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Image Processing in Spectral Domain Optical Coherence Tomography (SD-OCT)Vasilios Morikis1,2, Dan DeLahunta1,3, Md. Shahidul Islam4, Christian M. Oh4, Hyle Park4

1 Bioengineering Research Institute for Technical Excellence, UC Riverside2 Department of Nanotechnology, UC San Diego3 Department of Physics, University of Rochester

4 Department of Bioengineering, UC Riverside

Abstract

Optical Coherence Tomography (OCT) is an optical imaging technique based on low coherence interferometry of light waves. This method is mostly useful for obtaining high resolution cross-sectional images of biological tissues at a high speed. OCT is advantageous for some of its features which include non-invasive procedures, minimal contact with tissues, use of non toxic dyes, good lateral and axial resolution of images and better in-depth imaging than other optical methods. Because of these features, OCT has been an important imaging method in Ophthalmology, Dermatology, Cardiovascular imaging, Neuroimaging and many other fields. An OCT system utilizes low coherent light source and an optical set up to produce interference patterns in the spectrometer and these interference patterns, termed as sample depth profiles, are later processed in the computer to obtain the final image of the sample. This project is looking at the post processing steps in an OCT system and the goal is to analyze the data obtained from the spectrometer and perform the image processing techniques to generate the final image. A Fourier transform of the raw data from the spectrometer has a high degree of artifact. So, in order to remove the noises and obtained high quality images, we need to extensive post-processing of the data. Some of the basic post processing steps includes reading the image as a matrix, flipping the matrix if necessary, zero padding, interpolation, and fast Fourier transform (FFT). Once the images are processed, they can be arranged altogether to generate a cross sectional image of the sample.

Optical Coherence Tomography

Conclusions

Future Work

Acknowledgements

In Optical Coherence Tomography tissue is placed under a light source. The light is then reflected back, an interferometer must be used to detect the extremely short time delays.

The authors thank NSF and the UC Riverside BRITE program for funding, as well as the University of California, Riverside and NIH (R00 EB007241), as well as the entire BIONIL group for their guidance.

Project Overview and Methodoly

Read the Image

Flip Matrix (if

necessary)

Zero Padding

FFT Display Image

Interpolate

A two dimensional cross section or a three dimensional volume can be formed by scanning the beam across the tissue.

Fig. 4 Process in which the MATLAB code analyzes the data produced from OCT.

Results

The 1310 nanometer system we are utilizes a polarizing beam splitter cube and two cameras to acquire data. That way we can split the detected light and reconstruct the polarization state of light returning from the sample.

This project is a mathematical focusing of raw data obtained from an OCT system. A series of steps must be done prior to an FFT to increase the signal to noise ratio (SNR) and produce a clear high resolution image.

The processes are the ones illustrated in Figure 3. After MATLAB reads the image, it extracts a matrix from the image. Depending on which camera the image is from, it may have to be flipped.

The next step is to expand the matrix by adding zeroes so that a more accurate interpolation may occur. Interpolation is used to find linearly spaced values so that an FFT can be performed, in this case it takes K (wave number) and makes it linear.

Fast Fourier transforms are used to switch one complex variable to another one, in this case it transforms K into actual space.

An objective of this project is to find better depth profiles by adjusting parameters in the MATLAB code. More specifically the incident angle, the focal length, and the wavelength of the system.

Fig. 1 Outline of OCT system

Fig. 2 Sample image

Fig. 6 Raw data obtained from the straight camera when the reference and sample arm are 600 microns apart and

Fig. 7 Unprocessed Distance vs. Intensity graph and the corresponding image of Figure 6.

Fig. 8 Processed Distance vs. Intensity graph and the corresponding image of Figure 6.

The raw data taken of just a mirror, with no sample, from the camera comes in (Figure 6) and is then read by the MATLAB program, when no processing steps are done we get a graph that looks like Figure 7. To create an accurate image the point spread function should be narrow and high (ignore the noise in the middle). As you can see Figure 7 is wide and that is why at the top of the image we see a thick black blur. Whereas the image of a mirror should me a single thing line.

Once all the processing steps are complete (Figure 8) we get a much sharper point spread function which produces the desired result of a thin black line. As distance between reference and sample arm decreases the point spread function shifts right.

Fig. 9 Processed Distance vs. Intensity graph and the corresponding image when reference arm and sample arm are 400 microns apart.

Fig. 10 Parameters used to create better point spread functions

Side Camera Straight CameraIncident Angle 49*pi/180 51*pi/180;

Grating Spacing 1.0e-3/1145 1.0e-3/1145Focal Length 9.20E-02 9.50E-02Wavelength 1.35E-06 1.35E-06PixelWidth 2.50E-05 2.50E-05

Now that the systems are producing images, it is possible to venture into many fields. Such as looking at damaged rat nerves and, even further down the line, human tissue and nerves.

Fig. 12 Diagram of the sciatic nerve of a rat, and a potential crush site.

Fig. 13 OCT image of a crushed sciatic rat nerve one day after crush was applied, white areas are inflammation

Now that the parameters are working images can be retrieved from the camera and focused mathematically to obtain much clearer images.

Fig. 11 Images obtained from the 1310 nanometer system of a thin slice of apple (image width: 100 microns, image height: 500 microns approximately). The one on the left is completely unprocessed while the one on the right is mathematically focused using the parameters from this project.

In Figure 11 the unprocessed image is quite blurry but after mathematically focusing the image, the blurs become very sharp objects. The majority of an apple is water while the actual structure of the apple is a loose network so the dark spots would be the actual flesh of the apple while all the small white areas woven between the dark spots would be the juice of the apple. All the grey area beneath the apple would be air.

Collimator

Diffraction Grating

Focusing Lens

Fast Line Scan Cameras

Polarized beamsplitter cube

Fig. 3 Setup of the OCT system

Fig. 5 Equation that incorporates the parameters: incident angle, grating spacing, focal length, and initial wavelength.