1. Optical Coherence Tomography Assembly of the System and Analysis of Sciatic Rat Nerve Daniel deLahunta1,2, Vasilios Morikis2,3, Hyle Park4 1 Department of Physics and Astronomy, University of Rochester 2 BRITE Program, UC Riverside 3 Department of Chemical Engineering, UC San Diego 4 Department of Bioengineering, UC Riverside Abstract The System Results Optical Coherence Tomography (OCT) is an emerging biomedical imaging field that involves the use of low coherence, large bandwidth light to image subsurface tissues. Unlike more well known imaging techniques such as MRI and x-rays that can image large areas of the body, OCT takes much smaller images, but at a much higher order of resolution. How it works is that light from a single source is split and travels along two separate paths to the reference and sample arm. The sample arm directs light onto the tissue that is to be sampled and the reference arm simply has the light travel the same distance as it would in the sample arm. The light is then brought back together and depending on the properties of the tissue, an interference pattern forms that can be used to image subsurface structures. I analyzed images of the sciatic nerve of a rat 1, 7, 14, 21, and 28 days after it was crushed to determine the extent to which the myelin regrows. The data had been analyzed previously, but the degree of scatter in the resulting correlation plots was higher than desired due to large amounts of inflammation in the tissue. I attempted to quantify the fraction of tissue that was inflamed to then be able to clean up the data. The results were consistent with the fact that the control nerves should have less inflammation than the crushed nerves, but when applied to the data, the scatter was only marginally less. The other project I was involved in was the building of two new imaging systems. Both will be capable of polarization sensitive OCT which will provide better imaging than spectral domain OCT (used in imaging rat nerves). I worked on maximizing the power output of the reference arm of both systems and helped to build the sample arm on the system whose central wavelength is at 800 nm and will have a vertical resolution of 1.5 microns. The other system has a central wavelength of 1310 nm and due to this longer wavelength will have a slightly worse vertical resolution of 3 microns. Each sample contains 200 control and 200 crush site images. For each set of images, the inflammation percentage from all 200 images are averaged together to get the final percentage. The results for the images taken a day after the injury after are shown in the adjacent table Above is the basic setup of the two Polarization Sensitive OCT systems being built in the lab with the specifications below. The top left graph shows the original data. The PS-OCT slope values are data from images taken of the same rat nerves but with a different OCT system. The bottom two graphs are two examples of such data. The slope values for Day 1 were divided by one minus the inflammation percent. As a result, the slope values from Day 1 are shifted to the right as can be seen in the top right graph. This results in a slightly better linear fit. The images above are of a human knuckle showing the skin and tendon. Top image was taken using Spectral Domain OCT. The bottom image was taken with Polarization Sensitive OCT. Sciatic Rat Nerve Inflammation Analysis Previously, images were taken of the sciatic nerve of a rat after it was crushed. They were taken at seven day intervals starting a day after the injury and ending after four weeks. They were then analyzed to determine how well the myelin regrew around the nerve. The degree of myelin was assessed through the amount of accumulated phase retardation with depth of nerve tissue. Since inflammation leads to an inflated measurement of tissue depth, this effect appeared to be fouling up the results, especially in Day 1. The task then was to determine how much inflammation there was and see if this could be used to clean up the myelin regrowth data. Conclusion and Further Work Formation of Image The quantization of the of the inflammation from Day 1 resulted in slightly cleaner data for the analysis of the myelin regrowth. The imaging of the myelin regrowth can be used to monitor multiple sclerosis patients in order to determine the extent of their damage. However, this data still has a large amount of scatter. This could be due to the inflammation present in Days 7, 14, 21, and 28. Further work should be done to quantify the inflammation in images taken from these days, with the results being used in a similar manner as the Day 1 results were. If however the results do not make the data cleaner, then we know that we can ignore the inflammation in these images and in further studies. Each depth scan returns data looking like the graph to the left. It is an interference pattern formed by the light and from the reference and sample arm constructively and destructively interfering. ImageJ was used to analyze the images of the crushed rat nerve. The initial image (1) is converted from a gray scale to a black and white image (2) to highlight the lighter inflammation. Next, an outline of the desired region is formed and filled in with black (3). The blackened outline is then added to the black and white image. As a result, only the inflammation remains in the image (4). The area of the inflammation is computed from this image using the “Analyze Particles” function in ImageJ. The same is done for the blackened outline to find the total area. Dividing this area from the inflammation area gives the fraction of tissue that is inflamed. The amplitude and position of the peaks are used to determine the reflectivity, and from that the structure, of the tissue sample. 1 3 Acknowledgements Inflammation Multiple depth scans are taken in one direction across the tissue to form a 2-D image. Furthermore, the laser can be scanned over two dimensions to get a 3-D volume of the sample. The authors thank NSF and the UC Riverside BRITE program for funding, as well as  the University of California, Riverside and NIH (GRANT NUMBER), as well as the entire BIONIL group for their guidance. 2 4