PURPOSE: Raman spectroscopy is an optical imaging technique used to characterize tissue via molecular analysis. The use of Raman spectroscopy for real-time intraoperative tissue classification requires fast analysis with minimal human intervention. In order to have accurate predictions and classifications, a large and reliable database of tissue classifications with spectra results is required. We have developed a system that can be used to generate an efficient scanning path for robotic scanning of tissues using Raman spectroscopy. METHODS: A camera mounted to a robotic controller is used to take an image of a tissue slide. The corners of the tissue slides within the sample image are identified, and the size of the slide is calculated. The image is cropped to fit the size of the slide and the image is manipulated to identify the tissue contour. A grid set to fit around the size of the tissue is calculated and a grid scanning pattern is generated. A masked image of the tissue contour is used to create a scanning pattern containing only the tissue. The tissue scanning pattern points are transformed to the robot controller coordinate system and used for robotic tissue scanning. The pattern is validated using spectroscopic scans of the tissue sample. The run time of the tissue scan pattern is compared to a region of interest scanning pattern encapsulating the tissue using the robotic controller. RESULTS: The average scanning time for the tissue scanning pattern compared to region of interest scanning reduced by 4 minutes and 58 seconds. CONCLUSION: This method reduced the number of points used for automated robotic scanning, and can be used to reduce scanning time and unusable data points to improve data collection efficiency.
PURPOSE: The iKnife is a new surgical tool designed to aid in tumor resection procedures by providing enriched chemical feedback about the tumor resection cavity from electrosurgical vapors. We build and compare machine learning classifiers that are capable of distinguishing primary cancer from surrounding tissue at different stages of tumor progression. In developing our classification framework, we implement feature reduction and recognition tools that will assist in the translation of xenograft studies to clinical application and compare these tools to standard linear methods that have been previously demonstrated. METHODS: Two cohorts (n=6 each) of 12 week old female immunocompromised (Rag2−/−;Il2rg−/−) mice were injected with the same human breast adenocarcinoma (MDA-MB-231) cell line. At 4 and 6 weeks after cell injection, mice in each cohort were respectively euthanized, followed by iKnife burns performed on tumors and tissues prior to sample collection for future studies. A feature reduction technique that uses a neural network is compared to traditional linear analysis. For each method, we fit a classifier to distinguish primary cancer from surrounding tissue. RESULTS: Both classifiers can distinguish primary cancer from metastasis and surrounding tissue. The classifier that uses a neural network achieves an accuracy of 96.8% and the classifier without the neural network achieves an accuracy of 96%. CONCLUSIONS: The performance of these classifiers indicate that this device has the potential to offer real-time, intraoperative classification of tissue. This technology may be used to assist in intraoperative margin detection and inform surgical decisions to offer a better standard of care for cancer patients.
PURPOSE: Raman Spectroscopy is amongst several optical imaging techniques that have the ability to characterize tissue non-invasively. To use these technologies for intraoperative tissue classification, fast and efficient analysis of optical data is required with minimal operator intervention. Additionally, there is a need for a reliable database of optical signatures to account for variable conditions. We developed a software system with an inexpensive, flexible mechanical framework to facilitate automated scanning of tissue and validate spectroscopic scans with histologic ground truths. This system will be used, in the future, to train a machine learning algorithm to distinguish between different tissue types using Raman Spectroscopy. METHODS: A sample of chicken breast tissue is mounted to a microscope slide following a biopsy of fresh frozen tissue. Landmarks for registration and evaluation are marked on the specimen using a material that is recognizable in both spectroscopic and histologic analysis. The slides are optically analyzed using our software. The landmark locations are extraction from the spectroscopic scan of the specimen using our software. This information is then compared to the landmark locations extracted from images of the slide using the software, ImageJ. RESULTS: Target registration error of our system in comparison to ImageJ was found to be within 1.1 mm in both x and y directions. CONCLUSION: We demonstrated a system that can employ accurate spectroscopic scans of fixed tissue samples. This system can be used to spectroscopically scan tissue and validate the results with histology images in the future.
A new time domain sensing scheme using a compact single abrupt taper-based standard single mode fiber Mach-
Zehnder interferometer is proposed, tested and simulated. The interferometer consists of cascaded symmetrical abrupt 3
dB taper regions separated by a middle interfering section. After the interfering middle section, the cladding modes are
converted back into core mode. Due to the external stimuli applied to the interferometer, change of refractive index and
the optical path length are induced, resulting in phase difference of core and cladding modes, and hence, the output
optical signal in the time domain. The proposed scheme promises a simple and sensitive approach to optical sensing.
A new compact standard single mode fiber Michelson interferometer deflection sensor was proposed, tested and
simulated. The new interferometer consists of a symmetrical abrupt 3 dB taper region with a 40 μm waist diameter, a
700 μm length and a 500nm thick gold layer coating. Compared with similar interferometric devices based on long
period gratings that need microfabrication technology and photosensitive fibers, the proposed sensor uses a much
simplified fabrication process and normal single mode fiber, and has a linear response of 1.1nm/mm.
Multimode fiber (MMF) has found applications in high-speed computer interconnect, local area networks (LAN), and storage area networks (SAN) due to its ease of handling and high performance over short span. However, modal dispersion limits its bandwidth-distance product (BDP) to about 2 Gb/s-km. This limit has been extended by recent new generation of optimized MMF to 28 Gb/s-km, but there is evidence that a substantial portion of installed MMF have imperfect refractive index (RI) profiles due to defects during the manufacturing process, and the BDP might be at best no more than 500 Mbps-km. Different strategies have been proposed to address this issue by employing offset launch, multi-level subcarrier modulation, and mode spatial control. However, our studies have shown that end-to-end system performance of installed MMF can be highly dependent on input launch polarization. In this report, we investigate, for the first time to our knowledge, the relationship between RI profile defect, input launch condition, and transmission performance in commercial-grade MMF, both 50 μm and 62.5 μm. To this end, a number of techniques have been deployed. Two-dimensional (2D) MMF RI profile is obtained by a micro-reflectivity technique with a spatial resolution of ~400 nm. MMF transmission characteristics are interrogated using interferometric techniques. Data at 40 Gb/s are transmitted over the same MMF sample at different launch conditions, and the system performance is evaluated by bit-error rate measurements. These results are then analyzed to provide insights to correlate fiber RI profile defects and high-speed data transmission performance for installed commercial-grade MMF for optical access networks.