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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 Riverside 2 Department of Nanotechnology, UC San Diego 3 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 Project Overview and Methodoly Conclusions 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. Read the Image Flip Matrix (if necessary) 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. Focusing The next step is to expand the matrix by Lens Fig. 3 adding zeroes so that a more accurate Diffraction Setup of interpolation may occur. Interpolation is used Grating the OCT to find linearly spaced values so that an FFT system Fast Line can be performed, in this case it takes K Scan Cameras (wave number) and makes it linear. Collimator Interpolate Zero Padding Polarized beamsplitter cube 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. Display Image FFT Fig. 4 Process in which the MATLAB code analyzes the data produced from OCT. 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. 5 Equation that incorporates the parameters: incident angle, grating spacing, focal length, and initial wavelength. Results Fig. 6 Raw data obtained from the straight camera when the reference and sample arm are 600 microns apart and Now that the parameters are working images can be retrieved from the camera and focused mathematically to obtain much clearer images. 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. 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. Future Work 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. 1 Outline of OCT system 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. A two dimensional cross section or a three dimensional volume can be formed by scanning the beam across the tissue. Fig. 7 Unprocessed Distance vs. Intensity graph and the corresponding image of Figure 6. Fig. 9 Processed Distance vs. Intensity graph and the corresponding image when reference arm and sample arm are 400 microns apart. Fig. 2 Sample image 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. POSTER TEMPLATE BY: www.PosterPresentations.com Fig. 13 OCT image of a crushed sciatic rat nerve one day after crush was applied, white areas are inflammation Fig. 8 Processed Distance vs. Intensity graph and the corresponding image of Figure 6. Fig. 10 Incident Angle Parameters used to create Grating Spacing better point Focal Length spread Wavelength functions PixelWidth Side Camera 49*pi/180 1.0e-3/1145 9.20E-02 1.35E-06 2.50E-05 Straight Camera 51*pi/180; 1.0e-3/1145 9.50E-02 1.35E-06 2.50E-05 Acknowledgements 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.