2 [ T k ] + sin V n 2 [ T k ] )
Q n [ T k ] = ( cos H n 2 [ T k ] + sin H n 2 [ T k ] cos V n 2 [ T k ] sin V n 2 [ T k ] )
U n [ T k ] = 2 ( cos H n [ T k ] cos V n [ T k ] + sin H n [ T k ] sin V n [ T k ] )
V n [ T k ] = 2 ( cos H n [ T k ] sin V n [ T k ] sin H n [ T k ] cos V n [ T k ] )
ϕ H n , V n [ T k ] = tan 1 ( sin H n , V n [ T k ] cos H n , V n [ T k ] ) .
cos θ j = ( A × I j ) · ( A × I j ) A × I j A × I j .
θ = ( I 1 sin ( θ A , I 1 ) I 1 sin ( θ A , I 1 ) ) θ 1 + ( I 2 sin ( θ A , I 2 ) I 2 sin ( θ A , I 2 ) ) θ 2 ( I 1 sin ( θ A , I 1 ) I 1 sin ( θ A , I 1 ) ) + ( I 2 sin ( θ A , I 2 ) I 2 sin ( θ A , I 2 ) ) .
ω n [ z ] = ( I n [ z ] + Q n [ z ] ) Δ ϕ H n [ z ] + ( I n [ z ] Q n [ z ] ) Δ ϕ V n [ z ] ( I n [ z ] + Q n [ z ] ) + ( I n [ z ] Q n [ z ] ) · 1 2 T .
Var n [ z ] = ( I n [ z ] + Q n [ z ] ) Δ ϕ H n 2 [ z ] + ( I n [ z ] Q n [ z ] ) Δ ϕ V n 2 [ z ] ( I n [ z ] + Q n [ z ] ) + ( I n [ z ] Q n [ z ] ) .
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